The applicability of geographic information systems in the conduct of military operations in urban environments using ArcGIS [305182]

The applicability of geographic information systems in the conduct of military operations in urban environments using ArcGIS

Stud. [anonimizat], Romania

Table Of Contents

Table Of Contents 1

Abstract 4

Chapter 1 Introduction and purposes 7

1.1 Introduction 7

1.2 The necessity of this research 8

1.3 Scenario and purposes 9

Chapter 2 State of art and history 11

2.1 Evolution of GIS 11

2.2 The use of GIS in the military field 16

Chapter 3 Geographic Information System 20

3.1 Defining the concept of GIS 20

3.2 GIS Definition 22

3.3 GIS Components 23

3.3.1 People Component 24

3.3.2 Hardware Component 25

3.3.2 Software Component 28

3.3.3 Data Component 29

3.3.3 Methods Component 30

3.4 The disciplines that contribute to the founding of the GIS 31

3.5 Scope of application of GIS 33

3.6 The properties and purpose of a GIS 38

3.7 GIS Functions 40

3.7.1 Data entry and validation 40

3.7.2 Storage and management of databases 41

3.7.3 Analysis and modelling of data 41

3.7.4 Visualizing output data 42

3.8 GIS programs 42

3.9 GIS programs with military applicability 46

3.9.1 ArcGIS 46

3.9.2 ERDAS Imagine 51

Chapter 4 Specific data types to Geographic Information Systems 53

4.1 Genaral Information 53

4.2 The vectorial model 56

4.3 The Raster model 59

4.4 Comparative raster vs. vector analysis 64

4.5 Vector formats used in the military field and particular structures of geospatial databases 65

Chapter 5 Analysing the military scenario using ArcGIG 68

5.1 Input Data 68

5.1.1 Adding raster data to a map 72

5.1.2 NoData values in raster datasets 73

5.1.3 Merge multiple raster datasets into a new raster dataset. 74

5.1.4 Georeferencing a raster dataset 75

5.1.5 Vector map 76

5.2 Landing Areas 77

5.2.1 Introduction 77

5.2.2 Triangulated Irregular Network 77

5.2.3 Surface slope 79

5.2.4 Euclidean Distance 81

5.2.5 Intersect 85

5.2.6 Excluding areas 86

5.2.7 Select the areas of interest 88

5.2.8 Automation of the landing areas process 89

5.3 Observation posts 91

5.3.1 Introduction 91

5.3.2 Elevation values 92

5.3.3 The lines of sight 93

5.3.4 Landing areas Observation points 97

5.3.5 Hospital observation post 101

5.3.6 Profile Graphs 102

5.3.7 Automation of observation posts process 103

5.4 Shooter position 104

5.4.1 Introduction 104

5.4.2 Observation Posts tool and viewshed 105

5.4.3 Shooter profile graph 105

5.5 Routes 106

5.5.1 Introduction 106

5.5.2 Import Routes 106

Chapter 6 Analysis of the final products quality 111

Chapter 7 Conclusions 115

List of figures 120

Acronyms 123

Bibliography 126

Abstract

Subject:The applicability of geographic information systems in the conduct of military operations inurban environments using ArcGIS

Software: ArcGIS 10.4

Research domain:[anonimizat], Military tactics

Author:Stud. Eliza Manuela VOINEA

Institution:[anonimizat], Romania

Tutor: Col.(r) prof. univ. dr. ing. Raducanu Nicolae

Key words:[anonimizat], [anonimizat], [anonimizat], [anonimizat], ArcGIS.

Resume:

[anonimizat], map and environment analysis.

[anonimizat]d the environment possibilities using ArcGIS.

The scenario is defined by protecting a public event in Le RondePark, ZeeBrugge, Belgium considering that by the commander information, enemy troupes will use helicopters to land and attack the event. An overview on the scene and the possibilities is presented based on the environment and the field properties

The main steps of the project are: Preparing data, Create a database with the main environment features, Finding Landing Areas, Observation posts, Shooter Position, Routes and Areas that need extra protection.

Approach:

First of all, the data I needed are a Digital elevation Model and Orthophoto map of ZeeBrugge, provided by the Royal Military Academy of Brussels.

The next step represents studying the state of art and choosing the suitable Software for completing the Project, ArcGIS 10.4.

After that I read the ArcGIS official manual, learned how to use it and found the most helpful tools for each task. Then I combined the ArcGIS tools to create the dataset needed and complete every step.

For the automation of the process, I created two new tools in ArcMap, one used for the Landing Areas and another for the Observation Posts.

As a final step I analysed the results and take a conclusion regarding the areas that need extra protection and the time of react.

Conclusion:

As a conclusion I found four possible landing areas at a maximum distance of 400 meters from the event, two observation posts (one for the landing areas and another for the Hospital), and two possible location for shooters with clear visibility of the event, the routes between interest points and 5 areas that need extra protection.

Chapter 1 Introduction and purposes

Introduction

Over the last five decades, powerful technology has changed how people live and learn things in their neighborhoods, towns, and cities. Like many other technologies, most people are unaware of the information geographic systems and the impact they have. Even though geographic information systems have seen tremendous growth over the past 15-20 years, and hundreds of thousands of people now use this technology, very few realize how indirectly their daily lives are affected by these systems information.

To demonstrate this, we can analyze a person's daily activity. The first thing a person does when he wakes up in the morning is to turn on the light and possibly the TV. Both are powered by household electricity. Any electricity company uses GISs to manage the complex infrastructure of tens of thousands of transmission and distribution lines and hundreds of thousands of workstations that ensure optimal network performance. Then the coffee follows. The water from which the coffee is made is provided by a utility that manages a water distribution system made up of thousands of kilometers of pipelines to guide the flow of water. The utility uses GIS for water distribution, infrastructure maintenance, automated mapping, network tracking, and flow analysis. If we continue to do that, then drive to work and then do other activities involving geographic information systems.

In other words, without realizing it, we hit every step of geographic information systems, each specialized for a particular field. Therefore, GIS is nothing special. Like any information system, GIS is an accumulated data collection and procedures for creating, acquiring, integrating, transforming, viewing, analyzing and modeling information that helps people make decisions.

The work is structured in 7 chapters as follows:

Introduction and purposes

State of Art and concepts

Introduction to ArcGIS

High-Resolution LiDAR, RGB Data and Digital Surface Model

Realization

Conclusion

The current chapter is describing the importance of this project and research domain in nowadays and the purposes established in accordance with the applicability of the envisaged results and of Geographic Information Systems in the military field. Also, you can find the used scenario on which the project is based on and the purpose of the project.

The second chapter wants to highlight the most reliable results on using Geographic Information Systems in the military field. Besides that, some basic concepts related with the project realization will be defined.

The third Chapter represents GIS software programs and a short introduction about ArcGIS.

In the fourth Chapter are presented the data used and how it was obtained.

The fifth chapter represents a detailed explanation about each step of completing the project.

The sixth and also the last Chapter concludes all the steps and express the utility of all the information discovered through the project realisation.

The necessity of this research

Geographic Information Systems (GIS) play a pivotal role in military operations. The concept of Command, Control, Communication and Coordination in military operations is largely dependent on the availability of accurate, spatial information to arrive at quick decisions for operational orders. In the present digital era, GIS is an excellent tool for military commanders in the operations. The use of GIS applications in military forces has revolutionised the way in which these forces operate and function. Military forces use GIS in a variety of applications including  cartography, intelligence, battle field management, terrain analysis, remote sensing, military installation management and monitoring of possible terrorist activity.[1]

GIS is an increasingly important technology to the military. Spatial information has always been important to military commanders; an understanding of terrain, for example, is an essential military skill. Maps have been the principal mechanism for disseminating this knowledge and the ability to interpret a map the essence of understanding. The military is always seeking to improve capabilities in order to maintain a credible deterrent and increasingly to ensure efficient participation in peace-keeping missions. In the post-Cold War situation a key driver for international defence mapping agencies is their vastly increased area of interest. Forces can be deployed into almost any part of the world, yet prior knowledge of the terrain is unlikely. GIS have a key role to play in creating, editing, analysing, querying, and displaying geographical data in order to help the commander understand the influence of terrain on the conduct of the battle

Scenario and purposes

The proper way to illustrate the utility of GIS in the military field is by conducting a real military scenario. The scenario I have chosen implies a wide range of acts and analyses that are needed in the military field. Also, solving the scenario implies using many tools provided by ArcMap , GIS extensions and even the way GIS gives the user flexibility and customizable actions, allowing users to create new tools and automate tasks.

The scenario consists in the fact that a public military demonstration will happen in Le Ronde Park of ZeeBrugge, Belgium. The commander has information that the event will be attacked and troupes will land with helicopters in the area. For a full understanding of the possibilities we have to answer the following questions:

Where is the event happening?

What are the properties that define the environment?

Where are the hospitals?

Where is the Military Base?

Where are the areas that are suitable for landing?

Where are the best observation posts?

Where are the places with good visibility to the event for possible shooters?

What routes would enemies approach?

What areas need extra protection?

What areas need extra protection?

This project’s purpose is to answer those questions and make an overview of the scene and estimative scenarios depending on the field properties. In this project I will show the applicability of Geographic Information Systems in the military field by taking only one scenario as an example so in the next pages I will focus on solving the scenario.

Chapter 2 State of art and history

2.1 Evolution of GIS

The beginnings of Geographic Information Systems can be, if we consider that their ultimate product is a mapping map, still in the beginning of human history. The oldest known map dates back to 2500 BC, but there were probably maps before that date. Since then, people have continuously improved the methods of expressing spatial information. The eighteenth century brought novelty overlapping maps to relay troop movements during the US Independence War (maps mapped by French cartographer Louis-Alexandre Berthier for the Yorktown Battle). The manual overlay method was first described by Jacqueline Tyrwhitt in a 1950 planning manual. The first British census (1825) led to the creation of demography as a science, another GIS application. In the last century, more and more cartographers and scientists have discovered the power of the map overlay method to communicate increased levels of information for a particular area.

Inherited by CAO / DAO's computer development, automated mapping emerged in the 1950s. The Harvard University's "Computer Lab" (1965) was one of the most active in the field of fundamental research and the SYMAP system served as a model for most of the GIS raster that have been developed in the industry.

At the end of the 1950s and early 1960s, computers with increased processing capabilities appeared, which began to be used in new areas. Now there are the first applications in the field of meteorology, geology and geophysics, but inadequate printing technologies lead to rudimentary graphic products. In Europe and North America, demographics, urban planning, transport or distribution of natural resources are being undertaken in the use of electronic computers.

The first operational GISs appeared in the US and Canada in the 1960s. Among the pioneers, we can mention the Canadian Geographic Information System (1962), the New York Land Information System (1967) and the Minnesota Land Management System (1969) ) or, in France, the Urban Data Banks (BDU) made for Lille and Marseilles (1972). Over time, GISs had different names, most reflecting their immediate applications, as in the following examples:

LIS (Land Information System)

AM/FM (Automated Mapping and Facilities Management)

EIS (Environmental Information System)

RIS (Resources Information System)

PIS (Planning Information System)

SDHS (Spatial Data Handling System)

The emergence and development of GIS has been possible as a result of the spectacular advances in the fields of computing, computerized mapping and Database Management Systems (SGBDs). The birth of the Geographic Information System is only signed in 1962 in America, which holds the undisputed supremacy in the information technology market. The first achievement, titled GIS – the Canadian Geographic Information System – was developed by Roger Tomlinson of the Canada Land Inventory in 1962. Unlike previous systems that had been developed for specific applications, this system was designed to store digital map data, maps and attributes in digital format and accessible to all of Canada. The system is in operation today.

Geographic Information Systems is a computer-based tool that analyzes, stores, manipulates and visualizes geographic information, usually in a map.

Geographic Information Systems resumes to just 4 main purposes:

Create geographic data

Manage it

Analyze it

Display it on a map

Since 1969, ESRI has been helping people solve real-world geographic problems in a wide range of domains. For the last 15 years, ESRI’s software has been used in the most demanding of domains—the defense and intelligence community. Today, the ArcGIS platform is helping users in every service, throughout most defense domains, and in many nations around the world.[2]

Geographic information systems, in the current sense of the word, emerged in the 1970s, when analysts began to program computers to automate processes before handbooks. Maps from the beginning of this period contained no more than points, lines, straight lines, and text. Software companies (ESRI, ERDAS, etc.) have developed packages of programs that can import, process, and display geographic data to create new data and information categories. The rapid development of "hard" components and their rapid cost reduction, starting in the 1980s, made GIS a product accessible to a wide range of users.

In addition, ESRI launched in 1982 the ARC / INFO program for minicomputers, with the possibility of using the vectorial format in the context of calling to a georelative data model, with the distinct retention of site information and the attributes of each cartographic component. Subsequently, concerns about such an approach have been taken over and expanded by many other firms. At the end of the year 1982 the portability of large-scale programs on PC-based computing systems was assured.

With these remarkable conceptual advances in GIS, the preoccupations of refinement focused on the methodology of data collection, quality, standardization of working methods, expanding the possibilities of analysis and organizing the databases. In this way it became possible to integrate the vector format with the raster into a unitary one.

GIS evolution on the conceptual level has been matched by the remarkable advances made in computer performance that have stimulated the expansion of GIS application domains, and have in turn led to the need for more and more complex applications. Furthermore, programs have been developed that do not respond to "custom-made" problems, but satisfy a very diverse range of interests of an increasing number of potential users.

In this way, GIS has emerged as a distinct field of information technology, well represented by many program producers and by the collateral structures of computer manufacturers, manufacturers of data acquisition and imaging equipment, editors of magazines and specialized literature.

Since 1995, the development of GIS has mainly materialized through the creation of a specific information infrastructure so as to ensure that all interested stakeholders, even individuals, have access to GIS information available in the areas of governmental structures, public health, education and education and urban development.

This geographical information infrastructure tends to grow even globally to the extent that international organizations are interested in having easy access to information from outside the country on air and maritime navigation, weather forecasts, natural disasters and global climate change.

This "globalization" of GIS applications will allow data to be imported from the most diverse geographical areas instead of using local programs to create scenarios on the evolution of natural or economic phenomena in order to take timely more effective decisions to counteract their undesirable effects

The main events that contributed to the emergence and development of GIS:

• The digitizer was invented in 1950 (in 1960 the first Pencil Follower digitizer appears);

• 1958 Waldo Tobler imagines the MIMO (map in – map out) model;

• 1963 "Canada Geographic Information Systems" (CGIS), the first GIS application, used for cadastral inventory;

• 1964 Howard Fisher establishes at the Harvard University "Assisted Graphics and Space Analysis Laboratory";

1969 Jack and Laura Dangermond set up the Environmental Science Research Institute (ESRI), headquartered in Redlands, California;

• 1969 Intergraph is set up by Jim Meadlock

• 1969 is founded "Laser-Scan" by three academics from Cavendish, Cambridge Laboratories;

• 1970 GIS Symposium is held in Ottawa, Canada;

• 1973 ESRI creates the Maryland Automatic Geographic Information System (MAGIS), one of the first state-level GIS projects;

• 1974 Intergraph launches "Interactive Graphics Design System" (IGDS), hardware + software solution used for professional GIS implementations;

• 1979 appears ODYSSEY, the first modern GIS based on vector graphics, developed in Harvard laboratories.

• 1981 ESRI begins the development of GC ARC / INFO;

• 1982 John Walker sets up Autodesk to launch AutoCAD-80

• 1982 ESRI launches ARC / INFO 1.0, the first commercial GIS software package;

• 1984 is the first international symposium on spatial data manipulation; the first book on GIS is published: "Basic Readings in Geographic Information Systems", Marble, Calkins & Peuquet;

• 1985 is founded in the UK, the first GIS magazine: "Mapping Awareness";

• 1986 – Mapping Display and Analysis System (MIDAS), the first GIS desktop product to run on DOS operating systems only.

• 1986 MapInfo is founded by four students from the Rensalaer Polytechnic Institute

• 1987 publication of the International Journal of Geographic Information Systems;

• 1989 Intergraph launches the MGE (Modular GIS Environment) package;

• 1991: Geographical Information Systems: Principles and Applications, Maguire, Goodchild, Rhind;

• 1992 ESRI creates ArcView 1.0, designed for non-GIS users to access GIS data; it will run on Windows or MAC operating systems

• 1992 GIS Europe – the first European specialist magazine

• 1994 ESRI launches the reference editions for the three GIS product lines: ARC / INFO 7.0, ArcView 2.0, PC ARC / INFO 3.4.2; with ArcView 2.0 basically ESRI brings mapping to a new level: desktop GIS;

• 1999 ESRI launches the "geodatabase" format and ArcView GIS 3.2;

• March 2004 ESRI launches the ArcGIS 9 major edition, and Autodesk launches AutoCAD 2005;

• November 2009 gvSIG mobile, the first GIS for phones and tablets

• September 2010 Autodesk launches AutoCAD 360 for IOS

• April 2011 Autodesk launches AutoCAD 360 for Android

• October 2013 ESRI launches ArcGIS for Android

• December 2014 ESRI launches ArcGIS for Desktop version 10.3

• February 2016 ESRI launches ArcGIS 10.4

• March 2016 Autodesk launches AutoCAD 2017

2.2 The use of GIS in the military field

Since the beginning of civilization, military forces have played a dominant role. The focus on military efforts continues to be a major force with a heavy reliance on technology. Technology has made a major contribution to the way wars are taking place today and is also a decisive factor in planning military operations

Military forces use GIS in a variety of applications including:

Cartography

Intelligence

Battle field management

Terrain analysis

Remote sensing

Military installation management and monitoring of possible terrorist activity

GIS allows military land and facilities managers to:

Reduce base operation and maintenance costs

Improve mission effectiveness

Provide rapid modelling capabilities for analyzing alternative strategies

Improve communication and to store institutional knowledge

Terrain Evaluation in land based military operations

The need to swift and accuracy information was amply demonstrated during the Gulf war by Allied forces against Iraq. In an article published in „Electronic Today” (November 1996), Major General Gurbaksh Singh VSM, states

“The lessons gained from military history indicate that the key to military victory lies (regardless of military size of the opposing forces) in remaining ahead of the enemy in time sensitive SCORE loop of C4I2 process.If a defending force or weapon system can with some accuracy and sufficient warning finds out where the attacker is or his future course of action would be, it would be easier to defeat him by occupying position of advantage or by massing a superior force at the point of decision.”[3]

This statement reinforces how important spatial information is to field commanders for determining the best decision for military operations. “C4I2” in Major General Singh’s statement refers to Command, Control, Communication, Coordination, Information and Interoperability. He has rightly indicated the importance of Interoperability, which is very important aspect in the current scenario of proliferation of several computer systems and software systems used in the military operations.

Spatial data is as of crucial importance to the Military Commander in the battle as it is for a decision-maker in the planning and development of a state’s growth. The Ministry of Defense (MOD) in any country gathers data on routing, filtering, analyzing and presenting information for decision-making. The regional conflicts, rapid deployment and flexible response imposes heavy burden on military commanders, their staff and supporting system to keep up-to-date situation on the ground about enemy activities. Visualizing raw tabular data within a spatial framework has many benefits. Therefore digital mapping and GIS occupy center stage in activities as diverse as battlefield simulation, mission briefing and communications planning, logistics management and command control.

Terrain Evaluation:In land based military operations, Military field commanders would like to know terrain conditions, elevations for maneuvering Armour carriers, tanks and for use of various weapons. In addition, they need vegetation cover, road networks, and communication lines with pin pointing accuracy for optimizing resource utilization. A detailed land map with information on land use, terrain model and proximity of habitat is essential for military operations. All these details must be available to the field commanders in a datum to match with the equipment he uses for position fixing and communication in his area of operation. Any discrepancy in these inputs may endanger the operation. Target assessment can be done if the inputs are properly matched with the system used for firing the weapon. Magnetic variation, gravitational information are required for sensitive military operations.[3]

Viewing Spatial Data: Most potential users of GIS are viewers; ranging from field commanders to command staff. They need access to a geographic picture, map or photograph to help and assess a situation to carry out planned operations. Earlier GIS packages were proprietary in nature and restricted the use of data within confined specifications. A comprehensive database in multi-type data integration background needs an open GIS approach. An open GIS approach allows individual users choice of the most appropriate product and at the same time supports command requirements to specify an authorised map for operational reasons.[3]

Naval Operations: At sea naval vessels depend largely on extrapolative methods to navigate when there is no means of establishing position with visual aids. Global Positioning System (GPS) provides the means of determining the position at sea. Echo sounder provides measurement of the depth of the water below the vessel. Naval vessels operate at sea using several electronic gadgets. Recent technological advances have provided the means to assess the unknown to greater accuracy. At sea, complex natural features such as currents, wave conditions, sea surface temperatures and tides may prove at times deterrent to naval operations. A clear understanding of the complex ocean dynamics is an essential element for successful naval operations.

The recent introduction of Electronic Chart Display and Information System (ECDIS), helps navigate the ship safely in all weather conditions. Electronic Navigation Chart (ENC) is a replacement of conventional paper chart, which is used as tool for navigation provides in-put for detailed information about depth, hazards and navigational aids within the area. This supported by visual and audio alarms of ECDIS provide the navigator on bridge sufficient means to navigate the vessel safely. The display is used to provide selective information either spatial or textual information to the navigator for safe passage. ENC is the database for GIS operations and ECDIS is the real time GIS application in marine environment.

The United States military has used geospatial information in every conflict throughout its history of warfare. Until the last quarter century, geospatial information used by commanders on the battlefield was in the form of paper maps. Of note, these maps played pivotal roles on the littoral battlegrounds of Normandy, Tarawa and Iwo Jima (Greiss 1984; Ballendorf 2003). Digital geospatial data were employed extensively for the first time during military actions on Grenada in 1983 (Cole 1998). Since then, our military has conducted numerous operations while preparing for many like contingencies (Cole 1998; Krulak 1999). US forces have and will continue to depend on maps—both analog and digital—as baseline planning tools for military operations that employ both Legacy and Objective Forces (Murray and O’Leary 2002).[4]

Chapter 3 Geographic Information System

3.1 Defining the concept of GIS

Geographic Information Systems (GIS) are a part of the wider class of information systems. They have as their main characteristic the treatment of information taking into account its localization or spatial, geographical location in the territory, by coordinates. GIS technologies have emerged five decades ago in the need to facilitate complex geographic analysis operations for which existing systems did not offer any possibility or required a great amount of time or very difficult procedures.

Facilitating the processing and analysis of spatial data from both conventional and conventional sources (maps, plans, etc.) as well as from advanced technologies (aerial and satellite imagery, remote sensing, GPS), GIS systems are the only solution which can rationally, intelligently and efficiently solve the increasingly difficult problems related to the use of land resources. The applicability of GIS is virtually unlimited because the vast majority of human activities have an important feature in locating in space. Such a system is naturally used for the production of plans and maps, the management of public utility networks (water and sewage, district heating, electricity, telephone, gas, roads, railways, urban transport links, etc.) optimal location for an investment, study of the impact of an objective (nuclear power plant, airport, refinery) on the environment, etc

Thus, based on the definition of computer systems, we can say that the geographic information systems represent a technical and organizational set of equipment, people, methods, aiming at collecting, storing, analyzing and viewing geographic data.

SIG is a collection of hardware, software, geographic and personal data, designed to acquire, store, update, process, analyze, and display geographic information in accordance with the requirements of an application domain. In order to better understand this definition, I will make the following remarks:

• The hardware component means both the computing platform and the peripheral equipment for inputting data and displaying the results;

• The software component must offer a number of basic functions, with general applicability, and at the same time allow adaptation / extension to the specifics of any application; the functions provided must allow both vector analysis and automatic mapping as well as image processing and spatial modeling (raster) as well as multi-media database and access management;

• the geographic data component is determinant: the most expensive and longevous component of a GIS is the geographic database. Therefore, entering data is a matter of considerable importance. Data input can be acquired by: digitizing, scanning, field measurements, remote sensing, digital photogrammetry or conversion from other formats. Maintaining and updating geographic data is a second step, practically continuous in time and often requiring dedicated dedicated resources (hardware, software and personnel);

• Personal component means a team of three categories of specialists:

• Those who implement the basic software are involved in user training, technical assistance and consultancy;

• those who create and maintain the digital database are responsible for the content, accuracy and accuracy of data provided to users;

• Those who use the software and the geographic database to solve specific problems are involved in formulating the specifications for defining GIS projects / applications, developing specific technologies, generating GIS products and assisting decision-making processes.

A GIS approach necessarily implies the unitary treatment of a single and non-redundant database of graphical, cartographic, topological and tabular components. Although they play an important role in GIS, computer graphics are only one of the ways to consult or report the contents of a spatial database. The database allows for a diverse range of other types of exploration that require in particular processing and processing capacities on geographic and analytical criteria.

The main features of geographic information systems are:

• treatment of information, taking into account its spatial, geographical location in the territory by coordinates;

• assume unitary treatment in a single and non-redundant database of graphical, cartographic, topological and tabular components;

• Include a collection of spatial operators operating on a spatial database to geographically refer to actual information. A SIG data model is complex because it must represent and interconnect both graphical data (maps) and tabular data (attributes);

• simulation of actual situations and events.

3.2 GIS Definition

Geographic Information Systems have been the subject of numerous attempts to define, depending on the areas and academic disciplines involved. Some definitions have themselves reflected their evolution, simply considering them as instruments:

• "A geographic information system is a special case of an information system in which the database consists of observations on spatial distribution entities, activities or events that are defined in space as dots, lines, or areals. A geographic information system manipulates data about these points, lines, and areas to get information following ad-hoc queries and analyzes. " (Dueker, 1979).

• "A powerful set of tools for storing and retrieving, on request, transforming and displaying real-world spatial data for a defined purpose." (Burrough, 1986).

• "Tools that allow, from various sources, the acquisition, organization, management, analysis, development and presentation of localized information that contributes to space management." (Clarke, 1995).

Other definitions have in particular taken into account the automatic data processing side and software component of geographic information systems:

• "An information system that is designed to work with spatial reference data described by geographic or geodetic coordinates. In other words, a GIS is both a database with spatial reference data capabilities and a set of operators for be able to process those data. "(Star and Estes, 1990).

• "A geographic information system is a resident tool in an information system for researching and analyzing existing entities and events occurring on the Earth's surface. GIS technology integrates database-specific operations (e.g., query and statistical analysis) with the unique visualization and geographic analysis offered by mapping These capabilities distinguish between GIS and other information systems while giving them a great value for a wide range of public or private domains that are concerned with explaining events, planning situations and strategies. "(ESRI – Environmental Systems Research Institute).

There are even dictionary definitions: "Geographic Information Systems (GIS) are assemblies of techniques, methods, equipment, and people that aim to manage georeferenced information with the help of a computer." (British Encyclopaedic Dictionary).

3.3 GIS Components

Although the term GIS is most often used to name only a program package, a Geographic Information System has the following components:

• people;

• hardware;

• software;

• data;

• methods;

Fig 3. GIS Components

From an architectural point of view, a geographic information system is seen as being composed of a geospatial nucleus (tools for input, maintenance, analysis and output generation) that, together with implementation methods, give the geographic character of the system and geographic data which, together with access methods, gives it the title of computer system.

3.3.1 People Component

SIG staff includes both specialists who design and maintain the system, as well as those who use it as a tool for solving problems in their field of activity. SIG technology would be of limited value without highly trained personnel to manage the system and develop strategies for its application to real world issues. For proper data collection, processing, and querying, specialized staff are required to comply strictly between the appearance and language of the input data with those of the expected output data.

In order for a team to work with a GIS to have the maximum yield, specialists must be involved in at least four areas: quantitative geography, analysis and programming, system engineering, design. In addition, depending on the source of the data, a specialist in geodesy-topography and / or photogrammetry could be included in the team. There is also a need for a specialist with user training: urban planning, geology, environmental protection, defense, etc. An essential condition is the training of team members in the area of ​​space analysis and generally in the use of GIS programs.

3.3.2 Hardware Component

GIS hardware is nothing but a system, a computer on which to run the GIS application. In addition to the keypad, monitor, cable, internet connection, there are components such as: professional printers, scanners, special equipment that can scan maps and enter map data in the GIS database.

Fig 3. GIS Hardware Components

Within the hardware component, the constituent elements can be classified into three subcategories depending on the specifics of the actions carried out:

• Hardware used to collect and input data into the system,

• Hardware for processing and analyzing data

• Components for displaying the results of processing and analysis in different ways.

For the first subcategory, the data collection, the computer is linked to several types of data capture and digital conversion devices:

• mouse and keyboard (especially for non-volatile data);

• digitizer (for digitization);

• scanner (for scanning);

Fig 3. GIS Hardware Components

Also, all the devices used for field data collection: theodolites, levels, total stations, GPS receivers, field electronics cards can also be considered as this category.

The hardware components for processing and analyzing data include SIG software packages running on a wide range of machines, from central servers to individual or networked workstations. The minimum configuration that a SIG program runs is a GPU or CPU, basic memory (RAM), hard disk drive, and video card.

For showing the final results (component for different data display), the computer is linked to:

• a monitor or video projector (for viewing the final maps);

• a peripheral (printer or plotter) to allow maps to be printed on paper (paper, transparencies, photographic paper);

• a CD / DVD burner (for printing the results on an information medium);

• a modem (for transmitting network results).

3.3.2 Software Component

When talking about GIS software, we refer to the tools used to store, analyze and display geographic information. In any GIS system, data, in addition to spatial representation, has links to various attributes that are stored in a database. Most applications provide an easy-to-use interface for querying data and spatial manipulation by using tools such as zoom or pan.

SIG comprises a sum of programs grouped into modules or subsystems. Regardless of how you organize it, a complete GIS must include the following software components geared to geographically stored and processed data:

• Input, editing, transformation, verification, data validation system;

• Database management system;

• Image analysis processing system;

• Computerized cartography system;

• Statistical and spatial analysis system;

• Display and playback system.

The software component should meet the following requirements:

• Scalability – software applications present in a SIG must be designed to run on as many hardware platforms as possible: personal computers, workstations, servers, etc .;

• The possibility of networking – the technological advance in the computer industry has opened a new world of the possibility of implementing GIS, from small systems on a single computer to large networks and workstations sharing their access to databases;

• Integrated databases – software applications present in a SIG provide the ability to handle impressive capabilities databases that include data of different types: geographic information, numeric data in different formats, texts, images, photographs, etc .;

• data security – many of the data contained in a SIG are only for a specific category of staff, for the rest of the users they are secret; Given that a SIG can be run on a network, it is necessary to protect the data at different levels of security for the users;

• Modeling and decision options – A SIG must have tools to model phenomena; the analysis and modeling of such phenomena must result in the optimal decisions to be made on the issue in question;

• The ability to convert inherited data – a SIG must be able to inherit databases, images, and other types of map data from another geographic information system and convert them into the formats they work with.

3.3.3 Data Component

For apps to work properly, they need data. A spatial database consists of: the graphical database and attribute database.

In systems, the two databases integrate and form a single entity, the digital map, which is a collection of graphical symbols, to which a collection of attributes (attributes) is attached to each object represented on the map. Both the graphic symbols and the associated attributes are organized in numeric format so that they can be processed by the computer.

The data may come from the following sources: topographic measurements, GPS determinations, satellite and photogrammetric recordings, existing maps and plans, other digital sources.

Fig 3. GIS Data Components

3.3.3 Methods Component

It represents the techniques for collecting, validating, organizing, storing, supplying and using data and information in various forms. In different organizations, people need to draw up various types of reports, make decisions, and generally apply their own skills to solve all sorts of issues. The activities, the processes carried out to accomplish these things are called methods. To be successful, the geographic information system has to operate in accordance with a well-designed business plan and regulation that represent unique operating models and practices for each organization.

Designing an SIG as a real world model for a particular application involves methods of identifying and conceptualizing the problem to be solved. The manner in which data is entered, stored and analyzed within a GIS must reflect how the information is subsequently used in a research activity or in making a decision. Organizations using the GIS must establish the most appropriate procedures to ensure that the data are used correctly and efficiently and to maintain their quality.

3.4 The disciplines that contribute to the founding of the GIS

Geographic information systems represent an interdisciplinary science based on the knowledge of several disciplines

Geography is concerned with the understanding of the world and the place occupied by the human being within it. Geographers have a long tradition in working with spatial data and many techniques that have been taken over by the GIS.

Cartography deals with the representation of space information, most often in the form of maps. It is an area with a long experience in drawing up maps. The map is a very efficient way for storage spatial information, and for understanding and analyzing them. Existing maps are an important source of data for new computerized systems.

Photogrammetry uses aerial photographs and special techniques to obtain information on them. In the past, it has been an important source for most topographic data.

Remote sensing means for GIS information collected by satellites or planes. Currently, they are purchased in digital form with the help of satellites.

The topography provides accurate data on the location of land, buildings, and other entities.

Statistics provide many ways to build computational or data analysis models; statistics are important for understanding errors and uncertainties in GIS.

Mathematics and, in particular, topology, geometry and graph theory that provide many methods that can be exploited in GIS.

Artificial Intelligence provides many techniques useful in the decision-making process, for example in building expert systems that help the user in formulating questions that will draw useful answers.

The cadastre provides accurate data on properties and landlords.

CAD – Computer Aided Design – Provides software that can be used by the SIG in data input, representation, display and visualization.

SGBD – Database Management Systems – Contributes through program packages and methods to processing very large data sets needed in many GIS applications such as cadastral or census data.

Information technology provides the analyst with a wide range of methods and software tools to solve specific problems.

Each of the above-mentioned areas offers techniques and methods that make up a GIS. But no one can be expert at the same time in all these areas. The GIS analyst should have only a general idea of ​​the relationship between the GIS and each of these areas. It is more important to make the contribution of its own field of expertise in building a GIS.

3.5 Scope of application of GIS

GIS technology proves its applicability in any field of activity based on spatial information processing:

1.THE CADASTRE

– the complete integration of the cadastral process, starting from the field measurements and ending with the editing of the plans and registers of cadastral evidence

– communication facilities with the taxation system of the Ministry of Finance, other public bodies or individuals requesting cadastral data

2. LOCAL HISTORY, PLANNING AND ADMINISTRATION

– Urban transport optimization

– Establishment of the optimal location of the new objectives (urban endowments, housing districts, industrial objectives, social-cultural objectives, etc.)

– housing space

– arrangements on various criteria

– study of urbanism

– granting construction / demolition permits

– inventory of land use

– organizing the collection and storage of household waste

– organizing emergency interventions (rescue, police, firefighting, troubleshooting)

-reports necessary for police, firefighters, financial districts

3. CARTOGRAPHY

-Realization and updating of maps and topographical plans

-The making and updating of thematic maps

-integration of land data, photogrammetry and satellite data into the map content

4. HYDROLOGY, OCEANOGRAPHY

– watercourse and watercourse mapping

-the study of coastal areas

– Surface pollution of surface and deep water

– river transport analysis

– Surveillance of river basins

-preventing avalanches / floods

-batimetrie

5. GEOLOGY

– the geological formations

-Tectonic studies

– mapping, inventory and field supervision

6. TRANSPORT AND TELECOMMUNICATIONS

-design, maintenance and optimization of transport networks (roads, railways, cables, etc.)

– optimization of transport routes (supply, freight transport, passenger transport, public transport)

– special railroad (railways, roads, telecommunication)

-traffic surveillance (road, rail, etc.)

7. STATISTICS, POPULATION EVIDENCE, CENSUS, DEMOGRAPHY -registry of the population – territorial census data analysis -analysis of demographic movements – making and disseminating statistical directories

8. TRADE

-marking of wholesale stores according to auto access, competition, consumers

-organizing the distribution of goods to customers at the nearest warehouse

– inventory management

9. BANK FINANCE

– zoning on financial districts

-collecting taxes and taxes

-Guarantee of loans

-Investment of customers

10.PROTECTION OF THE ENVIRONMENT

-Supervision of nature reserves

-analysis of soil, water, air pollution

– the effects of various pollutants

-analysis of the areas affected by different pollutants (chemical, sound, physical, etc.)

-Analysis of areas affected by natural disasters

11. OIL AND GASES

– inventory, mapping and field surveying

-Project design, maintenance and optimization

12. AGRICULTURĂ, PEDOLOGIE, SILVICULTURĂ ȘI ÎMBUNĂTĂȚIRI FUNCIARE

-cartare pedologică

-cartare silvică

-cadastru silvic

-supravegherea stării de sănătate a pădurilor

-supravegherea culturilor

-proiectarea și supravegherea sistemelor de irigație

-urmărirea eroziunii solului

-analiza transportului agricol

-analiza stressului vegetal

13. EDILING ASSISTANCE: Automated Mapping / Facilities Management (AM / FM) applications for distribution companies for electricity, gas, water, etc.

(a) applications in the field of water and sewage distribution:

-planning the maintenance of the network and equipment in the water distribution and sewerage system

-inventory of consumer requirements

– mapping and surveillance of the water distribution and sewerage network

– recording malfunctions, planning intervention work and identifying affected customers in the event of an accident

-identifying routes affected by the infiltration of some pollutants, location of sources of pollution and warning to consumers

-planning the expansion of the water distribution and sewerage network

(b) applications in the field of electricity generation and distribution:

-elect the electrical equipment

-Inventory, analysis and surveillance of electrical equipment

-identifying the optimal location for a new lens

-planning maintenance, repairs

-design, maintenance and optimization of electrical networks Demographic analyzes for distribution planning and anticipation of peak peaks

-planning of complaints and consumer complaint handling operations

-optimization of meter readings and invoice collection by consumer pricing

-analysis of areas where frequent malfunctions occur

-identify and promptly notify all consumers affected by the temporary interruption of electricity supply for various reasons (damage, works)

-elect the charge of the electrical networks

14. SPECIAL APPLICATIONS

-topographic, hydrographic, aeronautical

-The military quad military strategy / tactics -navigation

– border control

– land analysis (visibility, accessibility, passage corridors, slopes) -information, counter-information

15. MILITAR

– Military maps

– military cadastre

– military applications

– spy plans and strategies

– information and counter-information

– tactical (defensive, offensive, counterattack, encircling) schemes.

– navigation

– virtual flights

3.6 The properties and purpose of a GIS

The purpose of GIS is to collect, validate, store, analyze and view geographic data. A geographic date is a feature of a particular object or phenomenon in the terrestrial space. The traditional form of storing and viewing geographic data is the map that we can consider as a model of the surrounding world used to perform geographic analysis. On the map, geographic data is represented by dots, lines and surfaces, drawn on a planar support (paper, cardboard, plastic, etc.) and coded with special symbols (conventional signs, hatches, colors) explained by a legend; or an accompanying text. The map, along with the explanatory elements necessary for its interpretation, is a geographic database.

On the analogue map, the analyzes mentioned above can only be made by man based on a personal image of the geographically formed space on his model in the form of graphical representation. The accuracy of decisions resulting from a geographic analysis depends both on the knowledge and experience accumulated in the specific field of the study conducted by the person making the analysis and on the quality of the available map (accuracy, timeliness, completeness, expressiveness, etc.).

The main problem that an GIS attempts to solve is to automatically perform geographic analysis using the computer for this purpose. A GIS must include facilities to answer the following five generic questions:

a) LOCATION: "What is at …?"

This question seeks to identify objects / phenomena located at a specific geographical position specified by name, postal address, or geographic / rectangular coordinates.

b) CONDITION: "Where are they …?"

This question seeks to find the exact position of an object / phenomenon or a set of specified requirements (eg: where can be placed an anti-missile shield in Romania in order to protect Europe against potential ballistic missile attacks or a deforested area of ​​at least 3000 sqm with ground suitable for building construction, located no more than 50 m from a national road).

c) TRENDS: "What has changed since …?" This question seeks to highlight changes in a geographical area over a period of time (for example: what is the wooded area of ​​the Albac commune, Alba County in 2016 as compared to 2010)

d) MODELING: "What particularities are there in the area …?" This question involves a complex analysis looking for cause and effect correlations (for example: is cancer the major cause of death for residents around a nuclear power plant?) Or anomalies that occurred at some point in an area with known characteristics.

e) SIMULATION: "What would happen if …?" This question requires a complex analysis to anticipate the impact of an event (adding / removing / transforming an object / phenomenon) to the environment (for example: what can happen if a new road, landfill, etc. if a toxic substance accidentally penetrates the drinking water pumping station?)

3.7 GIS Functions

Like any information system, the SIG also has certain functions:

-introduction and validation of data;

-storage and management of databases;

-analysis and modeling of data;

-view output data.

3.7.1 Data entry and validation

Geographic data sources have a very wide variety, but in general, the most important source is existing maps. Other data sources can be: existing databases, satellite imagery and aerial photographs, or data from field measurements.

Data input is the data conversion process in the form in which it exists in one that can be used by the GIS. Data entry can be done manually, by automatic transfer, by scanning or by vectorial digitization. Once entered into a GIS, data must be stored efficiently and must be available for quick access to multiple users.

Validation consists in checking the quality of the data. Verification can be done both for errors entered during the data loading process and for the original data.

3.7.2 Storage and management of databases

In a GIS, data can be stored in thematic tables, thematic layers, or in a database management system (SGBD).

Fig 3. GIS Layers

It is possible to structure a database with each type of data as separate layers. Once the data has been entered into the database, it is possible to modify it in attribute type information and update data according to the database. Effective data storage facilitates analysis and modeling. Database management functions are used to help organize, correct, and update data so you can perform effective analysis. Having data uploaded to the database, you can move to database analysis and modeling.

3.7.3 Analysis and modelling of data

GIS has the ability to perform data analysis and modeling. This is the function that fundamentally differs from other types of computer systems. A GIS allows queries and analysis to be performed quickly, which can be costly and long-lasting by other methods. It also has the ability to integrate data from different sources and produce new information. It is possible to process a single layer of one or more layers.

When data analysis and modeling is complete, it is necessary to present the results in a certain form for interpretation and connection to other data. So, the next set of SIG functions is the one that treats how to display and view output data.

3.7.4 Visualizing output data

The way the results will be presented after the analysis will depend on several factors: the cost of the results, the available time, the necessary equipment, etc. The visualization can be done on the monitor screen to detect possible errors before exporting the results. For final products, high quality plotters can be used if we need very good resolution or simple printers.

It should be outlined that the processing of a GIS does not end here. With the results, it can be noticed that if parameters change or additional data is included in the analysis, better results can be obtained. So, there must always be a return option for entering new data or changing existing ones, repeating analyzes, modifying modeling, and producing new results.

3.8 GIS programs

GIS technology is usable in all areas for which spatial information is relevant. Thus, SIG technology can be applied in all areas that use the geographical map for storing, analyzing, and representing the data it processes.

Within the GIS applications there are currently some main areas for which they are intended: the environment, transport, public services, cadastre, military, etc .; therefore, in short, it can be said that GIS is mostly used as a legal, operational and geographical instrument.

Before reviewing the major suppliers and their GIS products, we remember that we are dealing with both independent solutions and solutions resulting from the addition of GIS functionality to CAD platforms / environments. Most GIS vendors offer independent products, but there are also many elite producers who have resorted to CAD-GIS:

• Intergraph with "Modular GIS Environment" running on MicroStation

• Autodesk with Autodesk Map on AutoCAD

• Bentley with "MicroStation GeoGraphics" running on MicroStation

• ESRI with "ArcCAD" on AutoCAD.

Geographic Information Systems were, until recently, luxury applications. Today, powerful versions of personal computers have made these applications possible to address by a large number of users.

There are the following classes of GIS products:

• GIS Expert products;

• Desktop GIS products;

• GeoEngineering products;

• GIS Web products;

• AM / FM products;

• DBMS products

Because a spatial database is at the same time a graphical database and an attribute database that integrates and forms a single entity, to accomplish a GIS a single computer program is not enough. In general, the fundamental element of a GIS is the digital map (a collection of graphic symbols) to which a collection of attribute symbols corresponds. All of these are organized in a numerical form, to be compatible with the architecture and the way computers work. For example: For a city, streets are represented by lines, and housing blocks by polygons (graphics data elements), and street cover types and building characteristics of buildings are stored in the attribute database. As a result, even software companies had to design the program packages as a sum of modules or subsystems that are enabled and used as the database is being created, and the need for spatial analysis is required. In addition, as the possibilities of these program packages are still limited by the real possibilities of designing and writing, a single program package is not enough, and users have to carefully analyze them and use at least two, three packages to achieve all of the proposed goals.

Regardless of the internal organization of these programs, a GIS must include the following software components geared to georeferenced data:

• System for data acquisition, editing, transformation, verification and validation;

• Database management system;

• Image processing and analysis system;

• Computerized mapping system;

• Statistical and spatial analysis system;

• Graphic display and rendering system.

Because they are designed to be run on different platforms, the following table lists the most commonly used GIS packages:

ARC / VIEW is a software package created by ESRI (Environmental Systems Research Institute), originally designed as a data viewer from Arc / Info. Over time, it has become one of the most widespread GIS programs in the world due to its ease of use and modular structure that virtually accepts an unlimited number of extensions created by users according to their own needs. Moreover, ESRI has created a true forum for GIS application developers for ArcView, providing a portion of the source code.

Modulated structure, vector and raster file capability, and last but not least, the possibilities to create and add extensions specifically designed for specific army purposes made me choose ArcGIS and ERDAS Image as program packages for solving military problems .

3.9 GIS programs with military applicability

3.9.1 ArcGIS

ArcGIS is a geographic information system (GIS) created by Environmental Systems Research Institute (ESRI), the Microsoft of GIS software. It is used for creating and using maps, geographic data, analysing mapped information, sharing and discovering geographic information, using maps and geographic information in a range of applications, and managing geographic information in a database.[5]

ArcGIS is a software package that allows creation, processing, integration, analysis and display of geographic data at different levels. ArcGIS (ArcView, ArcEditor, ArcInfo) or "servers" (ArcSDE, ArcGIS server and ArcIMS) can be accessed by ArcGIS Architecture (Figure 3.9). ArcGIS Desktop provides access to different "customers" if the data is local.

Fig 3. ArcGIS Software Package

ArcGIS is the common name for three products: ArcView, ArcEditor and ArcInfo. These products are built on a common interface and central capability with each product offering a different level of functionality. The ArcGIS core blocks are:

• ArcMap – for displaying and querying maps.

• ArcCatalog – for managing geographic data.

• ArcToolbox – for data analysis.

ArcGIS for desktop is available at different product levels:

ArcView is the desktop version of ArcGIS meant for a general (non-professional) audience used to view and edit GIS data or view data stored in a relational database management system.

ArcEditor includes all the functionality of ArcGIS, adding the ability to edit features in a multiuser geodatabase so that multiuser editing and versioning are possible. ArcEditor also adds the ability to edit topologically integrated features in a geodatabase.

ArcInfo is ESRI’s professional GIS software. It includes all of the functionality in ArcGIS and ArcEditor, adding some advanced geoprocessing and data conversion capabilities.

ArcReader is a free product for viewing maps. You can explore and query map layers, but you cannot change symbology or create new data.

3.9.1.1 ArcMap

ArcMap is the main component of ESRI’s ArcGIS suite of geospatial processing programs and is used to view, edit, create and analyse geospatial data. ArcMap allows users to explore data within a data set, symbolize features and create maps. ArcMap is used to create and manipulate data sets. The software package includes a style set of features. [6]

In ArcMap, we can build maps from spatial data layers, choose colors and symbols (conventional signs), request attributes, analyze spatial relationships, and design the final map formats. The ArcMap interface contains a list of map layers (or table of contents), a display area for map viewing, menus, and map tools.

The basic component with which ArcMap is working is the map. Maps are documents stored on the disk, that is, files with the mxd extension. We can manage the maps with ArcCatalog. On a map, we display geographic information as layers, where each layer is a particular element type. A layer does not store existing geographic data, but it refers to the data contained in coverages, shapefiles, geodatabases, images, grids, etc. In this way, the layers of a map will reflect the up-to-date information from the database.

The table of contents lists all layers of the map and shows what the elements of each layer are. The order of layers is important because the layers at the top of the table are drawn above those found below. Mattress layers can be organized into frames for data (data frames). The layers we want to display together can be grouped into a data frame. When a map has multiple frames for data, one of them is the active frame. The active framework is the one in which it is currently working. It can be especially distinguished by the fact that the material table is highlighted and its name is displayed in bold letters.

ArcMap is divided in two distinct sections: the data frame where information is spatial and the table of contents where data is aligned in terms of importance.

Data frame

The data frame displays a collection of layers drawn in a particular order for a given map extent and map projection. The table of contents on the left side of the map window sows the list of layers in the data frame. Each layer in the data frame is used to display information from a dataset.

Table of contents

The table of contents lists all the layers on the map and shows what the feature in each layer represents. The map’s table of contents helps you manage the display order of map layers and symbol assignment, as well as set the display and other properties of each map layer.

Views are put into place to allow users to choose between either data view or the layout view.

Layout

The purpose of layout view is for the final design of a map. A page layout is a collection of map elements organized in a virtual page design for map printing. Common map elements include ore or more data frames, a scale bar, north arrow, map title, descriptive text and a legend. For geographic reference you can add grids or graticules. Page layouts can be exported and used electronically, they are primarily designed for printing.

Data View

Data view is the geographic view of the data users import, used to explore, display and query data, edit files, geographic processing such as symbology, import data, editing, coordinate system definition. Only one Data Frame can be viewed at a time in this view.

3.9.1.2 ArcCatalog

ArcCatalog helps us organize and administer all GIS data. ArcCatalog presents the current list of drives and folders on your computer. The application works similar to Windows Explorer. It contains tools for exploring and finding geographic information, for recording and viewing metadata, for quick viewing of spatial data and for defining the layout of geographic layers.

Spatial data can be searched on your computer's hard drive, your local intranet, or the Internet. We can look for spatial data, review and add as ArcMap layers. ArcCatalog has tools for creating and viewing metadata (information about spatial data, for example, who created it and when, its intent to use, precision, etc.). ArcCatalog manages spatial data that is collected in a variety of formats. Geographic database (geodatabase) presents a new spatial data format designed specifically for ArcGIS.

Many common formats, such as shape file (shapefiles), layers, coverages, CAD files (e.g., DXF) and GDBs (geodatabases), organize spatial data into detail classes. A detail class is a group of points (point objects), lines (linear objects), or polygons (areals) that represent similar geographic objects. A group of linear objects representing rivers is a class of details. A group of polygons representing parks is another class of details. A shape file consists of a single class of detail, while in GDB (geodatabases), coverages and CAD files can contain several classes of detail.

ArcCatalog performs copy, move, delete operations (similar to working with Windows Explorer), but the deleted files in ArcCatalog do not move into the Recycle Bin and can not be recovered!

3.9.1.3 ArcToolbox

The purpose of ArcToolbox is to simplify GIS tasks with tools or wizards. ArcToolbox is a simple application that contains many tools for geo-processing. There are two versions of ArcToolbox: the full version that comes with ArcInfo and a simplified version for ArcEditor and ArcView. With ArcToolbox tools, data analysis and conversions, as well as their management, can be performed.

3.9.1.4 Extensions for ArcGIS Desktop

The ArcGIS desktop products include an enormous amount of functionality but extensions can also be purchased for extending this functionality.Extensions are simply bundles of scripts that are added together to ArcGIS. These are generally written in Visual Basic, Python or Avenue by users or ESRI staff members.[6]

ArcGIS Spatial Analyst used for modelling and analysis with raster data, creating density surfaces and conducting map algebra.

ArcGIS 3D Analyst allows users to visualize and analyze spatial data in 3D, extruding polygons and draping surfaces on elevation models, create video animations that simulate flying through the study area.

ArcGIS Geostatistical Analyst allows users to analyse raster and point data using advanced statistical methods.

ArcGIS Desktop is part of a larger system that includes ArcSDE and ArcIMS.

ArcSDE (Spatial Database Engine) allows us to manipulate and edit stored geographic data in a multi-user, centralized multi-user database managed with SGBDR, for example Oracle, SQL Server.

ArcIMS (Internet Map Server) delivers SIG data over the Internet and Intranet and allows us to build Web sites that can provide maps, spatial data, and SIG applications.

Network Analyst used for network-based analysis such as routing, determining closest facility and service areas. Networks can store information about traffic flow, one-way streets and travel time.

Tracking Analyst used for animate point data representing events and discrete times and places. It gives users the possibility to view events happening across time and space using the “playback” feature.

Business Analyst is designed to support business decisions through a series of advanced tools and extensive collection of industry data.

ComunityViz is used for visualizing and analysing land-use decisions. This product is distributed by Placeways.

3.9.2 ERDAS Imagine

Erdas is a program that deals with the processing of geospatial data and images. It uses teletext and graphic editing capabilities to allow users to apply their data and images to geographic information systems (GIS) and respond to geographic questions. Hexagon Geospatial, formerly called ERDAS, develops this technology. The latest version is 2015. Professionals using Erdas include geospatial analysts, geographers, and analysts who detect and report suspicious activity.

By manipulating image values ​​and image data, we may see features that would not normally be visible and locate their geographic location. The level of brightness or reflection of the light on the surfaces in the image can be helpful in vegetation analysis, mineral prospecting. Erdas allows users to easily create value-added products such as 2D and 3D images, 3D virtual flights, high quality maps and maps from geospatial data.

With a simple interface and ordinary workspaces, we can create our favorite tools. ERDAS offers easier and faster access to what you need to work efficiently and productively. Erdas also offers advanced processing tools: spatial modeling, map production, mosaicking, and change detection.

ERDAS has two data display modes: 2D and 3D. In the 2D window we can display raster, vector and annotation data. We have land analysis tools (land slope, visibility between points), mosaic, georeferencing, re / design of coordinate system and coordinate calculation, etc.

The 3D window called is called VirtualGIS. VirtualGIS is a large database visualization tool with real-time performance. Several layers of raster data and vector images can be viewed and interrogated in a virtual environment. VirtualGIS features are rendered by 3D viewing in the workspace. VirtualGIS uses OpenGL as the basic graphical language. OpenGL language allows the use of hardware accelerators for geometry or texture rendering, and gives VirtualGIS the ability to run on both UNIX workstations and PCs.

To make a 3D scene, VirtualGIS reduces the amount of data needed for display: only the portion of the image in the display field is loaded into memory. With the perspective, the more distant objects are rendered at a lower resolution than objects that are closer to the observer. To increase the 3D effect of the screen, high resolution is used for high-resolution images, while low resolution is used for low-resolution images.

Multiple terrain numerals, overlayed with vector layers, annotations and raster images, can be displayed in the 3D window. A DEM must be the first open image, then the loaded layers are placed above the pattern. These layers can be opened as a Virtual World or as a Project File. VirtualGIS can store any number of layers that can be selected for display.

Numerical models of the terrain in a Virtual World directory are always displayed in multi-resolution mode. DMEMs are always displayed in single-resolution mode (except Virtual World). For a faster rendering of a single-resolution image, the level of detail can be reduced when displayed.

VirtualGIS has all the tools to create a virtual flight or to simulate a combat action. Of these instruments we mention: airplane board display, flight path selection, azimuth, speed. We also have the option to choose the illumination mode (we can select a light depending on the position of the sun), the level of detail of the numeric model of the terrain and of the raster data.

Chapter 4 Specific data types to Geographic Information Systems

4.1 Genaral Information

Geographical data means the set of spatial data (coordinates) and descriptive data (attributes) associated with geographical objects / phenomena (streets, plots).

A geographic database is a collection of geographically organized data to facilitate storage, querying, updating and displaying by a lot of users effectively.

Spatial data used in SIG technologies can be categorized by:

• precision;

• The primary documents used;

• Update cycle.

A GIS manages two types of data:

• spatial;

• descriptive.

Spatial data represents the position and form of terrestrial objects (phenomena) using three graphical entities: points, lines, polygons.

Descriptive data is information about terrestrial objects (phenomena) located on a map using: attributes (questions) and attribute values ​​(replies).

GIS allows integration of purchased data:

• at different times;

• at stairs and with different resolutions;

• by various methods, the linking element being given by the geographic location in the territory.

By geographical referencing (georeferencing) it is meant to establish the relationship between the coordinates of a point on a plane sheet (map – 2D) and the actual geographic coordinates in the field (on the Earth surface, which is a geoid – 3D).

Geographic data is the coordinates of spatial points and non-spatial attributes measured at certain time points. On a numeric calculator, data storage is in the form of numeric codes. The internal representation of a map is done in two systems: the vector system and the raster system.

In the vector system the map is constructed largely from points and lines, each point and the ends of the lines being defined by pairs of coordinates (x, y). They can form arcs, surfaces or volumes (if a z coordinate is still attached). The geographic features are expressed by these entities: a well will be a point, a geodetic point will also be a point; a river will be a bow, a road will also be a bow; a lake will be a polygon, but a forested area will be a polygon.

In the raster system, images are built from cells called pixels. The pixel, or image unit, is the smallest element on a display surface, which can be assigned an intensity or color independently. Each pixel will be assigned a number that will be associated with a color. Graphic entities are built from pixel sets / matrices. A path will be a sequence of pixels of the same value; a forested area will also be identified by the value of the pixels that contain it. There are differences between the two systems on how to store, manipulate and display data. In Figure 13, the two representation systems of the same reality are represented in a simplified way.

ArcGIS can work with many different types of data.

Tabular data

Tabular data includes delimited or fixed width text files, Excel worksheets, ACCESS files and dbase files. This is where attribute data with any information about a location is stored. In order to be mapped, tabular data generally needs to be linked to spatial/ geographic data.

XY data

Some tabular data include XY coordinates.

Geographic data

The Tabular data used in ArcGIS can be used in other programs like Excel, SPSS and others, but geographic data is used only in GIS programs. Geographic data store information about location and can be categorized as vector or raster.

Shapefiles

Shapefiles are the most common format for vector data in ArcGIS. Vector data use points, lines and polygons to represent map features. As the other formats of geographic data, shapefiles link information about the location and shape of the map features to their attributes. Shapefiles are made up of three or more files that need to be stored in the same directory in order for ArcGIS to recognize them as shapefiles.

.shp-the file that stores the feature geometry (point, line or polygon)

.shx – the file that stores the index of the feature geometry

.dbf – the dBASE file that stores the attribute information of features. When a shapefile is added as a theme to a view, this file is displayed as a feature table.

.sbn and .sbx – the files that store the spatial index of the features. These two files may not exist until you perform theme on theme selection, spatial join or create an index on a theme’s shape field.

.pjr – the file that stores information about the projection. This will only exist for shapefiles with defined projections

Images

ArcGIS allows users to import and export many different types of images. Images are like tables in that they may contain information about a particular location, but they do not store information so they cannot be mapped.

Raster map layers

Raster data use grids made up of regular cells or pixels to represent spatially continuous data. They look like regular images, but each pixel is assigned real world coordinates and an attribute value so the data can be mapped. The users define the cell size allowing for very fine or course raster surface. Raster layers can be viewed in ArcMap without any additional extensions but Spatial Analyst extension is needed to analyse or create new raster layers.

Geodatabases

ESRI has moved toward a new geographic data model called a geodatabase to store multiple tables, shapefiles and raster images.

4.2 The vectorial model

The vector model responds to the need to represent an object in an exact manner as close as possible to reality. Spatial objects are classified into point objects (meteorological stations, schools, hospitals), linear objects (roads, hydrographic network, railways) and field objects (agricultural parcels, built surfaces, shooting polygons). For spatial analysis in SIG, only geometry data (position, shape, size) is not sufficient; the typology must also be created and used. Spatial objects can thus be represented on a map by dots, lines, polygons, these elements being described by position, spatial relationships between objects, and non-spatial attributes.

The internal representation of digital maps is based on the fact that the connection between map and field mapping is done by means of a geodetic projection appropriate to the specificity of the cartographic elements represented. Although theoretically the number of geodesic projections is very high and the underlying principles are very diverse, the GIS technique is mainly operated by means of a coordinate system, usually Cartesian, defined by a rectangular axis system x, y . In our country the system officially used in this context is the stereographic projection 1970. The vector model is recommended when the elements to be represented are identifiable on the ground in the form of basic geometric figures such as points, lines or surfaces. They represent elementary graphical components for the vector information retrieval format.

The vector system is based on three graphical primitives (the primitive graphic is the smallest graphical element used to create and store a vector image): points, lines, and surfaces (polygons).

POINTS

The point is the elementary element in geometry. It should not be confused with the cell in the raster representation because it has neither surface nor size. It represents a space position with 2 or 3 dimensions.

The dot component is represented on a discrete site and defines a cartographic element with a negligible perimeter and a form irrelevant to be represented by another possible elemental graphic component. This component can be practically a point in its geometrical sense and can not be associated with a surface or shape.

Typical examples of such a component are a terminal, a pillar of a high voltage current network or a mountain peak. In some situations, by simplification, can be represented as points and cartographic objects, such as a building, when, due to the chosen ladder, the contour or its shape can not materially materialize. In these circumstances it is necessary to associate, in the database, descriptive information or attributes of characterization of the object.

LINES

The line component is rendered by an ordered set of points that, when connected in the specified sequence, form a linear form associated with a negligible cartographic object so that it can not hold the surface attribute.

Sometimes, this component does not really have a surface area, for example, with example level curves. In other situations, such as the representation of tracks in the form of lines, they can be given the width of an attribute, from which the surface occupied by them can be deduced.

Typically, in a GIS context, this component is known as arc or polyline. In the case of lines, points may be the beginning or end points of a line, or line break points, the set of lines defining the shape of each line. Each such point is also defined by a pair of coordinates x, y. The minimum number of coordinate pairs that can define a line is two in the case of a straight line. The line is delineated by two points. If these two points have distinct roles, ie starting point or "node to" and end point or "node to", the line has a direction and becomes, therefore, mathematically a vector. Hence the term of representation in vector format.

Sometimes lines can be subdivided, for example, parts of a road with a distinct superstructure (asphalt, pavement, earth) or of different quality (quality road, damaged road, etc.). Some lines may be smooth, such as level curves or serpentines, and can be described by using circular, parabolic or polynomial mathematical functions. In this case, the GIS data must also express the parameters of the equations defining the line, such as the radius of the circle or the coefficients of a form equation:

f (x) = a0 + a1x + a2x2

Smooth curves obtained by mathematical adjustment, starting from a set of points, are called splines and are of great utility in interpolations or representation of level curves. Along with the coordinates of each point, you also specify the list of connected points for line formation and the order of connection. This makes it possible to draw lines effectively.

SURFACES

The surface component is a closed geometric figure, defined by a line that includes a homogeneous area from a certain point of view, such as a real estate, a plot, a farmland cultivated with a particular crop, the territory of a commune, a basin hydrographic etc.

The surface also bears the name of the polygon. The line defining the surface has the same start and end points so that the polygon created is closed. The surface component is the most complex elementary component, and it can also contain other surfaces. Thus, a surface may have an outer contour line and one or more interior surfaces, each with its boundary line.

Each of these geometries is linked to a database that describes their attributes. In a concrete example, a database describing lakes can contain information about the depth of a lake, water quality, pollution level. This information can be used to make a map that describes a particular attribute of the data set. For example, lakes could be colored depending on the level of pollution. Different geometries can also be compared. In this regard, GIS could be used to identify all wells (points) within a radius of 1 km of a polar lake with a high level of pollution.

Fig. 4. Vectorial Data

4.3 The Raster model

In the raster model, images are built from cells called pixels. The pixel term is the abbreviation of the "picture element" and represents the smallest part of an image. The pixel is the smallest element on a display surface, which can be assigned an intensity or color independently. Each pixel has assigned a number that will be associated with a color. A geographic entity is composed of pixel sets. The conventional pixel succession is row by row, top-down, and column-by-column, from left to right. Each location is assigned two dimensional coordinates in the image: column number and row number, each location containing a single value. For example, a lacquer will be a sequence of pixels of the same color. In other words, a raster system is composed of small cells in square or rectangular shape, having the surface equal to the resolution of the system.

Fig. 4. Raster Model

The specific pattern of a raster structure is characterized by subdivision of a geographic space into a cellular network, their dimensions defining the spatial resolution of the data, which depends on the size of the smallest object to be represented. In principle, the size of the cell is recommended to be less than half the size of the smallest object represented. The raster model can be considered as a mathematical model because it is rendered by a regular set of square or rectangular cells. Therefore, raster models are also known as grid models.

The geometric resolution of the model is defined by the size of the cells. Usual sizes are 10x10m, 100x100m, 1x1km, 10x10km, but in some applications, cells of 5x5m or even smaller can be used. Each such cell is considered flat and having the same quota.

The features of the raster are:

• the surface area of ​​each cell is not explicitly delineated;

• lines are zigzag-like;

• the density of information is the same regardless of the variability of the represented geographical objects;

• the position of each cell is given by the line number and the associated column, so there is no need to use the x, y coordinates for this purpose;

• the numbering of structured cells as a matrix starts at the top left of the representation;

• The raster pattern cells are also known as pixels.

In the case of GIS, the pixel is the smallest component of an image that can be treated individually by machining or graphic representation.

Each pixel may contain a single value called attribute for its characterization from a certain point of view. Therefore, geographic objects that are characterized by multiple attributes are retained as separate rasters for each attribute. Attribute values ​​can be either numeric or alphanumeric and can represent:

• Physical variables such as altitude of a cell, ground temperature or precipitation amount;

• territorial administrative structures specifying the county, the locality or other units;

• nature of land use;

• the unit value of the land;

• the nature of the lithological substrate;

• the majority species identified, defined by an agricultural or forestry culture;

• soil solvency class

Generally the raster system is a great resource consumer. Thus, an image A4 (210×297 mm) represents about 9 million cells (300 dpi = 12 dots / mm and 12×12 = 144 dots / mm2 and 144x210x297 = 8.981.280) with a laser printer resolution.

The raster model is simple, it contains two entities: the cell and the image. It is important to note that a cell has only one value, and that this value is valid throughout the cell, even though finer information is available in the update process. Its position is defined by line number and column number in an image and only one. It is clear that this entity does not include geographic objects. The latter can only be recognized by the image theme and the attribute value of each cell. An image involves one or more cells. Each image is defined by its theme and an image number. The territory containing this image is defined by coordinates and extremes (spatial expansion). These features also contain the unit of measurement and the attribute of each cell.

Raster data files are retained in various possible formats, related to the specific compression methods used. Due to the large amount of memory required to retain raster data, they are kept in compressed form, with instantaneous decompression only at the time and during the use of each file.

4.4 Comparative raster vs. vector analysis

Both systems have advantages and disadvantages. The main advantage of the vector system over the raster is that data storage is more efficient. In this system only the coordinates describing the characteristic features of the image need to be coded. It is usually used in large scale maps. In the raster system, each pixel in the image needs to be encoded. The difference between storage capacity is not significant for small images, but for big ones it becomes very important. Raster graphics are used when it is necessary to integrate thematic maps with data captured by remote sensing.

Fig. 4. Raster vs Vector Data

Choosing the model type, vector, or raster used to represent cartographic elements is an important decision-making issue that should take into account the advantages and disadvantages of each alternative. Some of these aspects have been presented punctually in describing types of models.

The vectorial model:

Advantages:

– good display of models containing entities;

– compact data structure;

explicit description of the topology;

– easy coordinate transformation;

– precise graphic representation regardless of scale;

– the ability to download, update, and generalize graphics and attributes

b) Disadvantages

– complex data structures are reached;

– combining multiple polygon networks by intersections and overlapping involves a long processing time;

– presentation and display are time consuming and material resources;

– spatial analysis within the polygons is not possible, being considered homogeneous

The raster model:

Advantages:

– simple data structures;

– facility to handle attributes defined by location;

– the easy way of spatial analysis and data filtering;

– the ease of mathematical modeling due to the fact that the cells have a simple, regular shape;

– processing technologies are relatively inexpensive;

– data is available in a large number of available formats;

– It is possible to analyze satellite images and retrieve the information.

Disadvantages

– high volume of retained data;

– the use of large cells greatly reduces spatial resolution;

– the appearance of coarse raster maps is not satisfactory;

– the difficulty of transforming the coordinates and the great amount of time required to achieve them, even when it is possible to signal even loss of information and distortions of the shape of the cells

4.5 Vector formats used in the military field and particular structures of geospatial databases

Military institutions use GIS applications to manage, analyze and view large geographic databases. In order to achieve this goal, it is necessary to work with standardized geographic data.

Standards for Digital Data:

• MIL-STD-7074 DIGEST

• MIL-STD-2407 VPF

• MIL-STD-2412 VPF simbology

• MIL-STD-2408 Glossary of Feature and Attribute Definitions

Chapter 5 Analysing the military scenario using ArcGIG

5.1 Input Data

LiDAR

The name LIDAR is used as an acronym of Light Detection and Ranging.

LiDAR is a surveying method that measures distance to a target by illyminating the target with pulsed laser light and measuring the reflected pulses with a sesor. Differences in laser return times and wavelengths can then be used to make digital 3D representations of the target.

Lidar is commonly used to make high-resolution maps, with applications in geodesy, geomatics, archaeology, geography, geology, geomorphology, seismology, forestry, atmospheric physics,laser guidance, airborne laser swath mapping (ALSM), and laser altimetry. The technology is also used in control and navigation for some autonomous cars.[7]

RGB Data

The name of the model comes from the initials of the three additive primary colors, red, green and blue.

The RGB color model is an additive color model in which red, green and blue light are added together in various ways to reproduce a broad array of colors.

The main purpose of the RGB color model is for the sensing, representation and display of images in electronic systems.[8]

Orthophoto

An orthophoto is an aerial photograph or image geometrically corrected (orthorectified) such that the scale is uniform: the photo has the same lack of distorsion as a map. Unlike an uncorrected aerial photograph , an orthography can be used to measure true distances, because it is an accurate representation of the Earth’s surface, having been adjusted for topographic relief, lens distorsion and camera tild.

Orthophotographs are commonly used in Geographic Information Systems as a “map accurate” background image.

An orthophotomap is a raster image made by merging orthophotos, aerial or satellite photographs which have been transformed to correct for perspective so that they appear to have been taken from vertically above at an infinite distance.[9]

Digital Surface Model

A DEM is a representation of the elevation of the Earth’s surface above a certain datum (e.g. mean sea level) in digital form. This is achieved taking elevation measurements at regular (e.g. every 50 metres) or irregular spaced points (e.g. every 3 arc seconds) over the Earth’s surface.[10]

There are various acronyms used to describe digital elevation models. Two very popular ones are Digital Terrain Model (DTM) and Digital Surface Model (DSM).

A DSM is a DEM that represents the elevation of the surface a remote sensing system will first meet (i.e. when aerial photography is undertaken the top of a building, forest, etc.). Thus, the resulting DSM includes the elevation of the bare earth terrain plus the natural (e.g. trees, shrubs) and man-made features (e.g. buildings).

DSM's measure the height values of the first surface on the ground. This includes terrain features, buildings, vegetation and power lines etc. DSM's therefore provide a topographic model of the earth's surface. DSM's can be used to create 3D fly-throughs, support location-based systems and augmented simulated environments.

An example of a DSM is the NASA’s Shuttle Radar Topography Mapping (SRTM) mission, which covered about 80% of all the Earth’s land (approximately 3 arc seconds ≈ 90m spacing irregular points).[11]

Fig. 5. Passive and active sensors

Fig. 5. RGB Ortophoto

Fig. 5. DSM

Two datasets were acquired simultaneously by passive and active sensors (see Fig. 5.1). Both datasets were acquired on March 13, 2011, using an airborne platform flying at an altitude of 300 mover the harbour area of Zeebruges, Belgium (51.33◦N, 3.20◦E). The Department of Communication, Information, Systems and Sensors (CISS) of the Belgian

Royal Military Academy (RMA) provided the dataset and evaluated its accuracy while the service provider acquired and preprocessed the data.

The passive dataset is a 5-cm-resolution RGB orthophoto acquired in the visible wave length range (see Fig. 5.2 ).The active source is a LiDAR system that acquired the data using repetition rate, angle, and frequency of 125 kHz, 20◦, and 49 Hz, respectively. The laser repetition rate refers to the number of times per second a scanning device samples its field of view, the angle indicates the field of view, and the frequency refers to the number of emitted pulses per second. For obtaining a digital surface model (DSM) with a point spacing of 10 cm, the area of interest was scanned several times indifferent directions with a high-density point cloud rate of 65 pts/m2. The scanning mode was “last, first, and intermediate.” Multiple returns are capable of detecting the elevations of several objects within the laser footprint of an outgoing laser pulse. The first returned laser pulse is the most significant return and is generally associated with the highest feature in the landscape like a tree to the top of a building. The intermediate returns, in general, are used for vegetation structure, and the last return for bare-Earth terrain models. This scan mode was used to filter the cloud points and to facilitate the creation of the DSM. Indirect georeferencing using a large set of well-distributed ground control points was selected as the method for coregistration due to its accuracy and robustness against interior orientation parameters biases. This procedure completed the georeferencing obtained using the LiDAR system’s GPS and the inertial navigation system on boardtheaircraft.Boththeraw3-D point cloud (seeFig. 5.1)and the DSM (see Fig. 5.3) were distributed to the community.

This dataset corresponded to the finest spatial resolution addressed so far by the IEEE GRSS Data Fusion Contests, and made for a very challenging image analysis and fusion competition. The large data volume was possibly time consuming and expensive to process, but the highly detailed information allowed the users to provide results resembling a real-world situation. Nevertheless, this rich dataset demanded the innovation and the adaptation of many numerical tools for registration and for obtaining the relation between the target geometry, the measured scattering, and the corresponding height and target scattering properties in the 2-D and the 3-D dimensions.[12]

5.1.1 Adding raster data to a map

There are two main options to use when adding a raster data to a map:

Add it by dragging and dropping from the Catalog or the Windows file browser window.

Add it using the Add Data button on the Standard toolbar.

When a raster data is added via Add Data dialog box or from the Catalog window, each unique dataset type appears with its own icon.

Raster data can be single-band raster (Fig. 5.4) or containing multiple bands (Fig. 5.5) .

5.1.2 NoData values in raster datasets

Fig. 5. Orthophoto raster with black noData value

Fig. 5. DEM raster with black noData value

Cell values can be either positive or negative, integer or floating point. Cells can also have a NoData value to represent the absence of data.

NoData is stored as a mask that is part of the raster dataset, or using a pixel value in the dataset that is not used as a valid value elsewhere in the dataset. In my example the value stored for noData is 0 for the orthophoto raster (Fig. 5.6) and -1000 for the heightmap raster (Fig. 5.7) .

The noData value needs to be defined in raster properties from Catalog after identifying the value in the scene, and after that check Display Background Value in Table of Contents, Properties, Symbology (Fig. 5.8 , Fig 5.9).

Fig. 5. Orthophoto raster after the noData value is defined

Fig. 5. DEM raster after the noData value is defined

5.1.3 Merge multiple raster datasets into a new raster dataset.

When working with a large number of raster datasets Mosaic to New Raster tool is a solution for merging the raster datasets into one.

The inputs must have the same number of bands, same bit depth and the pixel type must be set to match the existing input raster datasets. The output can be saved to BIL, BIP, BMP, BSQ, DAT, Esri Grid, GIF, IMG, JPEG, JPEG 2000, PNG, TIFF, or any geodatabase raster dataset.

Fig. 5. DEM raster datasets mosaic

Fig. 5. Orthophoto raster datasests mosaic

5.1.4 Georeferencing a raster dataset

Georeferencing means that the internal coordinate system of a map or aerial photo image can be related to a ground system of geographic coordinates. Display software can show ground coordinates (latitude/longitude or UTM coordinates). Geographic locations are most commonly represented using a coordinate reference system, which in turn can be related to a geodetic reference system (WGS-84).

Generally, in ArcGIS raster datasets are georeferenced using existing spatial data (target data), in my case, the base map of the world. The process involves identifying a series of ground control points with known x,y coordinates that link locations on the raster dataset with locations in the spatially referenced data. Control points are locations that can be accurately identified on the raster dataset and in real-world coordinates. Many different types of features can be used as identifiable locations, such as road or stream intersections, buildings, street corners.[13]

The control points are used to build a polynomial transformation that will shift the raster dataset from its existing location to the spatially correct location. The connection between one control point on the raster dataset ( the from point) and the corresponding control point on the aligned target data (the to point) is a link.

The number of links needed to georeference the raster depends on the complexity of the transformation wanted. Typically, having at least one link near each corner of the raster dataset and few in the interior produces the best results.

5.1.5 Vector map

A feature class is a collection of geographic features that share the same geometry type (point, line or polygon) and the same attribute fields for a common area. I created feature classes for buildings, roads, bridges, beach, water and points of interest. In geodatabases, related feature classes are grouped together in a feature dataset.

There are four primary ways to create a new feature class. I created the feature class using ArcCatalog.

The buildings polygon feature class contains an attribute table with id, shape, importance, shape area, and type. Tor the type field I created 4 subtypes for Houses, Hospital, Military Base and Containers. Every subtype is symbolized differently depending on the value of the field.

The Water and Beach polygon feature classes attribute table have only Id and shape area fields.

Bridges are a point feature class and the attribute table contains only the Id of the point.

For Roads creation I used OpenStreetMap and exported the roades of zeeBrugge as KML. I converted the KML to shapefile and import the shapefile in the Database containing the others feature classes. The roads attribute table contains the following fields: Id, Shape, Name, Highway (for the type of the road).

5.2 Landing Areas

5.2.1 Introduction

In general, a landing area for a helicopter needs to be at least 20 feet wide and long in a square formation. A safety area of 35 feet on each side must be maintained with no buildings or obstructions. In order for the helicopter to conduct a proper landing and takeoff approach, the zone must also provide a 300 foot strip on either side of the landing area.[14]

For the landing areas there are three criteria I took into consideration: The slope of the field, the distance from obstacles (like buildings, containers, vegetation, etc.) and the type of the field (water, sand, etc.)

5.2.2 Triangulated Irregular Network

A triangulated irregular network (TIN) is a representation of a continuous surface consisting entirely of triangular facets. The vertices of these triangles are created from field recorded spot elevations through a variety of means including surveying through conventional, Global Positioning System Real-Time Kinematic (GPS RTK), photogrammetry, or some other means. Associated with three-dimensional data (x, y, and z) and topography, TINs are useful for the description and analysis of general horizontal (xand y) distributions and relationships.[15]

Digital TIN data structures are used in a variety of applications, including geographic information systems (GIS), and computer aided drafting (CAD) for the visual representation of a topographical surface. A TIN is an vector-based representation of the physical land surface or sea bottom, made up of irregularly distributed nodes and lines with three-dimensional coordinates (x, y, and z) that are arranged in a network of non-overlapping triangles.

TIN are based on a Delaunay triangulation or constrained Delaunay. Delaunay conforming triangulations are recommended over constrained triangulations. This is because the resulting TINs are likely to contain fewer long, skinny triangles, which are undesirable for surface analysis.[16]

I converted the digital elevation model to a Triangulated irregular network (TIN) surface model. For this task I have used Raster To TIN tool. The Raster To TIN tool is used to create a triangulated irregular network whose surface does not deviate from the input raster by more than a specified Z tolerance.

Raster To TIN first generates a candidate TIN using sufficient input raster points to fully cover the perimeter of the raster surface. After that incrementally improves the TIN surface until it meets the specified Z tolerance by adding more cell centers during an iterative process.

The Z factor is used to convert z-units (for example feet to meters). The output TIN heights will be multiplied by this value to perform the conversion.

The Z tolerance is given in the z-units of the output TIN (Fig. 5.13).

Fig. 5. Z tolerance

5.2.3 Surface slope

For each cell, I needed to calculate the maximum rate of change in value from that cell to its neighbours.The maximum change in elevation over the distance between the cell and its eight neighbours identifies the steepest downhill descent from the cell. If there is a cell location in the neighbourhood with NoData z-value, the z-value of the center cell will be assigned to the location. At the edge of the raster, at least three cells will contain NoData as their z-values.

The Surface Slope creates polygon features that represent ranges of slope values for triangulated surfaces.

The output can be calculated in two types of units, degrees or percent (percent rise). The percent rise can be better understood if you consider it as the rise divided by the run, multiplied by 100. Consider triangle B below. When the angle is 45 degrees, the rise is equal to the run, and the percent rise is 100 percent. As the slope angle approaches vertical (90 degrees), as in triangle C, the percent rise begins to approach infinity (Fig. 5.14).

The surface normal of each triangle, which is given by the vector cross-product of two triangle edges, is used to determine slope in percent or degrees. Each resulting polygon represents a range of slope values based on the classification breaks used when executing the tool. The default classification breaks divide slope measurement into nine groups and are indicated below.

Table 5.1 Slope ramp

Fig. 5. Slope Vector Map

From the feature class created after Surface Slope a new feature class must be created containing only the polygons that fulfil the condition for a suitable landing area (for example, lower than 5 degrees, which means the slope code 4 of the attributes table).

The select tool extracts features from an input feature class or input feature layer, typically using a select or Structured Query Language (SQL) expression and stores them in an output feature class.

The select or SQL expression gets build with the Query Builder, or simply typed in. If no expression is entered only the selected features are written to the output feature class and if an expression is entered for input features, the expression is only executed against selected features and the expression-based subset of the selected set is written to the output feature class.

5.2.4 Euclidean Distance

The next step is to find the areas that are suitable for landing depending on the second criteria which means that the areas must be further than 10 meters from an obstacle. For this task I will use the buildings and the containers areas as obstacles.

The Euclidean distance describes each cell’s relationship to a source or a set of sources based on the straight-line distance.

The Euclidean distance tool gives the distance from each cell in the raster to the closest source.

The source identifies the location of the objects of interest. In my case the objects of interest are buildings and containers, and because the object of interest is a feature class, it will internally be transformed into a raster when run the tool.

Euclidean distance is calculated form the center of the source cell to the center of each of the surrounding cells. For each cell, the distance to each source cell is determined by calculating the hypotenuse with maximum x and maximum y as the other two legs of the triangle. The shortest distance to a source is determined and if it is less than the specified maximum distance, the value is assigned to the cell location on the output raster.

The output values for the Euclidean distance raster are floating-point distance values.

The Euclidean distance output raster (Fig. 5.18) contains the measured distance from every cell to the nearest source. The distances are measured as the crow flies in the projection units of the raster, such as feet or meters, and are computed from cell center to cell center.

Fig. 5. Euclidean Distance output raster

To be able to convert the Euclidian Distance raster to polygons the pixel type must be integers.

The INT tool converts each cell value of a raster to an integer by truncation. The input values can be either positive or negative. If rounding is preferred to truncating, a 0.5 input raster prior is needed to perform the operation. The INT tool always truncates the number (0.5 becomes 0 and -1.5 becomes -1).

Storing data as an integer raster will use significantly less disk space than the same information stored as a floating-point raster.

Raster to Polygon tool converts a raster dataset to polygon features (Fig. 5.19).

The input raster is the output raster from the INT tool that represents the distance from the defined obstacles.

The Field parameter allows users to choose which attribute field of the input raster dataset will become an attribute in the output feature class. If a field is not specified, the cell values of the input raster (the VALUE field) will become a column with the headingGridcode in the attribute table of the output feature class(Fig. 5.20). The Gridcode field represents the distance from the obstacles in meters.

Fig. 5. Euclidean distance to polygon attribute table

From the feature class created by the Raster to Polygon tool a new feature class must be created containing only the polygons that fulfils the condition for a suitable landing area (for example further than 10 meters from the obstacles which means gridcode higher than 10) .

The select tool extracts features from an input feature class or input feature layer, typically using a select or Structured Query Language (SQL) expression and stores them in an output feature class.

The select or SQL expression gets build with the Query Builder, or simply typed in. If no expression is entered only the selected features are written to the output feature class and if an expression is entered for input features, the expression is only executed against selected features and the expression-based subset of the selected set is written to the output feature class.

5.2.5 Intersect

The output polygons with the right slope and the ones with the right distance must be intersected to polygons that fulfil both conditions.

The Intersect tool calculates the geometric intersection of any number of feature classes and feature layers. The features or portion of features that are common to all inputs (they intersect) will be written to the output feature class (Fig. 5.22).

Intersect determines the spatial reference for processing. This will also be the spatial reference of the output feature class. Cracks and clusters the features. Cracking inserts vertices at the intersection of feature edges. Clustering snaps together vertices that are within the xy tolerance. After that, it discovers geometric relationships (intersections) between features from all the feature classes or layers and writes these intersections as feature (point, line or polygon) to the output.

Polygons can intersect in three ways [6]:

Overlap: Area of overlap can be produced by leaving the Output Type to its default value (LOWEST).

Common boundary: touch at a line—this type of intersection can be produced by specifying LINE as the Output Type.

Touch at a point: this type of intersection can be produced by specifying POINT as the Output Type.

The way I used is Overlap with Polygon inputs and polygon outputs. The output polygon features are where a polygon from one of the input feature classes or layer intersects a polygon from the other input feature class or layer.

5.2.6 Excluding areas

The next step is to erase from the output areas, the ones that cannot be landing areas (for example, sand, water, streets)(Fig.5.23).

Erase tool creates a feature class by overlaying the input features with the polygons of the erase features. Only those portions of the input features falling outside the erase features outside boundaries are copied to the output feature class.

Input feature geometries coincident with erase feature geometries will be removed.

The erase feature can be point, line or polygon as long as the Input feature is of the same or lesser order feature type. In this case a polygon erase feature is used to erase polygons.

Attribute values from the input feature classes will be copied to the output feature class, which means that the gridcode and slope code attributes will be copied to the output feature class (Fig. 5.24).

This tool will use a tiling process to handle very large datasets for better performance and scalability.

Fig. 5. Landing areas from slope polygones, distance polygones and with water,sand and roads erased

Fig. 5. Erase output attribute table

5.2.7 Select the areas of interest

Area aggregation is a type of generalization operation. It combines polygon features that are in close proximity to each other, including adjacent polygons. The output features preserve the distinctive orthogonal or nonorthogonal characteristics of the input raster features.

The Aggregate Polygons tool converts vector data to raster and uses a number of raster functions to find features within the specified distance to each other and connect them. The result is then converted back to vector with proper construction of new boundaries.

The choice of cell size depends on the feature type, source data resolution and intended output data resolution.

The involved raster processes are constraint by the maximum number of approximately 2.1 billion cells.

The aggregation happens where different polygons are within the specified distance to each other but not where a polygon’s boundary is close to itself.

The process does not retain holes in the in_cover. It is possible to select the holes and apply Aggregate Polygons to holes and Polygons separately.

The Aggregate Polygons tool doesn’t keep the attribute fields of the input raster (Fig. 5.25).

Fig. 5. Aggregate polygons attribute table

From the feature class containing the polygons representing the landing areas, I used Select layer by location tool to select the areas that are within a 400 meters distance from Le Ronde park and Select Layer by Attributes to select the areas with an area higher than 100 meters (Shape_Area field from the polygons attribute table higher that 100, using SQL expression).

The final feature class contains four polygons suitable for landing.

Fig. 5. Final landing areas

5.2.8 Automation of the landing areas process

For the automation of the landing areas finding I created a tool that runs every step used for finding the Landing areas.

For this I used Model Builder interface.

ModelBuilder is an application used to create, edit, and manage models. Models are workflows that string together sequences of geoprocessing tools, feeding the output of one tool into another tool as input. ModelBuilder can also be thought of as a visual programming language for building workflows.

Fig. 5. Landing areas model builder

For the Observation Posts tool I used the following tools: Raster to TIN for the input raster, Surface slope for the feature class containing the slope polygons, Select to select the features that have the right slope, Euclidean Distance for the input obstacles feature, Int to convert the pixel type of the raster from float to integer, Raster to Polygon to convert the Euclidean distance raster to a feature class containing the polygons with the same distance from obstacles, Intersect for intersecting the feature class with the right slope polygons and the feature class with the right distance from obstacles, Erase to exclude the areas that are not suitable for landing (water, sand) and aggregate polygons to merge all the polygons that intersect.

As input parameters users must introduce the DEM raster, the feature class containing obstacles, the areas users want to erase and as an option users can choose the distance and the slope for the landing areas.

Fig. 5. Landing Areas tool

5.3 Observation posts

5.3.1 Introduction

An observation post, temporary or fixed is a position from which soldiers can watch enemy movements.

When selecting a (temporary) observation post, trained troops are to avoid obvious and conspicuous locations such as hilltops, water towers or other isolated terrain features, and to ensure that the observation post can be reached via a concealed route. This is especially important as the observer in the post should be rotated every 20–30 minutes, as vigilance decreases markedly after such a time.

Observation posts should be manned with at least two personnel (more, for defense and observer rotation, if the post is to be retained for longer durations), and should be provided a means of communication with their chain of command, preferably by phone instead of by radio. [17]

There are many instances where you will want the terrain to dictate the best locations for observation posts. Considering that the scenario analysed is placed in an urban environment, the observation posts are placed on the highest and secured buildings.

The next task is to find Observation posts from where the interest areas (in this case, the landing zones, the hospital and the bridge) are seen.

In order to find the Observation posts I created point feature classes for the highest points and the points of interest.

5.3.2 Elevation values

The point feature class for the interest areas and the highest point don’t have the elevation attribute.

Extract values to points extracts the cell values of a raster based on a set of point features and records the values in the attribute table of an output feature class.

All fields from the input point feature class will be included in the output point feature class.

The output feature class will have a new field added to id named RASTERVALU(Fig. 5.29).

For the RASTERVALU field of the attribute table NoData cells in the raster will be giver a value of -9999.

The interpolation option determines how the values will be obtained from the raster. The default option is to use the value at the center of the cell being sampled. The interpolation option uses bilinear interpolation to interpolate a value for the cell center.

The input raster of the ZeeBrugge height map is floating-point type, and the resulting output point dataset will only contain attributes from the input feature data and the value of the cell, as determined by the interpolation option.

Fig. 5. Extract values to points attribute table

5.3.3 The lines of sight

The Construct Sight Lines creates line features that represents sight lines from the highest posts to points of interest (Landing areas) (Fig. 5.30).

Sight lines are sampled from the perimeter of target lines and polygons based on the value specified in the Sampling Distance parameter.

A join field is used to specify one or more targets for a given observer. In this case no join field is used and all points are connected to all targets.

The following fields are added to the output feature class that contains the sight lines (Fig. 4.31):

OID_OBSERV: The OID of the observer point

OID_TARGET: The OID of the target feature

DIST_ALONG: The distance along the target feature if it is a line or polygon

Fig. 5. Construct Sight Lines output

Fig. 5. Construct Sight Lines attribute table

A line of sight is a graphic line between two points on a surface that shows where along the line of the view is obstructed. The color of the line indicates the locations where the surface is visible and where is hidden( Fig. 5.32). The status bar indicates whether the target is visible or hidden.

The Line of Sights tool determines the visibility of sight lines over obstructions consisting of a surface and an optional mutipatch dataset.

The Line of Sights tool calculates intervisibility between the first and last vertex of each line feature given its position in 3D space relative to the obstructions provided by a surface or multipatch feature class. The first vertex defines the observation point, whereas the last is the observation target. Visibility is determined along the sight line between these points and any intermediary vertices in lines with more than two vertices are ignored.

The output line feature's attribute table contains the following fields (Fig. 5.33):

SourceOID: The unique ID of the line feature used in calculating visibility.

VisCode: The visibility along the line. A value of 1 indicates visible, and a value of 2 indicates not visible. This field will only exist if the output geometry is a line.

TarIsVis: The target visibility along the line. A value of 1 indicates visible, and a value of 0 indicates not visible. This field will only exist if the output geometry is a line.

OBSTR_MPID: The unique ID of the multipatch that obstructs the line of sight. If no multipatch obstructs the line of sight, then the field contains a value of -1 or -9999. If the target is obstructed by the surface, the value will be -1. If the target is visible, the value will be -9999.[6]

An optional obstruction point feature class can be produced to visualize the first location along the sight line that impedes the observer’s visibility of the target. It contains a SourceOID field that identifies the input line associated with the obstruction.

The curvature option makes adjustments to take the curvature of the earth into consideration when performing line-of-sight calculations. It only can be used when the spatial reference of the input surface is in a projected coordinate system (Web Mercator UTM 31N zone in this case) and z-coordinate units are defined.

Enabling the refraction option will compensate for the influence of atmospheric refraction, which forces light to bend as it passes through the atmosphere. The amount of this deviation is controlled by variations in air pressure and density, humidity, temperature, and elevation. As with curvature, correcting for atmospheric refraction requires the surface's spatial reference to be in a projected coordinate system and have its z-coordinate units defined.[6]

The formula used for the correction is:

Z = Z0 + D2(R – 1) ÷ d

where:

Z—corrected elevation after factoring the impact of atmospheric refraction.

Z0—surface elevation of the observed location.

D—planimetric distance between the observation feature and the observed location.

d—diameter of the earth, which is 12,740 km.

R—refractivity coefficient of light. The default value of 0.13 is considered appropriate under standard atmospheric pressure for daytime conditions with a clear sky for locations whose elevation varies between 40 and 100 meters (Yoeli, The Making of Intervisibility Maps with Computer and Plotter, Cartographica 22:88–103, 1985). Different refractivity values can be used to simulate the visibility impact of varying atmospheric conditions and elevations.

Fig. 5. Lines of Sight

Fig. 5. Lines of Sight attribute table

From the feature class created by the Line of Sight tool a new feature class must be created containing only the lines that are visible from one point to another (TarlVis field from attribute table equals 1) (Fig. 5.34).

The select tool extracts features from an input feature class or input feature layer, typically using a select or Structured Query Language (SQL) expression and stores them in an output feature class.

The select or SQL expression gets build with the Query Builder, or simply typed in. If no expression is entered only the selected features are written to the output feature class and if an expression is entered for input features, the expression is only executed against selected features and the expression-based subset of the selected set is written to the output feature class.

Fig. 5. Visible lines of sight

5.3.4 Landing areas Observation points

First we need only the points that can see the landing areas points. Using Select Layer by Location, intersect the visible lines of sight with all possible observation posts (Fig. 5.35).

Select Layer by Location tool lets users select features based on their location relative to features in another layer. There are a variety of selection methods to select the point, line, or polygon features in one layer that are near or overlap the features in the same or another layer.

The spatial relationship to be evaluated:

Intersect: The features in the input layer will be selected if they intersect a selecting feature.

Intersect_3D: The features in the input layer will be selected if they intersect a selecting feature in three-dimensional space (x, y, and z).

WITHIN_A_DISTANCE:The features in the input layer will be selected if they are within a specified distance of a selecting feature.

WITHIN_A_DISTANCE_3D:The features in the input layer will be selected if they are within a specified distance of a selecting feature in three-dimensional space.

WITHIN_A_DISTANCE_GEODESIC:The features in the input layer will be selected if they are within a specified distance of a selecting feature.

CONTAINS:The features in the input layer will be selected if they contain a selecting feature.

COMPLETELY_CONTAINS:The features in the input layer will be selected if they completely contain a selecting feature.

CONTAINS_CLEMENTINI:This spatial relationship yields the same results as COMPLETELY_CONTAINS with the following exception: if the selecting feature is entirely on the boundary of the input feature (no part is properly inside or outside), the feature will not be selected.

WITHIN:The features in the input layer will be selected if they are within a selecting feature.

COMPLETELY_WITHIN:The features in the input layer will be selected if they are completely within or contained by a selecting feature.

WITHIN_CLEMENTINI:The result will be identical to WITHINwith the exception that if the entirety of the feature in the input layer is on the boundary of the feature in the selecting layer, the feature will not be selected.

ARE_IDENTICAL_TO:The features in the input layer will be selected if they are identical (in geometry) to a selecting feature.

BOUNDARY_TOUCHES:The features in the input layer will be selected if they have a boundary that touches a selecting feature.

SHARE_A_LINE_SEGMENT_WITH:The features in the input layer will be selected if they share a line segment with a selecting feature.

CROSSED_BY_THE_OUTLINE_OF:The features in the input layer will be selected if they are crossed by the outline of a selecting feature.

HAVE_THEIR_CENTER_IN:The features in the input layer will be selected if their center falls within a selecting feature.

Using the ralationship INTERSECT, with the highest points as an input and tha visible lines of sight as the selecting layer, the result is 4 points from the 10 highest points (Fig. 5.35).

Fig. 5. Observation posts

Using Copy Features tool I copied the selected points from Select Layer by Location tool to a new feature class.

Both the geometry and attributes of the Input Features will be copied to the output feature class.

The next step is to identify the best observation post between the 4 Observation posts founded.

A viewshed identifies the cells in an input raster that can be seen from one or more observation locations. Each cell in the output raster receives a value that indicates how many observer points can be seen from each location. All cells that cannot see the observer point are given the value of 0. The observer points feature class can contain points or lines. The nodes and vertices of lines will be used as observation points.

The Viewshed tool creates a raster, recording the number of times each area can be seen from the input point or polyline observer feature locations. This value is recorded in the VALUE item in the table of the output raster (Fig. 5.36). All cell locations assigned NoData on the input raster are assigned NoData on the output raster.

Applying viewed tool for all the 4 observation posts, I found one point that sees all the landing areas (Fig.5.37).

Fig. 5. Viewshed attribute table

5.3.5 Hospital observation post

On the same principles as before, I found an observation post for the hospital(Fig. 5.38).

Considering that is the only hospital in the area and the route from the hospital to Le RondePark implies one bridge it is important for both the Hospital and the bridge to be supervised (Fig. 5.39).

Fig. 5. Hospital observation post

Fig. 5. Viewshed map for the hospital observation post

5.3.6 Profile Graphs

a) Landing areas Observation posts profile graph

Fig. 5. Profile graph from observation post to Le Rond Park

Hospital Observation posts profile graph

5.3.7 Automation of observation posts process

For the automation of the observation posts finding I created a tool that runs every step used for finding the observation post (Fig. 5.47).

For this I used Model Builder interface.

Fig. 5. Model builder for Observation posts

For the Observation Posts tool I combined the following tools : Extract Values to points for both the highest points and the landing points, Construct Sight Lines between them, Line of Sight, Select the visible lines, Select Layer By Location for the visible points and Copy Features for the final feature class containing the Observation Posts.

As input parameters users must introduce the highest points, the points of interest that must be seen from the observation posts, and the TIN of the map.

Fig. 5. Observation posts tool

5.4 Shooter position

5.4.1 Introduction

“To know your enemy, you must become your enemy”Sun Tzu (The art of War)

Knowing your enemies, where they are and what they can see from their vantage points are some of the most integral aspects of warfare. It is important to know not only the best locations for enemy observation points, but also which areas are not visible from them. This knowledge allows you to plan your defensive positions and avenues of approach so that they are concealed from enemy observation and covered from enemy fire. [18]

Generally, enemies are not going to hide in a lower location where they can see less, they are going to look for the best vantage point over what they are observing, and those areas we must identify.

5.4.2 Observation Posts tool and viewshed

Using the Observation Posts tool created by me I found the points that can be used to see Le Ronde park for a possible shooter.

5.4.3 Shooter profile graph

Fig. 5. Profile Graph Shooter 1 – Le Ronde Park

5.5 Routes

5.5.1 Introduction

Understanding avenues of approach on a battlefield can often mean the difference between life and death. In this case I have to determine likely avenues of approach along known roads through an identified area of interest. After landing zones have been identified it is very important to predict the routes from the landing areas to the interest area. Considering that this example is about routes for troupes and not vehicles the only criteria I took into consideration was the distance and the speed. It is also important to know the route from Hospital and from the Military base to the event.

The routes needed are: from the four landing areas, Hospital and Military Base to Le Ronde Park.

5.5.2 Import Routes

For the routes I used Google maps (Fig. 5.53 ).

Fig. 5. Google maps route example

I exported the routes as KML (Fig. 5.54).

Fig. 5. Export routes

The next task is to import the routes to ArcMap. For this step the KML routes must be converted to Shapefiles.

The KML to Layer tool converts a KML or KMZ file into feature classes and a layer file. The layer file maintains the symbology found within the original KML or KMZ file.

This tool creates a file geodatabase containing a feature class within a feature dataset. The feature class name will be named point, line, polygon, or multipatches, dependent on the original features of the KML file. At the same folder level as the file geodatabase will be a layer file which can be added to a map to draw the features. This layer file draws features based on their schema of point, line, or polygon, while maintaining the original KML symbology.

Each feature class created will have attributes which maintain information about the original KML file. The original folder structure, name, and pop-up information, as well as fields that help define how the features sit on a surface, all make up the attributes of each feature.

Output will be generated in the WGS84 coordinate system. The output features can be reprojectedto another coordinate system, if desired, using the Project tool.

Routes :

Landing area 1/2 to park

Distance: 400 m

Estimative time: 3-5 minutes by foot

Fig. 5.

Landing area 3 to park

Distance: 210 m

Estimative time: 2-3 minutes by foot

Fig. 5.

Landing area 4 to park

Distance: 800 m

Estimative time: 7-10 minutes by foot

Fig. 5.

Military base to park

Distance: 1,6 km

Estimative time: 4 minutes by car

Fig. 5.

Hospital to park

Distance: 2,2 km

Estimative time: 5 minutes by car

Fig. 5.

Chapter 6 Analysis of the final products quality

The quality of the product resulting from a GIS processing depends on the validity of the data that has been entered.

Until recent years, less importance has been attached to the accuracy of GIS data. Data is known to contain systematic errors and random errors, but no emphasis has been placed on how SIG procedures and solutions account for these errors. A good treatment of this problem allows the choice of the best data sources, the choice of the most accurate methods of data collection and the choice of the most accurate procedures for processing and final products. Several types of data from multiple sources are combined in SIG.

In the case of maps, precision refers to the relative accuracy of the position of objects on a map relative to their actual position on the earth's surface. Absolute precision refers to the level of accuracy of the object's position. Absolute precision is the estimated error for a single point relative to a spatial reference system (Krasovski, WGS84, etc.)

Relative accuracy is the estimated error for a distance between two points or the precision of a point compared to another point. If the errors of two points are not correlated, the relative error is the mean square error of the absolute points errors.

The precision of a digital map does not depend on the display scale. It depends on the accuracy of the original data used, the processing algorithms and the resolution at which the map is printed. In the case of digitizing an analogue map, the result of digitization can not be more precise than the original sources.

There are four types of errors in the digital mapping:

• positional error;

• error of attributes (thematic data);

• conceptual error;

• Consistency error.

Positioning error is the deviation of an object on the map from its actual position in the field. The topography map instructions provide a planimetric precision of 0.4 mm map scale with a confidence level α = 95% (probability of 0.95). This accuracy for map 1: 50,000 is 20m in the field.

Attribute error is defined as the approximation of attribute values to their actual value. While the item's position does not change over time, the attributes can change frequently. The accuracy of the attributes is analyzed in different ways depending on the nature of the data:

• For continuous values of attributes, precision is expressed as a measurement error. Precision assessment is done by methods similar to positioning accuracy.

• For discrete attribute values, precision is expressed as a classification error (Figure 6.1 – a built area was mistakenly classified as forest). The assessment of classification accuracy is influenced by the number of classes defined in the database.

Fig. 6. Attribute Error

The conceptual error refers to the errors of omission of the information in the database and is defined by the degree of generalization and abstraction. The most generalized database can be considered complete if it contains all the objects described in the specifications. Conceptual precision gives the user information about the amount of spatial, temporal and thematic data available.

Fig. 6. Conceptual Error

Consistency error refers to apparent contradictions in a geographic database. Consistency accuracy is a measure of the internal validity of the database. Several types of database consistency can be distinguished:

– spatial consistency refers to compliance with topological rules (eg, the common boundary of two polygons must coincide – Figure 6.3)

– temporal consistency, at one point, at one point there can only be one event;

– Thematic consistency refers to the lack of contradictions in thematic data (storing data on the number of inhabitants, the size of the localities and population density, errors can occur due to the redundancy of the information).

– logical consistency refers both to topological aspects and to the validity of the domain of definition of data stored in databases.

Fig. 6. Consistency Error

These errors are reduced or even eliminated in the cartographic editing process.

Also, the quality of the final product may be affected by errors due to mapping or topological errors. Spatial data may be perceived differently by a person sometimes due to a lack of knowledge about symbolism. The user must understand the symbols used to describe spatial data for the purpose of understanding the data. In fact, the author of the data source must correctly display the data so that it can be interpreted correctly. Topological errors can lead to incorrect data manipulation and incorrect topological analysis. These errors are due to the faulty design of the spatial database (there can be no video areas in a layer with continuous surfaces) and inappropriate digitization (in one layer the polygons can not overlap).

The quality of tactical schemes is influenced by the relative accuracy of the position of the objects on the map relative to their real position on the Earth's surface. Also, quality can also be influenced by misinterpretation of enemy forces (we have an enemy tanks battalion and we only symbolize a company or a platoon of tanks on the map).

The quality and accuracy of the representation of a helicopter landing area depends on the resolution of the images being analyzed. In my application, the quality of the product is good because the image resolution is 5 meters.

The quality of military maps depends on the accuracy of the original data used and the processing algorithms. There may also be errors due to mapping and generalization. In the case of vector data these errors are more due to the operator that generates the database. There may be code input errors or interpreter errors. In SIG, it is possible to generate a new layer by reducing the amount of detail in an existing layer. This generalization may, but not necessarily, reduce the number of objects in the layer. For example, a layer of forests can be generalized by aggregating adjacent polygons with similar features. This method reduces the number of objects in the layer. Instead, a hydrograph layer can be generalized by reducing the number of vertices of a line without removing elements in the layer.

To improve the quality of military maps prior to printing or exporting, cartographic editing is performed to eliminate as many of the errors as occurred during processing.

Chapter 7 Conclusions

GIS has evolved in a very short time to a technology accessible to most researchers and institutions. It seems that in the coming years GIS will become a technology as common as today is holding a PC.

GIS is one of the information technologies that radically transformed the way geographers carry out their research for the benefit of society. The same major impact had on the military by relieving them in a visible way of daily work.

For the military domain, the most promising direction for developing GIS applications can be the creation of standard military maps.

Even though the main and easiest advantage seems to be the one of the ease of charging any information (maps have always been a means of representing us to the world and the environment), it must be remembered that GIS can be used today for to get useful maps for both the researcher and the general public.

Due to the positive impact, the GIS software systems have developed a lot. There is a large number of products on the market, both of established developers (ESRI, Intergraph, Autodesk, MapInfo, etc.), but also Open Source (Grass GIS, Quantum GIS, GVSIG, OpenJump, etc.).

A GIS stores data that have a geographical reference and stores the field details and attributes in the attribute tables.

Topological data structures allow spatial operations to be used in SIG analysis, such as buffer pool generation, polygonal overlay, and combination of graphics. These structures are most commonly used by SIG program packages.

The choice of a particular geographic data structure is based on several criteria: data volume, data types, application complexity, types of algorithms, etc.

An important role in structuring, organizing and collecting thematic data is given by location data, positioning techniques, field or photogrammetric measurement methods, digital maps and records, and methods of measuring and processing position data.

The quality of a geographic information system is largely influenced by the quantity and quality of the geographic data stored in its database, whose collection costs 70-80% of the price of the system. Geographic data is structured either in raster or vector format.

One of the disadvantages of raster data is the large storage space for information. It has also been found that the main advantage of vector data is that it allows for complicated spatial analysis (for example, the optimal route) as well as the addition of additional information to each element of the map. One of the disadvantages of this type of data is the complicated and time consuming way of obtaining them.

The main method of data collection and transformation in vector format is the digitization of existing maps.

Geospatial databases allow us to: Manage multiple data types; we apply rules and types of complex relationships; we are accessing large volumes of geographic data stored both as files and in databases.

The ability of a geographic information system to integrate various information is the source of flexibility and ability to meet user requirements. By integrating the information, GIS users can have a unified picture of data of various types and can establish a single and coherent information system.

GIS is not a hardware component, no software component, but rather a process. GIS is for making decisions. The way data is input, stored and analyzed within a GIS must reflect how the information will be used for a particular type of research or for a task that requires making certain decisions. Seeing GIS just as a software or hardware system means losing the crucial role on which can play it in a full decision-making process.

The advantages of using the SGI in the military are numerous, the most important of which is to use it as a support in decision-making on material management and the operation of troops on the battlefield.

Analyzing the capabilities of an SGI with military applicability, we can conclude that it has advantages and disadvantages.

The advantages of using a GIS in the military domain:

• Data is better organized;

• Eliminates redundancy in data storage;

• Enhancements facility;

• Allows much easier analysis, statistics and new searches.

disadvantages:

• Complexity

• High costs

• Field changes

• Difficulties in staff training

As a conclusion for the military scenario analysed, I will answer the questions I have begun with:

1. Where is the event happening?

The event is happening in Le Ronde Park from ZeeBugge, Belgium. The park has an area of 5250 meters and is surrounded by buildings with an approximate height of 53 meters. This fact gives the event a high protection against enemy’s observation posts and shooter positions.

2. What are the properties that define the environment?

The event is happening in an urban environment. The urban environment is very different from open space when talking about both operational and tactical level. Complicating factors in urban environment include the presence of civilians and the complexity of the urban terrain. It can also have some advantages like the fact that buildings can be used as observation posts, the possibility to hide, the possibility to predict enemies routes due to the existent roads, the restrictions for landing, etc.

3. Where are the hospitals?

In the scene there is only one hospital at 2.2 km from the event. The routes from the hospital are conditioned by the bridge between the two destinations so protecting the Hospital implies also protecting the bridge.

4. Where is the Military Base?

The Military base is at 1.6 km from the event which means 5 minutes by car. The distance between the Military base and the event allows troupes to arrive fast at the event
if reinforcement is required.

5. Where are the areas that are suitable for landing?

In the scene there are 4 areas suitable for landing at a maximum distance of 400 meters from the event with areas from 1300 to 9000 meters.

Establishing a helicopter landing area may be a military application of major importance for save and rescue missions as well. This application is useful on the battlefield to save the troups.

6. Where are the best observation posts?

I found 4 observation posts with a good visibility to the possible landing areas. The best observation post has a height of 85 meters and good visibility to all the areas of interest. The position of the observation point isn’t on the estimate enemy’s routes and this fact protects it from discovering and destroying.

7. Where are the places with good visibility to the event for possible shooters?

Due to the height of the surrounding buildings there are only 2 points with a visibility of about 60% of the area where the event is happening.

8. What routes would enemies approach?

An advantage of the urban environment in this situation is that there aren’t areas suitable for landing right next the event and the enemies have to walk about 400 meters to the event. That gives the troupes the advantage of a better react possibility.

9. What areas need extra protection?

I found five areas that need extra protection: The two possible shooter positions or enemy’s observation posts, two of the entrances to the park and one for the hospital.

10. How did Geographic Information Systems helped in solving the scenario?

ArcGIS had a great contribution in solving the scenario, providing a multitude of tools used for create, manage, analyse and display the geographic data. All the steps were completed using ArcGIS, starting with displaying the Orthophoto map and the digital surface model and using it for the analyses needed.

In this project I have used the following tools provided by ArcGIS: Mosaic to new Raster, georeferencing tools, Slope, Raster to TIN, Surface Slope, Select Layer by Attribute, Euclidean Distance, Int, Raster to Polygon, Intersect, Erase, Select Layer by Location, Aggregate polygons, Extract values to points, Construct Sight Lines, Line of sight, Select, Viewshed, Kml to layer.

The Model builder interface implemented in ArcGIS gives the possibility to create customized tools and improving the efficiency of the process.

ArcGIS also gives the possibility of creating feature classes, and databases and construct a vector map with all the interest features in the scene. The database created allows SQL queries using the fields created in the attribute table.

In the presented applications, spatial information analysis provides greater efficiency in understanding the land and utilizing it in its own advantage, providing a more realistic view and opening new directions in the planning of military operations, and the best way to analyze the field the battle is to have a continuous image of the land surface, where the field details are superimposed on the numeric model of the 3D plot.

The precision of a digital map does not depend on the display scale, it depends on the accuracy of the original data used, the processing algorithms, and the resolution at which the map is printed.

Errors that occur when creating digital maps are removed within the cartographic editing process.

List of figures

Chapter 3

Fig 3. 1 GIS Components 23

Fig 3. 2 GIS Hardware Components 24

Fig 3. 3 GIS Hardware Components 25

Fig 3. 4 Digitizer 25

Fig 3. 5 Scanner 25

Fig 3. 6 Data Input Instruments 26

Fig 3. 7 GIS Data Components 29

Fig 3. 8 GIS Layers 40

Fig 3. 9 ArcGIS Software Package 45

Chapter 4

Fig. 4. 1 Vectorial Data 58

Fig. 4. 2 Raster Model 59

Fig. 4. 3 Raster Data examples 60

Fig. 4. 4 Raster vs Vector Data 63

Chapter 5

Fig. 5. 1 Passive and active sensors 69

Fig. 5. 2RGB Ortophoto 69

Fig. 5. 3 DSM 70

Fig. 5. 4 Single band rasters 71

Fig. 5. 5 Multiple bands rasters 71

Fig. 5. 6 Orthophoto raster with black noData value 72

Fig. 5. 7 DEM raster with black noData value 72

Fig. 5. 8 Orthophoto raster after the noData value is defined 73

Fig. 5. 9 DEM raster after the noData value is defined 73

Fig. 5. 10 DEM raster datasets mosaic 74

Fig. 5. 11 Orthophoto raster datasests mosaic 74

Fig. 5. 12 Vector map of ZeeBrugge 76

Fig. 5. 13 Z tolerance 77

Fig. 5. 14 Slope 78

Fig. 5. 16 Slope Vector Map 79

Fig. 5. 15 Surface Slope attribute table 79

Fig. 5. 17 Polygons with slope under 5 degrees 80

Fig. 5. 18 Euclidean Distance output raster 81

Fig. 5. 19 Euclidean distance to polygon 82

Fig. 5. 20 Euclidean distance to polygon attribute table 83

Fig. 5. 21 The polygones that are further than 10 meters from obstacles 84

Fig. 5. 22 Intersect between the slope polygones and the Euclidean Distance polygones 85

Fig. 5. 23 Landing areas from slope polygones, distance polygones and with water,sand and roads erased 86

Fig. 5. 24 Erase output attribute table 86

Fig. 5. 25 Aggregate polygons attribute table 87

Fig. 5. 26 Final landing areas 88

Fig. 5. 27 Landing areas model builder 89

Fig. 5. 28 Landing Areas tool 90

Fig. 5. 29 Extract values to points attribute table 91

Fig. 5. 30 Construct Sight Lines output 92

Fig. 5. 31 Construct Sight Lines attribute table 93

Fig. 5. 32 Lines of Sight 95

Fig. 5. 33 Lines of Sight attribute table 95

Fig. 5. 34 Visible lines of sight 96

Fig. 5. 35 Observation posts 98

Fig. 5. 36 Viewshed attribute table 99

Fig. 5. 37 Viewshed map from the observation post 99

Fig. 5. 38 Hospital observation post 100

Fig. 5. 39 Viewshed map for the hospital observation post 100

Fig. 5. 44 Profile graph from observation post to Le Rond Park 101

Fig. 5. 40 Profile Graph Observation post – Landing area 2 101

Fig. 5. 41 Profile Graph Observation post- Landing area 1 101

Fig. 5. 42 Profile Graph Observation post – Landing area 3 101

Fig. 5. 43 Profile Graph Observation post – Landing area 4 101

Fig. 5. 47 Model builder for Observation posts 102

Fig. 5. 45 Profile Graph Observation Post – Hospital 102

Fig. 5. 46 Profile Graph Observation post – bridge 102

Fig. 5. 48 Observation posts tool 103

Fig. 5. 52 Profile Graph Shooter 1 – Le Ronde Park 104

Fig. 5. 49 Shooter position points 104

Fig. 5. 50 Shooter points viewshed map 104

Fig. 5. 51 Profile Graph Shooter 2 – Le Ronde Park 104

Fig. 5. 53 Google maps route example 105

Fig. 5. 54 Export routes 106

Fig. 5. 55 107

Fig. 5. 56 107

Fig. 5. 57 108

Fig. 5. 58 108

Fig. 5. 59 109

Chapter 6

Fig. 6. 1 Attribute Error 111

Fig. 6. 2 Conceptual Error 112

Fig. 6. 3 Consistency Error 112

Acronyms

AM/FM – Automated Mapping and Facilities Management

ArcIMS – Internet Map Server

ArcSDE – Spatial Database Engine

BMP – Bitmap

CAD – Computer Aided Design

CAO/DAO – Computer-aided design

CGIS – Canada Geographic Information Systems

DBMS – Database Management System

DFAD – Digital Feature Analysis Data

DIGEST – Digital Geographic Information Exchange Standard

DNC – Digital Nautical Chart

EIS – Environmental Information System

ESRI – Environmental Science Research Institute

FACC – Feature and Attribute Coding Catalog

FFD- Foundation Feature Data

GEOINT – Geospatial Intelligence

GIF – Graphics Interchange Format

GIS- Geographic Information System

GML – Geographic Markup Language

GTDS – Global Topographic Data Store

HLZ – Helicopter Landing Zone

HUMIT – Human intelligence

ICM – Image City Map

IGDS – Interactive Graphics Design System

JOG – Joint Operations Graphics

JPEG – Joint Photographic Experts Group

KML – Keyhole Markup Language

LDG – Local Derived Graphic

LIS – Land Information System

LTDS – Local Topographic Data Store

MGCP – Multinational Geospatial Co-production Program

MGE – Modular GIS Environment

MIDAS – Mapping Display and Analysis System

MrSID – Multi-resolution Seamless Image Database

NATO – North-Atlantic Treaty Organization

NGA – National Geospatial-Intelligence Agency

NIMA – National Imagery and Mapping Agency

NRMM – NATO Reference Mobility Model

NSG – National System for Geospatial Intelligence

OCOKA – observation, cover, obstacles, key terrain, avenues of approach

OGC- Open Geospatial Consortium

PDF – Portable Document Format

PIS – Planning Information System

PNG – Portable Network Graphics

RIS – Resources Information System

RTDS – Regional Topographic Data Store

SAC – Stereo Airfield Collections

SDHS – Spatial Data Handling System

SGBD – Database Management Systems

GIS – Geographic Information Dystem

SYMAP – Synteny Mapping and Analysis Program

TDG – Theatre Geospatial Database

TDS – Topographic Data Store

TIFF – Tagged Image File Format

TLM – Topographic Line Maps

UTDS – Urban Topographic Data Store

UTM – Universal Transverse Mercator

UVMap – Urban Vector Map

VITD – VPF Interim Terrain Data

VMap- Vector Map

VPF – Vector Product Format

WGS – World Geodetic System

Bibliography

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[3] Graff L H, Visone D L 1996 The seamless integration of commercial-off-the-shelf (COTS) and government-off-theshelf (GOTS) products to meet the Army’s terrain analysis requirements. Proceedings, Sixteenth Annual ESRI User Conference. Redlands, ESRI: 4pp

[4] Peuquet D J, Bacastow T 1991 Organizational issues in the development of geographical information systems: a case study of US Army topographic information automation. International Journal of Geographical Information Systems 5: 303–19

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[6] ArcGIS desktop 10 official ArcMap manual

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[8] http://whatis.techtarget.com/definition/RGB-red-green-and-blue

[9] http://www.photomapping.com.au/digital-orthophoto

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[17] Optimal distribution of observation posts in the operation area

Ondřej Litvaj; Petr Stodola

International Conference on Military Technologies (ICMT) 2015Year: 2015

[18] Representation of urban operations in military models and simulationsS. T. CrinoProceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304)Year:2001, Volume: 1

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Castraveț, T., Inițiere în SIG, Chișinău, 2013

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ESRI, GIS in the Defense and Intelligence Communities, Volumul 1

Swann, D., Military applications of GIS

Neira Gutierrez, J.C., Spatial Information Management and Workflow at IGM GDB, Chile, 2014

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