COMPARASION OF NDBI AND NDVI AS INDICATORS OF [307100]

COMPARASION OF NDBI AND NDVI AS INDICATORS OF

SURFACE URBAN HEAT ISLAND EFFECT IN LANDSAT 8 IMAGERY: A CASE STUDY OF IASI

Paul Macarof 1, Florian Statescu2

1PhD student: [anonimizat]., „Gheorghe Asachi” [anonimizat], Geodesy and Environmental Engineering, e-mail [anonimizat],

2Professor Dr. Eng., Gheorghe Asachi”[anonimizat], Geodesy and Environmental Engineering, e-mail [anonimizat]

Key words: NDBI, NDVI, [anonimizat], Landsat-8

Abstract: This study compares NDVI (normalized difference vegetation index) [anonimizat] (NDBI) as indicators of surface urban heat island (UHI) effects in Landsat-8 OLI imagery by investigating the relationships between the LST (land surface temperature), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural zones due to urbanization. [anonimizat]. [anonimizat]. This paper indicates than there is a [anonimizat]. [anonimizat] a complementary metric to the traditionally applied NDVI.

1.Introduction

The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural zones due to urbanization (Oke & Voogt, 2005). UHI is most noticeable during the summer and winter. The main cause of the UHI effect is from the modification of land surfaces (Solecki et al., 2005). Waste heat generated by energy usage is a secondary contributor (Li, Zhao, 2012). [anonimizat]. Therefore, [anonimizat], and evaluate the urban heat island effect quantitatively has become one of the prime important research among current urban climate and environment studies.

[anonimizat]. [anonimizat]. Results was indicated there is a linear relationship between Land surface temperature and NDVI. [anonimizat] (Carlson et al., 1994). [anonimizat]. Furthermore, the relationship between NDVI and LST is well known to be nonlinear. Thus, NDVI alone may not be a enought metric to study surface UHI quantitatively. According to studies the relationship between percent impervious surface area (ISA) [anonimizat]+ data in an urbanized environment. Yuan et al. found that percent ISA was an accurate indicator of surface UHI effects with strong linear relationships between LST and percent ISA for all the four seasons (Yuan et al., 2011). The normalized difference build-up index (NDBI) represents one of the major land cover types, that is, build-up areas (Zha, Gao and Ni, 2005). Morever, like NDVI, NDBI is very simple and easy to obtain, so it is feasible to use NDBI to substitute for percent impervious surface area for study of surface urban heat island. NDBI can be used as indicator of intensity of development and as indicator of urban impervious surface.

2.Data and methods

2.1. Study Area

Iași is the largest city in eastern Romania and the seat of Iași County. Located in the historical region of Moldavia, Iași has traditionally been one of the leading centres of Romanian social, cultural, academic and artistic life. The city is positioned on the Bahlui River. This is an affluent of Jijia that flows into the Prut River. This is one of the "legendary city of the seven hills", namely Bucium, Cetățuia, Galata, Copou, Șorogari, Breazu and Repedea, just like so many cities around world, one such example being Rome. The local climate is continental with low rainfall and with large temperature differences between the seasons. Summer is hot and it lasts from the end of the month of May up to the half of September. Autumn is a short season, of transition. In the second half of November there is usually frost and snow. Winter is a freezing season with temperatures dropping to –20 șC (https://en.wikivoyage.org/wiki/Iași).

Study Area is geographically situated on latitude 47°12'N to 47°06'N and longitude 27°32'E to 27°40'E.

Figure 1. Study Area (wikipedia, rotravel)

2.2. Landsat data

The main objective of this study was to compare the relationships of LST to NDBI and NDVI using Landsat data obtained from four different seasons using Iasi metropolitan area as a case study.

Landsat 8 measures different ranges of frequencies along the electromagnetic spectrum – a color, although not necessarily a color visible to the human eye. Each range is called a band. Landsat 8 has 11 bands. In the thermal infrared (TIR) are band 10 and 11, which see heat. According NASA, instead of measuring the temperature of the air, like weather stations do, they report on the ground itself, which is often much hotter (NASA Landsat Science).

Table 1-Landsat data

2.3. Data processing

The Normalized Difference Vegetation Index (NDVI), according to Rouse is a numerical indicator that uses the visible (Vis) and near-infrared (NIR) bands of the electromagnetic spectrum, and is adopted to analyze remote sensing measurements and assess whether the aim being observed contains live green vegetation or not (John Rouse, 1973). The NDVI algorithm subtracts the red (R) reflectance values from the near-infrared (NIR) and divides it by the sum of them.

NDVI=

NDBI in below equation is a normalized difference built-up index which is used to extract built-up area. Beginning, this index, was used for TM data by Zha, Gao and Ni, in 2003.

NDBI=

3. RESLUTS AND DISCUSSION

3.1. LST, NDBI and NDVI spatial patterns

LST (land surface temperature) is used to establish the temperature distribution at the change global, regional and local scale. Also it’s used in climate and acclimate change models in particular. LST, calculated from remote sensing data is used in a lot of sphere of science, like: climate change, hydrology, forestry, urban planning, oceanography, agriculture etc. Obtaining surface temperatures and using them in different analysis is important to determine the problem associated with the environment (Orhan et al. 2014).

The LST maps are presents in figure 1.

LST statistical data for the four dates are shown in Table 1.

Table1. Statistical data of LST for four seasons (oC)

The most important parameters are mean and variations. These two parameters reflect change extent of the LST which spring and summer are much higher than autumn and winter. This also indicates spring and summer SUHI effects are more striking which autumn and winter are not so obvious.

Figure 2. LST maps for four seasons (a-2016-02-11; b-2016-05-01;

c-2016-08-08; d-2016-11-09)

NDBI statistical data for the four dates are shown in Table 2. The variations of NDBI are higher summer and spring than smaller winter and autumn.

Table 2. Statistical data of NDBI

The NDBI maps are presents in figure 2. The NDBI in bulit-up area of summer and spring are obviously higher than winter and autumn.

Figure 3. NDBI maps for four dates (a-winter, b-spring, c-summer, d-autumn)

As can be seen from Table 2 and Figure 3, the NDBI values for four seasons are quite close, these indicate NDBI varies less with the season.

NDVI maps and statistical data for the four dates are shown in Fig. 4 and Tab. 3.

Table 3. Statistical data of NDVI

Figure 4.NDVI maps for four dates (a-winter, b-spring, c-summer,

d-autumn)

The parameters: variations, mean and standard deviations of NDVI for four seasons showing that this index are higher spring and summer than autumn and winter when all the vegetation was defoliated. The variations for spring and summer are over 0.6 while for autumn and winter are about 0.3-0.4. The mean for May-August is about 0.2-0.3 while for November-February is 0.05-0.1. These all indicate NDVI has evident seasonal change which is exactly different to NDBI.

3.2. LST relationships to NDBI and NDVI

Figure 7 indicates consistent linear patterns between LST and NDBI for all the seasons, while in Figure 8 the relationship between LST-NDVI is non-linear and strongly affected by season.

Research over the past two decades, according to Price, has shown that the surface radiant temperature response is determined by both surface vegetation cover and soil water content (Goetz, 1994; Price, 1990). The variation in the pixel temperatures may be mostly explained to built-up site amounts and characteristics since vegetated surfaces vary less in temperature than urban surfaces. Moreover, NDBI is sensitive to the build-up area.

Irregular patterns with NDVI values are shown in the scatter plots for the autumn and winter data. These results indicate that although NDVI may be used for analysis of surface UHI effects during spring and summer. So, NDBI is suitable for LST studies for all the seasons (Gillies et al., 1997).

Fig. 5 Scatter plots of NDBI and LST for four dates

A linear relationship is shown between LST and NDBI for all the four seasons (Fig.7), suggesting the fluctuation in LST can be accounted for very well by NDBI for all seasons.

No evident association between the surface UHI and the NDVI, which further implies using NDVI to understand surface UHI is complicated since NDVI itself suffers strong seasonal changes. The NDBI reflects seasonal surface UHI fluctuations in similar linear patterns, which makes it a valuablel tool for analysis and understanding of multi-temporal surface UHI effects. Figure 8 show relationship of NDVI and mean LST for four dates.

Fig. 6. Scatter plots of NDVI and LST for four dates

Figure 7.Relationship of NDBI and LST for four dates

Figure 8.Relationship of NDVI and LST for four dates

4. Conclusions

This paper investigated the relationships between the LST, NDBI and NDVI in Iasi. Results indicate there is a strong linear relationship between NDBI and LST for all the four date, whereas the relationship between LST and NDVI varies by season. The linear relationship between the LST and NDBI suggests that urban zone accounts for most of the fluctuation in land surface temperature dynamics. Therefore, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

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* https://en.wikivoyage.org/wiki/Iași

**https://en.wikipedia.org/wiki/Iași