Monitoring Spatiotemporal Changes of Heat Island in Babol City Due to Land Use Changes

Panah, Seyed Kazem Alavi; Mogaddam, M. Kiavarz; Firozjaei, Mohammad Karimi

doi:10.5194/isprs-archives-xlii-4-w4-17-2017

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Vegetation covered 60.9% of the area in Banjarmasin City in vegetated areas can lead to changes in surface physical characteristics that ultimately cause changes in surface temperature and air temperature [46]. Several studies show that increased surface temperatures happen due to changes in the urban environment, namely, more built-up areas [49][50][51][52][53]. However, our fndings contradict the previous studies.…”

Section: Te Correlation Between Surface Temperature and Vegetation De...contrasting

confidence: 70%

The Effect of Built-Up Area Density and Vegetation Density on Surface Temperature in Banjarmasin City

Adyatma

Muhaimin

Arisanty

et al. 2022

International Journal of Forestry Research

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Banjarmasin City continues to develop rapidly. Malls and settlements are the newly built-up area that has reduced vegetation cover leading to changes in surface temperature in Banjarmasin City. Analysis of temperature changes is needed to determine the effect of increasing built-up areas and decreasing vegetation cover. The surface temperature can be detected and analyzed using satellite imagery. The study aimed to analyze the built-up area and vegetation density index and their effect on changes in surface temperature in Banjarmasin City from 2015 to 2019. We employed remote sensing and surveys to monitor and detect regional changes in urban areas due to rapid development. Built-up areas can be mapped using the Normalized Difference Built-Up Index (NDBI) algorithm, and vegetation density can be mapped using the Normalized Difference Vegetation Index (NDVI) algorithm. The correlation value between building density and surface temperature in 2015 was 0.826, and in 2019, it was 0.969. This means that the NDBI of the built-up area density in 2015 and 2019 strongly correlates with surface temperature. Correlation values between the vegetation density score and the surface temperature were -0.860 in 2015 and -0.949 in 2019. All correlation results are negative, which means that the vegetation density has an inverse ratio to surface temperature; in other words, high vegetation density causes low surface temperature.

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Section: Te Correlation Between Surface Temperature and Vegetation De...contrasting

confidence: 70%

The Effect of Built-Up Area Density and Vegetation Density on Surface Temperature in Banjarmasin City

Adyatma

Muhaimin

Arisanty

et al. 2022

International Journal of Forestry Research

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“…LST shows the amount of emitted radiation from the surface and subsurface and dictates the exchange of energy between the Earth's surface and atmosphere. Satellitederived LST is of paramount importance in various applications including energy balance [101][102][103][104], environmental monitoring [105], geological structure [106], climate change Land 2022, 11,2025 and urban studies [107][108][109][110][111], evapotranspiration surface moisture, and water resources management [112][113][114][115].…”

Section: Lst and Urban Heat Islands (Uhis)mentioning

confidence: 99%

The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review

Alavipanah

Firozjaei

Seddighi

et al. 2022

Land

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In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by remote sensors. It is, therefore, of the utmost importance to detect and model the shadow effect in RS data as well as the information that is obtained from them, particularly when the data are to be used in further environmental studies. Shadows can generally be categorized into four types based on their sources: cloud shadows, topographic shadows, urban shadows, and a combination of these. The main objective of this study was to review the recent literature on the shadow effect in remote sensing. A systematic literature review was employed to evaluate studies published since 1975. Various studies demonstrated that shadows influence significantly the estimation of various properties by remote sensing. These properties include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, and land surface temperature. It should be noted that shadows also affect the outputs of remote sensing processes such as spectral indices, urban heat islands, and land use/cover maps. The effect of shadows on the extracted information is a function of the sensor–target–solar geometry, overpass time, and the spatial resolution of the satellite sensor imagery. Meanwhile, modeling the effect of shadow and applying appropriate strategies to reduce its impacts on various environmental and surface biophysical variables is associated with many challenges. However, some studies have made use of shadows and extracted valuable information from them. An overview of the proposed methods for identifying and removing the shadow effect is presented.

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“…The region's population has grown by more than 20% from 1985 to 2017 [19]. The population growth has led to built-up expansion of the city and changed agricultural lands and green spaces around the city [50,51]. The results of previous studies indicate that the built-up land increased from 19% of the total area in 1985 to 36.52% in 2015.…”

Section: Study Areamentioning

confidence: 99%

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

et al. 2019

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Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

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Monitoring Spatiotemporal Changes of Heat Island in Babol City Due to Land Use Changes

Cited by 12 publications

References 18 publications

The Effect of Built-Up Area Density and Vegetation Density on Surface Temperature in Banjarmasin City

The Effect of Built-Up Area Density and Vegetation Density on Surface Temperature in Banjarmasin City

The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

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