Bangkok Metropolitan Administration (BMA) is the capital city of Thailand. In recent years, city surface has been changed greatly by rapid urbanization. Many green spaces in the urban area are destroyed and replaced with the commercial area. It can have effect on the climate and weather that could lead to the Urban Heat Island (UHI). This research analyzes the correlation between Urban Green Spaces (UGS) and Land Surface Temperature (LST) using quantitative remote sensing technology in BMA. More than five years of Landsat-5 TM and Landsat-8 images are used to study urban temperature. The LST, UGS and their correlation of whole area of BMA are analyzed. The LST is retrieved from mono-window algorithm, which used only one thermal band. Several indices of UGS, including the Normalized Difference Vegetation Index (NDVI), and measurement of vegetation cover percentage are also used to study on UGS. The result showed that from 2008 to 2018 LST is increased while UGS is decreased. In addition, Pearson product-moment correlation coefficient is used to analyze the linear correlation between UGS and LST. The correlation between LST and NDVI indicates the negative correlation. The average correlation coefficient is -0.44. That can imply that the higher vegetated area, the LST was lower. To get more detail relationship between LST and UGS, three typical regional areas are selected to study. LST contour is used to analyze LST and HERCULES system is used to classify land cover. The result showed that firstly, the density of tree affect LST. Secondly, the location of the public park is important. Thirdly, water bodies help to decrease LST. The results of this study have key implications for BMA sustainable urban planning and development; to mitigate UHI effects it is important to not only increase canopy cover or the size of UGS, but also to optimize their spatial configuration.
Land degradation is a serious environmental issue in the world. Both space and ground-based observations could be used to define the land changes and develop the assessment of land degradation. This study assessed land degradation in the Orkhon sub-province, the best representation sample in the prominent agricultural zone of Mongolia, using Landsat Thermal Mapper (TM) and Landsat Operation Land Imager (OLI) satellite images during the periods of 1990, 1994, 2000, 2006, 2010, and 2015. The land degradation of a region could be detected by changes in spectral indices and correlation of these indices. The most frequently used spectral indices include Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). These indices were selected as indicators for representing land surface conditions vegetation biomass, landscape pattern, micrometeorology and human activities. The land degradation analysis was described by descriptive statistics, correlation distributions and correlation coefficients of changes in index outputs. In addition, the validations of these indices were also verified by comparing LST and NDWI index values with in-situ, realtime climate data from 1984 to 2010. The Land Degradation Risk Mapping (LDRM) analysis shows that the agricultural and urban areas experienced degradation due to human activities and this has led to decline in the soil moisture in this region.
Urban growth can profoundly alter the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical to develop strategies for sustainable development and to improve the urban residential environment and living quality. Ulaanbaatar city was urbanized very rapidly caused by herders and farmers, many of them migrating from rural places, have played a big role in this urban expansion (sprawl). Today, 1.3 million residents for about 40% of total population are living in the Ulaanbaatar region. Those human activities influenced stronger to green environments. Therefore, the aim of this study is determined to change detection of land use/land cover (LULC) and estimating their areas for the trend of future by remote sensing and statistical methods. The implications of analysis were provided by change detection methods of LULC, remote sensing spectral indices including normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI). In addition, it can relate to urban heat island (UHI) provided by Land surface temperature (LST) with local climate issues. Statistical methods for image processing used to define relations between those spectral indices and change detection images and regression analysis for time series trend in future. Remote sensing data are used by Landsat (TM/ETM+/OLI) satellite images over the period between 1990 and 2016 by 5 years. The advantages of this study are very useful remote sensing approaches with statistical analysis and important to detecting changes of LULC. The experimental results show that the LULC changes can image on the present and after few years and determined relations between impacts of environmental conditions.
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