Abstract
Devastation possibility of disenfranchising poor people of emerging countries like India is due to urban climate change. Hence, an urgent and efficient urban planning strategy shall be adopted for the creation/making of sustainable and amicable cities. This research is focused on the interlinked impacts of urbanization and land cover change on urban climate for Aurangabad city, India using Google Earth Engine. Aurangabad city areas are occupied by industrial areas and historical places and thus the city can be converted into a metropolitan city in the future through well planning. Important indicators such as land cover, change detection, Normalized Difference Vegetation Index (NDVI), and land surface temperature (LST) are used for the research assessment. Machine learning (ML) model (i.e., random forest (RF)) is developed using google earth engine (GEE) platform and satellite datasets for Land use and land cover (LULC) classification. GEE platform is used for the computation of LST and NDVI (2015–2020) based on Landsat-8 satellite. The vegetation “agriculture land” is observed to be covered more than half of the total area under study (113.48 km2) followed by Wasteland (61.70 km2), Built-up land (34.68 km2), and Water body (3.44 km2). Significantly, over for the years of 2015 and 2020, an increment in the water body area noticed by 11.24 km2 followed by Wasteland (66.30 km2) and urban area (36.70 km2). Whereas the vegetation covered is decreased during period of 2020 with area ratio of 98.95 km2. Study of vegetation's index for the years of 2015 and 2020 revealed NDVI values are decreased. Interlinked land cover vegetation area and NDVI values is showed vegetation land decreased in the city. The LST is identified in the urban area about 2 °C in rising in comparison to the year of 2015. The major highlight of this research that LST, NDVI and land cover classes are dramatically changed over the last five years due to built-up land expansion, pollution increase, vegetation land decrease and pollution.
Published Version
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