Use of Cellular Automata-Based Artificial Neural Networks For Detection and Prediction of Land Use Changes in North Eastern Dhaka City

Abstract

The purpose of this study was to examine the pattern of change in Land Use Land Cover (LULC) and Land Surface Temperature (LST) in Mirpur and its surrounding area (north-eastern part of Dhaka) over the last 30 years using Landsat Satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, five-year intervals of Landsat 4-5 TM and Landsat 8 OLI pictures were utilized to track the relationship between LULC changes and LST. Cellular Automata-based Artificial Neural Network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. Two environmental indices were analyzed to determine their link with LST: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). The link between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature has been increasing steadily for the previous 30 years. For the year 2019, it was determined that roughly 86 percent of total land area has been converted to built-up area and that 89 percent of land area has an LST greater than 28°C. According to the study, if the current trend continues, 72 percent of the Mirpur area is predicted to see temperatures near 32°C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a Kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.