Analyzing environmental and land use changes by combining remote sensing and geographic information system

Abstract

Arid and semi-arid regions exhibit distinct spectrum features compared to other climatic regions. Hence, this work used remote sensing and geographical information system to identify environmental and land use alterations in the city of Basrah, located in southern Iraq. Various picture indices are employed. The analysis utilizes three mean Landsat photos from the years 2002, 2012, and 2022 to identify alterations in the environment and land utilization. This study suggests utilizing various indices to chart the alterations in the environment using Landsat 7 and 8. The mapping process would be facilitated by the cloud computing-based Google Earth Engine (GEE) platform, which efficiently manages images and processes spatiotemporal data on a large scale. Nevertheless, this platform necessitates the utilization of index formulae or combinations to facilitate the classification and enhance the precision in mapping the surface of the planet. The combination is made up of four indexes namely: vegetation index (NDVI), soil index (BSI), water index (NDWI), and buildings index (NDBI). In the change detection analysis, between 2012 and 2002, the vegetation cover had a serious degradation. The change detection results from BSI illustrate that the year of 2002 have the majority of bare soil rather than 2012 and 2022.According to the analysis, which was made on the Normalized Difference Moisture Index (NDMI) of Landsat (2012) image, the water level in the research area was increasing securely, while it was a decrease in 2002 compared to today. The process of urbanization is progressing as shows in the result of change detection analysis with the utilization of Normalized Difference Built-up Index (NDBI). The study investigates the changes in land use and land cover (LULC) patterns and its extent for the last twenty years using Maximum Likelihood Algorithm (MLA) and GIS technology. The maximum likelihood approach was used to classify the land use land cover (LULC) classes by using supervised classification technique.Within the 2002 and 2022 interval, there was a huge decrease of the area of vegetative land coverage, which decreased from 20.21% to 2.79%. On the other hand, the area of barren land experiences a little decrease, which changes from 67.18% to 64.57%. In the meanwhile, with the analyzed land use and land cover (LULC) classes water body and built-up area in the research area are growing, from 5.6618% to 13.31% and from 6.95% to 19.33% separately.The results suggest that prioritizing and implementing integrated land management and land use planning within the research area is essential.