Geo-informatics techniques for detecting changes in land use and land cover in response to regional weather variation.

Representing and measuring the status of land use and land cover (LU and LC) alterations and drivers of alteration are essential for classifying susceptible regions for alteration and planning sustainable environmental services. This study presents the contributing factors of LU and LC and the extent and effects of these changes on sustainable LU in the Diyala River drainage area, north of Iraq. Five major LU and LC types (cultivated and settled land, bare land, water bodies, and palm) from Landsat images of 2013, 2016, 2019, and 2022 were mapped. The images were classified using a classification algorithm and a maximum likelihood classifier. The normalized difference vegetation index (NDVI), vegetation condition index (VCI), and land surface temperature (LST) maps are derived using multi-temporal Landsat 8 and 9 OLI/TIRS satellites. In the past decade, the buildup and barren lands have expanded from 1295.8 km2 (3.9%) to 1677.4 km2 (5.1%) and 5770.3 km2 (17.5%) to 8501.1 km2 (25.8%), whereas the vegetation cover has declined from 25,273.9 km2 (76.6%) to 22,421.8 km2 (68.0%). NDVI saw a significant rise in 2019 and 2022. There were maximum changes in NDVI in the northeast and southeast, where rainfall was higher than before in some parts. The VCI results showed that in 2022, there was a larger class area, with a prolonged extreme drought extent of 5727.1 km2 (47.8%). The findings imply that NDVI and LST are more closely correlated drought indices and are appropriate for use in arid and semi-arid areas to monitor drought with limited data. These studies will advance our knowledge of the connections between drought indices from remote sensing and meteorology. It is suggested that this research methodology be used again in the future, taking into account the impact of changes in climate that affect LU and LC patterns in similar climates. [ABSTRACT FROM AUTHOR]