Environmental Assessment For Mapping Land Degradation and Lands Changes Using Remotely Sensed Data with Geospatial Analysis

Lands degradation is one of the problems that facing the humanity throughout the world as well as the abandonment of farming on their lands by farmers, in addition to the fragmentation of most orchards and agricultural fields and their conversion into residential areas, has a negative impact on the Economic, Environmental and Social (Reduced Agricultural Productivity, Economic Loss, Soil Degradation, Agricultural productivity. Water Scarcity, Biodiversity Loss, Rural-Urban Migration, Food Security, Conflict and Instability). However, in Karbala Province, Iraq, most the Agriculture lands are facing this dilemma since 2003. Therefore, in order to start solving this problem and, it is important to detect all the changes throughout the study area and then put recommendations for overcoming this dilemma. The aim of this study to monitor and detect the changes in LCLU the study area and detect the lands degradation and the reasons behind that. For that, Authors employed pixel based classification techniques (Maximum Likelihood Method) on four Landsat satellite (9 ,7 ETM+, TM5, TM4) images acquired at intervals (1990, 2000, 2010, and 2023). The first step in this research is applied the pre-processing stages (radiometric and geometric corrections) to correct the images, secondly, processing stage (layer stacking, and study area sub-setting) to all satellite images, then the corrected images classified using supervise classification to six regions. The results show that the desertification has markedly intensified in the city of Karbala since the last three decades. In 2023, the water volume, decreased by 14.21%, and both Urban area and dark soil increased by 3.05%, and 8.63% respectively, and that give a negative indicator about what happen in research area, it evidences of land degradation processes was seen, mostly due to Human activities such as urban expansion and unsustainable land use practices. The confusion matrix was applied to evaluate the results. The overall accuracy and kappa statistic were above the 90% and 0.90 respectively.