A Review for Vegetation Vulnerability Using Artificial Intelligent (AI) Techniques

Abstract

Because it is detrimental to all living things, including people, directly and indirectly, vegetation vulnerability has gained international attention. The vegetation cover is essential for maintaining the ecological balance on the surface of the earth, so it can be considered one of the most important renewable natural resources with significant economic and environmental feasibility. According to the review, topography, human involvement (cover, land use, and people density), climatic parameters (precipitation, air temp, sunlight length), and topographic features all contribute to a decrease in vegetation area (aspect, slope, elevation). Owing to their adaptability to data, neural networks have been implemented in Remote Sensing (RS) technologies as they gained prominence. To improve categorization and accuracy, image segments eventually took the place of the neural network's initial input layer, which in neural networks is the smallest unit of an image, known as the pixel. To study and accurately anticipate the vegetation susceptibility, the earlier studies Systems used GIS and machine learning to create an Artificial Neural Network (ANN) based model.