Abstract
Land use is an essential theme in monitoring environmental phenomena. The supervised maximum likelihood classification algorithm has been shown to provide the best results from remotely sensed data. This work is aimed at the application of a supervised classification (maximum likelihood) based on a priori knowledge of the terrain under study and information extracted from the two remote sensing indices NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-up Index) for mapping land use of the area of Ain Abid Constantine (located in eastern Algeria) for the year 2020. The obtained result showed that this city is an agricultural area with a percentage of 68.49% of agricultural land and a low percentage of 2.02% of Buildings.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
