Application of remote sensing and clustering for the sustainable management of green oak forests in western Algeria

Application of remote sensing and clustering for the sustainable management of green oak forests in western Algeria

Blog Article

The identification of ecologically homogeneous zones is crucial and represents one of the key steps in ecosystem planning, management, and restoration.It allows targeted ecological restoration efforts based on the interactions between environmental factors.The approach of this study is pixel-based to identify ecologically homogeneous zones within the green oak forest in Elhassana area, western Algeria.

This approach uses remote sensing indicators such as the normalized difference vegetation index (NDVI), leaf area index (LAI), soil-adjusted vegetation index (SAVI), normalized difference moisture index (NDMI), elevation, slope, aspect, relative slope position (RSP), and topographic Baggies wetness index (TWI), derived from Landsat 8 and Terra/ASTER satellite imagery to identify homogeneous zones.We use principal component analysis (PCA) to reduce the dimensionality of the data and identify the most important variables.This analysis helps to better understand the structure of the data and determine which variables have the most influence on the unsupervised classification using the iso cluster algorithm.

The results of this study allowed us to visualize and map four types of homogeneous zones and characterize their ecological attributes.This information is invaluable for forest planners, enabling sustainable environmental Mushroom Gummies management and the development of an ecological restoration plan for the green oak forest.