The management of forests plays a crucial role in ensuring long-term ecological sustainability and the provision of valuable resources. Self-seeding forests, characterized by their ability to regenerate naturally without human intervention, are of particular interest due to their potential for sustainable resource management. This study explores the application of Geographic Information Systems (GIS) as a tool to identify and map self-seeding forests, facilitating informed decision-making for forest managers and policymakers.
The research leverages remote sensing data, including satellite imagery and digital elevation models, to analyze landscape patterns and ecological factors associated with self-seeding forests. Various GIS techniques, such as image classification, spatial analysis, and geostatistics, are employed to delineate and evaluate self-seeding forest patches within a defined study area. Additionally, field surveys and ground truthing are conducted to validate the GIS-derived results and assess the accuracy of the identified self-seeding forests.
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