Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan
Author(s):
Bilal Jan HAJI MUHAMMAD1, Wang PING1, Muhammad Jalal MOHABBAT 2
1 Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, Jilin, China. 2 Department of Geological Engineering and Exploration of Mines, Kabul Polytechnic University, Kabul, Afghanistan.
Forests are an essential component of the natural environment and are essential to the advancement of sustainable development. But each year, natural forests are being destroyed by human endeavors. For this reason, forest management is essential to sustainable development. The forest canopy density (FCD) model is a valuable tool for assessing the condition of forests and their alterations over time. Three criteria are chosen to evaluate FCD: shadow index (SI), bare soil (BI), and advanced vegetation (AVI). Satellite images are used to calculate these characteristics. To compute the FCD, the Landsat 8 OLI image from 2023 is first normalized and then worked with in ArcGIS and ENVI software. When comparing the categorization result with the land cover map, the total accuracy is 86.6%. The distribution of forest canopy density in the study region is depicted in the final result, which includes non-forest, low, moderate and intense forest densities.
Keywords:
Forest canopy density, Remote Sensing and GIS, AVI, SI, BSI.
