Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, 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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editura Univeristatii "Stefan cel Mare" din Suceava
2024-05-01
|
| Series: | Georeview |
| Subjects: | |
| Online Access: | https://georeview.usv.ro/wp-content/uploads/2024/06/Article.1-Vol.34-1.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850080014577434624 |
|---|---|
| author | Bilal Jan HAJI MUHAMMAD Wang PING Muhammad Jalal MOHABBAT |
| author_facet | Bilal Jan HAJI MUHAMMAD Wang PING Muhammad Jalal MOHABBAT |
| author_sort | Bilal Jan HAJI MUHAMMAD |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-2be8d7e70dbe4e7e8d113b46f347e77f |
| institution | DOAJ |
| issn | 2343-7391 2343-7405 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Editura Univeristatii "Stefan cel Mare" din Suceava |
| record_format | Article |
| series | Georeview |
| spelling | doaj-art-2be8d7e70dbe4e7e8d113b46f347e77f2025-08-20T02:45:03ZengEditura Univeristatii "Stefan cel Mare" din SuceavaGeoreview2343-73912343-74052024-05-0134111210.4316/GEOREVIEW.2024.01.01Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, AfghanistanBilal Jan HAJI MUHAMMAD0Wang PING1Muhammad Jalal MOHABBAT2Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, Jilin, ChinaKey Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, Jilin, ChinaDepartment of Geological Engineering and Exploration of Mines, Kabul Polytechnic University, Kabul, AfghanistanForests 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 densitieshttps://georeview.usv.ro/wp-content/uploads/2024/06/Article.1-Vol.34-1.pdfforest canopy densityremote sensing and gisavisibsi |
| spellingShingle | Bilal Jan HAJI MUHAMMAD Wang PING Muhammad Jalal MOHABBAT Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan Georeview forest canopy density remote sensing and gis avi si bsi |
| title | Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan |
| title_full | Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan |
| title_fullStr | Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan |
| title_full_unstemmed | Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan |
| title_short | Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan |
| title_sort | integration of gis and remote sensing for evaluating forest canopy density index in kunar province afghanistan |
| topic | forest canopy density remote sensing and gis avi si bsi |
| url | https://georeview.usv.ro/wp-content/uploads/2024/06/Article.1-Vol.34-1.pdf |
| work_keys_str_mv | AT bilaljanhajimuhammad integrationofgisandremotesensingforevaluatingforestcanopydensityindexinkunarprovinceafghanistan AT wangping integrationofgisandremotesensingforevaluatingforestcanopydensityindexinkunarprovinceafghanistan AT muhammadjalalmohabbat integrationofgisandremotesensingforevaluatingforestcanopydensityindexinkunarprovinceafghanistan |