Main Article Content
Abstract
The purpose of this study was to identify the uses of Geographic Information Systems (GIS) and remote sensing in forest resource management and to identify their opportunities and limitations. This technology has brought significant changes in the management, conservation, and monitoring of forests. Methodologically, the study is descriptive and analytical, utilizing both literature sources and satellite data. The results show that (GIS) and remote sensing have wide applications in determining vegetation cover, estimating biomass and carbon stocks, monitoring climate change, mapping, wildfire management, assessing biodiversity, and creating tourist areas and other fields. However, these technologies have the potential to provide free information, but the lack of reliable data, high costs, seasonal effects, and other similar problems are among their limitations. The presence of analytical capabilities and technical tools is considered essential for their effective use in forests.
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References
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- Bahramzi, F. A. (2010). Structure and Interpretation of Domain Knowledge with Special Reference to Mineral Deposit in Afghanistan. A Report Submitted to the School of Engineering, Technology and Media National University. Link
- Dossa, K. F., & Miassi, Y. E. (2024). Remote Sensing Methods and GIS Approaches for Carbon Sequestration Measurement: A General Review. International Journal of Environment and Climate Change, 14(7), 222-233.
- https://doi.org/10.9734/ijecc/2024/v14i74265
- Eniolorunda, N. (2014). Climate change analysis and adaptation: the role of remote sensing (Rs) and geographical information system (Gis). International Journal of Computational Engineering Research, 4(1), 41-51. Link
- Food and Agriculture Organization of the United Nations. (2020). Global forest resources assessment 2020—Key findings. Rome. Link
- Gao, Y., Skutsch, M., Paneque-Gálvez, J., & Ghilardi, A. (2020). Remote sensing of forest degradation: a review. Environmental Research Letters, 15(10), 103001. https://doi.org/10.1088/1748-9326/abaad7
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- Grigolato, S., Mologni, O., & Cavalli, R. (2017). GIS applications in forest operations and road network planning: An overview over the last two decades. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 38(2), 175-186. Link
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- Jovanović, M. M., & Milanović, M. M. (2017). Remote sensing and Forest conservation: Challenges of illegal logging in Kursumlija municipality (Serbia). Forest Ecology and Conservation, 99-118. https://doi.org/10.5772/67666
- Keshtkar, H. (2024). Monitoring Land Surface Temperature Changes in Afghanistan over the Last Two Decades: A Satellite Data Analysis. Link
- Khan, K., Khan, S. N., Ali, A., Khokhar, M. F., & Khan, J. A. (2025). Estimating Aboveground Biomass and Carbon Sequestration in Afforestation Areas Using Optical/SAR Data Fusion and Machine Learning. Remote Sensing, 17(5), 934. https://doi.org/10.3390/rs17050934
- Kshetri, T. (2018). Ndvi, ndbi & ndwi calculation using landsat 7, 8. GeoWorld, 2, 32-34. Link
- Lechner, A. M., Foody, G. M., & Boyd, D. S. (2020). Applications in remote sensing to forest ecology and management. One Earth, 2(5), 405-412. https://doi.org/10.1016/j.oneear.2020.04.008
- Liang, L., Li, X., Huang, Y., Qin, Y., & Huang, H. (2017). Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance. Ecological Modelling, 354, 1-10. https://doi.org/10.1016/j.ecolmodel.2017.03.003
- Lin, Z., Zhao, C. J., & Li, P. S. (2014). The Application of RS and GIS in Forest Park Planning: A Case Study of Diaoluoshan National Forest Park. Ecosystem Assessment and Fuzzy Systems Management, 353-365. https://doi.org/10.1007/978-3-319-03449-2_32
- Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), 63-105. https://doi.org/10.1080/17538947.2015.1053667
- Mani, J. K., & Varghese, A. O. (2018). Remote sensing and GIS in agriculture and forest resource monitoring. Geospatial technologies in land resources mapping, monitoring and management, 377-400. https://doi.org/10.1007/978-3-319-78711-4_19
- Matouq, M., Al-Bilbisi, H., El-Hasan, T., & Eslamian, S. (2014). GIS applications in a changing climate. Handbook of Engineering Hydrology, 2, 297-312. https://doi.org/10.1201/b16683-16.
- McDowell, N. G., Coops, N. C., Beck, P. S., Chambers, J. Q., Gangodagamage, C., Hicke, J. A., ... & Allen, C. D. (2015). Global satellite monitoring of climate-induced vegetation disturbances. Trends in plant science, 20(2), 114-123. https://doi.org/10.1016/j.tplants.2014.10.008
- Mertes, C. M., Schneider, A., Sulla-Menashe, D., Tatem, A. J., & Tan, B. (2015). Detecting change in urban areas at continental scales with MODIS data. Remote Sensing of Environment, 158, 331-347. https://doi.org/10.1016/j.rse.2014.09.023
- Muhammad, B. J. H., PING, W., & MOHABBAT, M. J. (2024). Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan. https://doi.org/10.4316/GEOREVIEW.2024.01.01
- Najmuddin, O., Deng, X., & Bhattacharya, R. (2018). The dynamics of land use/cover and the statistical assessment of cropland change drivers in the Kabul River Basin, Afghanistan. Sustainability, 10(2), 423. https://doi.org/10.3390/su10020423
- Parthasarathy, S., Konyak, L. M., Bupesh, G., & Tharumasivam, S. V. (2024). THE ROLE OF REMOTE SENSING AND GIS IN FORESTRY. CHALLENGES, 58. Link
- Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. (2014). Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology, 51(4), 839-848. https://doi.org/10.1111/1365-2664.12261
- Phayayam, C., Panngom, K., Howpinjai, I., & Kamyo, T. (2023). Applying Geographic Information System to Evaluate Risk Areas of Illegal Logging in Conservation Areas at Maehongson Province. Thai Forest Ecological Research Journal, 7(2), 159-170. https://doi.org/10.34044/j.tferj.2023.7.2.02
- Pirasteh, S., & Varshosaz, M. (2019). Geospatial information technologies in support of disaster risk reduction, mitigation and resilience: Challenges and recommendations. In Sustainable development goals connectivity dilemma (pp. 93-108). CRC Press. https://doi.org/10.1201/9780429290626-6
- Popal, S. (2018). Object-based Forest cover change mapping using remote sensing in Nuristan Province, Afghanistan--Poster Summary. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 10(1), 31-31. Link
- Potter, C. R., Green, K. C., Peters, D. L., & Niemann, K. O. (2024). Investigating hydrological recovery in regenerating coniferous stands in snow‐dominated watersheds using simultaneous localization and mapping‐enabled mobile terrestrial LiDAR. Hydrological Processes, 38(7), e15247. https://doi.org/10.1002/hyp.15247
- Rousta, I., Olafsson, H., Moniruzzaman, M., Zhang, H., Liou, Y. A., Mushore, T. D., & Gupta, A. (2020). Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sensing, 12(15), 2433. https://doi.org/10.3390/rs12152433
- Roy, L., Ganchaudhuri, S., Pathak, K., Dutta, A., & Gogoi Khanikar, P. (2022). Application of Remote Sensing and GIS in Agriculture. International Journal of Research and Analytical Reviews, 9(1), 460. https://doi.org/10.1729/Journal.29398
- Singh, M., Shahina, N. N., Das, S., Arshad, A., Siril, S., Barman, D., ... & Chakravarty, S. (2022). Forest resources of the world: present status and future prospects. Land Degradation Neutrality: Achieving SDG 15 by Forest Management, 1-23. https://doi.org/10.1007/978-3-030-98297-3_1
- Singh, S. K., & Noori, A. R. (2022). Groundwater quality assessment and modeling utilizing water quality index and GIS in Kabul Basin, Afghanistan. Environmental Monitoring and Assessment, 194(10), 673. https://doi.org/10.1007/s10661-022-10340-0
- Sritart, H., & Miyazaki, H. (2022). Geographic Information System (GIS) and Data Visualization. In Disaster Nursing, Primary Health Care and Communication in Uncertainty (pp. 297-307). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-98297-3_26
- Thangaperumal, S., Surya, K. J., & Bervin, A. B. (2019). Deforestation: A quantitative analysis using remote sensing and GIS. Link
- Turner, M. G., Gardner, R. H., Turner, M. G., & Gardner, R. H. (2015). Introduction to landscape ecology and scale. Landscape ecology in theory and practice: Pattern and process, 1-32. https://doi.org/10.1007/978-1-4939-2794-4
- Zhao, P., Zhang, F., Lin, H., & Xu, S. (2021). GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing. Remote Sensing, 13(18), 3704. https://doi.org/10.3390/rs13183704
References
Ahmad, F., Uddin, M. M., & Goparaju, L. (2018). Assessment of remote sensing and GIS application in identification of land suitability for agroforestry: A case study of Samastipur, Bihar, India. Contemporary Trends in Geoscience, 7(2), 214-227. https://doi.org/10.2478/ctg-2018-0015
Archard, F., de OLIVEIRA, Y. M. M., & Mollicone, D. (2017). Monitoring forest cover and deforestation. Link
Ariez, M., & Larwai, M. I. (2022). Forest cover change detection in Paktia province of Afghanistan using remote sensing and GIS: 1998-2018. Jurnal Belantara, 5(2), 169-177. https://doi.org/10.29303/jbl.v5i2.887
Aziz, G., Minallah, N., Saeed, A., Frnda, J., & Khan, W. (2024). Remote sensing-based forest cover classification using machine learning. Scientific Reports, 14(1), 69. https://doi.org/10.1038/s41598-023-50863-1
Bahramzi, F. A. (2010). Structure and Interpretation of Domain Knowledge with Special Reference to Mineral Deposit in Afghanistan. A Report Submitted to the School of Engineering, Technology and Media National University. Link
Dossa, K. F., & Miassi, Y. E. (2024). Remote Sensing Methods and GIS Approaches for Carbon Sequestration Measurement: A General Review. International Journal of Environment and Climate Change, 14(7), 222-233.
https://doi.org/10.9734/ijecc/2024/v14i74265
Eniolorunda, N. (2014). Climate change analysis and adaptation: the role of remote sensing (Rs) and geographical information system (Gis). International Journal of Computational Engineering Research, 4(1), 41-51. Link
Food and Agriculture Organization of the United Nations. (2020). Global forest resources assessment 2020—Key findings. Rome. Link
Gao, Y., Skutsch, M., Paneque-Gálvez, J., & Ghilardi, A. (2020). Remote sensing of forest degradation: a review. Environmental Research Letters, 15(10), 103001. https://doi.org/10.1088/1748-9326/abaad7
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., & Justice, C. O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. Remote sensing of environment, 217, 72-85. https://doi.org/10.1016/j.rse.2018.08.005
Grigolato, S., Mologni, O., & Cavalli, R. (2017). GIS applications in forest operations and road network planning: An overview over the last two decades. Croatian Journal of Forest Engineering: Journal for Theory and Application of Forestry Engineering, 38(2), 175-186. Link
Jenicka, S. (2021). Introduction to remote sensing. In Land Cover Classification of Remotely Sensed Images: A Textural Approach (pp. 1-16). Cham: Springer International Publishing. https://doi.org.10.1007/978-3-030-66595-1
Jovanović, M. M., & Milanović, M. M. (2017). Remote sensing and Forest conservation: Challenges of illegal logging in Kursumlija municipality (Serbia). Forest Ecology and Conservation, 99-118. https://doi.org/10.5772/67666
Keshtkar, H. (2024). Monitoring Land Surface Temperature Changes in Afghanistan over the Last Two Decades: A Satellite Data Analysis. Link
Khan, K., Khan, S. N., Ali, A., Khokhar, M. F., & Khan, J. A. (2025). Estimating Aboveground Biomass and Carbon Sequestration in Afforestation Areas Using Optical/SAR Data Fusion and Machine Learning. Remote Sensing, 17(5), 934. https://doi.org/10.3390/rs17050934
Kshetri, T. (2018). Ndvi, ndbi & ndwi calculation using landsat 7, 8. GeoWorld, 2, 32-34. Link
Lechner, A. M., Foody, G. M., & Boyd, D. S. (2020). Applications in remote sensing to forest ecology and management. One Earth, 2(5), 405-412. https://doi.org/10.1016/j.oneear.2020.04.008
Liang, L., Li, X., Huang, Y., Qin, Y., & Huang, H. (2017). Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance. Ecological Modelling, 354, 1-10. https://doi.org/10.1016/j.ecolmodel.2017.03.003
Lin, Z., Zhao, C. J., & Li, P. S. (2014). The Application of RS and GIS in Forest Park Planning: A Case Study of Diaoluoshan National Forest Park. Ecosystem Assessment and Fuzzy Systems Management, 353-365. https://doi.org/10.1007/978-3-319-03449-2_32
Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., & Moran, E. (2016). A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), 63-105. https://doi.org/10.1080/17538947.2015.1053667
Mani, J. K., & Varghese, A. O. (2018). Remote sensing and GIS in agriculture and forest resource monitoring. Geospatial technologies in land resources mapping, monitoring and management, 377-400. https://doi.org/10.1007/978-3-319-78711-4_19
Matouq, M., Al-Bilbisi, H., El-Hasan, T., & Eslamian, S. (2014). GIS applications in a changing climate. Handbook of Engineering Hydrology, 2, 297-312. https://doi.org/10.1201/b16683-16.
McDowell, N. G., Coops, N. C., Beck, P. S., Chambers, J. Q., Gangodagamage, C., Hicke, J. A., ... & Allen, C. D. (2015). Global satellite monitoring of climate-induced vegetation disturbances. Trends in plant science, 20(2), 114-123. https://doi.org/10.1016/j.tplants.2014.10.008
Mertes, C. M., Schneider, A., Sulla-Menashe, D., Tatem, A. J., & Tan, B. (2015). Detecting change in urban areas at continental scales with MODIS data. Remote Sensing of Environment, 158, 331-347. https://doi.org/10.1016/j.rse.2014.09.023
Muhammad, B. J. H., PING, W., & MOHABBAT, M. J. (2024). Integration of GIS and remote sensing for evaluating forest canopy density index in Kunar Province, Afghanistan. https://doi.org/10.4316/GEOREVIEW.2024.01.01
Najmuddin, O., Deng, X., & Bhattacharya, R. (2018). The dynamics of land use/cover and the statistical assessment of cropland change drivers in the Kabul River Basin, Afghanistan. Sustainability, 10(2), 423. https://doi.org/10.3390/su10020423
Parthasarathy, S., Konyak, L. M., Bupesh, G., & Tharumasivam, S. V. (2024). THE ROLE OF REMOTE SENSING AND GIS IN FORESTRY. CHALLENGES, 58. Link
Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. (2014). Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology, 51(4), 839-848. https://doi.org/10.1111/1365-2664.12261
Phayayam, C., Panngom, K., Howpinjai, I., & Kamyo, T. (2023). Applying Geographic Information System to Evaluate Risk Areas of Illegal Logging in Conservation Areas at Maehongson Province. Thai Forest Ecological Research Journal, 7(2), 159-170. https://doi.org/10.34044/j.tferj.2023.7.2.02
Pirasteh, S., & Varshosaz, M. (2019). Geospatial information technologies in support of disaster risk reduction, mitigation and resilience: Challenges and recommendations. In Sustainable development goals connectivity dilemma (pp. 93-108). CRC Press. https://doi.org/10.1201/9780429290626-6
Popal, S. (2018). Object-based Forest cover change mapping using remote sensing in Nuristan Province, Afghanistan--Poster Summary. Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS), 10(1), 31-31. Link
Potter, C. R., Green, K. C., Peters, D. L., & Niemann, K. O. (2024). Investigating hydrological recovery in regenerating coniferous stands in snow‐dominated watersheds using simultaneous localization and mapping‐enabled mobile terrestrial LiDAR. Hydrological Processes, 38(7), e15247. https://doi.org/10.1002/hyp.15247
Rousta, I., Olafsson, H., Moniruzzaman, M., Zhang, H., Liou, Y. A., Mushore, T. D., & Gupta, A. (2020). Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sensing, 12(15), 2433. https://doi.org/10.3390/rs12152433
Roy, L., Ganchaudhuri, S., Pathak, K., Dutta, A., & Gogoi Khanikar, P. (2022). Application of Remote Sensing and GIS in Agriculture. International Journal of Research and Analytical Reviews, 9(1), 460. https://doi.org/10.1729/Journal.29398
Singh, M., Shahina, N. N., Das, S., Arshad, A., Siril, S., Barman, D., ... & Chakravarty, S. (2022). Forest resources of the world: present status and future prospects. Land Degradation Neutrality: Achieving SDG 15 by Forest Management, 1-23. https://doi.org/10.1007/978-3-030-98297-3_1
Singh, S. K., & Noori, A. R. (2022). Groundwater quality assessment and modeling utilizing water quality index and GIS in Kabul Basin, Afghanistan. Environmental Monitoring and Assessment, 194(10), 673. https://doi.org/10.1007/s10661-022-10340-0
Sritart, H., & Miyazaki, H. (2022). Geographic Information System (GIS) and Data Visualization. In Disaster Nursing, Primary Health Care and Communication in Uncertainty (pp. 297-307). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-98297-3_26
Thangaperumal, S., Surya, K. J., & Bervin, A. B. (2019). Deforestation: A quantitative analysis using remote sensing and GIS. Link
Turner, M. G., Gardner, R. H., Turner, M. G., & Gardner, R. H. (2015). Introduction to landscape ecology and scale. Landscape ecology in theory and practice: Pattern and process, 1-32. https://doi.org/10.1007/978-1-4939-2794-4
Zhao, P., Zhang, F., Lin, H., & Xu, S. (2021). GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing. Remote Sensing, 13(18), 3704. https://doi.org/10.3390/rs13183704