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Fas’ın Idmine Ormanı’ndaki Bozulmuş Orman Alanlarının Yerbilim Yeteneklerini Kullanarak Mekansal Analizi

Year 2021, Volume: 21 Issue: 1, 1 - 11, 03.04.2021
https://doi.org/10.17475/kastorman.908568

Abstract

Çalışmanın Amacı: Çalışmanın amacı, 2019 yılının (28/08/2019 tarihli) Sentinel 2 uydu görüntülerini kullanarak CBS ve Uzaktan Algılama yoluyla orman bozulma durumu için bir tanı sunmaktır.
Çalışma Alanı: Çalışma, yarı kurak biyoklimatik bölgede bulunan Güney Batı Fas'taki Idmine orman komününde gerçekleştirilmiştir.
Materyal ve Yöntem: Bu çalışmada, iki yöntem denenmiştir. Bunlar; (i) Vejetasyon indisleri (VIs) [Normalize Fark Vejetasyon İndeksi (NDVI), Normalize Fark Su İndeksi (NDWI), Toprak-uyarlı Vejetasyon İndeksi (SAVI), Parlaklık İndeksi (IB)] ve bunların kombinasyonu ile (ii) Denetimli sınıflandırma ve spektral analizdir.
Temel sonuçlar: Orman degradasyon durumunu tanımlamak için iki yöntem aynı sonuçları (Kappa katsayısı=%90) vermiştir. Sonuç olarak, çalışma alanı içindeki orman degredasyonuna ilişkin üç sınıf; düşük (%34), orta (%44) ve kritik bozulma (%22)’dır.
Araştırma Vurguları: Bu izleme, yöneticilerin orman yönetim planları oluşturmasına ve ormansızlaşma ve orman degredasyonu hızını değerlendirmesine yardımcı olabilir.

References

  • Adedeji, O.H. & Adeofun, C.O. (2014). Spatial pattern of land cover change using Remotely Sensed Imagery and GIS: A Case study of Omo-Shasha-Oluwa Forest Reserve, SW Nigeria (1986-2002). Journal of Geographic Information System, 6(04), 375-385. https://doi.org/10.4236/jgis.2014.64033.
  • Afify, H.A. (2011). Evaluation of change detection techniques for monitoring land-cover changes: A case study in New Burg El-Arab Area. Alexandria Engineering Journal, 50(2), 187-195. https://doi.org/10.1016/j.aej.2011.06.001.
  • Alaoui, A., Laaribya, S. & Ayan, S. (2020). The evolution of the forest cover with the effect of anthropic pressure (The Case Study of Sehoul Cork-Oak Forest in Morocco, North Atlantic). Kastamonu University Journal of Forestry Faculty, 20(1), 62-73.
  • Bannari, A., Huete, A.R., Morin, D. & Zagolski, F. (1996). Effets de la couleur et de la brillance des sols sur les indices de végétation. International Journal of Remote Sensing, 17(10), 1885-1906.
  • Baumann, M., Ozdogan, M., Wolter, P.T., Krylov, A., Vladimirova, N. & Radeloff, V.C. (2014). Landsat remote sensing of forest windfall disturbance. Remote Sensing of Environment, 143, 171-179. https://doi.org/10.1016/j.rse.2013.12.020.
  • Cuenca, P., Arriagada, R. & Echeverría, C. (2016). How much deforestation do protected areas avoid in Tropical Andean Landscapes? Environmental Science & Policy, 56, 56-66. https://doi.org/10.1016/j.envsci.2015.10.014.
  • Gao, B. (1996). NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266, Dec.
  • Ghebrezgabher, M. G., Yang, T., Yang, X., Wang, X. & Khan, M. (2016). Extracting and analysing forest and woodland cover change in Eritrea based on Landsat data using supervised classification. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 37-47. https://doi.org/10.1016/j.ejrs.2015.09.002.
  • Hansen, M. C., Roy, D. P., Lindquist, E., Adusei, B., Justice, C.O. & Altstatt, A. (2008). A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sensing of Environment, 112 (5), 2495-2513. https://doi.org/10.1016/j.rse.2007.11.012
  • Haque, M. I. & Basak, R. (2017). Land cover change detection using GIS and Remote Sensing Techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 20(2), 251-263. https://doi.org/10.1016/j.ejrs.2016.12.003
  • Healey, S. P., Cohen, W. B., Zhiqiang, Y. & Krankina, O. N. (2005). Comparison of Tasseled Cap-Based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97(3), 301-310. https://doi.org/10.1016/j.rse.2005.05.009.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G, (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2.
  • Huete, A. R. (1988). A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25, 295-309. https://doi.org/10.1016/0034-4257(88)90106-X IFN, (1994). Inventaire forestier National – Département des eaux et forêts Maroc.
  • Khalile, L., Kaoukaya, H., Lahlaoi, H. & Rhinane, H. (2015). Monitoring deforestation and forest degradation in Benslimane Forest (Morocco) using Landsat time series, November 2015, Conference: 4th International Geosciences & Geomatics Conference, 23rd- 25th November 2015, Manama, Bahrain.
  • Kinoshita, T., Inoue, K., Iwao, K., Kagemoto, H. & Yamagata, Y. (2009). A spatial evaluation of forest biomass usage using GIS. Applied Energy, 86(1), 1-8. https://doi.org/10.1016/j.apenergy.2008.03.017
  • Kumar, P., Rani, M., Pandey, P.C., Majumdar, A. & Nathawat, M.S. (2010). Monitoring of deforestation and forest degradation using Remote Sensing and GIS: A case study of Ranchi in Jharkhand (India). Report and Opinion, 2 (4), 14-20.
  • Laaribya, S. & Alaoui, A. (2017). Rural women and the forest issues preservation - Case study, Morocco. Biological Diversity and Conservation, 10/2 (S2), 8-15.
  • Laaribya, S., Alaoui, A. & Gmira, N. (2017). The Moroccan forest and sustainable development case of the argan tree (Argania spinosa (L.) Skeels) in Morocco, Biological Diversity and Conservation. 10/2 (S2), 1-7.
  • Laaribya, S., Najib, G. & Assmaa, A. (2010). Towards a coordinated development of the forest in Maamora (Morocco), Kastamonu Univ., Journal of Forestry Faculty, 10(2), 172-179.
  • Lea, R., Blodgett, C., Diamond, D. & Schanta, M. (2004). Using the tasselled cap transformation to identify change in the Missouri Ozark Forests. Proceedings of the ASPRS Fall Conference Images to Decisions: Remote Sensing Foundations for GIS Applications, 1-12.
  • Lyon, J. G., Yuan, D., Lunetta, R.S. & Elvidge, C.D. (1998). A change detection experiment using vegetation indices. Photogrammetric Engineering and Remote Sensing, 64(2), 143-150.
  • McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432.
  • Morton, D.C., DeFries, R.S., Shimabukuro, Y.E., Anderson, L.O., Arai, E., del Bon Espirito-Santo, F., Freitas, R. & Morisette, J.T. (2006). Cropland expansion changes deforestation dynamics in the Southern Brazilian Amazon. Proceedings of the National Academy of Sciences, 103(39), 14637-14641. https://doi.org/10.1073/pnas.0606377103
  • Nori, W., Elsidding, E.N. & Niemeyer, I. (2008). Detection of land cover changes using Multi-Temporal Satellite Imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(7), 947-951.
  • Ochego, H. (2003). Application of remote sensing in deforestation monitoring: A case study of the Aberdares (Kenya). 2nd FIG Regional Conference, Marrakech, Morocco, 2-5 December 2003, 1-10.
  • Onojeghuo, A.O, & Onojeghuo, A.R. (2015). Dynamics of forest landscape transition across protected areas in the Niger Delta from 1986 to 2014. Journal of Geoscience and Environment Protection, 3(7), 1.
  • Romero-Sanchez, M.E. & Ponce-Hernandez, R. (2017). Assessing and monitoring forest degradation in a deciduous tropical forest in Mexico via remote sensing indicators. Forests, 8(9), 302. https://doi.org/10.3390/f8090302.
  • Sahebjalal, E. & Dashtekian, K. (2013). Analysis of land use-land covers changes using Normalized Difference Vegetation Index (NDVI) differencing and classification methods. African Journal of Agricultural Research, 8(37), 4614-4622.
  • Schultz, M., Clevers, J.G., Carter, S., Verbesselt, J., Avitabile, V., Quang, H.V. & Herold, M. (2016). Performance of vegetation indices from Landsat time series in deforestation monitoring. International Journal of Applied Earth Observation and Geoinformation, 52, 318-327. https://doi.org/10.1016/j.jag.2016.06.020
  • Siegmund, A. & Menz, G. (2005). Fernes nah gebracht –Satelliten- und Luftbildeinsatz zur Analyse von Umweltveränderungen im Geographieunterricht. In: Geographie & Schule, 27. Jg., H. 154, 2-10.
  • Song, X.P., Huang, C., Sexton, J.O., Channan, S. & Townshend, J.R. (2014). Annual detection of forest cover loss using time series satellite measurements of percent tree cover. Remote Sensing, 6(9), 8878-8903. https://doi.org/10.3390/rs6098878.
  • Torahi, A.A. & Rai, S.C. (2011). Land cover classification and forest change analysis, using satellite imagery - A case study in Dehdez Area of Zagros Mountain in Iran. Journal of Geographic Information System, 3(1), 1-11. https://doi.org/10.4236/jgis.2011.31001
  • Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 8 (2), 127-150.
  • URL 1. FAO (Forest and Agricultural Organization of the United States), Forests and the forestry sector: Morocco, http://www.fao.org/forestry/country/57478/en/mar/, Access Date: 21.04.2020.
  • Villa, P., Lechi, G. & Gomarasca, M.A. (2009). Multivariate differencing techniques for land cover change detection: The normalized difference reflectance approach. In: Geoscience and Remote Sensing, In-Tech., 277-300. https://doi.org/10.5772/8312
  • Wang, T. (2016). Vegetation NDVI change and its relationship with climate change and human activities in Yulin, Shaanxi Province of China. Journal of Geoscience and Environment Protection, 4, 28-40. https://doi.org/10.4236/gep.2016.410002.
  • Xue, J. & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 1-17. https://doi.org/10.1155/2017/1353691.
  • Yang, L., Xian, G., Klaver, J. M. & Deal, B. (2003). Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering & Remote Sensing, 69(9), 1003-1010. https://doi.org/10.14358/PERS.69.9.1003

Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities

Year 2021, Volume: 21 Issue: 1, 1 - 11, 03.04.2021
https://doi.org/10.17475/kastorman.908568

Abstract

Aim of study: The aim of the study is to present a diagnosis for the state of Argan forest degradation in Morocco through GIS and remote sensing utilizing Sentinel 2 satellite images of the year 2019 (dated 28/08/2019).
Area of study: The study was carried out in a forest commune in Idmine, South West Morocco, which is located in semi-arid bioclimatic region.
Material and methods: In the study, two methods were tested. These are; (i) the vegetation indices (VIs) [Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Soil-Adjusted Vegetation Index (SAVI), Brilliance Index (IB)] and their combination and (ii) the supervised classification and spectral analysis.
Main results: Two methods have given the same results (Kappa coefficient=90%) to describe the state of forest degradation. Consequently, three classes pertaining to forest degradation within the study area were; low (34%), medium (44%) and critical degradation (22%).
Highlights: This monitoring might help managers to create forest management plans and to evaluate the speed of deforestation and degradation.

References

  • Adedeji, O.H. & Adeofun, C.O. (2014). Spatial pattern of land cover change using Remotely Sensed Imagery and GIS: A Case study of Omo-Shasha-Oluwa Forest Reserve, SW Nigeria (1986-2002). Journal of Geographic Information System, 6(04), 375-385. https://doi.org/10.4236/jgis.2014.64033.
  • Afify, H.A. (2011). Evaluation of change detection techniques for monitoring land-cover changes: A case study in New Burg El-Arab Area. Alexandria Engineering Journal, 50(2), 187-195. https://doi.org/10.1016/j.aej.2011.06.001.
  • Alaoui, A., Laaribya, S. & Ayan, S. (2020). The evolution of the forest cover with the effect of anthropic pressure (The Case Study of Sehoul Cork-Oak Forest in Morocco, North Atlantic). Kastamonu University Journal of Forestry Faculty, 20(1), 62-73.
  • Bannari, A., Huete, A.R., Morin, D. & Zagolski, F. (1996). Effets de la couleur et de la brillance des sols sur les indices de végétation. International Journal of Remote Sensing, 17(10), 1885-1906.
  • Baumann, M., Ozdogan, M., Wolter, P.T., Krylov, A., Vladimirova, N. & Radeloff, V.C. (2014). Landsat remote sensing of forest windfall disturbance. Remote Sensing of Environment, 143, 171-179. https://doi.org/10.1016/j.rse.2013.12.020.
  • Cuenca, P., Arriagada, R. & Echeverría, C. (2016). How much deforestation do protected areas avoid in Tropical Andean Landscapes? Environmental Science & Policy, 56, 56-66. https://doi.org/10.1016/j.envsci.2015.10.014.
  • Gao, B. (1996). NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266, Dec.
  • Ghebrezgabher, M. G., Yang, T., Yang, X., Wang, X. & Khan, M. (2016). Extracting and analysing forest and woodland cover change in Eritrea based on Landsat data using supervised classification. The Egyptian Journal of Remote Sensing and Space Science, 19(1), 37-47. https://doi.org/10.1016/j.ejrs.2015.09.002.
  • Hansen, M. C., Roy, D. P., Lindquist, E., Adusei, B., Justice, C.O. & Altstatt, A. (2008). A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sensing of Environment, 112 (5), 2495-2513. https://doi.org/10.1016/j.rse.2007.11.012
  • Haque, M. I. & Basak, R. (2017). Land cover change detection using GIS and Remote Sensing Techniques: A spatio-temporal study on Tanguar Haor, Sunamganj, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 20(2), 251-263. https://doi.org/10.1016/j.ejrs.2016.12.003
  • Healey, S. P., Cohen, W. B., Zhiqiang, Y. & Krankina, O. N. (2005). Comparison of Tasseled Cap-Based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97(3), 301-310. https://doi.org/10.1016/j.rse.2005.05.009.
  • Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Ferreira, L.G, (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2.
  • Huete, A. R. (1988). A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25, 295-309. https://doi.org/10.1016/0034-4257(88)90106-X IFN, (1994). Inventaire forestier National – Département des eaux et forêts Maroc.
  • Khalile, L., Kaoukaya, H., Lahlaoi, H. & Rhinane, H. (2015). Monitoring deforestation and forest degradation in Benslimane Forest (Morocco) using Landsat time series, November 2015, Conference: 4th International Geosciences & Geomatics Conference, 23rd- 25th November 2015, Manama, Bahrain.
  • Kinoshita, T., Inoue, K., Iwao, K., Kagemoto, H. & Yamagata, Y. (2009). A spatial evaluation of forest biomass usage using GIS. Applied Energy, 86(1), 1-8. https://doi.org/10.1016/j.apenergy.2008.03.017
  • Kumar, P., Rani, M., Pandey, P.C., Majumdar, A. & Nathawat, M.S. (2010). Monitoring of deforestation and forest degradation using Remote Sensing and GIS: A case study of Ranchi in Jharkhand (India). Report and Opinion, 2 (4), 14-20.
  • Laaribya, S. & Alaoui, A. (2017). Rural women and the forest issues preservation - Case study, Morocco. Biological Diversity and Conservation, 10/2 (S2), 8-15.
  • Laaribya, S., Alaoui, A. & Gmira, N. (2017). The Moroccan forest and sustainable development case of the argan tree (Argania spinosa (L.) Skeels) in Morocco, Biological Diversity and Conservation. 10/2 (S2), 1-7.
  • Laaribya, S., Najib, G. & Assmaa, A. (2010). Towards a coordinated development of the forest in Maamora (Morocco), Kastamonu Univ., Journal of Forestry Faculty, 10(2), 172-179.
  • Lea, R., Blodgett, C., Diamond, D. & Schanta, M. (2004). Using the tasselled cap transformation to identify change in the Missouri Ozark Forests. Proceedings of the ASPRS Fall Conference Images to Decisions: Remote Sensing Foundations for GIS Applications, 1-12.
  • Lyon, J. G., Yuan, D., Lunetta, R.S. & Elvidge, C.D. (1998). A change detection experiment using vegetation indices. Photogrammetric Engineering and Remote Sensing, 64(2), 143-150.
  • McFeeters, S. K. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432.
  • Morton, D.C., DeFries, R.S., Shimabukuro, Y.E., Anderson, L.O., Arai, E., del Bon Espirito-Santo, F., Freitas, R. & Morisette, J.T. (2006). Cropland expansion changes deforestation dynamics in the Southern Brazilian Amazon. Proceedings of the National Academy of Sciences, 103(39), 14637-14641. https://doi.org/10.1073/pnas.0606377103
  • Nori, W., Elsidding, E.N. & Niemeyer, I. (2008). Detection of land cover changes using Multi-Temporal Satellite Imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(7), 947-951.
  • Ochego, H. (2003). Application of remote sensing in deforestation monitoring: A case study of the Aberdares (Kenya). 2nd FIG Regional Conference, Marrakech, Morocco, 2-5 December 2003, 1-10.
  • Onojeghuo, A.O, & Onojeghuo, A.R. (2015). Dynamics of forest landscape transition across protected areas in the Niger Delta from 1986 to 2014. Journal of Geoscience and Environment Protection, 3(7), 1.
  • Romero-Sanchez, M.E. & Ponce-Hernandez, R. (2017). Assessing and monitoring forest degradation in a deciduous tropical forest in Mexico via remote sensing indicators. Forests, 8(9), 302. https://doi.org/10.3390/f8090302.
  • Sahebjalal, E. & Dashtekian, K. (2013). Analysis of land use-land covers changes using Normalized Difference Vegetation Index (NDVI) differencing and classification methods. African Journal of Agricultural Research, 8(37), 4614-4622.
  • Schultz, M., Clevers, J.G., Carter, S., Verbesselt, J., Avitabile, V., Quang, H.V. & Herold, M. (2016). Performance of vegetation indices from Landsat time series in deforestation monitoring. International Journal of Applied Earth Observation and Geoinformation, 52, 318-327. https://doi.org/10.1016/j.jag.2016.06.020
  • Siegmund, A. & Menz, G. (2005). Fernes nah gebracht –Satelliten- und Luftbildeinsatz zur Analyse von Umweltveränderungen im Geographieunterricht. In: Geographie & Schule, 27. Jg., H. 154, 2-10.
  • Song, X.P., Huang, C., Sexton, J.O., Channan, S. & Townshend, J.R. (2014). Annual detection of forest cover loss using time series satellite measurements of percent tree cover. Remote Sensing, 6(9), 8878-8903. https://doi.org/10.3390/rs6098878.
  • Torahi, A.A. & Rai, S.C. (2011). Land cover classification and forest change analysis, using satellite imagery - A case study in Dehdez Area of Zagros Mountain in Iran. Journal of Geographic Information System, 3(1), 1-11. https://doi.org/10.4236/jgis.2011.31001
  • Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 8 (2), 127-150.
  • URL 1. FAO (Forest and Agricultural Organization of the United States), Forests and the forestry sector: Morocco, http://www.fao.org/forestry/country/57478/en/mar/, Access Date: 21.04.2020.
  • Villa, P., Lechi, G. & Gomarasca, M.A. (2009). Multivariate differencing techniques for land cover change detection: The normalized difference reflectance approach. In: Geoscience and Remote Sensing, In-Tech., 277-300. https://doi.org/10.5772/8312
  • Wang, T. (2016). Vegetation NDVI change and its relationship with climate change and human activities in Yulin, Shaanxi Province of China. Journal of Geoscience and Environment Protection, 4, 28-40. https://doi.org/10.4236/gep.2016.410002.
  • Xue, J. & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 1-17. https://doi.org/10.1155/2017/1353691.
  • Yang, L., Xian, G., Klaver, J. M. & Deal, B. (2003). Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering & Remote Sensing, 69(9), 1003-1010. https://doi.org/10.14358/PERS.69.9.1003
There are 38 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Said Laarıbya This is me 0000-0003-3864-0612

Assmaa Alaouı This is me 0000-0001-7844-2683

Sezgin Ayan This is me 0000-0001-8077-0512

Abdelkader Benabou This is me 0000-0002-3274-6398

Publication Date April 3, 2021
Published in Issue Year 2021 Volume: 21 Issue: 1

Cite

APA Laarıbya, S., Alaouı, A., Ayan, S., Benabou, A. (2021). Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities. Kastamonu University Journal of Forestry Faculty, 21(1), 1-11. https://doi.org/10.17475/kastorman.908568
AMA Laarıbya S, Alaouı A, Ayan S, Benabou A. Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities. Kastamonu University Journal of Forestry Faculty. April 2021;21(1):1-11. doi:10.17475/kastorman.908568
Chicago Laarıbya, Said, Assmaa Alaouı, Sezgin Ayan, and Abdelkader Benabou. “Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities”. Kastamonu University Journal of Forestry Faculty 21, no. 1 (April 2021): 1-11. https://doi.org/10.17475/kastorman.908568.
EndNote Laarıbya S, Alaouı A, Ayan S, Benabou A (April 1, 2021) Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities. Kastamonu University Journal of Forestry Faculty 21 1 1–11.
IEEE S. Laarıbya, A. Alaouı, S. Ayan, and A. Benabou, “Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities”, Kastamonu University Journal of Forestry Faculty, vol. 21, no. 1, pp. 1–11, 2021, doi: 10.17475/kastorman.908568.
ISNAD Laarıbya, Said et al. “Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities”. Kastamonu University Journal of Forestry Faculty 21/1 (April 2021), 1-11. https://doi.org/10.17475/kastorman.908568.
JAMA Laarıbya S, Alaouı A, Ayan S, Benabou A. Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities. Kastamonu University Journal of Forestry Faculty. 2021;21:1–11.
MLA Laarıbya, Said et al. “Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities”. Kastamonu University Journal of Forestry Faculty, vol. 21, no. 1, 2021, pp. 1-11, doi:10.17475/kastorman.908568.
Vancouver Laarıbya S, Alaouı A, Ayan S, Benabou A. Spatial Analysis of the Degraded Forest Areas in Idmine Forest-Morocco Using Geoscience Capabilities. Kastamonu University Journal of Forestry Faculty. 2021;21(1):1-11.

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