Araştırma Makalesi
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Hindistan Sikkim'de Yanan Alanların Uydu Haritalaması Yoluyla Değerlendirilmesi

Yıl 2023, Cilt: 23 Sayı: 3, 199 - 219, 06.12.2023
https://doi.org/10.17475/kastorman.1394888

Öz

Çalışmanın amacı: Yangın biyolojik çeşitliliği ve ekosistemleri etkiler ve yangın nedenlerinin anlaşılması açısından çok önemlidir. Bu makalede, Sikkim'in 2004-2019 yılları arasındaki orman yangını vaka verilerindeki yanan alanların ve şiddet seviyelerinin değerlendirilmesi amaçlanmıştır.
Çalışma alanı: Himalaya Dağı'nın Kuzeydoğu bölgesinde yer alan Sikkim eyaletidir.
Materyal ve Yöntem: Çalışmada Landsat 8 ve Landsat 5 uydu görüntüleri kullanılmış ve Delta Normalize Yanma Oranı (dNBR) ve Göreceli Yanma Oranı (RBR) gibi standart bitki örtüsü indisleri hesaplanmıştır. Ayrıca 2009-2019 arasında Sikkim'de yaşanan orman yangınlarının sıcaklık (°C), rüzgar (Km/h), yağış (mm) gibi hava parametreleri ile yanma şiddeti (dNBR sınıfları) arasında doğrusal regresyon analizi yapılmıştır.
Temel sonuçlar: Bulgulara göre, 2004 ile 2019 yılları arasında Sikkim'de meydana gelen 557 orman yangını olayından 250 tanesi Yanmamış (%46.21), 199 tanesi Bitki Örtüsünün Yeniden Gelmesi, Düşük (%35.72) ve 43 tanesi Bitki Örtüsünün Yeniden Gelmesi, Yüksek (%7.94), 32 tanesi Düşük seviye (%5.92), 9 tanesi Orta-düşük seviye (%1.66), 5 tanesi Orta-yüksek seviye (%0.92) ve 2 tanesi Yüksek seviye (%0.36) olarak sınıflandırılmıştır. Rüzgarın (r=0.80, Eğim=0.57, SD=0.70) ve yağışın (r=0.77, Eğim=-0.18, SD=7.00) yanma şiddetini (dNBR) etkilemede sırasıyla güçlü pozitif ve güçlü negatif doğrusal ilişki gösterdiği sıcaklığın ise (r=0.69, Eğim=0.74, SD=0.01) yanma şiddetini etkilemede orta derecede pozitif rol oynadığı belirlenmiştir.
Araştırma vurguları: Çalışma, sınırlı kaynaklar, çeşitli yer şekilleri ve bitki örtüsüne sahip orman yangını bölgelerinin analizinde yanan alan haritalama ve uzaktan algılama veri ürünlerinin etkinliğini göstermiştir. Araştırmacılar, orman yangınlarından etkilenen ve yangından bu yana iyileşmeyen bölgeleri tespit edebilecektir. Bu araştırmanın amacı, yanan bölgelerdeki bitki örtüsü bozulma modelini değerlendirmek ve orman yangınlarının etkisini tahmin etmek için sorumlu makamlara yardım sağlayarak orman yangını planlamasını ve yönetimini iyileştirmektir

Kaynakça

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  • Axel, A. C. (2018). Burned area mapping of an escaped fire into tropical dry forest in Western Madagascar using multi-season Landsat OLI Data. Remote Sensing, 10(3), 371. https://doi.org/10.3390/rs10030371
  • Bar, S., Parida, B. R. & Pandey, A. C. (2020). Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18, 100324. https://doi.org/10.1016/j.rsase.2020.100324
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  • Bajocco, S., Koutsias, N. & Ricotta, C. (2017). Linking fire ignitions hotspots and fuel phenology : The importance of being seasonal. Ecological Indicators, 82, 433-440.
  • Cansler, C. A. & McKenzie, D. (2012). How robust are burn severity indices when applied in a new region ? Evaluation of alternate field-based and remote-sensing methods. Remote Sensing, 4(2), 456-483. https://doi.org/10.3390/rs4020456
  • Chuvieco, E., Mouillot, F., Van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., et al. (2019). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45-64. https://doi.org/10.1016/j.rse.2019.02.013
  • Elmahdy, S. I., Mohamed, M. M., Ali, T. A., Abdalla, J. E. D. & Abouleish, M. (2022). Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain, UAE. Geocarto International, 37(1), 315-331. https://doi.org/10.1080/10106049.2020.1716398
  • Fitriana, H. L., Prasasti, I. & Khomarudin, M. R. (2018). Mapping burned areas from landsat-8 imageries on mountainous region using reflectance changes. In MATEC Web of Conferences, 229, 04012. EDP Sciences. https://doi.org/10.1051/matecconf/201822904012
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  • Gaveau, D. L., Descals, A., Salim, M. A., Sheil, D. & Sloan, S. (2021). Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning. Earth System Science Data, 13(11), 5353-5368. https://doi.org/10.5194essd-13-5353-2021
  • Ghorbanzadeh, O., Blaschke, T., Gholamnia, K. & Aryal, J. (2019). Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire, 2(3), 50. https://doi.org/10.3390/fire2030050
  • Gigović, L., Jakovljević, G., Sekulović, D., & Regodić, M. (2018). GIS multi-criteria analysis for identifying and mapping forest fire hazard: Nevesinje, Bosnia and Herzegovina. Tehnički vjesnik, 25(3), 891-897.
  • Gomez, C., Alejandro, P., Hermosilla, T., Montes, F., Pascual, C., Ruiz Fernández, L. Á., et al. (2019). Remote sensing for the Spanish forests in the 21st century: A review of advances, needs, and opportunities. Forest Systems, 28(1), 1-33. https://doi.org/10.5424/fs/2019281-14221
  • Han-Qiu, X. U. (2005). A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5), 589-595. https://www.researchgate.net/publication/284418225
  • Harvey, B. J., Andrus, R. A. & Anderson, S. C. (2019). Incorporating biophysical gradients and uncertainty into burn severity maps in a temperate fire‐prone forested region. Ecosphere, 10(2), e02600. https://doi.org/10.1002/ecs2.2600
  • Jafarzadeh, A. A., Mahdavi, A., & Jafarzadeh, H. (2017). Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering. Journal of forest Science, 63(8), 370-380.
  • Joseph, S., Anitha, K. & Murthy, M. S. R. (2009). Forest fire in India: a review of the knowledge base. Journal of Forest Research, 14(3), 127-134. https://doi.org/10.1007/s10310-009-0116-x
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Assessing Burned Areas in Sikkim, India through Satellite Mapping

Yıl 2023, Cilt: 23 Sayı: 3, 199 - 219, 06.12.2023
https://doi.org/10.17475/kastorman.1394888

Öz

Aim of study: Fire impacts biodiversity and ecosystems, and is crucial for understanding fire causes. This paper aimed to assess burned areas and severity levels in Sikkim's forest fire incidence data from 2004-2019.
Area of the study: The study area for the work is the state of Sikkim, situated in the Himalayan Mountain's North-eastern region.
Material and methods: Landsat 8 and Landsat 5 satellite image were used for the study and Standard vegetation indices like Delta Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) are computed. Also, a linear regression analysis was performed between weather parameters like temperature (℃), wind (Km/h), rainfall (mm) on burn severity (dNBR classes) of forest fires in Sikkim between the year 2009-2019.
Main results: According to the findings, out of 557 numbers forest fire incidents in Sikkim between 2004 and 2019, 250 numbers were classified as Unburned (46.21 %), 199 numbers as Enhanced Regrowth, Low (35.72 %), and 43 numbers as Enhanced Regrowth, High (7.94 %), while 32 numbers were classified as Low Severity (5.92 %), 9 numbers as Moderate-Low Severity (1.66 %), 5 numbers as Moderate-High Severity (0.92 %), and 2 numbers as High Severity (0.36 %). It was found that the wind (r=0.80, Slope=0.57, SD=0.70) and rainfall (r=0.77, Slope=-0.18, SD=7.00) showed a strong positive and strong negative linear relationships respectively in influencing the burn severity (dNBR). While, temperature (r=0.69, Slope=0.74, SD=0.01) plays a moderate positive role in influencing the burn severity (dNBR).
Highlights: The study has shown the effectiveness of burn area mapping and remote sensing data products in analyzing forest fire regions with limited resources and diverse landforms and vegetation. Researchers will be able to identify the regions affected by forest fires and those that have not recovered since the fire. Goal of this research is to improve forest fire planning and management by fostering aid to the responsible authorities to evaluate the pattern of vegetation degradation in burn regions and estimate the impact of forest fires

Kaynakça

  • Adab, H., Kanniah, K. D., & Solaimani, K. (2013). Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural hazards, 65, 1723-1743.
  • Ahmad, F., Goparaju, L. & Qayum, A. (2018). Himalayan forest fire characterization in relation to topography, socio-economy and meteorology parameters in Arunachal Pradesh, India. Spatial Information Research, 26(3), 305-315. https://doi.org/10.1007/s41324-018-0175-1
  • Axel, A. C. (2018). Burned area mapping of an escaped fire into tropical dry forest in Western Madagascar using multi-season Landsat OLI Data. Remote Sensing, 10(3), 371. https://doi.org/10.3390/rs10030371
  • Bar, S., Parida, B. R. & Pandey, A. C. (2020). Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18, 100324. https://doi.org/10.1016/j.rsase.2020.100324
  • Bowman, D. M., Williamson, G. J., Abatzoglou, J. T., Kolden, C. A., Cochrane, M. A. & Smith, A. (2017). Human exposure and sensitivity to globally extreme wildfire events. Nature Ecology & Evolution, 1(3), 1-6. https://doi.org/10.1038/s41559-016-0058
  • Bajocco, S., Koutsias, N. & Ricotta, C. (2017). Linking fire ignitions hotspots and fuel phenology : The importance of being seasonal. Ecological Indicators, 82, 433-440.
  • Cansler, C. A. & McKenzie, D. (2012). How robust are burn severity indices when applied in a new region ? Evaluation of alternate field-based and remote-sensing methods. Remote Sensing, 4(2), 456-483. https://doi.org/10.3390/rs4020456
  • Chuvieco, E., Mouillot, F., Van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., et al. (2019). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45-64. https://doi.org/10.1016/j.rse.2019.02.013
  • Elmahdy, S. I., Mohamed, M. M., Ali, T. A., Abdalla, J. E. D. & Abouleish, M. (2022). Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain, UAE. Geocarto International, 37(1), 315-331. https://doi.org/10.1080/10106049.2020.1716398
  • Fitriana, H. L., Prasasti, I. & Khomarudin, M. R. (2018). Mapping burned areas from landsat-8 imageries on mountainous region using reflectance changes. In MATEC Web of Conferences, 229, 04012. EDP Sciences. https://doi.org/10.1051/matecconf/201822904012
  • Forest Survey of India. (2011b). India State of Forest Report 2011. In India State of Forest Report 2011: 2. https://doi.org/http://www.fsi.org.in/cover_2011/uttarakhand.pdf
  • FSI (2019). Forest Survey of India. The state of Forest Report. Government of India-Ministry of Environment and Forest. 233-241. https://fsi.nic.in/forest-report-2019
  • Gaveau, D. L., Descals, A., Salim, M. A., Sheil, D. & Sloan, S. (2021). Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning. Earth System Science Data, 13(11), 5353-5368. https://doi.org/10.5194essd-13-5353-2021
  • Ghorbanzadeh, O., Blaschke, T., Gholamnia, K. & Aryal, J. (2019). Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables. Fire, 2(3), 50. https://doi.org/10.3390/fire2030050
  • Gigović, L., Jakovljević, G., Sekulović, D., & Regodić, M. (2018). GIS multi-criteria analysis for identifying and mapping forest fire hazard: Nevesinje, Bosnia and Herzegovina. Tehnički vjesnik, 25(3), 891-897.
  • Gomez, C., Alejandro, P., Hermosilla, T., Montes, F., Pascual, C., Ruiz Fernández, L. Á., et al. (2019). Remote sensing for the Spanish forests in the 21st century: A review of advances, needs, and opportunities. Forest Systems, 28(1), 1-33. https://doi.org/10.5424/fs/2019281-14221
  • Han-Qiu, X. U. (2005). A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5), 589-595. https://www.researchgate.net/publication/284418225
  • Harvey, B. J., Andrus, R. A. & Anderson, S. C. (2019). Incorporating biophysical gradients and uncertainty into burn severity maps in a temperate fire‐prone forested region. Ecosphere, 10(2), e02600. https://doi.org/10.1002/ecs2.2600
  • Jafarzadeh, A. A., Mahdavi, A., & Jafarzadeh, H. (2017). Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering. Journal of forest Science, 63(8), 370-380.
  • Joseph, S., Anitha, K. & Murthy, M. S. R. (2009). Forest fire in India: a review of the knowledge base. Journal of Forest Research, 14(3), 127-134. https://doi.org/10.1007/s10310-009-0116-x
  • Key, C. & Center, G. F. S. (2006). Evaluate sensitivities of burn-severity mapping algorithms for different ecosystems and fire histories in the United States. Final Report to the Joint Fire Science Program.
  • Miller, J. D., Safford, H. D., Crimmins, M. & Thode, A. E. (2009). Quantitative evidence for increasing forest fire severity in the Sierra Nevada and southern Cascade Mountains, California and Nevada, USA. Ecosystems, 12, 16-32.
  • Key, C. H., Benson, N. C. (2006) Landscape Assessment (LA). FIREMON: Fire effects monitoring and inventory system. LA-1-55. https://www.fs.fed.us/rm/pubs/rmrs_gtr164/rmrs_gtr164_13_land_assess.pdf
  • Kolden, C. A., Smith, A. M., van Wagtendonk, J. W. & Lutz, J. A. (2012). Effects of prior wildfires on vegetation response to subsequent fire in a reburned mixed-conifer forest. International Journal of Wildland Fire, 21(3), 293-305.
  • Kumari, B. & Pandey, A. C. (2020). Geo-informatics based multi-criteria decision analysis (MCDA) through analytic hierarchy process (AHP) for forest fire risk mapping in Palamau Tiger Reserve, Jharkhand state, India. Journal of Earth System Science, 129(1), 1-16. https://doi.org/10.1007/s12040-020-01461-6
  • Lareau, N. P., Clements, C. B., Rebeca, A. & Mahoney, C. M. (2020). Wind influences on wildfire behavior, containment, and wildland firefighter exposure: A review. Current Forestry Reports, 6(4), 241-251.
  • Liu, Q., Shan, Y., Shu, L., Sun, P. & Du, S. (2018). Spatial and temporal distribution of forest fire frequency and forest area burnt in Jilin Province, Northeast China. Journal of Forestry Research, 29(5), 1233-1239. https://doi.org/10.1007/s11676-018-0605-x
  • Malik, T., Rabbani, G., & Farooq, M. (2013). Forest fire risk zonation using remote sensing and GIS technology in Kansrao forest range of Rajaji National Park, Uttarakhand, India. India. Inter. J. of advanced RS and GIS, 2(1), 86-95.
  • Mallinis, G. & Koutsias, N. (2012). Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data. International Journal of Remote Sensing, 33(14), 4408-4433. https://doi.org/10.1080/01431161.2011.648284
  • Mallinis, G., Maris, F., Kalinderis, I. & Koutsias, N. (2009). Assessment of post-fire soil erosion risk in fire-affected watersheds using remote sensing and GIS. GIScience & Remote Sensing, 46(4), 388-410. DOI:10.2747/1548-1603.46.4.388
  • Miller, J. D. & Thode, A. E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (DNBR). Remote Sensing of Environment, 109(1), 66-80. https://doi.org/10.1016/j.rse.2006.12.006
  • Mouillot, F., Schultz, M. G., Yue, C., Cadule, P., Tansey, K., Ciais, P. & Chuvieco, E. (2014). Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, 26, 64-79. https://doi.org/10.1016/j.jag.2013.05.014
  • Nikhil, S., Danumah, J. H., Saha, S., Prasad, M. K., Rajaneesh, A., Mammen, P. C., et al. (2021). Application of GIS and AHP Method in Forest Fire Risk Zone Mapping: a Study of the Parambikulam Tiger Reserve, Kerala, India. Journal of Geovisualization and Spatial Analysis, 5(1), 1-14. https://doi.org/10.1007/s41651-021-00082-x
  • Özelkan, E. (2020). Water body detection analysis using NDWI indices derived from landsat-8 OLI. Polish Journal of Environmental Studies, 29(2), 1759-1769. DOI: https://doi.org/10.15244/pjoes/110447
  • Parajuli, A., Gautam, A. P., Sharma, S. P., Bhujel, K. B., Sharma, G., Thapa, P. B., et al. (2020). Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomatics, Natural Hazards and Risk, 11(1), 2569-2586. https://doi.org/10.1080/19475705.2020.1853251
  • Parks, S. A., Dillon, G. K. & Miller, C. (2014). A new metric for quantifying burn severity: the relativized burn ratio. Remote Sensing, 6(3), 1827-1844. https://doi.org/10.3390/rs6031827 Parks, S. A., Holsinger, L. M., Miller, C., Parisien, M. A., Dobrowski, S. Z. & Abatzoglou, J. T. (2016). How will climate change affect wildland fire severity in the western US?. Environmental Research Letters, 11(3), 035002.
  • Pausas, J. G., Llovet, J., Rodrigo, A. & Vallejo, R. (2008). Are wildfires a disaster in the Mediterranean basin? –A review. International Journal of Wildland Fire, 17(6), 713-723. https://doi.org/10.1071/WF07151
  • Pereira, A. A., Pereira, J., Libonati, R., Oom, D., Setzer, A. W., Morelli, F., et al. (2017). Burned area mapping in the Brazilian Savanna using a one-class support vector machine trained by active fires. Remote Sensing, 9(11), 1161. https://doi.org/10.3390/rs9111161
  • Petković, M., Garvanov, I., Knežević, D. & Aleksić, S. (2020). Optimization of Geographic Information Systems for Forest Fire Risk Assessment. In 2020 21st International Symposium on Electrical Apparatus & Technologies (SIELA), 1-4, IEEE. 10.1109/SIELA49118.2020.9167162
  • Pourghasemi, H. R. (2016). GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models. Scandinavian Journal of Forest Research, 31(1), 80-98.
  • Pourtaghi, Z. S., Pourghasemi, H. R., Aretano, R. & Semeraro, T. (2016). Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological indicators, 64, 72-84.
  • Reddy, C. S., Jha, C. S., Manaswini, G., Alekhya, V. P., Pasha, S. V., Satish, K. V., et al. (2017). Nationwide assessment of forest burnt area in India using Resourcesat-2 AWiFS data. Current Science, 1521-1532. https://www.jstor.org/stable/24912700
  • Sachdeva, S., Bhatia, T. & Verma, A. K. (2018). GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping. Natural Hazards, 92(3), 1399-1418. https://doi.org/10.1007/s11069-018-3256-5
  • Safi, Y. & Bouroumi, A. (2013). Prediction of forest fires using artificial neural networks. Applied Mathematical Sciences, 7(6), 271-286.
  • Satendra, K. A. (2014). Forest fire disaster management. National Institute of Disaster Management, Ministry of Home Affairs, New Delhi.http://nidm.gov.in/PDF/pubs/Forest%20Fire%202013.pdf
  • Sevinc, V., Kucuk, O. & Goltas, M. (2020). A Bayesian network model for prediction and analysis of possible forest fire causes. Forest Ecology and Management, 457, 117723. https://doi.org/10.1016/j.foreco.2019.117723
  • Sewak, R., Vashisth, M. & Gupta, L. (2021). Forest Fires in India: A Review. Journal University Shanghai Science and Technology, 23(7),247-259.DOI:10.51201/JUSST/21/07129
  • Sharma, K. & Thapa, G. (2021). Analysis and interpretation of forest fire data of Sikkim. Fores and Society, 261-276. https://doi.org/10.24259/fs.v5i2.10931
  • Sharma, R. K., Sharma, N., Shrestha, D. G., Luitel, K. K., Arrawatia, M. L. & Pradhan, S. (2012). Study of forest fires in Sikkim Himalayas, India using remote sensing and GIS techniques. Climate Change in Sikkim–Patterns, impacts and initiatives, 233-244.
  • Sharma, S., Joshi, V. & Chhetri, R. K. (2014). Forest fire as a potential environmental threat in recent years in Sikkim, Eastern Himalayas, India. Climate Change and Environmental Sustainability, 2(1), 55-61.DOI: 10.5958/j.2320-642X.2.1.006
  • Taylor, A. H., Trouet, V., Skinner, C. N. & Stephens, S. L. (2015). Socioecological transitions trigger fire regime shifts and modulate fire–climate interactions in the Sierra Nevada, USA, 1600–2015 CE. Proceedings of the National Academy of Sciences, 112(13), 3931-3936.
  • Thakur, A. K. & Singh, D. (2014). Forest Fire Risk Zonation Using Geospatial Techniques and Analytic Hierarchy Process in Dehradun District, Uttarakhand, India. Universal Journal of Environmental Research & Technology, 4(2).
  • Tien Bui, D., Le, K. T. T., Nguyen, V. C., Le, H. D. & Revhaug, I. (2016). Tropical forest fire susceptibility mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, using GIS-based kernel logistic regression. Remote Sensing, 8(4), 347.
  • Tiwari, A., Shoab, M. & Dixit, A. (2021). GIS-based forest fire susceptibility modeling in Pauri Garhwal, India : a comparative assessment of frequency ratio, analytic hierarchy process and fuzzy modeling techniques. Natural Hazards, 105(2), 1189-1230. https://doi.org/10.1007/s11069-020-04351-8
  • Tonini, M., Pereira, M. G., Parente, J. & Vega Orozco, C. (2017). Evolution of forest fires in Portugal: from spatio-temporal point events to smoothed density maps. Natural Hazards, 85, 1489-1510.
  • Toujani, A., Achour, H. & Faïz, S. (2018). Estimating forest fire losses using stochastic approach: case study of the Kroumiria Mountains (northwestern Tunisia). Applied Artificial Intelligence, 32(9-10), 882-906. https://doi.org/10.1080/08839514.2018.1514808
  • U.S. Geological Survey (2019) Earth Explorer-Home. Satellite Data. https://earthexplorer.usgs.gov/
  • Vega Orozco, C., Tonini, M., Conedera, M. & Kanveski, M. (2012). Cluster recognition in spatial-temporal sequences : the case of forest fires. Geoinformatica, 16, 653-673.
  • Whitman, E., Parisien, M. A., Thompson, D. K., Hall, R. J., Skakun, R. S. & Flannigan, M. D. (2018). Variability and drivers of burn severity in the northwestern Canadian boreal forest. Ecosphere, 9(2), e02128. https://doi.org/10.1002/ecs2.2128
  • World Bank, 2018. (2018). “Strengthening Forest Fire Management in India”. World Bank, Washington DC. www.fsi.nic.in
  • World Bank. (2005). India Unlocking Opportunities for Forest-Dependent People in India. I (34481).
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ormancılık (Diğer)
Bölüm Makaleler
Yazarlar

Kapila Sharma

Gopal Thapa Bu kişi benim

Salghuna Nn Bu kişi benim

Erken Görünüm Tarihi 1 Aralık 2023
Yayımlanma Tarihi 6 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 23 Sayı: 3

Kaynak Göster

APA Sharma, K., Thapa, G., & Nn, S. (2023). Assessing Burned Areas in Sikkim, India through Satellite Mapping. Kastamonu University Journal of Forestry Faculty, 23(3), 199-219. https://doi.org/10.17475/kastorman.1394888
AMA Sharma K, Thapa G, Nn S. Assessing Burned Areas in Sikkim, India through Satellite Mapping. Kastamonu University Journal of Forestry Faculty. Aralık 2023;23(3):199-219. doi:10.17475/kastorman.1394888
Chicago Sharma, Kapila, Gopal Thapa, ve Salghuna Nn. “Assessing Burned Areas in Sikkim, India through Satellite Mapping”. Kastamonu University Journal of Forestry Faculty 23, sy. 3 (Aralık 2023): 199-219. https://doi.org/10.17475/kastorman.1394888.
EndNote Sharma K, Thapa G, Nn S (01 Aralık 2023) Assessing Burned Areas in Sikkim, India through Satellite Mapping. Kastamonu University Journal of Forestry Faculty 23 3 199–219.
IEEE K. Sharma, G. Thapa, ve S. Nn, “Assessing Burned Areas in Sikkim, India through Satellite Mapping”, Kastamonu University Journal of Forestry Faculty, c. 23, sy. 3, ss. 199–219, 2023, doi: 10.17475/kastorman.1394888.
ISNAD Sharma, Kapila vd. “Assessing Burned Areas in Sikkim, India through Satellite Mapping”. Kastamonu University Journal of Forestry Faculty 23/3 (Aralık 2023), 199-219. https://doi.org/10.17475/kastorman.1394888.
JAMA Sharma K, Thapa G, Nn S. Assessing Burned Areas in Sikkim, India through Satellite Mapping. Kastamonu University Journal of Forestry Faculty. 2023;23:199–219.
MLA Sharma, Kapila vd. “Assessing Burned Areas in Sikkim, India through Satellite Mapping”. Kastamonu University Journal of Forestry Faculty, c. 23, sy. 3, 2023, ss. 199-1, doi:10.17475/kastorman.1394888.
Vancouver Sharma K, Thapa G, Nn S. Assessing Burned Areas in Sikkim, India through Satellite Mapping. Kastamonu University Journal of Forestry Faculty. 2023;23(3):199-21.

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