Forest fire detection based on earlier pre-fire conditions using analysis hi-erarchic process (AHP) in a semi-arid climate. A case study: Belezma National Park, Algeria
Słowa kluczowe:
forest fires, prediction, Belezma National Park, vulnerability, dendrochronology, AHP, semi-arid climates, AlgeriaAbstrakt
GIS and remote sensing are the main techniques for spatial phenomena analysis, especially phenomena triggered by different factors such as fire hazards, climate change, transportation and land use. This study intends to measure forest fire vulnerability in Belezma National Park, one of Algeria's most important national parks and of high environmental value.
The NDVI, NDWI and LST indices are used to evaluate the vulnerability, using satellite data obtained in the same year between May and August. The AHP is then used to integrate the suggested fire model and validated using dendrochronology and Moran's methods.
The results demonstrated that vulnerability decreases relative to altitude; furthermore, the dry season is the period when most fires are triggered (timeline vulnerability factor), which was confirmed by finding out of 150–300-year-old non-burned trees in the low vulnerability area using dendrochronology method. In addition, a significant spatial correlation between elevation and vulnerability maps was found, with Moran's I score of 0.28.
Bibliografia
Abedi Gheshlaghi H. 2019. Using GIS to Develop a Model for Forest Fire Risk Mapping. Journal of the Indian Society of Remote Sensing 47(7): 1173-1185. https://doi.org/10.1007/s12524-019-00981-z.
Akay A.E., Şahin H. 2019. Forest Fire Risk Mapping by using GIS Techniques and AHP Method: A Case Study in Bodrum (Turkey). European Journal of Forest Engineering 5(1): 25-35. https://doi.org/10.33904/ejfe.579075
Akbulak C., Tatlı H., Aygün G., Sağlam B. 2018. Forest fire risk analysis via integration of GIS, RS and AHP: The Case of Çanakkale, Turkey. Journal of Human Sciences 15(4): 2127-2143.
ArcGis (ArcGIS Pro Documentation). 2023. Online: https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-spatial-autocorrelation-moran-s-i-spatialst.htm (accessed: 08.12.2023)
Avdan U., Jovanovska G. 2016. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of sensors 2016: 1-8.
Bagnouls F., Gaussen H. 1957. Les climats biologiques et leur classification. Annales de géographie 355: 193-220.
Barbache A., Beghami Y., Benmessaoud H. 2018. Study and diachronic analysis of forest cover changes of Belezma-Algeria. Geographica Pannonica 22(4): 253-263. https://doi.org/10.5937/gp22-18806
Becker F., Choudhury B.J. 1988. Relative sensitivity of normalized difference vegetation index (NDVI) and microwave polarization difference index (MPDI) for vegetation and desertification monitoring. Remote Sensing of Environment 24(2): 297-311.
Boulghobra N. 2021. Sentinel 2 imagery and burn ratios for assessing the July 5, 2021 wildfires severity in the region of Khenchela (northeast Algeria). Geographia Technica 16(2): 99-104.
CFFDRS. 2023. Canadian Wildland Fire Information System/Canadian Forest Fire Danger Rating System. Online: https://cwfis.cfs.nrcan.gc.ca/back ground/summary/fdr (accessed: 08.11.2023).
Chen Y. 2023. Spatial autocorrelation equation based on Moran’s index. Scientific Reports 13(1): 19296. https://doi.org/10.1038/s41598-023-45947-x
Chen Y., Yang K., He J., Qin J., Shi J., Du J., He Q. 2011. Improving land surface temperature modeling for dry land of China. Journal of Geophysical Research: Atmospheres 116: D20104.
Curt T., Aini A., Dupire S. 2020. Fire Activity in Mediterranean Forests (The Algerian Case). Fire 3(4): 58. https://www.mdpi.com/2571-6255/3/4/58
Eskandari S. 2017. A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arabian Journal of Geosciences 10: 190.
Fekir Y., Hamadouche M.A., Anteur D. 2022. Integrated approach for the assessment of forest fire risk and burn severity mapping using GIS, AHP method, and Google Earth Engine in Western Algeria. Euro-Mediterranean Journal for Environmental Integration 7(4): 531-544. https://doi.org/10.1007/s41207-022-00338-y
Fenniche K. 2018. Application du Processus Hiérarchique Analytique (AHP) a l’évaluation de la performance des banques commerciales. Les cahiers du CREAD 34(4): 39-59.
Gao L., Wang X., Johnson B.A., Tian Q., Wang Y., Verrelst J., Mu X., Gu X. 2020. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing 159: 364-377.
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.
Guha S., Govil H. 2021. Relationship between land surface temperature and normalized difference water index on various land surfaces: A seasonal analysis. International Journal of Engineering and Geosciences 6(3): 165-173. https://doi.org/10.26833/ijeg.821730
Guibal F., Guiot J. 2021. Dendrochronology. In: G. Ramstein, A. Landais, N. Bouttes, P. Sepulchre, A. Govin (eds) Paleoclimatology. Springer International Publishing: 117-122. https://doi.org/10.1007/978-3-030-24982-3_8
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 5: 589-595.
Huang S., Tang L., Hupy J.P., Wang Y., Shao G. 2021. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research 32(1): 1-6. https://doi.org/10.1007/s11676-020-01155-1
Iban M.C., Sekertekin A. 2022. Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey. Ecological Informatics 69: 101647. https://doi.org/10.1016/j.ecoinf.2022.101647
Karnieli A., Agam N., Pinker R.T., Anderson M., Imhoff M.L., Gutman G.G., Panov N., Goldberg A. 2010. Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of climate 23(3): 618-633.
Lebiar I.E. 2023. Modeling Wildfire Risk in the Aurès Region: Spatial and Statistical Analysis for Assessment and Conservation. University of Batna, Algieria.
Li Z.L., Wu H., Duan S.B., Zhao W., Ren H., Liu X., Leng P., Tang R., Ye X., Zhu J., Sun Y., Si M., Liu M., Li J., Zhang X., Shang G., Tang B.H., Yan G., Zhou C. 2023. Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications. Reviews of Geophysics 61(1): 1-77. https://doi.org/https://doi.org/10.1029/2022RG000777
Lionello P., Malanotte-Rizzoli P., Boscolo R. 2006. Mediterranean Climate Variability. Developments in Earth and Environmental Sciences, 4. Elsevier.
Lionello P., Malanotte-Rizzoli P., Boscolo R., Alpert P., Artale V., Li L., Luterbacher J., May W., Trigo R., Tsimplis M., Ulbrich U., Xoplaki E. 2006. The Mediterranean climate: An overview of the main characteristics and issues. In: P. Lionello, P. Malanotte-Rizzoli, R. Boscolo (eds.) Developments in Earth and Environmental Sciences (4). Elsevier: 1-26.
LST. 2021. Land Surface Temperature – Appli-cations – Sentinel-3 SLSTR. Online: https://sentinels.copernicus.eu/web/sentinel/usergu-ides/sentinel-3-slstr/overview/geophyscalm- easurements/land-surface-temperature (acce-ssed: 07.01.2022).
Merchant C.J., Minnett P.J., Beggs H., Corlett G.K., Gentemann C., Harris A.R., Hoyer J., Maturi E. 2019. Global Sea Surface Temperature. In: G.C. Hulley, D. Ghent (eds.) Taking the Temperature of the Earth. Elsevier: 5-55.
Mokhtari S. 2018. Contribution à la gestion des connaissances en gestion des risques: application aux feux de foret dans le massif des Aurès. Université de Batna, Algieria.
Mumtaz F., Tao Y., de Leeuw G., Zhao L., Fan C., Elnashar A., Bashir B., Wang G., Li L., Naeem S., Arshad A., Wang D. 2020. Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST). Remote Sensing 12(18): 2987.
Nikhil S., Danumah J.H., Saha S., Prasad M.K., Rajaneesh A., Mammen P.C., Ajin R.S., Kuriakose S.L. 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): 14.
Nuthammachot N., Stratoulias D. 2021. Multi-criteria decision analysis for forest fire risk assessment by coupling AHP and GIS: method and case study. Environment, Development and Sustainability 23(12): 17443-17458. https://doi.org/10.1007/s10668-021-01394-0.
Pausas J.G., Keeley J.E. 2021. Wildfires and global change. Frontiers in Ecology and the Environment 19(7): 387-395. https://doi.org/10.1002/fee.2359.
Pettorelli N. 2013. The normalized difference vegetation index. Oxford University Press, New York.
Piton G., Philippe F., Tacnet J.M., Gourhand A. 2018. Aide à la décision par l’application de la méthode AHP (Analytic Hierarchy Pro-cess) à l’analyse multicritère des stratégies d’aménagement du Grand Büech à la Faurie. Sciences Eaux Territoires 2: 54-57.
Pourghasemi H.R., Beheshtirad M., Pradhan B. 2016. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards and Risk 7(2): 861-885.
Prasomsup W., Piyatadsananon P., Aunphoklang W., Boonrang A. 2020. Extraction Technic for Built-up Area Classification in Landsat 8 Imagery. International Journal of Environmental Science and Development 11: 15-20. https://doi.org/10.18178/ijesd.2020.1 1. 1.1219.
Rahmani S., Benmassoud H. 2019. Modelling of forest fire risk spatial distribution in the region of Aures, Algeria. Geoadria 24(2): 79- -91.
Rajeshwari A., Mani N.D. 2014. Estimation of land surface temperature of Dindigul district using Landsat 8 data. International Journal of Research in Engineering and Technology 3(5): 122-126.
Rasooli S.B., Bonyad A.E., Pir Bavaghar M. 2018. Forest fire vulnerability map using remote sensing data, GIS and AHP analysis (Case study: Zarivar Lake surrounding area). Caspian Journal of Environmental Sciences 16(4): 369-377. https://doi.org/10.22124/cjes.2018.3205.
Rogers P. 2011. Development of resilient Australia: enhancing the PPRR approach with anticipation, assessment and registration of risks. Australian Journal of Emergency Management 26(1): 54-58.
Saaty R.W. 1987. The analytic hierarchy process – what it is and how it is used. Mathematical modelling 9(3–5): 161-176.
Saaty T.L. 2008. Decision making with the analytic hierarchy process. International Journal of Services Sciences 1(1): 83-98.
Sakr G.E., Elhajj I.H., Mitri G., Wejinya U.C. 2010. Artificial intelligence for forest fire prediction. International Conference on Advanced Intelligent Mechatronics, Montreal.
Santos S.M.B., Bento-Gonçalves A., Vieira A. 2021. Research on Wildfires and Remote Sensing in the Last Three Decades: A Bibliometric Analysis. Forests 12(5): 604.
Serrano J., Shahidian S., Marques da Silva J. 2019. Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Sea-sonal and Inter-Annual Variability in a Mediterranean Agro-Silvo-Pastoral System. Water 11(1): 62.
Sivrikaya F., Küçük Ö. 2022. Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics 68: 101537. https://doi.org/10.1016/j.ecoinf.2021.101537
Spatial Autocorrelation (Global Moran's I –ArcGIS Pro Documentation). 2021. Online: https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-spatial-autocorrelation-moran-s-i-spatial-st.htm (accessed: 12.12.2023)
Sun Q., Wu Z., Tan J. 2012. The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environmental Earth Sciences 65(6): 1687-1694.
Thomas D., Butry D., Gilbert S., Webb D., Fung J. 2017. The Costs and Losses of Wildfires: A Literature Review. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.1215
UGS. 2023. Online: https://earthexplorer.usgs. gov/ (accessed: 08.03.2024)
Vicente-Serrano S.M., Camarero J.J., Olano J.M., Martín-Hernández N., Peña-Gallardo M., Tomás-Burguera M., Gazol A., Azorin-Molina C., Bhuyan U., El Kenawy A. 2016. Diverse relationships between forest growth and the Normalized Difference Vegetation Index at a global scale. Remote Sensing of Environment 187:14-29.
Vorovencii I. 2020. Analyzing the Sprawl of Built-up Areas Using Landsat Data and the Normalized Difference Built-up Index. RevCAD. Journal of Geodesy and Cadastre 28: 139-146.
Wehner M.F., Arnold J.R., Knutson T., Kunkel K.E., LeGrande A.N. 2017. Chapter 8: Droughts, Floods, and Wildfires. In: D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, T.K. Maycock (eds.) Climate Science Special Report: Fourth National Climate Assessment, Volume I. U.S. Global Change Research Program, Washington DC, USA: 231-256.
Wu Y., Li W., Wang Q., Liu Q., Yang D., Xing M., Pei Y., Yan S. 2016. Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arabian Journal of Geosciences 9(2): 84. https://doi.org/10.1007/s12517-015-2112-0.
Yang J., Wong M.S., Menenti M., Nichol J. 2015. Study of the geometry effect on land surface temperature retrieval in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing 109: 77-87.
Yang S., Lupascu M., Meel K.S. 2021. Predicting Forest Fire Using Remote Sensing Data And Machine Learning. https://doi.org/10.48550/arXiv.2101.01975
Zheng Y., Tang L., Wang H. 2021. An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of cleaner production 328: 129488.
Pobrania
Opublikowane
Jak cytować
Numer
Dział
Licencja
Prawa autorskie (c) 2024 Łódzkie Towarzystwo Naukowe
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Użycie niekomercyjne – Bez utworów zależnych 4.0 Międzynarodowe.
