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Mapping burnt areas in the semi-arid savannahs: an exploration of SVM classification and field surveys

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Accurate and precise mapping of burnt areas in the fire-prone semi-arid savannah zones is relevant for making decisions on the management of environmental resources. In this study, we apply the Support Vector Machine (SVM) algorithm to freely available Sentinel-2A&B data to delineate and map burnt areas and compare our findings with conventional field surveys to enable resource managers decide on the most appropriate method to use when seeking to rapidly conduct post-fire assessment. We surveyed three burnt patches of varying sizes and compared with estimates from the SVM classification algorithm. Accuracy assessment was based on reference data collected from field surveys. We obtained an average overall accuracy of 94.8% ± 5.2% for all kernel functions in the SVM. The classification estimated the average total of the three patches at 42.99 km2 but with variations among the different kernels, while the field measurements produced 42.29 km2. A follow-up field survey showed that the earlier survey either over- or under-estimated the burnt patches. Our micro-level analysis demonstrates that any kernel function in the SVM algorithm can be used with freely available remote sensing data to accurately and cost-effectively map wildfire hazards, especially in resource-poor settings, for efficient decision making when managing environmental resource.

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References

  • Ajin, R. S., Loghin, A.-M., Karki, A., Vinod, P. G., & Jacob, M. K. (2016). Delineation of forest fire risk zones in Thenmala forest division, Kollam, Kerala, India: A study using geospatial tools. Journal of Wetlands Biodiversity, 6, 139–148.

    Google Scholar 

  • Amos, C., Petropoulos, G. P., & Ferentinos, K. P. (2019). Determining the use of Sentinel-2A MSI for wildfire burning & severity detection. International Journal of Remote Sensing, 40(3), 905–930.

    Google Scholar 

  • Andela, N., & Van Der Werf, G. R. (2014). Recent trends in African fires driven by cropland expansion and El Nino to La Nina transition. Nature Climate Change, 4(9), 791–795.

    Google Scholar 

  • Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.

  • Archibald, S., Roy, D. P., Wilgen, V., Brian, W., & Scholes, R. J. (2009). What limits fire? An examination of drivers of burnt area in Southern Africa. Global Change Biology, 15(3), 613–630.

    Google Scholar 

  • Barbosa, P. M., Stroppiana, D., Grégoire, J., & Cardoso Pereira, J. M. (1999). An assessment of vegetation fire in Africa (1981–1991): Burned areas, burned biomass, and atmospheric emissions. Global Biogeochemical Cycles, 13(4), 933–950.

    Google Scholar 

  • Beckage, B., Bucini, G., Gross, L. J., Platt, W. J., Higgins, S. I., Fowler, N. L., et al. (2019). Water limitation, fire, and savanna persistence: A conceptual model. Savanna woody plants and large herbivores, pp. 643–659.

  • Bond, W. J., Woodward, F. I., & Midgley, G. F. (2005). The global distribution of ecosystems in a world without fire. New Phytologist, 165(2), 525–538.

    Google Scholar 

  • Bowd, E. J., Banks, S. C., Strong, C. L., & Lindenmayer, D. B. (2019). Long-term impacts of wildfire and logging on forest soils. Nature Geoscience, 12(2), 113.

    Google Scholar 

  • Bowman, D., Zhang, Y., Walsh, A., & Williams, R. J. (2003). Experimental comparison of four remote sensing techniques to map tropical savanna fire-scars using Landsat-TM imagery. International Journal of Wildland Fire, 12(4), 341–348.

    Google Scholar 

  • Brown, A. R., Petropoulos, G. P., & Ferentinos, K. P. (2018). Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal’s fires of 2017. Applied Geography, 100, 78–89.

    Google Scholar 

  • Chang, C., & Lin, C. (2001). {LIBSVM}: A library for support vector machines (Version 2.3).

  • Chen, Y., Su, W., Li, J., & Sun, Z. (2009). Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research, 43(7), 1101–1110.

    Google Scholar 

  • Chen, Y., Sun, L., Wang, W., & Pei, Z. (2019). Application of Sentinel 2 data for drought monitoring in Texas, America. In: 2019 8th international conference on agro-geoinformatics (agro-geoinformatics), pp. 1–4. IEEE.

  • Chubarova, N., Nezval, Y., Sviridenkov, I., Smirnov, A., & Slutsker, I. (2012). Smoke aerosol and its radiative effects during extreme fire event over Central Russia in summer 2010. Atmospheric Measurement Techniques, 5(3), 557–568.

    Google Scholar 

  • Collier, P., Conway, G., & Venables, T. (2008). Climate change and Africa. Oxford Review of Economic Policy, 24(2), 337–353.

    Google Scholar 

  • Colson, D., Petropoulos, G. P., & Ferentinos, K. P. (2018). Exploring the potential of Sentinels-1 & 2 of the Copernicus Mission in support of rapid and cost-effective wildfire assessment. International Journal of Applied Earth Observation and Geoinformation, 73, 262–276.

    Google Scholar 

  • Corona, P., Lamonaca, A., & Chirici, G. (2008). Remote sensing support for post fire forest management. IForest-Biogeosciences and Forestry, 1(1), 6–12.

    Google Scholar 

  • Crowley, M. A., Cardille, J. A., White, J. C., & Wulder, M. A. (2019). Multi-sensor, multi-scale, Bayesian data synthesis for mapping within-year wildfire progression. Remote Sensing Letters, 10(3), 302–311.

    Google Scholar 

  • Escuin, S., Navarro, R., & Fernandez, P. (2008). Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images. International Journal of Remote Sensing, 29(4), 1053–1073.

    Google Scholar 

  • Ettehadi Osgouei, P., Kaya, S., Sertel, E., & Alganci, U. (2019). Separating built-up areas from bare land in mediterranean cities using sentinel-2A imagery. Remote Sensing, 11(3), 345.

    Google Scholar 

  • Farasin, A., Nini, G., Garza, P., & Rossi, C. (2019). Unsupervised Burned Area Estimation through Satellite Tiles: A multimodal approach by means of image segmentation over remote sensing imagery.

  • Fasullo, J. T., Otto-Bliesner, B. L., & Stevenson, S. (2018). ENSO’s changing influence on temperature, precipitation, and wildfire in a warming climate. Geophysical Research Letters, 45(17), 9216–9225.

    Google Scholar 

  • Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel, M., & Lampin, C. (2013). A review of the main driving factors of forest fire ignition over Europe. Environmental Management, 51(3), 651–662.

    Google Scholar 

  • Giglio, L., Randerson, J. T., & Werf, G. R. (2013). Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). Journal of Geophysical Research: Biogeosciences, 118(1), 317–328.

    Google Scholar 

  • Gómez, D., Salvador, P., Sanz, J., & Casanova, J. L. (2019). Potato yield prediction using machine learning techniques and sentinel 2 data. Remote Sensing, 11(15), 1745.

    Google Scholar 

  • Gómez-González, S., González, M. E., Paula, S., Díaz-Hormazábal, I., Lara, A., & Delgado-Baquerizo, M. (2019). Temperature and agriculture are largely associated with fire activity in Central Chile across different temporal periods. Forest Ecology and Management, 433, 535–543.

    Google Scholar 

  • Graw, V., Dubovyk, O., Duguru, M., Heid, P., Ghazaryan, G., de León, J. C. V., et al. (2019). Assessment, monitoring, and early warning of droughts: the potential for satellite remote sensing and beyond. In Current directions in water scarcity research (Vol. 2, pp. 115–131). Amsterdasm: Elsevier.

  • GSS. (2014). 2010 Population census: District analytical report. Sissala East.

  • Hansen, P. M., Semenova-Nelsen, T. A., Platt, W. J., & Sikes, B. A. (2019). Recurrent fires do not affect the abundance of soil fungi in a frequently burned pine savanna. Fungal Ecology, 42, 100852.

    Google Scholar 

  • Holden, Z. A., Swanson, A., Luce, C. H., Jolly, W. M., Maneta, M., & Oyler, J. W. (2018). Decreasing fire season precipitation increased recent western US forest wildfire activity. Proceedings of the National Academy of Sciences, 115(36), E8349–E8357.

    Google Scholar 

  • Hudak, A. T., & Brockett, B. H. (2004). Mapping fire scars in a southern African savannah using Landsat imagery. International Journal of Remote Sensing, 25(16), 3231–3243.

    Google Scholar 

  • Huiping, H., Bingfang, W., & Jinlong, F. (2003). Analysis to the relationship of classification accuracy, segmentation scale, image resolution. Proceedings of 2003 IEEE international geoscience and remote sensing symposium, 2003. IGARSS’03 (Vol, 6, pp. 3671–3673). IEEE.

  • Ireland, G., & Petropoulos, G. P. (2015). Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada. Applied Geography, 56, 232–248.

    Google Scholar 

  • Issaka, Y. B. (2018). Non-timber Forest Products, Climate Change Resilience, and Poverty Alleviation in Northern Ghana. In Strategies for building resilience against climate and ecosystem changes in sub-saharan Africa (pp. 179–192). Springer.

  • Jarvis, A., Reuter, H. I., Nelson, A., & Guevara, E. (2008). Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90 m Database.

  • Jensen, R. J. (1986). Introductory digital image processing, a remote sensing perspective. New Jersey: Prentice Hall.

    Google Scholar 

  • Jusuf, S. K., Wong, N. H., Hagen, E., Anggoro, R., & Hong, Y. (2007). The influence of land use on the urban heat island in Singapore. Habitat International, 31(2), 232–242.

    Google Scholar 

  • Kalivas, D. P., Petropoulos, G. P., Athanasiou, I. M., & Kollias, V. J. (2013). An intercomparison of burnt area estimates derived from key operational products: The Greek wildland fires of 2005–2007. Nonlinear Processes in Geophysics, 20(3), 397–409.

    Google Scholar 

  • Kerr, G. H., DeGaetano, A. T., Stoof, C. R., & Ward, D. (2018). Climate change effects on wildland fire risk in the Northeastern and Great Lakes states predicted by a downscaled multi-model ensemble. Theoretical and Applied Climatology, 131(1–2), 625–639.

    Google Scholar 

  • Koutsias, N., Karteris, M., Fernandez-Palacios, A., Navarro, C., Jurado, J., Navarro, R., et al. (1999). Burnt land mapping at local scale. Remote sensing of large wildfires (pp. 157–187). Berlin: Springer.

    Google Scholar 

  • Kpienbaareh, D. L. (2016). Assessing the relationship between climate and patterns of wildfires in Ghana. International Journal of Humanities and Social Sciences, 8(3), 1–20.

    Google Scholar 

  • Kpienbaareh, D., Kansanga, M., & Luginaah, I. (2018). Examining the potential of open source remote sensing for building effective decision support systems for precision agriculture in resource-poor settings. GeoJournal. https://doi.org/10.1007/s10708-018-9932-x.

    Article  Google Scholar 

  • Kpienbaareh, D., & Luginaah, I. (2019a). After the flames then what? Exploring the linkages between wildfires and household food security in the northern Savannah of Ghana. International Journal of Sustainable Development and World Ecology, 26(7), 612–624.

    Google Scholar 

  • Kpienbaareh, D., & Luginaah, I. (2019b). Modeling the internal structure, dynamics and trends of urban sprawl in Ghanaian cities using remote sensing, spatial metrics and spatial analysis. African Geographical Review. https://doi.org/10.1080/19376812.2019.1677482.

    Article  Google Scholar 

  • Le Page, Y., Morton, D., Bond-Lamberty, B., Pereira, J. M. C., & Hurtt, G. (2015). HESFIRE: A global fire model to explore the role of anthropogenic and weather drivers. Biogeosciences, 12, 887–903.

    Google Scholar 

  • Leuenberger, M., Parente, J., Tonini, M., Pereira, M. G., & Kanevski, M. (2018). Wildfire susceptibility mapping: Deterministic vs. stochastic approaches. Environmental Modelling and Software, 101, 194–203.

    Google Scholar 

  • Levine, J. S. (1999). Wildland fires and the environment: A global synthesis. UNEP/Earthprint.

  • Li, R.-R., Kaufman, Y. J., Hao, W. M., Salmon, J. M., & Gao, B.-C. (2004). A technique for detecting burn scars using MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1300–1308.

    Google Scholar 

  • Liu, J. G. (2000). Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21(18), 3461–3472.

    Google Scholar 

  • Mayor, A. G., Bautista, S., Llovet, J., & Bellot, J. (2007). Post-fire hydrological and erosional responses of a Mediterranean landscpe: Seven years of catchment-scale dynamics. CATENA, 71(1), 68–75.

    Google Scholar 

  • Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57.

    Google Scholar 

  • Padma, S., & Sanjeevi, S. (2014). Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis. International Journal of Applied Earth Observation and Geoinformation, 32, 138–151.

    Google Scholar 

  • Pal, M., & Mather, P. M. (2006). Some issues in the classification of DAIS hyperspectral data. International Journal of Remote Sensing, 27(14), 2895–2916.

    Google Scholar 

  • Pereira, J. M. C. (2003). Remote sensing of burned areas in tropical savannas. International Journal of Wildland Fire, 12(4), 259–270.

    Google Scholar 

  • Petropoulos, G. P., Kontoes, C., & Keramitsoglou, I. (2011). Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using support vector machines. International Journal of Applied Earth Observation and Geoinformation, 13(1), 70–80.

    Google Scholar 

  • Robichaud, P. R., Lewis, S. A., Brown, R. E., & Ashmun, L. E. (2009). Emergency post-fire rehabilitation treatment effects on burned area ecology and long-term restoration. Fire Ecology, 5(1), 115–128.

    Google Scholar 

  • Roy, A., Choi, Y., Souri, A. H., Jeon, W., Diao, L., Pan, S., & Westenbarger, D. (2018). Effects of biomass burning emissions on air quality over the continental USA: A three-year comprehensive evaluation accounting for sensitivities due to boundary conditions and plume rise height. In Environmental contaminants (pp. 245–278). Springer.

  • Rozenstein, O., Haymann, N., Kaplan, G., & Tanny, J. (2019). Estimating cotton water requirements using Sentinel-2: Model development and validation. In Precision agriculture’19 (pp. 223–243). Wageningen Academic Publishers.

  • Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222.

    Google Scholar 

  • Srivastava, P. K., Petropoulos, G. P., Gupta, M., Singh, S. K., Islam, T., & Loka, D. (2019). Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining. Modeling Earth Systems and Environment, 5(2), 627–643.

    Google Scholar 

  • Stavi, I. (2019). Wildfires in grasslands and shrublands: A review of impacts on vegetation, soil, hydrology, and geomorphology. Water, 11(5), 1042.

    Google Scholar 

  • Stehman, S. V., Olofsson, P., Woodcock, C. E., Herold, M., & Friedl, M. A. (2012). A global land-cover validation data set, II: Augmenting a stratified sampling design to estimate accuracy by region and land-cover class. International Journal of Remote Sensing, 33(22), 6975–6993.

    Google Scholar 

  • Stroppiana, D., Bordogna, G., Carrara, P., Boschetti, M., Boschetti, L., & Brivio, P. A. (2012). A method for extracting burned areas from Landsat TM/ETM + images by soft aggregation of multiple Spectral Indices and a region growing algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 88–102.

    Google Scholar 

  • Sunar, F., & Özkan, C. (2001). Forest fire analysis with remote sensing data. International Journal of Remote Sensing, 22(12), 2265–2277.

    Google Scholar 

  • Thenkabail, P. S., Knox, J. W., Ozdogan, M., Gumma, M. K., Congalton, R. G., & Wu, Z. (2012). Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help? Photogrammetric Engineering and Remote Sensing, 78(8), 773–782.

    Google Scholar 

  • Tonini, M., Pereira, M. G., Parente, J., & Orozco, C. V. (2017). Evolution of forest fires in Portugal: From spatio-temporal point events to smoothed density maps. Natural Hazards, 85(3), 1489–1510.

    Google Scholar 

  • Toscano, P., Castrignanò, A., Di Gennaro, S. F., Vonella, A. V., Ventrella, D., & Matese, A. (2019). A precision agriculture approach for durum wheat yield assessment using remote sensing data and yield mapping. Agronomy, 9(8), 437.

    Google Scholar 

  • Van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M., & Kasibhatla, P. S. (2010). Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry and Physics, 10(23), 11707–11735.

    Google Scholar 

  • van Straaten, O., Doamba, S. W. M. F., Corre, M. D., & Veldkamp, E. (2019). Impacts of burning on soil trace gas fluxes in two wooded savanna sites in Burkina Faso. Journal of Arid Environments, 165, 132–140.

    Google Scholar 

  • Vapnik, V. (1995). The nature of statistical learning theory Springer New York Google Scholar.

  • Verbyla, D. L., & Boles, S. H. (2000). Bias in land cover change estimates due to misregistration. International Journal of Remote Sensing, 21(18), 3553–3560.

    Google Scholar 

  • Verhegghen, A., Eva, H., Ceccherini, G., Achard, F., Gond, V., Gourlet-Fleury, S., et al. (2016). The potential of Sentinel satellites for burnt area mapping and monitoring in the Congo Basin forests. Remote Sensing, 8(12), 986.

    Google Scholar 

  • Vert, J.-P. (2001). Introduction to support vector machines and applications to computational biology. Seminar Report. Cambridge, MA: MIT Press.

  • Vigneshwaran, S., & Kumar, S. V. (2019). Urban land cover mapping and change detection analysis using high resolution sentinel-2A data. Environment and Natural Resources Journal, 17(1), 22–32.

    Google Scholar 

  • Vitolo, C., Di Napoli, C., Di Giuseppe, F., Cloke, H. L., & Pappenberger, F. (2019). Mapping combined wildfire and heat stress hazards to improve evidence-based decision making. Environment International, 127, 21–34.

    Google Scholar 

  • Vizzari, M., Santaga, F., & Benincasa, P. (2019). Sentinel 2-based nitrogen VRT fertilization in wheat: Comparison between traditional and simple precision practices. Agronomy, 9(6), 278.

    Google Scholar 

  • Wang, S., Baig, M. H. A., Liu, S., Wan, H., Wu, T., & Yang, Y. (2018). Estimating the area burned by agricultural fires from Landsat 8 Data using the Vegetation Difference Index and Burn Scar Index. International Journal of Wildland Fire, 27(4), 217–227.

    Google Scholar 

  • Whitman, E., Parisien, M., 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.

    Google Scholar 

  • Wu, T.-F., Lin, C.-J., & Weng, R. C. (2004). Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5, 975–1005.

    Google Scholar 

  • Yan, G., Mas, J., Maathuis, B. H. P., Xiangmin, Z., & Van Dijk, P. M. (2006). Comparison of pixel-based and object-oriented image classification approaches—A case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27(18), 4039–4055.

    Google Scholar 

  • Zammit, O., Descombes, X., & Zerubia, J. (2006). Burnt area mapping using support vector machines. Forest Ecology and Management, 234(1), S240.

    Google Scholar 

  • Zhu, G., & Blumberg, D. G. (2002). Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel. Remote Sensing of Environment, 80(2), 233–240.

    Google Scholar 

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  1. Department of Geography, Social Science Centre, Western University, Room 1424, 1151 Richmond Street, London, ON, N6A 5C2, Canada

    Daniel Kpienbaareh & Isaac Luginaah

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  1. Daniel Kpienbaareh
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Kpienbaareh, D., Luginaah, I. Mapping burnt areas in the semi-arid savannahs: an exploration of SVM classification and field surveys. GeoJournal 86, 979–992 (2021). https://doi.org/10.1007/s10708-019-10107-0

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