Automated mapping of burned areas in semi-arid ecosystems using modis time-series imagery
- 1National Patagonian Center-Argentinean National Research Council, Terrestrial Ecology Unit, U9120ACD Puerto Madryn, Chubut, Argentina
- 2of Environmental Studies, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia
- 3Autonomous University of Entre Ríos, CP 3100 Paraná, Entre Ríos, Argentina
- 4UnLu-PRODITEL, 6700 Lujan, Buenos Aires, Argentina
Keywords: Bushfires, Time Series, Image Segmentation, MODIS, Normalized Burn Ratio, Rangelands
Abstract. Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail - able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica - tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R2 = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.