Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest

Abstract

Abstract:The objective of this study was to analyze the spatial and temporal distribution of burned areas in Rondônia State, Brazil during the years 2000 to 2011 and evaluate the burned area maps. A Linear Spectral Mixture Model (LSMM) was applied to MODIS surface reflectance images to originate the burned areas maps, which were validated with TM/Landsat 5 and ETM+/Landsat 7 images and field data acquired in August 2013. The validation presented a correlation ranging from 67% to 96% with an average value of 86%. The lower correlation values are related to the distinct spatial resolutions of the MODIS and TM/ETM+ sensors because small burn scars are not detected in MODIS images and higher spatial correlations are related to the presence of large fires, which are better identified in MODIS, increasing the accuracy of the mapping methodology. In addition, the 12-year burned area maps of Rondônia indicate that fires, as a general pattern, occur in areas that have already been converted to some land use, such as vegetal extraction, large animal livestock areas or diversified permanent crops. Furthermore, during the analyzed period, land use conversion associated with climatic events significantly influenced the occurrence of fire in Rondônia and amplified its impacts.

Spatial Pattern of the Seasonal Drought/Burned Area Relationship across Brazilian Biomes: Sensitivity to Drought Metrics and Global Remote-Sensing Fire Products

“…This peculiar result then points out a potential bias in model developments based on BA observation from global remote sensing, and DGVMs/fire module benchmarking for this region when using the large fire dataset only. Recent studies actually pointed out the BA underestimation from these global products when compared to finer resolution Landsat-based remote sensing analysis [63,66,67].…”

“…The MERIS FIRE_CCI v4.1 is a recently BA grid dataset [60] delivered from the ESA Fire Cci project [61] and generated by an algorithm based on processing in two-phase [62]. The MERIS FIRE_CCI v4.1 provides monthly burned area (m 2 ) in NetCDF format files from 2002 to 2011 and is a dataset validated from multi-temporal pairs of Landsat images sites with stratified random sample [63]. different time-scales were computed, respectively, by functions developed in fwi.fbp library [54] from R cran program [55] and the monthly data available in SPEI database [51], which covers the global frequency of SPEI at scales from 1 to 48 months at 0.5° gridded spatial resolution [56], using the same CRU-NCEP climate variables.…”

Development of a Point-based Method for Map Validation and Confidence Interval Estimation: A Case Study of Burned Areas in Amazonia

“…Facing the challenge of providing burned area information for the Amazon region a great effort has been made for developing a methodology based on the Linear Spectral Mixture Model (LSMM) that more accurately detects burned area in both productive lands and forests, aiming to support the construction of an operational burned area product for the region [28,30,[32][33][34]. The experiences developed in the above mentioned studies pointed out that the global and regional validation schemes, such as the SAFNet protocol developed in Africa burned areas [14], are not adequate for the complexity and dynamics of the Amazonian burn scar characteristics.…”

“…Because of the complex mosaic of land cover changes that emerge rapidly through the use of fire in Amazonia, the performance of automated global burned area methods to detect fire affected area is undermined [30][31][32]. Facing the challenge of providing burned area information for the Amazon region a great effort has been made for developing a methodology based on the Linear Spectral Mixture Model (LSMM) that more accurately detects burned area in both productive lands and forests, aiming to support the construction of an operational burned area product for the region [28,30,[32][33][34].…”

“…Secondly, we test this method in a study case of burned areas in the Amazon. The methodology for detecting burned areas in the Amazon is built on the previous experiences of the research group [28,30,[32][33][34]. The development of an operational burned area product with accuracy assessment and confidence interval is a joint initiative in Brazil: the National Institute for Space Research, INPE, and the National Center for Monitoring and Early Warning of Natural Disasters -Cemaden.…”