Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets

[1]  Yosio Edemir Shimabukuro,et al.  The least-squares mixing models to generate fraction images derived from remote sensing multispectral data , 1991, IEEE Trans. Geosci. Remote. Sens..

[2]  M. Cochrane,et al.  Fire as a large-scale edge effect in Amazonian forests , 2002, Journal of Tropical Ecology.

[3]  Ciro Abbud Righi,et al.  Biomass and greenhouse-gas emissions from land-use change in Brazil's Amazonian “arc of deforestation”: The states of Mato Grosso and Rondônia , 2009 .

[4]  Y. Shimabukuro,et al.  Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni‐temporal perspective , 2006 .

[5]  Stuart E. Marsh,et al.  Broad-Scale Environmental Conditions Responsible for Post-Fire Vegetation Dynamics , 2010, Remote. Sens..

[6]  M. Hansen,et al.  Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013 , 2017, Science Advances.

[7]  J. Handmer,et al.  Estimating the economic, social and environmental impacts of wildfires in Australia , 2013 .

[8]  Nelson D. A. Mascarenhas,et al.  Use of synthetic bands derived from mixing models in the multispectral classification of remote sensing images , 1999 .

[9]  Assessment of fire emission inventories during the South American Biomass Burning Analysis (SAMBBA) experiment , 2016 .

[10]  C. Justice,et al.  Characterizing Vegetation Fire Dynamics in Brazil through Multisatellite Data: Common Trends and Practical Issues , 2005 .

[11]  W. Cohen,et al.  Testing a Landsat-based approach for mapping disturbance causality in U.S. forests , 2017 .

[12]  J. Grégoire,et al.  Lessons to be learned from the comparison of three satellite‐derived biomass burning products , 2004 .

[13]  N. Koutsias,et al.  Historical background and current developments for mapping burned area from satellite Earth observation , 2019, Remote Sensing of Environment.

[14]  W. Hao,et al.  A VIIRS direct broadcast algorithm for rapid response mapping of wildfire burned area in the western United States , 2018, Remote Sensing of Environment.

[15]  E. Chuvieco,et al.  Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence , 2013 .

[16]  D. Roy,et al.  The Collection 6 MODIS burned area mapping algorithm and product , 2018, Remote sensing of environment.

[17]  André C. P. L. F. de Carvalho,et al.  TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping , 2019, ISPRS Int. J. Geo Inf..

[18]  Gilberto Câmara,et al.  Spring: integrating remote sensing and gis by object-oriented data modelling , 1996, Comput. Graph..

[19]  D. Roy,et al.  The MODIS fire products , 2002 .

[20]  Yosio Edemir Shimabukuro,et al.  Vegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil , 2020, Remote. Sens..

[21]  S. Stehman,et al.  Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation , 2015 .

[22]  Michelle Coppoletta,et al.  Post-fire vegetation and fuel development influences fire severity patterns in reburns. , 2015, Ecological applications : a publication of the Ecological Society of America.

[23]  David P. Roy,et al.  Southern Africa Validation of the MODIS, L3JRC, and GlobCarbon Burned-Area Products , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[24]  R. DeFries,et al.  The Amazon basin in transition , 2012, Nature.

[25]  Carmen Quintano Pastor,et al.  A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots , 2020, Remote. Sens..

[26]  Thiago F. Morello,et al.  Policy instruments to control Amazon fires:a simulation approach , 2017 .

[27]  Mathias Disney,et al.  Theoretical uncertainties for global satellite-derived burned area estimates , 2019, Biogeosciences.

[28]  L. Anderson,et al.  Detecção de cicatrizes de áreas queimadas baseada no modelo linear de mistura espectral e imagens índice de vegetação utilizando dados multitemporais do sensor MODIS/TERRA no estado do Mato Grosso, Amazônia brasileira , 2005 .

[29]  Giselda Durigan,et al.  The need for a consistent fire policy for Cerrado conservation , 2016 .

[30]  F. Rossi,et al.  Fire dynamics in Mato Grosso State, Brazil: the relative roles of gross primary productivity , 2020 .

[31]  E. Chuvieco,et al.  Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa , 2019, Remote Sensing of Environment.

[32]  Kaio Allan Cruz Gasparini,et al.  Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon , 2017, IEEE International Geoscience and Remote Sensing Symposium.

[33]  J. Randerson,et al.  Interannual variability in global biomass burning emissions from 1997 to 2004 , 2006 .

[34]  D. Roy,et al.  Burned area mapping using multi-temporal moderate spatial resolution data—a bi-directional reflectance model-based expectation approach , 2002 .

[35]  Yosio Edemir Shimabukuro,et al.  Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest , 2014, Remote. Sens..

[36]  M. Chin,et al.  Satellite contributions to the quantitative characterization of biomass burning for climate modeling , 2012 .

[37]  A. Masiero,et al.  Mapping fire regimes in China using MODIS active fire and burned area data , 2017 .

[38]  Susan I. Stewart,et al.  Detection rates of the MODIS active fire product in the United States , 2008 .

[39]  J. Dwyer,et al.  Mapping burned areas using dense time-series of Landsat data , 2017 .

[40]  E. Scott,et al.  Increased fire severity alters initial vegetation regeneration across Calluna-dominated ecosystems. , 2019, Journal of environmental management.

[41]  D. Morton,et al.  Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data , 2008 .

[42]  Andrea Melchiorre,et al.  Global Analysis of Burned Area Persistence Time with MODIS Data , 2018, Remote. Sens..

[43]  M. Keller,et al.  Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity , 2016, Global change biology.

[44]  F. S. Recuero,et al.  Monitoring the transport of biomass burning emissions in South America , 2002 .

[45]  José M. C. Pereira,et al.  How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[46]  Christopher O. Justice,et al.  Spatial and temporal intercomparison of four global burned area products , 2018, Int. J. Digit. Earth.

[47]  F. Kawakubo,et al.  Satellite observations for describing fire patterns and climate-related fire drivers in the Brazilian savannas , 2018 .

[48]  Johannes W. Kaiser,et al.  Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite , 2016 .

[49]  L. Aragão,et al.  Deforestation-Induced Fragmentation Increases Forest Fire Occurrence in Central Brazilian Amazonia , 2018, Forests.

[50]  Manoel Cardoso,et al.  Regional climate change over eastern Amazonia caused by pasture and soybean cropland expansion , 2007 .

[51]  Leonardo Vanneschi,et al.  Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[52]  Kevin C. Ryan,et al.  Fire and fire ecology: Concepts and principles , 2009 .

[53]  D. Roy,et al.  Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data , 2005 .

[54]  T. Loboda,et al.  Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data , 2007 .

[55]  Y. Shimabukuro.  Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images , 1998 .