Using Remote Sensing Products for Environmental Analysis in South America

Land cover plays a major role in many biogeochemical models that represent processes and connections with terrestrial systems; hence, it is a key component for public decisions in ecosystems management. The advance of remote sensing technology, combined with the emergence of new operational products, offers alternatives to improve the accuracy of environmental monitoring and analysis. This work uses the GLOBCOVER, the Vegetation Continuous Field (VCF), MODIS Fire Radiative Power (FRP) and the Tropical Rainfall Measuring Mission (TRMM) remotely sensed databases to analyze the biomass burning distribution, the land use and land cover characteristics and the percent of tree cover in South America during the years 2000 to 2005. Initially, GLOBCOVER was assessed based on VCF product, and subsequently used for quantitative analysis of the spatial distribution of the South America fires with the fire radiative power (FRP). The results show that GLOBCOVER has a tendency to overestimate forest classes and to underestimate urban and mangroves areas. The fire quantification based on GLOBCOVER product shows that the highest incidence of fires can be observed in the arc of deforestation, located in the Amazon forest border, with vegetation cover composed mainly of broadleaved evergreen or semi-deciduous forest. A time series analysis of FRP database indicates that biomass burning occurs mainly in areas of broadleaved evergreen or semi-deciduous forest and in Brazilian Cerrado associated with grassland management, agricultural land clearing and with the deforestation of Amazon tropical rainforest. Also, variations in FRP intensity and spread can be attributed to rainfall anomalies, such as in 2004, when South America had a positive anomaly rainfall.

[1]  Frédéric Achard,et al.  The GLOBCOVER Initiative , 2005 .

[2]  Minhua Wang,et al.  Modeling errors in remote sensing image classification , 1993 .

[3]  Y. Kaufman,et al.  Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release , 2005 .

[4]  J. Fishman,et al.  NASA GTE TRACE A experiment (September–October 1992): Overview , 1996 .

[5]  Gareth Roberts,et al.  Fire Detection and Fire Characterization Over Africa Using Meteosat SEVIRI , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Jan Feranec,et al.  THE CORINE LAND COVER 2000 PROJECT , 2004 .

[7]  Ann Henderson-Sellers,et al.  Current Global Land-surface Data Sets for Use in Climate-related Studies , 1986 .

[8]  C. Nobre,et al.  Overview of atmospheric conditions during the Smoke, Clouds, and Radiation-Brazil (SCAR-B) field experiment , 1998 .

[9]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[10]  G. Huffman,et al.  The TRMM Multi-Satellite Precipitation Analysis (TMPA) , 2010 .

[11]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

[12]  Steffen Fritz,et al.  Comparison of land cover maps using fuzzy agreement , 2005, Int. J. Geogr. Inf. Sci..

[13]  B. Holben,et al.  Remote sensing of smoke from MODIS airborne simulator during the SCAR‐B experiment , 1998 .

[14]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[15]  C. Justice,et al.  Potential global fire monitoring from EOS‐MODIS , 1998 .

[16]  Ruth S. DeFries,et al.  Estimation of tree cover using MODIS data at global, continental and regional/local scales , 2005 .

[17]  Yoram J. Kaufman,et al.  A method to derive smoke emission rates from MODIS fire radiative energy measurements , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Ronald M. Welch,et al.  Estimation of Surface and Top-of-Atmosphere Shortwave Irradiance in Biomass-Burning Regions during SCAR-B , 2000 .

[19]  A. Ansmann,et al.  Comparison of the radiative impact of aerosols derived from vertically resolved (lidar) and vertically integrated (Sun photometer) measurements: Example of an Indian aerosol plume , 2001 .

[20]  M. Hansen,et al.  A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products , 2000 .

[21]  T. Eck,et al.  Assessment of smoke aerosol impact on surface solar irradiance measured in the Rondônia region of Brazil during Smoke, Clouds, and Radiation ‐ Brazil , 1999 .

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

[23]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[24]  Aaron Moody,et al.  Scale-dependent errors in the estimation of land-cover proportions. Implications for global land-cover datasets , 1994 .

[25]  Guoqing Sun,et al.  MODIS tree cover validation for the circumpolar taiga-tundra transition zone , 2009 .

[26]  Christiane Schmullius,et al.  Comparative assessment of CORINE2000 and GLC2000: Spatial analysis of land cover data for Europe , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[27]  Yoram J. Kaufman,et al.  Remote sensing of biomass burning in the tropics , 1990 .

[28]  J. Townshend,et al.  Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm , 2003 .

[29]  T. Eck,et al.  Modeling of gaseous, aerosol, and cloudiness effects on surface solar irradiance measured in Brazil's Amazonia 1992–1995 , 2000 .

[30]  S. Freitas,et al.  Estimating trace gas and aerosol emissions over South America: Relationship between fire radiative energy released and aerosol optical depth observations , 2009 .

[31]  L. Remer,et al.  Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy , 2008 .

[32]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[33]  P. Crutzen,et al.  Biomass Burning in the Tropics: Impact on Atmospheric Chemistry and Biogeochemical Cycles , 1990, Science.

[34]  Piers J. Sellers,et al.  Amazonian Deforestation and Regional Climate Change , 1991 .

[35]  B. Holben,et al.  Biomass Burning Airborne and Spaceborne Experiment in the Amazonas (BASE-A) , 1992 .

[36]  G. Roberts,et al.  Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI imagery , 2005 .

[37]  Bicheron Patrice,et al.  GlobCover - Products Description and Validation Report , 2008 .

[38]  Christiane Schmullius,et al.  Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields , 2011 .

[39]  R. D. Ramsey,et al.  Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south‐western USA , 2005 .

[40]  R. Welch,et al.  The 1985 Biomass Burning Season in South America: Satellite Remote Sensing of Fires, Smoke, and Regional Radiative Energy Budgets , 1998 .

[41]  Joyce Chou,et al.  Estimation of Shortwave Direct Radiative Forcing of Biomass-Burning Aerosols Using New Angular Models , 2000 .

[42]  E. Vermote,et al.  MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season, north India. , 2011, Environmental pollution.

[43]  Charles Ichoku,et al.  Relationships between energy release, fuel mass loss, and trace gas and aerosol emissions during laboratory biomass fires , 2008 .

[44]  Mingshi Li,et al.  Comparing forest fragmentation and its drivers in China and the USA with Globcover v2.2. , 2010, Journal of environmental management.

[45]  Sundar A. Christopher,et al.  Use of the Ångstrom exponent to estimate the variability of optical and physical properties of aging smoke particles in Brazil , 1999 .

[46]  Teresa L. Campos,et al.  REMOTE MEASUREMENT OF ENERGY AND CARBON FLUX FROM WILDFIRES IN BRAZIL , 2004 .

[47]  J. Levine.  Global biomass burning - Atmospheric, climatic, and biospheric implications , 1990 .

[48]  Rasim Latifovic,et al.  Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data , 2004 .

[49]  S. Nilsson,et al.  A spatial comparison of four satellite derived 1 km global land cover datasets , 2006 .

[50]  A. Belward,et al.  The IGBP-DIS global 1km land cover data set, DISCover: First results , 1997 .

[51]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[52]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[53]  Michael D. King,et al.  SCAR‐B fires in the tropics: Properties and remote sensing from EOS‐MODIS , 1998 .