Relationships between Human Population Density and Burned Area at Continental and Global Scales

We explore the large spatial variation in the relationship between population density and burned area, using continental-scale Geographically Weighted Regression (GWR) based on 13 years of satellite-derived burned area maps from the global fire emissions database (GFED) and the human population density from the gridded population of the world (GPW 2005). Significant relationships are observed over 51.5% of the global land area, and the area affected varies from continent to continent: population density has a significant impact on fire over most of Asia and Africa but is important in explaining fire over < 22% of Europe and Australia. Increasing population density is associated with both increased and decreased in fire. The nature of the relationship depends on land-use: increasing population density is associated with increased burned are in rangelands but with decreased burned area in croplands. Overall, the relationship between population density and burned area is non-monotonic: burned area initially increases with population density and then decreases when population density exceeds a threshold. These thresholds vary regionally. Our study contributes to improved understanding of how human activities relate to burned area, and should contribute to a better estimate of atmospheric emissions from biomass burning.

[1]  José M. C. Pereira,et al.  Exploratory spatial data analysis of global MODIS active fire data , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[2]  C. Justice,et al.  Regional fire monitoring and characterization using global NASA MODIS fire products in dry lands of Central Asia , 2012, Frontiers of Earth Science.

[3]  Trisalyn A. Nelson,et al.  Spatial and temporal patterns of wildfire ignitions in Canada from 1980 to 2006 , 2012 .

[4]  S. Levin,et al.  Evolution of human-driven fire regimes in Africa , 2011, Proceedings of the National Academy of Sciences.

[5]  J. Carreiras,et al.  Greenhouse gas emissions from shifting cultivation in the tropics, including uncertainty and sensitivity analysis , 2011 .

[6]  P. Friedlingstein,et al.  Modeling fire and the terrestrial carbon balance , 2011 .

[7]  Ana C. L. Sá,et al.  The pyrogeography of sub-Saharan Africa: a study of the spatial non-stationarity of fire–environment relationships using GWR , 2011, J. Geogr. Syst..

[8]  Andrew Aldersley,et al.  Global and regional analysis of climate and human drivers of wildfire. , 2011, The Science of the total environment.

[9]  M. Charlton,et al.  An Assessment of the Effectiveness of Multiple Hypothesis Testing for Geographical Anomaly Detection , 2011 .

[10]  C. Ryan,et al.  How does fire intensity and frequency affect miombo woodland tree populations and biomass? , 2011, Ecological applications : a publication of the Ecological Society of America.

[11]  D. Morton,et al.  Modeling fire-driven deforestation potential in Amazonia under current and projected climate conditions , 2010 .

[12]  Erle C. Ellis,et al.  Anthropogenic transformation of the biomes, 1700 to 2000 , 2010 .

[13]  João M. N. Silva,et al.  Seasonality of vegetation fires as modified by human action: observing the deviation from eco-climatic fire regimes , 2010 .

[14]  Sandy P. Harrison,et al.  The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model , 2010 .

[15]  W. Landman.  Climate change 2007: the physical science basis , 2010 .

[16]  J. Dodson.  Changing Climates, Earth Systems and Society , 2010 .

[17]  J. Randerson,et al.  Assessing variability and long-term trends in burned area by merging multiple satellite fire products , 2009 .

[18]  Christopher I. Roos,et al.  Fire in the Earth System , 2009, Science.

[19]  M. Moritz,et al.  Global Pyrogeography: the Current and Future Distribution of Wildfire , 2009, PloS one.

[20]  S. Pyne.  The human geography of fire: a research agenda , 2009 .

[21]  D. Roy,et al.  What limits fire? An examination of drivers of burnt area in Southern Africa , 2009 .

[22]  Martin Charlton,et al.  Multiple Dependent Hypothesis Tests inGeographically Weighted Regression , 2009 .

[23]  F. Joos,et al.  Climate and human influences on global biomass burning over the past two millennia , 2008 .

[24]  Erle C. Ellis,et al.  Putting people in the map: anthropogenic biomes of the world , 2008 .

[25]  R. Neilson,et al.  Response of vegetation distribution, ecosystem productivity, and fire to climate change scenarios for California , 2008 .

[26]  Stan Openshaw,et al.  Modifiable Areal Unit Problem , 2008, Encyclopedia of GIS.

[27]  Susan I. Stewart,et al.  Human influence on California fire regimes. , 2007, Ecological applications : a publication of the Ecological Society of America.

[28]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[29]  R. Koenker,et al.  Regression Quantiles , 2007 .

[30]  W. Knorr,et al.  A climate-change risk analysis for world ecosystems , 2006, Proceedings of the National Academy of Sciences.

[31]  Kelly K. Caylor,et al.  Determinants of woody cover in African savannas , 2005, Nature.

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

[33]  R. Koenker.  Quantile Regression: Name Index , 2005 .

[34]  J. Pereira,et al.  Vegetation burning in the year 2000: Global burned area estimates from SPOT VEGETATION data , 2004 .

[35]  Navin Ramankutty,et al.  Geographic distribution of major crops across the world , 2004 .

[36]  B. Cade,et al.  A gentle introduction to quantile regression for ecologists , 2003 .

[37]  M. Lesperance,et al.  PIECEWISE REGRESSION: A TOOL FOR IDENTIFYING ECOLOGICAL THRESHOLDS , 2003 .

[38]  R. Guyette,et al.  Dynamics of an Anthropogenic Fire Regime , 2003, Ecosystems.

[39]  S. Sitch,et al.  Simulating fire regimes in human‐dominated ecosystems: Iberian Peninsula case study , 2002 .

[40]  E. Sanderson,et al.  The Human Footprint and the Last of the Wild , 2002 .

[41]  P. Laris.  Burning the Seasonal Mosaic: Preventative Burning Strategies in the Wooded Savanna of Southern Mali , 2002 .

[42]  Grace S. Chiu.  Bent-Cable Regression for Assessing Abruptness of Change , 2002 .

[43]  M. Flannigan,et al.  Climate change and forest fires. , 2000, The Science of the total environment.

[44]  Jose M. Cardoso Pereira,et al.  An assessment of vegetation fire in Africa (1981–1991): Burned areas, burned biomass, and atmospheric emissions , 1999 .

[45]  S. Fotheringham,et al.  Geographically weighted regression : modelling spatial non-stationarity , 1998 .

[46]  S. Fotheringham,et al.  Geographically Weighted Regression , 1998 .

[47]  X. Lee,et al.  Introduction to wildland fire , 1997 .

[48]  B. W. Wilgen,et al.  Fire and Plants , 1995, Population and Community Biology Series.

[49]  E. Johnson.  VEGETATION DYNAMICS : STUDIES FROM THE NORTH AMERICAN BOREAL FOREST , 1993 .

[50]  H. Akaike.  Likelihood of a model and information criteria , 1981 .