Challenges and opportunities in mapping land use intensity globally

Future increases in land-based production will need to focus more on sustainably intensifying existing production systems. Unfortunately, our understanding of the global patterns of land use intensity is weak, partly because land use intensity is a complex, multidimensional term, and partly because we lack appropriate datasets to assess land use intensity across broad geographic extents. Here, we review the state of the art regarding approaches for mapping land use intensity and provide a comprehensive overview of available global-scale datasets on land use intensity. We also outline major challenges and opportunities for mapping land use intensity for cropland, grazing, and forestry systems, and identify key issues for future research.

[1]  Andrew C. Millington,et al.  A hybrid approach to mapping land-use modification and land-cover transition from MODIS time-series data: A case study from the Bolivian seasonal tropics , 2011 .

[2]  Navin Ramankutty,et al.  Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world? , 2010 .

[3]  R. DeFries,et al.  Decoupling of deforestation and soy production in the southern Amazon during the late 2000s , 2012, Proceedings of the National Academy of Sciences.

[4]  E. Boserup The conditions of agricultural growth: The economics of agrarian change under population pressure , 1966 .

[5]  H. Steinfeld,et al.  Livestock's long shadow: environmental issues and options. , 2006 .

[6]  Changsheng Li,et al.  Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images , 2006 .

[7]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[8]  Kathleen Neumann,et al.  Challenges in using land use and land cover data for global change studies , 2011 .

[9]  C. Woodcock,et al.  Resolution dependent errors in remote sensing of cultivated areas , 2006 .

[10]  Kjeld Philip,et al.  Ester Boserup: The Conditions of Agricultural The Economics of Agrarian Change under Population Pressure. George Allen & Unwin Ltd. London 1965. 124 sider. 22/6 sh. , 1965 .

[11]  C. Müller,et al.  Climate‐driven simulation of global crop sowing dates , 2012 .

[12]  N. Ramankutty,et al.  Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production , 2010 .

[13]  J. Hill,et al.  Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece , 2008 .

[14]  Martin Jung,et al.  Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .

[15]  D. Molden,et al.  Fourier analysis of historical NOAA time series data to estimate bimodal agriculture , 2007 .

[16]  Patrick Hostert,et al.  Using image texture to map farmland field size: a case study in Eastern Europe , 2009 .

[17]  Liangzhi You,et al.  Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach , 2009 .

[18]  M. Goodchild,et al.  Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice , 2012 .

[19]  P. Verburg,et al.  A Land System representation for global assessments and land‐use modeling , 2012, Global change biology.

[20]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[21]  G. Nabuurs,et al.  Statistical mapping of tree species over Europe , 2011, European Journal of Forest Research.

[22]  Food Security Agriculture Organization of the United Nations (FAO) , 2004 .

[23]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[24]  Jin Li,et al.  A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors , 2011, Ecol. Informatics.

[25]  Javier Gallego,et al.  The European Land Use and Cover Area‐Frame Statistical Survey , 2010 .

[26]  Mark Rounsevell,et al.  Please Scroll down for Article International Journal of Geographical Information Science Exploring Spatial Data Uncertainties in Land-use Change Scenarios Exploring Spatial Data Uncertainties in Land-use Change Scenarios , 2022 .

[27]  Petra Döll,et al.  Development and validation of the global map of irrigation areas , 2005 .

[28]  Lars Eklundh,et al.  Using 250-meter spatial resolution MODIS data and regression tree modeling to map fractional land cover across the highlands of mainland Southeast Asia , 2007 .

[29]  John R. Miller,et al.  Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model , 2010 .

[30]  Risto Päivinen,et al.  The growing stock of European forests using remote sensing and forest inventory data , 2009 .

[31]  V. Radeloff,et al.  Author's Personal Copy Mapping Abandoned Agriculture with Multi-temporal Modis Satellite Data , 2022 .

[32]  Spatial occurrence of major tree species groups in Europe derived from multiple data sources , 2009 .

[33]  Richard A. Birdsey,et al.  Age structure and disturbance legacy of North American forests , 2010 .

[34]  Petra Döll,et al.  Global Patterns of Cropland Use Intensity , 2010, Remote. Sens..

[35]  P. Jönsson,et al.  Mapping fractional forest cover across the highlands of mainland Southeast Asia using MODIS data and regression tree modelling , 2007 .

[36]  Helmut Haberl,et al.  A comprehensive global 5 min resolution land-use data set for the year 2000 consistent with national census data , 2007 .

[37]  Karl-Heinz Erb,et al.  How a socio-ecological metabolism approach can help to advance our understanding of changes in land-use intensity , 2012, Ecological economics : the journal of the International Society for Ecological Economics.

[38]  K. Neumann,et al.  Modelling the spatial distribution of livestock in Europe , 2009, Landscape Ecology.

[39]  D. Deryng,et al.  Crop planting dates: an analysis of global patterns. , 2010 .

[40]  E. Lambin,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:Global land use change, economic globalization, and the looming land scarcity , 2011 .

[41]  N. Ramankutty,et al.  Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000 , 2008 .

[42]  P. Döll,et al.  MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling , 2010 .

[43]  Steffen Fritz,et al.  Highlighting continued uncertainty in global land cover maps for the user community , 2011 .

[44]  Christopher J. Kucharik,et al.  Data and monitoring needs for a more ecological agriculture , 2011 .

[45]  N. Ramankutty,et al.  Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000 , 2008 .

[46]  Mutlu Ozdogan,et al.  A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US , 2008 .

[47]  P. Thornton,et al.  Global livestock production systems. , 2011 .

[48]  A. D. Gregorio,et al.  Land Cover Classification System (LCCS): Classification Concepts and User Manual , 2000 .

[49]  R. Netting,et al.  Smallholders, Householders: Farm Families and the Ecology of Intensive, Sustainable Agriculture. , 1994 .

[50]  Elke Stehfest,et al.  Exploring global changes in nitrogen and phosphorus cycles in agriculture induced by livestock production over the 1900–2050 period , 2011, Proceedings of the National Academy of Sciences.

[51]  S. Carpenter,et al.  Solutions for a cultivated planet , 2011, Nature.

[52]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[53]  M. Lefsky A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System , 2010 .

[54]  Peter H. Verburg,et al.  Exploring global irrigation patterns : a multilevel modelling approach , 2011 .

[55]  D. Tilman,et al.  Global food demand and the sustainable intensification of agriculture , 2011, Proceedings of the National Academy of Sciences.

[56]  G. Hoogenboom,et al.  Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation , 2011 .

[57]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[58]  M. Keller,et al.  Selective Logging in the Brazilian Amazon , 2005, Science.

[59]  Volker C. Radeloff,et al.  Using Landsat imagery to map forest change in southwest China in response to the national logging ban and ecotourism development , 2012 .

[60]  H. Haberl,et al.  Quantifying and mapping the human appropriation of net primary production in earth's terrestrial ecosystems , 2007, Proceedings of the National Academy of Sciences.

[61]  S. Goetz,et al.  Carbon Balance and Management , 2009 .

[62]  Christoph Schmitz,et al.  Measuring agricultural land-use intensity -A global analysis using a model-assisted approach , 2012 .

[63]  C. Müller,et al.  The yield gap of global grain production: A spatial analysis , 2010 .

[64]  Giles M. Foody,et al.  IDENTIFICATION OF SPECIFIC TREE SPECIES IN ANCIENT SEMI-NATURAL WOODLAND FROM DIGITAL AERIAL SENSOR IMAGERY , 2005 .

[65]  Mark R. Rosenzweig,et al.  Behavioural and material determinants of production relations in agriculture , 1986 .

[66]  Billie Turner,et al.  The concept and measure of agricultural intensity , 1978 .

[67]  Ronald E. McRoberts,et al.  Comprar National Forest Inventories · Pathways for Common Reporting | Tomppo, Erkki | 9789048132324 | Springer , 2010 .

[68]  Curtis E. Woodcock,et al.  Changes in Summer Irrigated Crop Area and Water Use in Southeastern Turkey from 1993 to 2002: Implications for Current and Future Water Resources , 2006 .