A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses

Global Change Biology

Volumn 24, Issue 1, 2018, Pages 322-337

  Estes, Lyndon ^a,b,c   Chen, Peng ^d   Debats, Stephanie ^c   Evans, Tom ^d   Ferreira, Stefanus ^e   Kuemmerle, Tobias ^f   Ragazzo, Gabrielle ^c   Sheffield, Justin ^c,g   Wolf, Adam ^h   Wood, Eric ^c   Caylor, Kelly ^c,i  

^a CLARK UNIVERSITY   (United States)

^b PRINCETON UNIVERSITY   (United States)

^c Princeton University   (United States)

^d INDIANA UNIVERSITY   (United States)

^e GeoTerraImage   (South Africa)

^f HUMBOLDT UNIVERSITY   (Germany)

^g UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^h Arable Labs Inc   (United States)

ⁱ UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

agent based model; agriculture; bias; carbon; crop yield; evapotranspiration; land cover; remote sensing

Indexed keywords

AGRICULTURAL LAND; CARBON CYCLE; CROP YIELD; ERROR ANALYSIS; EVAPOTRANSPIRATION; LAND COVER; MAPPING; REMOTE SENSING; SPATIAL ANALYSIS;

CROP; CROP PRODUCTION; ENVIRONMENTAL MONITORING; ENVIRONMENTAL PROTECTION; FOREST; GEOGRAPHIC INFORMATION SYSTEM; GEOGRAPHIC MAPPING; PROCEDURES; SOUTH AFRICA; STANDARDS; STATISTICS AND NUMERICAL DATA;

CONSERVATION OF NATURAL RESOURCES; CROP PRODUCTION; CROPS, AGRICULTURAL; ENVIRONMENTAL MONITORING; FORESTS; GEOGRAPHIC INFORMATION SYSTEMS; GEOGRAPHIC MAPPING; SOUTH AFRICA;

EID: 85031104924     PISSN: 13541013     EISSN: 13652486     Source Type: Journal    
DOI: 10.1111/gcb.13904     Document Type: Article

References (92)

1. Araújo, M., & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22, 42–47.
2. Arino, O., Perez, J. J. R., Kalogirou, V., Bontemps, S., Defourny, P., & Bogaert, E. V. (2012) Global Land Cover Map for 2009 (GlobCover 2009). PANGAEA.
3. Asner, G. P., Powell, G. V. N., Mascaro, J., Knapp, D. E., Clark, J. K., Jacobson, J., … Flint Hughes, R. (2010). High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Sciences of the United States of America, 107, 16738–16742.
4. Asseng, S., Ewert, F., Rosenzweig, C., Jones, J., Hatfield, J., Ruane, A., … Wolf, J. (2013). Uncertainty in simulating wheat yields under climate change. Nature Climate Change, 3, 827–832.
5. Avelino, A. F. T., Baylis, K., & Honey-Rosés, J. (2016). Goldilocks and the raster grid: Selecting scale when evaluating conservation programs. PLoS One, 11, e0167945.
6. Berger, T., & Schreinemachers, P. (2006). Creating agents and landscapes for multiagent systems from random samples. Ecology and Society, 11, 19.
7. Carletto, C., Gourlay, S., & Winters, P. (2015). From guesstimates to GPStimates: Land area measurement and implications for agricultural analysis. Journal of African Economies, 24, 593–628.
8. Carletto, C., Jolliffe, D., & Banerjee, R. (2013). The emperor has no data! Agricultural statistics in sub-Saharan Africa. World Bank Working Paper. Retrieved from http://mortenjerven.com/wp-content/uploads/2013/04/Panel-3-Carletto.pdf.
9. Cattaneo, A., Lubowski, R., Busch, J., Creed, A., Strassburg, B., Boltz, F., & Ashton, R. (2010). On international equity in reducing emissions from deforestation. Environmental Science & Policy, 13, 742–753.
10. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., … Mills, J. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7–27.
11. Chen, P., Plale, B., & Evans, T. (2013). Dependency Provenance in Agent Based Modeling. In: 2013 IEEE 9th International Conference on eScience (eScience), pp. 180–187.
12. Dark, S. J., & Bram, D. (2007). The modifiable areal unit problem (MAUP) in physical geography. Progress in Physical Geography, 31, 471–479.
13. Debats, S. R., Luo, D., Estes, L. D., Fuchs, T. J., & Caylor, K. K. (2016). A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes. Remote Sensing of Environment, 179, 210–221.
14. Dendoncker, N., Schmit, C., & Rounsevell, M. (2008). Exploring spatial data uncertainties in landuse change scenarios. International Journal of Geographical Information Science, 22, 1013–1030.
15. Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., … Bargellini, P. (2012). Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36.
16. Estes, L. D., Beukes, H., Bradley, B. A., Beukes, H., Hole, D., Lau, M., … Turner, W. (2013). Projected climate impacts to South African maize and wheat production in 2055: A comparison of empirical and mechanistic modeling approaches. Global Change Biology, 19, 3762–3774.
17. Estes, L. D., McRitchie, D., Choi, J., Debats, S., Evans, T., Guthe, W., … Caylor, K. (2016). A platform for crowdsourcing the creation of representative, accurate landcover maps. Environmental Modelling & Software, 80, 41–53.
18. Estes, L. D., Searchinger, T., Spiegel, M., Tian, D., Sichinga, S., Mwale, M., … Caylor, K. (2016). Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 371, 20150316.
19. Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., … Zaks, D. P. (2011). Solutions for a cultivated planet. Nature, 478, 337–342.
20. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185–201.
21. Fourie, A. (2009). Better Crop Estimates in South Africa. ArcUser Online, Winter, 2009. Retrieved from http://www.esri.com/news/arcuser/0109/crop_estimates.html.
22. Frey, K. E., & Smith, L. C. (2007). How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground-truth database for West Siberia. Global Biogeochemical Cycles, 21, GB1016.
23. Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168–182.
24. Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D., … Obersteiner, M. (2012). Geo-Wiki: An online platform for improving global land cover. Environmental Modelling & Software, 31, 110–123.
25. Fritz, S., & See, L. (2008). Identifying and quantifying uncertainty and spatial disagreement in the comparison of global land cover for different applications. Global Change Biology, 14, 1057–1075.
26. Fritz, S., See, L., McCallum, I., Schill, C., Obersteiner, M., Velde, M., … Achard, F. (2011). Highlighting continued uncertainty in global land cover maps for the user community. Environmental Research Letters, 6, 044005.
27. Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., … Obersteiner, M. (2015). Mapping global cropland and field size. Global Change Biology, 21, 1980–1992.
28. Fritz, S., See, L., & Rembold, F. (2010). Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa. International Journal of Remote Sensing, 31, 2237–2256.
29. Fritz, S., See, L., You, L., Justice, C., Becker-Reshef, I., Bydekerke, L., … Woodcock, C. (2013). The need for improved maps of global cropland. Eos, Transactions American Geophysical Union, 94, 31–32.
30. Fritz, S., You, L., Bun, A., See, L., McCallum, I., Schill, C., … Obersteiner, M. (2011). Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets. Geophysical Research Letters, 38, L04404.
31. Fry, J., Coan, M., Homer, C., Meyer, D., & Wickham, J. (2009). Completion of the National Land Cover Database (NLCD) 1992-2001 Land Cover Change Retrofit Product. USGS Numbered Series 2008-1379, U.S. Geological Survey.
32. Gasparri, N. I., Kuemmerle, T., Meyfroidt, P., le Polain de Waroux, Y., & Kreft, H. (2016). The emerging soybean production frontier in Southern Africa: Conservation challenges and the role of South-South telecouplings. Conservation Letters, 9, 21–31.
33. Gaveau, D. L. A., Salim, M. A., Hergoualc'h, K., Locatelli, B., Sloan, S., Wooster, M., … Sheil, D. (2014). Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: Evidence from the 2013 Sumatran fires. Scientific Reports, 4, 6112.
34. Ge, J., Qi, J., Lofgren, B. M., Moore, N., Torbick, N., & Olson, J. M. (2007). Impacts of land use/cover classification accuracy on regional climate simulations. Journal of Geophysical Research: Atmospheres, 112, D05107.
35. GeoTerraImage (2013). 2011 KZN Province land-cover mapping. Data Users Report and Meta Data.
36. Giorgi, F., & Mearns, L. O. (2002). Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “reliability ensemble averaging” (REA) method. Journal of Climate, 15, 1141–1158.
37. Goetz, S. J., Baccini, A., Laporte, N. T., Johns, T., Walker, W., Kellndorfer, J., … Sun, M. (2009). Mapping and monitoring carbon stocks with satellite observations: A comparison of methods. Carbon Balance and Management, 4, 2.
38. Gross, D., Dubois, G., Pekel, J. F., Mayaux, P., Holmgren, M., Prins, H., … Boitani, L. (2013). Monitoring land cover changes in African protected areas in the 21st century. Ecological Informatics, 14, 31–37.
39. Hand, E. (2015). Startup liftoff. Science, 348, 172–177.
40. Hardy, M., Dziba, L., Kilian, W., & Tolmay, J. (2011). Rainfed farming systems in South Africa. In P. Tow, I. Cooper, I. Partridge, & C. Birch (Eds.), Rainfed farming systems (pp. 395–432). Netherlands: Springer.
41. Hastie, T., & Tibshirani, R. (1990). Generalized additive models. London: Chapman and Hall.
42. Hay, G. J., Blaschke, T., Marceau, D. J., & Bouchard, A. (2003). A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry and Remote Sensing, 57, 327–345.
43. Jain, A. K., Meiyappan, P., Song, Y., & House, J. I. (2013). CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Global Change Biology, 19, 2893–2906.
44. Jain, M., Mondal, P., DeFries, R. S., Small, C., & Galford, G. L. (2013). Mapping cropping intensity of smallholder farms: A comparison of methods using multiple sensors. Remote Sensing of Environment, 134, 210–223.
45. Kuemmerle, T., Erb, K., Meyfroidt, P., Müller, D., Verburg, P. H., Estel, S., … Reenberg, A. (2013). Challenges and opportunities in mapping land use intensity globally. Current Opinion in Environmental Sustainability, 5, 484–493.
46. Lambin, E. F. (1997). Modelling and monitoring land-cover change processes in tropical regions. Progress in Physical Geography, 21, 375–393.
47. Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, 205–241.
48. Lambin, E., Gibbs, H., Ferreira, L., Grau, R., Mayaux, P., Meyfroidt, P., … Munger, J. (2013). Estimating the world's potentially available cropland using a bottom-up approach. Global Environmental Change, 23, 892–901.
49. Langford, W. T., Gergel, S. E., Dietterich, T. G., & Cohen, W. (2006). Map misclassification can cause large errors in landscape pattern indices: Examples from habitat fragmentation. Ecosystems, 9, 474–488.
50. Lark, T. J., Salmon, J. M., & Gibbs, H. K. (2015). Cropland expansion outpaces agricultural and biofuel policies in the United States. Environmental Research Letters, 10, 044003.
51. Larsen, A. E., Hendrickson, B. T., Dedeic, N., & MacDonald, A. J. (2015). Taken as a given: Evaluating the accuracy of remotely sensed crop data in the USA. Agricultural Systems, 141, 121–125.
52. Latham, J., Cumani, R., Rosati, I., & Bloise, M. (2014). Global land cover share (GLC-SHARE) database beta-release version 1.0-2014. Tech. rep., FAO, Rome, Italy.
53. Liang, X., Lettenmaier, D. P., Wood, E. F., & Burges, S. J. (1994). A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research, 99, 14415.
54. Licker, R., Johnston, M., Foley, J. A., Barford, C., Kucharik, C. J., Monfreda, C., & Ramankutty, N. (2010). Mind the gap: How do climate and agricultural management explain the ‘yield gap’ of croplands around the world? Global Ecology and Biogeography, 19, 769–782.
55. Linard, C., Gilbert, M., & Tatem, A. J. (2010). Assessing the use of global land cover data for guiding large area population distribution modelling. GeoJournal, 76, 525–538.
56. Luoto, M., Virkkala, R., Heikkinen, R. K., & Rainio, K. (2004). Predicting bird species richness using remote sensing in boreal agricultural-forest mosaics. Ecological Applications, 14, 1946–1962.
57. Marceau, D. J. (1999). The scale issue in the social and natural sciences. Canadian Journal of Remote Sensing, 25, 347–356.
58. Marceau, D. J., & Hay, G. J. (1999). Remote sensing contributions to the scale issue. Canadian Journal of Remote Sensing, 25, 357–366.
59. Monfreda, C., Ramankutty, N., & Foley, J. A. (2008). Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles, 22, GB1022.
60. Moody, A., & Woodcock, C. E. (1995). The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing. Landscape Ecology, 10, 363–379.
61. Newbold, T., Hudson, L. N., Hill, S. L. L., Contu, S., Lysenko, I., Senior, R. A., … Purvis, A. (2015). Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50.
62. Nol, L., Verburg, P. H., Heuvelink, G. B. M., & Molenaar, K. (2008). Effect of land cover data on nitrous oxide inventory in fen meadows. Journal of Environmental Quality, 37, 1209–1219.
63. Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57.
64. Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2013). Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, 122–131.
65. Olofsson, P., Stehman, S. V., Woodcock, C. E., Sulla-Menashe, D., Sibley, A. M., Newell, J. D., … Herold, M. (2012). A global land-cover validation data set, part I: Fundamental design principles. International Journal of Remote Sensing, 33, 5768–5788.
66. Openshaw, S., & Taylor, P. J. (1979). A million or so correlation coefficients: Three experiments on the modifiable areal unit problem. Statistical Applications in the Spatial Sciences, 21, 127–144.
67. Ozdogan, M., & Woodcock, C. E. (2006). Resolution dependent errors in remote sensing of cultivated areas. Remote Sensing of Environment, 103, 203–217.
68. Pax-Lenney, M., & Woodcock, C. E. (1997). The effect of spatial resolution on the ability to monitor the status of agricultural lands. Remote Sensing of Environment, 61, 210–220.
69. Phelps, J., Carrasco, L. R., Webb, E. L., Koh, L. P., & Pascual, U. (2013). Agricultural intensification escalates future conservation costs. Proceedings of the National Academy of Sciences of the United States of America, 110, 7601–7606.
70. Quaife, T., Quegan, S., Disney, M., Lewis, P., Lomas, M., & Woodward, F. I. (2008). Impact of land cover uncertainties on estimates of biospheric carbon fluxes. Global Biogeochemical Cycles, 22, GB4016.
71. Ramankutty, N., Evan, A. T., Monfreda, C., & Foley, J. A. (2008). Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22, 19.
72. Ruesch, A., & Gibbs, H. K. (2008). New IPCC Tier-1 global biomass carbon map for the year 2000. Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, Oak Ridge, Tennessee. Retrieved from http://cdiac.ornl.gov/epubs/ndp/global_carbon/carbon_documentation.html
73. Rulli, M. C., Saviori, A., & D'Odorico, P. (2013). Global land and water grabbing. Proceedings of the National Academy of Sciences of the United States of America, 110, 892–897.
74. Samberg, L. H., Gerber, J. S., Ramankutty, N., Herrero, M., & West, P. C. (2016). Subnational distribution of average farm size and smallholder contributions to global food production. Environmental Research Letters, 11, 124010.
75. SANBI (2009). National Landcover 2009. Tech. rep., South African National Biodiversity Institute; National Department of Environmental Affairs and Tourism, Pretoria, South Africa.
76. Schierhorn, F., Müller, D., Beringer, T., Prishchepov, A. V., Kuemmerle, T., & Balmann, A. (2013). Post-Soviet cropland abandonment and carbon sequestration in European Russia, Ukraine, and Belarus. Global Biogeochemical Cycles, 27, 1175–1185.
77. Schmit, C., Rounsevell, M. D. A., & La Jeunesse, I. (2006). The limitations of spatial land use data in environmental analysis. Environmental Science & Policy, 9, 174–188.
78. Searchinger, T. D., Estes, L., Thornton, P. K., Beringer, T., Notenbaert, A., Rubenstein, D., … Herrero, M. (2015). High carbon and biodiversity costs from converting Africa's wet savannahs to cropland. Nature Climate Change, 5, 481–486.
79. See, L., Fritz, S., You, L., Ramankutty, N., Herrero, M., Justice, C., … Obersteiner, M. (2015). Improved global cropland data as an essential ingredient for food security. Global Food Security, 4, 37–45.
80. Sexton, J. O., Noojipady, P., Song, X. P., Feng, M., Song, D., Kim, D., … Townshend, J. (2015). Conservation policy and the measurement of forests. Nature Climate Change, 6, 192–196.
81. Shackelford, G. E., Steward, P. R., German, R. N., Sait, S. M., & Benton, T. G. (2015). Conservation planning in agricultural landscapes: Hotspots of conflict between agriculture and nature. Diversity and Distributions, 21, 357–367.
82. Sheffield, J., Wood, E. F., Chaney, N., Guan, K., Sadri, S., Yuan, X., … Ogallo, L. (2013). A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bulletin of the American Meteorological Society, 95, 861–882.
83. Statistics South Africa (2007). Building statistics, 2007. Tech. Rep. 50-11-01, Statistics South Africa, Pretoria.
84. Stehman, S. V., Olofsson, P., Woodcock, C. E., Herold, M., & Friedl, M. A. (2012). A global land-cover validation data set, II: Augmenting a stratified sampling design to estimate accuracy by region and land-cover class. International Journal of Remote Sensing, 33, 6975–6993.
85. Sweeney, S., Ruseva, T., Estes, L., & Evans, T. (2015). Mapping cropland in smallholder-dominated savannas: Integrating remote sensing techniques and probabilistic modeling. Remote Sensing, 7, 15295–15317.
86. Tuanmu, M. N., & Jetz, W. (2014). A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Global Ecology and Biogeography, 23, 1031–1045.
87. Verburg, P. H., Neumann, K., & Nol, L. (2011). Challenges in using land use and land cover data for global change studies. Global Change Biology, 17, 974–989.
88. Waldner, F., Fritz, S., Di Gregorio, A., & Defourny, P. (2015). Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps. Remote Sensing, 7, 7959–7986.
89. Waldner, F., Fritz, S., Di Gregorio, A., Plotnikov, D., Bartalev, S., Kussul, N., & … Defourny, P. (2016). A unified cropland layer at 250 m for global agriculture monitoring. Data, 1, 3.
90. Wood, S. N. (2001). mgcv: GAMs and generalized ridge regression for R. R News, 1, 20–25.
91. Wright, C. K., & Wimberly, M. C. (2013). Recent land use change in the Western Corn Belt threatens grasslands and wetlands. Proceedings of the National Academy of Sciences of the United States of America, 110, 4134–4139.
92. Yu, L., Liang, L., Wang, J., Cheng, Q., Hu, L., Liu, S., … Gong, P. (2014). Meta-discoveries from a synthesis of satellite-based land-cover mapping research. International Journal of Remote Sensing, 35, 4573–4588.