Validation of Land Use Cover Maps: A Guideline

Referencias bibliográficas

• Aldwaik SZ, Pontius RG (2012) Intensity analysis to unify measurements of size and stationarity of land changes by interval, category and transition. Landsc Urban Plan 106:103–114
• Anderson JR (1971) Land-use classification schemes used in selected recent geographic applications of remote sensing. Photogramm Eng Remote Sens 37:379–387
• Camacho Olmedo MT, Paegelow M, Mas J-F (2013) Interest in intermediate soft-classified maps in land change model validation: suitability versus transition potential. Int J Geogr Inf Sci 27(12):2343–2361
• Camacho Olmedo MT, Pontius RG Jr, Paegelow M, Mas JF (2015) Comparison of simulation models in terms of quantity and allocation of land change. Environ Model Softw 69(2015):214–221. https://doi.org/10.1016/j.envsoft.2015.03.003
• Foody GM (2008) Harshness in image classification accuracy assessment. Int J Remote Sens 29(11):3137–3158
• Forman RTT (1995) Land mosaics: the ecology of landscapes and regions. Cambridge University Press, Cambridge, United Kingdom
• García-Álvarez D, Camacho Olmedo MT (2017) Changes in the methodology used in the production of the Spanish CORINE: Uncertainty analysis of the new maps. Int J Appl Earth Obs Geoinf 63:55–67. https://doi.org/10.1016/j.jag.2017.07.001
• Gómez C, White JC, Wulder MA (2016) Optical remotely sensed time series data for land cover classification: A review. ISPRS J Photogramm Remote Sens 116:55–72. https://doi.org/10.1016/j.isprsjprs.2016.03.008
• Hao G, Gen-Suo J (2014) Assessing MODIS land cover products over China with probability of interannual change. Atmosph Ocean Sci Lett 7(6):564–570. https://doi.org/10.1080/16742834.2014.11447225
• Hargrove WW, Hoffman FM, Hessburg PF (2006) Mapcurves: a quantitative method for comparing categorical maps. J Geogr Syst 8:187–208. https://doi.org/10.1007/s10109-006-0025-x
• Islam S, Zhang M, Yang H, Ma M (2019) Assessing inconsistency in global land cover products and synthesis of studies on land use and land cover dynamics during 2001 to 2017 in the southeastern region of Bangladesh. J Appl Remote Sens 13(04):1. https://doi.org/10.1117/1.JRS.13.048501
• Krüger C, Lakes T (2016) Revealing uncertainties in land change modeling using probabilities. Trans GIS 20(4):526–546. https://doi.org/10.1111/tgis.12161
• Olofsson P, Foody GM, Stehman SV, Woodcock CE (2013) Making better use of accuracy data in land change studies: estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens Environ 129(2013):122–131. https://doi.org/10.1016/j.rse.2012.10.031
• Olofsson P, Foody GM, Herold M et al (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ 148:42–57. https://doi.org/10.1016/j.rse.2014.02.015
• Pérez-Vega A, Mas JF, Ligmann-Zielinska A (2012) Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environ Model Softw 29(1):11–23
• Pontius RG Jr, Malanson J (2005) Comparison of the structure and accuracy of two land change models. Int J Geogr Inf Sci 19:243–265. https://doi.org/10.1080/13658810410001713434
• Pontius RG Jr, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens 32:4407–4429. https://doi.org/10.1080/01431161.2011.552923
• Pontius RG Jr, Parmentier B (2014) Recommendations for using the relative operating characteristic (ROC). Landscape Ecol 29(3):367–382
• Pontius RG Jr, Shusas E, McEachern M (2004) Detecting important categorical land changes while accounting for persistence. Agr Ecosyst Environ 101:251–268
• Pontius RG Jr, Krithivasan R, Sauls L et al (2017) Methods to summarize change among land categories across time intervals. J Land Use Sci 12:218–230. https://doi.org/10.1080/1747423X.2017.1338768
• Puyravaud J-P (2003) Standardizing the calculation of the annual rate of deforestation. For Ecol Manage 177(1–3):593–596
• Runfola DSM, Pontius RG (2013) Measuring the temporal instability of land change using the Flow matrix. Int J Geogr Inf Sci 27:1696–1716. https://doi.org/10.1080/13658816.2013.792344
• Stehman SV (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62(1):77–89. https://doi.org/10.1016/S0034-4257(97)00083-7
• Stehman SV (1999) Basic probability sampling designs for thematic map accuracy assessment. Int J Remote Sens 20(12):2423–2441. https://doi.org/10.1080/014311699212100
• Stehman SV, Czaplewski RL (1998) Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sens Environ 64:331–344
• Wulder MA, Franklin SE, White JC, Linke J, Magnussen S (2006) An accuracy assessment framework for large-area land cover classification products derived from medium-resolution satellite data. Int J Remote Sens 27(4):663–683. https://doi.org/10.1080/01431160500185284
• Yang Y, Xiao P, Feng X, Li H (2017) Accuracy assessment of seven global land cover datasets over China. ISPRS J Photogramm Remote Sens 125:156–173. https://doi.org/10.1016/j.isprsjprs.2017.01.016
• Bonham-Carter GF (1994) Tools for map analysis: map pairs. In: Bonham-Carter GF (ed) Geographic information systems for geoscientists, pp 221–266. Pergamon. ISBN 9780080418674, https://doi.org/10.1016/B978-0-08-041867-4.50013-8
• Botequilha Leitao A, Miller J, Ahern J, McGarigal K (2006) Measuring landscapes: a planner's handbook. Island Press, Washington, Covelo, London
• Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28(4):281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
• Camacho Olmedo MT., Mas JF, Paegelow M (2018) The Simulation Stage in LUCC modeling. In: Camacho Olmedo M, Paegelow M, Mas JF, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Lecture Notes in Geoinformation and Cartography. Springer, Cham, pp 27–51. Publisher Name Springer, Cham Print ISBN 978-3-319-60800-6 Online ISBN 978-3-319-60801-3 eBook Packages Earth and Environmental Science. https://doi.org/10.1007/978-3-319-60801-3_3
• Comber AJ (2013) Geographically weighted methods for estimating local surfaces of overall, user and producer accuracies. Remote Sens Lett 4(4):373–380. https://doi.org/10.1080/2150704X.2012.736694
• Eastman JR, Van Fossen ME, Solarzano LA (2005) Transition potential modelling for land cover change. In: Maguire D, Goodchild M, Batty M (eds), GIS, Spatial analysis and modeling. ESRI Press, Redlands, California
• FAO (1995) Forest resources assessment 1990. Global synthesis. FAO, Rome
• Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sensing Environ 80(1):185–201. https://doi.org/10.1016/S0034-4257(01)00295-4
• Gwet K (2002) Kappa statistic is not satisfactory for assessing the extent of agreement between raters. Series: statistical methods for inter-rater reliability assessment
• Kerr GHG, Fischer C, Reulke R (2015) Reliability assessment for remote sensing data: beyond Cohen's kappa. In: International geoscience and remote sensing symposium (IGARSS), pp 4995–4998. https://doi.org/10.1109/IGARSS.2015.7326954
• Paegelow M, Camacho Olmedo MT, Mas JF and Houet T (2014) Benchmarking of LUCC modelling tools by various validation techniques and error analysis. Cybergeo Eur J Geogr [En ligne] Systèmes, Modélisation, Géostatistiques, document 701, mis en ligne le 22 décembre 2014. ISSN: 1278-3366. CNRS-UMR Géographie-cités 8504. https://doi.org/10.4000/cybergeo.26610
• Paegelow M, Camacho Olmedo MT, Mas JF (2018) Techniques for the validation of LUCC modeling outputs. In: Camacho Olmedo M, Paegelow M, Mas JF, Escobar F (eds) Geomatic approaches for modeling land change scenarios. Lecture Notes in Geoinformation and Cartography. Springer, Cham, pp 53–80. Publisher Name Springer, Cham Print ISBN 978-3-319-60800-6 Online ISBN 978-3-319-60801-3 eBook Packages Earth and Environmental Science
• Pérez-Hoyos A, Udías A, Rembold F (2020) Integrating multiple land cover maps through a multi-criteria analysis to improve agricultural monitoring in Africa. Int J Appl Earth ObsGeoinf 88:102064. https://doi.org/10.1016/j.jag.2020.102064
• Pontius Jr RG, Boersma W, Castella JC, Clarke K, de Nijs T, Dietzel C, Duan Z, Fotsing E, Goldstein N, Kok K, Koomen E, Lippitt CD, McConnell W, MohdSood A, Pijanowski B, Pithadia S, Sweeney S, Trung TN, Veldkamp AT, Verburg PH (2008) Comparing input, output, and validation maps for several models of land change. Ann Reg Sci 42(1):11e47
• Pouliot D, Latifovic R (2013) Accuracy assessment of annual land cover time series derived from change-based updating. In: Proceedings of the 7th International workshop on the analysis of multi-temporal remote sensing images: “our dynamic environment” (MultiTemp 2013), pp 1–3. https://doi.org/10.1109/Multi-Temp.2013.6866005
• Strahler AH, Boschetti L, Foody GM, Friedl MA, Hansen MC, Herold M, Mayaux P, Morisette JT, Stehman SV, Woodcock CE (2006) Global land cover validation: recommendations for evaluation and accuracy assessment of global land cover maps. Office for official publications of the European communities. GOFC-GOLD Report No 25. Luxemburg
• Van Vliet J, Bregt AK, Hagen-Zanker A (2011) Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecol Model 222(8):1367-1375. https://doi.org/10.1016/j.ecolmodel.2011.01.017
• Woodcock CE, Gopal S (2000) Fuzzy set theory and thematic maps: accuracy assessment and area estimation. Int J Geogr Inf Sci 14(2):153–172. https://doi.org/10.1080/136588100240895