Evolution of MapBiomas Collections’ Accuracy for the Highly Fragmented São Paulo landscape
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Abstract
The assessing the accuracy of land cover mappings is essential for the scientific, practical and policy uses of maps. In Brazil, the MapBiomas project has been annually mapping the land cover across the entire territory via automatic classification of Landsat images of medium spatial resolution (30 m) since 1985. Each new version of the classification algorithm generates a new collection of maps that are subject to an accuracy assessment at the national level. However, MapBiomas is increasingly used for regional, municipal or local studies for which the assessment of accuracy at the national level is not adequate. Here we evaluate the accuracy and evolution of accuracy of the main categories of MapBiomas coverage for the state of São Paulo (SP), the most urbanized in the country and the objetic of many studies and public policies related to land cover. We analyzed collections 3.1, 4.1, 5.0, 6.0 and 7.0 for the year 2017, the most recent with coincidence of classes in all collections, considering the classes: Forest Formation, Planted Forest, Pasture, Sugar Cane, Urban Infrastructure and River , lake and ocean. The global accuracy (GA) had its lowest value in collection 4.1 (91%) and highest in 7.0 (96%). Producer (PA) and user accuracies (UA) improved from collection 3.0 to 7.0, with the exception of PA for Planted Forest, which remained virtually unchanged, and for Urban Infrastructure, which has been showing a tendency to worsen over the course of the collections, reaching its lowest value at 7.0 (0,87). Even so, the fact that the GA, and particularly the PA and UA of collection 7.0 are above 0.84 indicates that in Sâo Paulo MapBiomas is accurate enough for, for example, many common analyzes at the landscape scale, in ecology (e.g. distribution modeling of species of mammal or bird), agriculture (e.g. harvest estimation), or engineering (e.g. choice of airport site)
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References
Alves, C.J.P.; Da Silva, E.J.; Müller, C.; Borille, G.M.R.; Guterres, M.X.; Arraut, E.M.; Dos Santos, R.J. (2020). Towards an objective decision-making framework for regional airport site selection. Journal of Air Transport Management, 89, 101888.
Arraut, E. M. et al. (2021). Anticipation of common buzzard population patterns in the changing UK landscape. Proceedings of the Royal Society B-biological Sciences, v. 288, p. 20210993. https://royalsocietypublishing.org/doi/10.1098/rspb.2021.0993.
Castojo, A. M. Z.; Jesus, S. C. de. (2022). The Conservation Units of the State of São Paulo - Management Plans and Representativeness. Brazilian Journal of Physical Geography v.15, n.06.
CANASAT, Sugarcane Monitoring. http://www.dsr.inpe.br/laf/canasat/
Carmo, A. B. G.; Gavioli, F. R.; Molin, P. G.(2023). Landscape Dynamics and Qualitative Classification of Conservation Unit Buffer Zones in São Paulo. Proceedings of the XX Brazilian Symposium on Remote Sensing. INPE - Florianópolis-SC.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data.
Remote Sensing of Environment, v.49, n.12, p.1671-1678.
Costa, V. M. (2022). Assessment of the Situation of Permanent Preservation Areas in the State of São Paulo.
[Course Conclusion Paper Environmental Engineering]. Federal University of São Carlos, Buri.
Gindon, B.; Zhang, Y.; Dillabaugh, C. (2004) Landsat Urban Mapping Based on a Combined Spectral–Spatial Methodology. Remote Sensing of Environment. v 92. Issue 2, p 218-232, ISSN 0034-4257. https://doi.org/10.1016/j.rse.2004.06.015
IBGE, Brazilian Institute of Geography and Statistics. (2022, jun 07). Cities and States. https://www.ibge.gov.br/cidades-e estados/sp.html.
IBGE, Brazilian Institute of Geography and Statistics. (2022, out 13). Social Statistics. August 27, 2020. https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/28668- ibge-divulga-estimativa-dapopulacao-dos-municipios-para-2020.
Kenward, R. E.; Arraut, E. M.; Robertson, P. A.; Paredes, S. S.; Casey, N. M.; Aebischer, N. J. Resource-Area-Dependence Analysis: inferring animal resource needs from home-range and mapping data. PLOS ONE. Article-journal. https://doi.org/10.1371/journal.pone.0206354.
MAPBIOMAS. (2022, jun 01). Accuracy Analysis. https://mapbiomas.org/analise-de-acuracia>.
MAPBIOMAS. (2024, april 04). Urban Area - Appendix. 2023.
https://brasil.mapbiomas.org/wp-content/uploads/sites/4/2023/08/Urban-Area-Appendix-ATBD-Col.8-v1
_revisado.pdf.
QGIS Development Team e GRASS Development Team. (2022). QGIS 3.22.0 "Białowieża" with GRASS GIS 7.2.2 [Software]. https://qgis.org e https://grass.osgeo.org.
Ribeiro, F.O. (2022). The use of MapBiomas to analyze the loss of natural vegetation and support current Forestry Legislation in Bragança (Pará). Brazilian Journal of the Environment. v.10, n3, 150-167. https://revistabrasileirademeioambiente.com/index.php/RVBMA/article/view/1252.
Santos Junior, E. R. dos. (2023). Analysis of the effects of land cover change on the supply of ecosystem services in peri-urban wetlands in São Paulo. 150p. [Master's Dissertation] - São Carlos School of Engineering, University of São Paulo, São Carlos.
SÃO PAULO. Department of Infrastructure and Environment (2023, jun 16). Thematic Mapping of Land Cover in the State of São Paulo on a scale of 1:100,000. https://www.infraestruturameioambiente.sp.gov.br/cpla/mapa-de-cobertura-da-terra-do-estado-de-sao-pa ulo/.
Sena-Souza, J.P. et al. (2022). Influence of relief and temporal dynamics of land use and land cover in northern Minas Gerais, Brazil. Brazilian Journal of Physical Geography. v.15, n.05. 2475-2485. https://periodicos.ufpe.br/revistas/rbgfe/article/view/252707.
Smith, C.C , Espírito-Santo, FDB , Healey, Jr , et al. (2020). Secondary forests offset less than 10% of deforestation-mediated carbon emissions in the Brazilian Amazon. Globo. Troque Biol. 26: 7006-7020. https://doi.org/10.1111/gcb.15352.
Souza Jr, C. M. et al. (2020). Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote Sensing. 12 10.3390/rs12172735.
Stephen V. Stehman, Giles M. Foody. (2019) Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment, v 231, 111199, ISSN 0034-4257.
https://doi.org/10.1016/j.rse.2019.05.018.
Wickham, J.; Stehman, S. V.; Homer, C. G. (2017). Spatial patterns of the United States National Land Cover Dataset (NLCD) land-cover change thematic accuracy (2001–2011). International Journal of Remote Sensing, 39(6). 1729–1743. https://doi.org/10.1080/01431161.2017.1410298.