Accounting for alternation in temporal quality analysis in MapBiomas Brazil

Land use and land cover maps are an important resource for understanding interactions between humans and their environment across space and time with current mapping efforts often spanning upwards of 20 years. We present here a new method for a robust assessment of land cover transitions over time a...

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Main Authors: Ana Paula Matos, Maria Hunter, Robert Gilmore Pontius, Luis Rodrigo Baumann, Leandro Leal Parente, Laerte Guimarães Ferreira
Format: Article
Language:English
Published: Taylor & Francis Group 2025-08-01
Series:International Journal of Digital Earth
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Online Access:https://www.tandfonline.com/doi/10.1080/17538947.2025.2528604
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author Ana Paula Matos
Maria Hunter
Robert Gilmore Pontius
Luis Rodrigo Baumann
Leandro Leal Parente
Laerte Guimarães Ferreira
author_facet Ana Paula Matos
Maria Hunter
Robert Gilmore Pontius
Luis Rodrigo Baumann
Leandro Leal Parente
Laerte Guimarães Ferreira
author_sort Ana Paula Matos
collection DOAJ
description Land use and land cover maps are an important resource for understanding interactions between humans and their environment across space and time with current mapping efforts often spanning upwards of 20 years. We present here a new method for a robust assessment of land cover transitions over time and apply this methodology to the yearly MapBiomas land use land cover maps of Brazil spanning 1985–2022. Based on a reference sample of 85,152 points, we find MapBiomas to have limited accuracy as an indicator of yearly land use change, but consistent over the full mapping period. Alternation, a newly defined error component, captures the number of land use transitions a location experiences throughout time. It is the primary reason for differences in estimates of annual change and is 4.6 times more frequent in the MapBiomas product than reference data. Differences in alternations are particularly prevalent in transitions from pasture to savanna and forest classes. The total land use changes detected over the 37 year study period are consistent between the reference data and the MapBiomas classification with 232 million hectares and 252 million hectares, or 27% and 29% of the Brazilian territory respectively.HIGHLIGHTS 1. We present a new method, Alternation, to measure quality in land cover transitions over time.2. The methodology is applied to MapBiomas land cover maps of Brazil from 1985 to 2022.3. Land cover change is consistent across the full time series, but inconsistent at the annual scale.4. MapBiomas has 4.6 times more annual transitions than the reference data.
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spelling doaj-art-0cfd8dab5fa8446f909c3f88ef4333852025-08-25T11:28:53ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2528604Accounting for alternation in temporal quality analysis in MapBiomas BrazilAna Paula Matos0Maria Hunter1Robert Gilmore Pontius2Luis Rodrigo Baumann3Leandro Leal Parente4Laerte Guimarães Ferreira5Remote Sensing and GIS Lab (Lapig), Federal University of Goiás (UFG), Goiânia, BrazilRemote Sensing and GIS Lab (Lapig), Federal University of Goiás (UFG), Goiânia, BrazilSchool of Geography, Clark University, Worcester, MA, USAInstitute of Mathematics and Statistics (IME), Federal University of Goiás (UFG), Goiânia, BrazilRemote Sensing and GIS Lab (Lapig), Federal University of Goiás (UFG), Goiânia, BrazilRemote Sensing and GIS Lab (Lapig), Federal University of Goiás (UFG), Goiânia, BrazilLand use and land cover maps are an important resource for understanding interactions between humans and their environment across space and time with current mapping efforts often spanning upwards of 20 years. We present here a new method for a robust assessment of land cover transitions over time and apply this methodology to the yearly MapBiomas land use land cover maps of Brazil spanning 1985–2022. Based on a reference sample of 85,152 points, we find MapBiomas to have limited accuracy as an indicator of yearly land use change, but consistent over the full mapping period. Alternation, a newly defined error component, captures the number of land use transitions a location experiences throughout time. It is the primary reason for differences in estimates of annual change and is 4.6 times more frequent in the MapBiomas product than reference data. Differences in alternations are particularly prevalent in transitions from pasture to savanna and forest classes. The total land use changes detected over the 37 year study period are consistent between the reference data and the MapBiomas classification with 232 million hectares and 252 million hectares, or 27% and 29% of the Brazilian territory respectively.HIGHLIGHTS 1. We present a new method, Alternation, to measure quality in land cover transitions over time.2. The methodology is applied to MapBiomas land cover maps of Brazil from 1985 to 2022.3. Land cover change is consistent across the full time series, but inconsistent at the annual scale.4. MapBiomas has 4.6 times more annual transitions than the reference data.https://www.tandfonline.com/doi/10.1080/17538947.2025.2528604Brazildata qualityland use land covermapbiomastime seriesuncertainty
spellingShingle Ana Paula Matos
Maria Hunter
Robert Gilmore Pontius
Luis Rodrigo Baumann
Leandro Leal Parente
Laerte Guimarães Ferreira
Accounting for alternation in temporal quality analysis in MapBiomas Brazil
International Journal of Digital Earth
Brazil
data quality
land use land cover
mapbiomas
time series
uncertainty
title Accounting for alternation in temporal quality analysis in MapBiomas Brazil
title_full Accounting for alternation in temporal quality analysis in MapBiomas Brazil
title_fullStr Accounting for alternation in temporal quality analysis in MapBiomas Brazil
title_full_unstemmed Accounting for alternation in temporal quality analysis in MapBiomas Brazil
title_short Accounting for alternation in temporal quality analysis in MapBiomas Brazil
title_sort accounting for alternation in temporal quality analysis in mapbiomas brazil
topic Brazil
data quality
land use land cover
mapbiomas
time series
uncertainty
url https://www.tandfonline.com/doi/10.1080/17538947.2025.2528604
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