An Algorithm for Maximum Concordance to Harmonize Legends of Land Use and Land Cover Maps

Land use and land cover maps (LULC) are abstractions of the physical space of a chosen region. Comparison of LULC maps is essential to understand landscape dynamics, alteration patterns, and environmental implications. This article has the objective of propose an algorithm for harmonizing LULC maps based on the spatial distribution of their classes and applies it in a case study to harmonize the maps of Brazil’s National Inventory of Anthropogenic Emissions by Sources and Removals of Greenhouse Gases (Fourth Version) and MapBiomas (Collection 7) based on their spatial distribution of LULC classes. The purpose of this paper is to compute the agreement between two initiatives. Furthermore, the results highlight the classes and areas of potential inconsistency or ambiguity, allowing to identify and correct discrepancies, proposing a harmonized legend between then. At the national level, we reached maximum agreement 81% between the two maps. Of the 44 equivalences, the algorithm accurately recognized 36 of the connections between the classes. At the biome level, the algorithm achieved its highest concordance within the Amazonia biome, surpassing Brazil’s level by 11%, mainly due to the size and homogeneity of the forest classes. In biomes with a predominance of nonforest vegetation, an increased confusion was observed among the classes ‘Grassland’, ‘Pasture’, and ‘Forest’ was observed between the maps, especially in Pampa and Caatinga.

Authors

Institutions

Publication Details

Journal
Revista Brasileira de Cartografia
Published
2025-03-28
DOI
https://doi.org/10.14393/rbcv76n0a-72784
Citations
1
Primary Topic
Data Management and Algorithms
Type
article
Field-Weighted Citation Impact
1.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An Algorithm for Maximum Concordance to Harmonize Legends of Land Use and Land Cover Maps

Aline C. Soterroni, Maria Isabel Sobral Escada, Pedro R. Andrade, Sabrina Guilherme Marques
1 citations
Revista Brasileira de Cartografia
Data Management and Algorithms
1.49
article

An Algorithm for Maximum Concordance to Harmonize Legends of Land Use and Land Cover Maps

Aline C. Soterroni, Maria Isabel Sobral Escada, Pedro R. Andrade, Sabrina Guilherme Marques
article en
1 citations

Abstract

Land use and land cover maps (LULC) are abstractions of the physical space of a chosen region. Comparison of LULC maps is essential to understand landscape dynamics, alteration patterns, and environmental implications. This article has the objective of propose an algorithm for harmonizing LULC maps based on the spatial distribution of their classes and applies it in a case study to harmonize the maps of Brazil’s National Inventory of Anthropogenic Emissions by Sources and Removals of Greenhouse Gases (Fourth Version) and MapBiomas (Collection 7) based on their spatial distribution of LULC classes. The purpose of this paper is to compute the agreement between two initiatives. Furthermore, the results highlight the classes and areas of potential inconsistency or ambiguity, allowing to identify and correct discrepancies, proposing a harmonized legend between then. At the national level, we reached maximum agreement 81% between the two maps. Of the 44 equivalences, the algorithm accurately recognized 36 of the connections between the classes. At the biome level, the algorithm achieved its highest concordance within the Amazonia biome, surpassing Brazil’s level by 11%, mainly due to the size and homogeneity of the forest classes. In biomes with a predominance of nonforest vegetation, an increased confusion was observed among the classes ‘Grassland’, ‘Pasture’, and ‘Forest’ was observed between the maps, especially in Pampa and Caatinga.

Revista Brasileira de CartografiaVol. 76
University of Oxford (GB), Instituto Nacional de Pesquisas Espaciais (BR)
Openalex Percentile: Top 21%
Data Management and Algorithms
1.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.