Classification; Land cover transition; Landsat; Mass movement; Remote sensing; Time series analysis; Geography, Planning and Development; Computers in Earth Sciences
Abstract :
[en] Land use and land cover (LULC) analysis provides valuable information to understand environmental changes and their effects on landslide occurrence. However, LULC time series can be affected by errors in classifications that lead to invalid transitions and, therefore, to misinterpretations. One solution is to include temporal approaches that reduce the effects of invalid transitions. Here, we aimed to evaluate how such methods can improve the LULC analysis for a landslide-affected area. For that, we integrated the Random Forest (RF) class likelihoods with the temporal approach provided by the Compound Maximum a Posteriori (CMAP) algorithm, named here as RF-CMAP. Results from RF-CMAP were compared to those obtained from the traditional RF in a post-classification comparison approach. Although both methods presented high performance, with overall accuracy (OA) values greater than 0.87, RF-CMAP reached higher OA than RF for all the analysed years and corrected 99.92 km2 (12% of the total area) of invalid transitions presented by the traditional RF. Furthermore, RF-CMAP was capable of correctly classifying more areas than RF in landslides (e.g., 66% and 21% for RF-CMAP and RF in 2000, respectively). Finally, this study contributes to exploring the integration between RF and CMAP algorithms to avoid invalid transitions and to assess how the existence of LULC invalid transitions can impact subsequent analyses.
Maciel, Daniel Andrade; Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São Paulo, Brazil
Reis, Mariane Souza ; Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São Paulo, Brazil
Rennó, Camilo Daleles ; Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São Paulo, Brazil
Dutra, Luciano Vieira ; Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São Paulo, Brazil
Andrades-Filho, Clódis de Oliveira ; Department of Geodesy, Federal University of Rio Grande Do Sul (UFRGS), Porto Alegre, Brazil ; Postgraduate Program in Remote Sensing, Federal University of Rio Grande Do Sul (UFRGS), Porto Alegre, Brazil
Velástegui-Montoya, Andrés ; Centro de Investigación y Proyectos Aplicados a las Ciencias de la Tierra (CIPAT), ESPOL Polytechnic University, Guayaquil, Ecuador ; Faculty of Engineering in Earth Sciences FICT, ESPOL Polytechnic University, Guayaquil, Ecuador
Zhang, Tingyu; Key Laboratory of Degraded and Unused Land Consolidation Engineering, the Ministry of Natural Resources, Xi'an, China ; Institute of Land Engineering and Technology, Shaanxi Provincial Land Engineering Construction Group Co., Ltd, Xi'an, China
Körting, Thales Sehn; Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São Paulo, Brazil
Anderson, Liana Oighenstein ; National Center for Monitoring and Early Warning of Natural Disaster, São José dos Campos, Brazil
Language :
English
Title :
Land use and land cover changes without invalid transitions: A case study in a landslide-affected area
Publication date :
2024
Journal title :
Remote Sensing Applications: Society and Environment
eISSN :
2352-9385
Publisher :
Elsevier
Volume :
36
Pages :
101314
Peer reviewed :
Peer Reviewed verified by ORBi
Funding text :
This research was financed in part by the Coordena\u00E7\u00E3o de Aperfei\u00E7oamento de Pessoal de N\u00EDvel Superior - Brasil (CAPES), Brazil - Finance Code 001 and in part by the National Council of Technological and Scientific Development (CNPq), Brazil project number 422354/2023-6 (Monitoring and Alerts of Land Cover Changes in Brazilian Biomes - Training and Semi-Automation of the BiomasBR Program), supported by the National Institute for Space Research (INPE), Brazil. This research was partly financed by the ESPOL Polytechnic University, Ecuador project \u201CIPUS: Identification and Prediction of Urban Sprawl\u201D, with code FICT-8-2023. LOA acknowledge the National Council for Scientific and Technological Development (CNPq), process number 314473/2020-3. The authors thank the S\u00E3o Paulo Research Foundation (FAPESP), Brazil grant no 2023/09118-6, and the Brazilian National Council for Scientific and Technological Development (CNPq, grant no 302205/2023-3).This research was financed in part by the Coordena\u00E7\u00E3o de Aperfei\u00E7oamento de Pessoal de N\u00EDvel Superior - Brasil (CAPES) - Finance Code 001 and in part by the National Council of Technological and Scientific Development (CNPq) project number 422354/2023-6 (Monitoring and Alerts of Land Cover Changes in Brazilian Biomes - Training and Semi-Automation of the BiomasBR Program), supported by the National Institute for Space Research (INPE). This research was partly financed by the ESPOL Polytechnic University project \u201CIPUS: Identification and Prediction of Urban Sprawl\u201D, with code FICT-8-2023. LOA acknowledge the National Council for Scientific and Technological Development (CNPq), process number 314473/2020-3. The authors thank the S\u00E3o Paulo Research Foundation (FAPESP, grant no 2023/09118-6), and the Brazilian National Council for Scientific and Technological Development (CNPq, grant no 302205/2023-3).
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