19 January 2016 Markov chains–cellular automata modeling and multicriteria analysis of land cover change in the Lower Nhecolândia subregion of the Brazilian Pantanal wetland
Vitor Matheus Bacani, Arnaldo Yoso Sakamoto, Hervé Quénol, Clémence Vannier, Samuel Corgne
Author Affiliations +
Abstract
The dynamics of land use/land cover change in the Lower Nhecolândia wetland are marked by deforestation for pasture expansion, resulting in a real threat to the ecological stability. The aim of our work was to analyze the spatial distribution of land cover changes in the Lower Nhecolândia from 1985 to 2013 and to predict changes in trends for 2040. The mapping of land cover changes was developed using Landsat satellite images of 1985, 1999, 2007, and 2013, based on geographic object-based image analysis approach. This study uses integrated Markov chains and cellular automata modeling and multicriteria evaluation techniques to produce transition probability maps and describe the trajectory analysis methodology to construct a continuity of spatial and temporal changes for the wetland. The results of the multitemporal change detection classification show that, from 1985 to 2013, the forest woodland decreased by 6.89% and the grassland class increased by 18.29%. On the other hand, all water bodies showed a reducing trend, while the bare soil class increased compared to 1985, but did not present a regular trend of increase or decrease. From the present day, the trend for the future is a reduction of almost 6.4% by 2040. We found that deforestation actions will be concentrated in the areas with the highest concentration of saline lakes, constituting a serious threat to the natural functioning of this environmental system.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2016/$25.00 © 2016 SPIE
Vitor Matheus Bacani, Arnaldo Yoso Sakamoto, Hervé Quénol, Clémence Vannier, and Samuel Corgne "Markov chains–cellular automata modeling and multicriteria analysis of land cover change in the Lower Nhecolândia subregion of the Brazilian Pantanal wetland," Journal of Applied Remote Sensing 10(1), 016004 (19 January 2016). https://doi.org/10.1117/1.JRS.10.016004
Published: 19 January 2016
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CITATIONS
Cited by 13 scholarly publications.
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KEYWORDS
Image classification

Data modeling

Earth observing sensors

Climate change

Process modeling

Landsat

Vegetation

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