Journal article Open Access

Remote‐sensing image mining: detecting agents of land‐use change in tropical forest areas

Silva, Marcelino; Camara, Gilberto; Escada, Isabel; Souza, Ricardo


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    <subfield code="a">Landscape patterns, data mining, tropical deforestation, land use change</subfield>
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    <subfield code="a">Escada, Isabel</subfield>
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    <subfield code="u">INPE (National Institute for Space Research), Brazil</subfield>
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    <subfield code="c">4803-4822</subfield>
    <subfield code="n">16</subfield>
    <subfield code="p">International Journal of Remote Sensing</subfield>
    <subfield code="v">29</subfield>
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    <subfield code="u">UERN (Universidade do Estado do Rio Grande do Norte)</subfield>
    <subfield code="a">Silva, Marcelino</subfield>
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    <subfield code="a">Remote‐sensing image mining: detecting agents of land‐use change in tropical forest areas</subfield>
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    <subfield code="a">&lt;p&gt;Land remote-sensing images are the primary means of assessing land change. There have been major land changes in the planet in the last decades, especially in tropical forest areas. Identifying the agents of deforestation is important for establishing public policies that can help preserve the environment. This paper proposes a method for detecting the agents of land change in remote-sensing image databases. We associate each land-change pattern, detected in a remote sensing image, to one of the agents of change. The proposed method uses a decision-tree classifier to describe shapes found in land-use maps extracted from remote-sensing images and then associates these shape descriptions to the different types of social agents involved in land-use change. We support our proposal with two case studies for detecting land-change agents in Amazonia, using the remote-sensing image database of the Brazilian National Institute for Space Research (INPE).&lt;/p&gt;</subfield>
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