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

Abstract

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).

Acknowledgements

We would like to thank the anonymous reviewers of this paper for their useful comments. Gilberto Camara's work is partially funded by CNPq (grants PQ—300557/19996‐5 and 550250/2005‐0) and FAPESP (grant 04/11012‐0). Marcelino Silva's work is supported by UERN and funded by CAPES. We thank Wilson Pagani from INCRA (Jaru) and Romain Taravella for the helpful information achieved during the fieldwork in Vale do Anari (Rondônia) and São Félix do Xingu (Pará) regions.