Vergara-AsenjoSharmaPotvin2015
Référence
Vergara-Asenjo, G., Sharma, D., Potvin, C. (2015) Engaging Stakeholders: Assessing Accuracy of Participatory Mapping of Land Cover in Panama. Conservation Letters, 8(6):432-439. (Scopus 
)
Résumé
Full and effective participation of indigenous peoples and local communities, and high accuracy estimates are two current requirements for the purposes of monitoring forests at international level. We produced two land cover maps, both of which were based on digital image processing (decision trees) using Rapideye imagery, and a land cover participatory map, for indigenous territories of eastern Panama. Accuracy of the three maps was evaluated using field data. Classification that was based on participatory mapping gave best overall accuracy of 83.7% (κ = 0.783), followed by the decision tree that included textural variables (DT2: overall accuracy of 79.9%, κ = 0.757). We have demonstrated for the first time that local knowledge can improve land cover classification and facilitate the identification of forest degradation. The plea of the UNFCC for the full and effective participation of local and indigenous people could, therefore, improve the accuracy of monitoring. Copyright © 2015 Wiley Periodicals, Inc.
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@ARTICLE { Vergara-AsenjoSharmaPotvin2015,
AUTHOR = { Vergara-Asenjo, G. and Sharma, D. and Potvin, C. },
TITLE = { Engaging Stakeholders: Assessing Accuracy of Participatory Mapping of Land Cover in Panama },
JOURNAL = { Conservation Letters },
YEAR = { 2015 },
VOLUME = { 8 },
PAGES = { 432-439 },
NUMBER = { 6 },
NOTE = { cited By 2 },
ABSTRACT = { Full and effective participation of indigenous peoples and local communities, and high accuracy estimates are two current requirements for the purposes of monitoring forests at international level. We produced two land cover maps, both of which were based on digital image processing (decision trees) using Rapideye imagery, and a land cover participatory map, for indigenous territories of eastern Panama. Accuracy of the three maps was evaluated using field data. Classification that was based on participatory mapping gave best overall accuracy of 83.7% (κ = 0.783), followed by the decision tree that included textural variables (DT2: overall accuracy of 79.9%, κ = 0.757). We have demonstrated for the first time that local knowledge can improve land cover classification and facilitate the identification of forest degradation. The plea of the UNFCC for the full and effective participation of local and indigenous people could, therefore, improve the accuracy of monitoring. Copyright © 2015 Wiley Periodicals, Inc. },
AUTHOR_KEYWORDS = { Accuracy; Decision trees; Indigenous peoples; Land cover; Participatory mapping },
DOCUMENT_TYPE = { Article },
DOI = { 10.1111/conl.12161 },
SOURCE = { Scopus },
URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84955411710&partnerID=40&md5=d5ba7443b4b68b01d121bb2b052f542b },
}
