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Relevance of Global Forest Change Data Set to Local Conservation: Case Study of Forest Degradation in Masoala National Park, Madagascar
Author(s) -
Burivalova Zuzana,
Bauert Martin R.,
Hassold Sonja,
Fatroandrianjafijasolomiovazo Nandinanjakana T.,
Koh Lian Pin
Publication year - 2015
Publication title -
biotropica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.813
H-Index - 96
eISSN - 1744-7429
pISSN - 0006-3606
DOI - 10.1111/btp.12194
Subject(s) - deforestation (computer science) , logging , geography , national park , slash and burn , land cover , vegetation (pathology) , environmental resource management , scale (ratio) , agroforestry , environmental science , land use , agriculture , forestry , ecology , cartography , computer science , medicine , archaeology , pathology , biology , programming language
A global data set on forest cover change was recently published and made freely available for use (Hansen et al . 2013. Science 342: 850–853). Although this data set has been criticized for inaccuracies in distinguishing vegetation types at the local scale, it remains a valuable source of forest cover information for areas where local data is severely lacking. Masoala National Park, in northeastern Madagascar, is an example of a region for which very little spatially explicit forest cover information is available. Yet, this extremely diverse tropical humid forest is undergoing a dramatic rate of forest degradation and deforestation through illegal selective logging of rosewood and ebony, slash‐and‐burn agriculture, and damage due to cyclones. All of these processes result in relatively diffuse and small‐scale changes in forest cover. In this paper, we examine to what extent Hansen et al .'s global forest change data set captures forest loss within Masoala National Park by comparing its performance to a locally calibrated, object‐oriented classification approach. We verify both types of classification with substantial ground truthing. We find that both the global and local classifications perform reasonably well in detecting small‐scale slash‐and‐burn agriculture, but neither performs adequately in detecting selective logging. We conclude that since the use of the global forest change data set requires very little technical and financial investment, and performs almost as well as the more resource‐demanding, locally calibrated classification, it may be advantageous to use the global forest change data set even for local conservation purposes.