Abstract
This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.
Acknowledgements
This work forms part of the project ‘Reinforcing REDD+ readiness in Mexico and enabling South-South cooperation’, in which Centro de Investigaciones en Geografia Ambiental and Centro de Investigaciones en Ecosistemas at Universidad Nacional Autonoma de Mexico are collaborating with the Mexican forest authority (CONAFOR). The authors would like to thank MSc. Ignacio Paniagua for the land cover data for the Monarch Butterfly Biosphere Reserve.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1. In the context of UNFCCC climate mitigation policy, deforestation refers to a permanent change in land cover from forest to non-forest while degradation, although not formally defined yet, refers to reduced biomass levels (and thus carbon stocks) in forests that remain forests. The definition of forest rests on three parameters, for which countries may select threshold values within the following ranges: canopy cover (10–30%), height of trees at maturity (2–5 m) and area (0.1–1 ha).
2. Based on the data sets available from http://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.1.html.