Abstract
Current estimates of CO2 emissions from forest degradation are generally based on insufficient information and are characterized by high uncertainty, while a global definition of ‘forest degradation’ is currently being discussed in the scientific arena. This study proposes an automated approach to monitor degradation using a Landsat time series. The methodology was developed using the Google Earth Engine (GEE) and applied in a pine forest area of the Dominican Republic. Land cover change mapping was conducted using the random forest (RF) algorithm and resulted in a cumulative overall accuracy of 92.8%. Forest degradation was mapped with a 70.7% user accuracy and a 91.3% producer accuracy. Estimates of the degraded area had a margin of error of 10.8%. A number of 344 Landsat collections, corresponding to the period from 1990 to 2018, were used in the analysis. Additionally, 51 sample plots from a forest inventory were used. The carbon stocks and emissions from forest degradation were estimated using the RF algorithm with an R2 of 0.78. GEE proved to be an appropriate tool to monitor the degradation of tropical forests, and the methodology developed herein is a robust, reliable, and replicable tool that could be used to estimate forest degradation and improve monitoring, reporting, and verification (MRV) systems under the reducing emissions from deforestation and forest degradation (REDD+) mechanism.
Highlights
Forest monitoring has been an important scientific objective mainly due to the large number of ecosystem services that serve humanity
The current study developed a method for automated forest degradation measuring and monitoring using field data from a National Forest Inventory and a time series of Landsat images using GGE
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Summary
Forest monitoring has been an important scientific objective mainly due to the large number of ecosystem services that serve humanity. The first step in measuring forest degradation is to define key concepts, such as (i) the forest and (ii) forest degradation. These concepts have been widely debated [5], and their definitions vary between institutions and organizations. The Intergovernmental Panel on Climate Change (IPCC) defines forest degradation as “direct human-induced long-term loss (persisting for X years or more) of at least Y% of forest carbon stocks [and forest values] since time T and not qualifying as deforestation or an elected activity under Article 3.4 of the Kyoto Protocol” [6]. Defining a carbon (C) stock baseline is the first step to monitor this continual C loss
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