Abstract
To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR) images, which relies on a geometric artifact that appears when deforestation happens, in the form of a shadow at the border of the deforested patch. The conditions for the appearance of these shadows are analyzed, as well as the methods that can be employed to exploit them to detect deforestation. The approach involves two steps: (1) detection of new shadows; (2) reconstruction of the deforested patch around the shadows. The launch of Sentinel-1 in 2014 has opened up opportunities for a potential exploitation of this approach in large-scale applications. A deforestation detection method based on this approach was tested in a 600,000 ha site in Peru. A detection rate of more than 95% is obtained for samples larger than 0.4 ha, and the method was found to perform better than the optical-based UMD-GLAD Forest Alert dataset both in terms of spatial and temporal detection. Further work needed to exploit this approach at operational levels is discussed.
Highlights
At present, forest destruction is a major source of carbon emissions and a primary cause of biodiversity loss [1]
We introduce a new, more reliable indicator of deforestation based on Sentinel-1 time series, and we demonstrate how this indicator can be used for near-real time (NRT) deforestation mapping over a test site in Peru
For the S1-only approach, we considered only detections occurring effectively between the dates of the two Sentinel-2 images used for the sample extractions (10 March to 16 October 2016), while for the University of Maryland (UMD)-Global Land Analysis and Discovery (GLAD) dataset, we considered all the detections occurring in 2016, because of the lower observation rate linked to the poor availability of cloud-free Landsat observations
Summary
Forest destruction is a major source of carbon emissions and a primary cause of biodiversity loss [1]. Deforestation and forest degradation are estimated to account for approximately 20% of anthropogenic CO2 emissions [2], though the rate of net forest loss has been reported to have halved from 7.3 Mha·year−1 in the 1990s to 3.3 Mha·year−1 between 2010 and 2015 [3]. The call to reduce uncertainties in estimating changes in forest cover is driven by the reporting needs outlined in the Reducing Emissions from Deforestation and forest Degradation (REDD+) program. A major limitation for optical-based NRT applications is the presence of haze in the dry season (caused by fire) and, more importantly, of clouds in the wet season [7,8,9,10].
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