Abstract
The use of Sentinel-1 (S1) radar for wide-area, near-real-time (NRT) tropical-forest-change monitoring is discussed, with particular attention to forest degradation and deforestation. Since forest change can relate to processes ranging from high-impact, large-scale conversion to low-impact, selective logging, and can occur in sites having variable topographic and environmental properties such as mountain slopes and wetlands, a single approach is insufficient. The system introduced here combines time-series analysis of small objects identified in S1 data, i.e., segments containing linear features and apparent small-scale disturbances. A physical model is introduced for quantifying the size of small (upper-) canopy gaps. Deforestation detection was evaluated for several forest landscapes in the Amazon and Borneo. Using the default system settings, the false alarm rate (FAR) is very low (less than 1%), and the missed detection rate (MDR) varies between 1.9% ± 1.1% and 18.6% ± 1.0% (90% confidence level). For peatland landscapes, short radar detection delays up to several weeks due to high levels of soil moisture may occur, while, in comparison, for optical systems, detection delays up to 10 months were found due to cloud cover. In peat swamp forests, narrow linear canopy gaps (road and canal systems) could be detected with an overall accuracy of 85.5%, including many gaps barely visible on hi-res SPOT-6/7 images, which were used for validation. Compared to optical data, subtle degradation signals are easier to detect and are not quickly lost over time due to fast re-vegetation. Although it is possible to estimate an effective forest-cover loss, for example, due to selective logging, and results are spatiotemporally consistent with Sentinel-2 and TerraSAR-X reference data, quantitative validation without extensive field data and/or large hi-res radar datasets, such as TerraSAR-X, remains a challenge.
Highlights
IntroductionThe average annual net forest/non-forest conversion loss was estimated at 4.74 million ha [1]
A recent study of the Amazon region showed that losses in carbon were almost evenly split between cases attributable to forest conversion and cases due to forest degradation and disturbance
The objective of this paper is to discuss the suitability of Sentinel-1 radar for wide-area, NRT monitoring of tropical forest change in terms of deforestation and degradation, for a variety of landscapes in Indonesia and the Amazon, including tropical peat swamp forest landscapes
Summary
The average annual net forest/non-forest conversion loss was estimated at 4.74 million ha [1]. Less well-known, but of equal concern, are losses of peat underneath tropical peat swamp forests. In degraded peat swamp forests in Indonesia, on average, approximately 0.4 GtC is lost annually because of oxidization and fires [6]. Forest degradation, and peat degradation are important components of carbon accounting. Cloud cover, which can be very persistent in tropical rainforests, may pose problems when continuous timelines and timeliness of information is important. Radar data, such as Sentinel-1 data, offers an alternative. This may not be the only advantage, as is explored later in this paper
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