Abstract
ABSTRACT To combat global deforestation, monitoring forest disturbances at sub-annual scales is a key challenge. For this purpose, the new Planetscope nano-satellite constellation is a game changer, with a revisit time of 1 day and a pixel size of 3-m. We present a near-real time forest disturbance alert system based on PlanetScope imagery: the Thresholding Rewards and Penances algorithm (TRP). It produces a new forest change map as soon as a new PlanetScope image is acquired. To calibrate and validate TRP, a reference set was constructed as a complete census of five randomly selected study areas in Tuscany, Italy. We processed 572 PlanetScope images acquired between 1 May 2018 and 5 July 2019. TRP was used to construct forest change maps during the study period for which the final user’s accuracy was 86% and the final producer’s accuracy was 92%. In addition, we estimated the forest change area using an unbiased stratified estimator that can be used with a small sample of reference data. The 95% confidence interval for the sample-based estimate of 56.89 ha included the census-based area estimate of 56.19 ha.
Highlights
Monitoring forest changes and areas of changes at sub-annual scales using satellite imagery is an increas ingly important part of initiatives aimed at reducing global deforestation, primarily because sub-annual results can support a fast response to illegal deforesta tion (Hamunyela et al, 2016)
We present a new Thresholding Rewards and Penances Thresholding Rewards and Penances algorithm (TRP) algorithm using PlanetScope imagery for near-real time forest change detection
The revisit time is strictly related to cloudiness and the number of in-orbit PlanetScope satellites
Summary
Monitoring forest changes and areas of changes at sub-annual scales using satellite imagery is an increas ingly important part of initiatives aimed at reducing global deforestation, primarily because sub-annual results can support a fast response to illegal deforesta tion (Hamunyela et al, 2016). Landsat time series analyzed with spectral trajectory systems were used to map yearly clearcuts in several regions including western Oregon (Schroeder et al, 2007) Canada (Hermosilla et al, 2015), Finland (White et al, 2018) and Italy (Giannetti et al, 2020). Another example consists in the Global Forest Change map GFC (Hansen et al, 2013). It was obtained using a machine learning approach in order to map tree cover extent, loss, and gain at the global scale but only on an annual basis
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