Abstract
Synthetic Aperture Radar has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths, such as L-band, are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is much interest due to its high temporal resolution (five days) and “free, full, and open” data policy. We tested its ability to distinguish forest from non-forest in six study sites, located in Alaska, Colombia, Finland, Florida, Indonesia, and the UK. Using the time series for a full year significantly increases the classification accuracy compared to a single scene (a mean of 85 % compared to 77 % across the study sites for the best classifier). Our results show that we can further improve the mean accuracy to 87 % when only considering the annual mean and standard deviation of co-polarized (VV) and cross-polarized (VH) backscatter. In this case, separation accuracies of up to 93 % (in Finland) are possible, though in the worst case (Alaska), the highest possible accuracy using these variables was 80 % . The best overall performance was observed when using a Support Vector Machine classifier, outperforming random forest, k-Nearest-Neighbors, and Quadratic Discriminant Analysis. We further show that the small information content we found in the phase data is an artifact of terrain slope orientation and has a negligible impact on classifier performance. We conclude that for the purposes of forest mapping the smaller file size and easier to process GRD products are sufficient, unless the SLC products are used to compute the temporal coherence which was not tested in this study.
Highlights
Forests play a major role in our fragile ecosystem
Contrary to what has been found by Quegan et al [13], the histograms of the temporal standard deviation of VV and VH backscatter seem to suggest that forested areas have a larger rather than smaller temporal variability compared to non-forested areas
In our study of six partially forested areas in Alaska, Colombia, Finland, Florida, Indonesia, and the UK, we looked at the separation between forest and non-forest pixels for different feature sets derived from Sentinel-1 Single Look Complex (SLC) data
Summary
Forests play a major role in our fragile ecosystem. When planted they act as a carbon sink, but when cut down or burnt they act as a carbon source. In light of the universal awareness of the reality and consequences of global climate change, significant international efforts are being undertaken to preserve forests and reforest areas that have experienced large scale deforestation. Examples of these undertakings include the Bonn Challenge, the New York Declaration on Forests, and the Paris Agreement. All countries require good maps of their changes in forest areas as part of their reporting on changes in all carbon pools, related to their territory-wide reporting to the UNFCCC on carbon sinks and sources and how these compare to their commitments under the Paris Agreement
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