Abstract
The European Space Agency’s (ESA) “SAR for REDD” project aims to support complementing optical remote sensing capacities in Africa with synthetic aperture radar (SAR) for Reducing Emissions from Deforestation and Forest Degradation (REDD). The aim of this study is to assess and compare Sentinel-1 C-band, ALOS-2 PALSAR-2 L-band and combined C/L-band SAR-based land cover mapping over a large tropical area in the Democratic Republic of Congo (DRC). The overall approach is to benefit from multi-temporal observations acquired from 2015 to 2017 to extract statistical parameters and seasonality of backscatters to improve forest land cover (FLC) classification. We investigate whether and to what extent the denser time series of C- band SAR can compensate for the L-band’s deeper vegetation penetration depth and known better FLC mapping performance. The supervised classification differentiates into forest, inundated forest, woody savannah, dry and wet grassland, and river swamps. Several feature combinations of statistical parameters from both, single and multi-frequency observations in a multivariate maximum-likelihood classification are compared. The FLC maps are reclassified into forest, savannah, and grassland (FSG) and validated with a systematic sampling grid of manual interpretations of very-high-resolution optical satellite data. Using the temporal variability of the dual-polarized backscatters, in the form of either wet/dry seasonal averages or using the statistical variance, in addition to the average backscatter, increased the classification accuracies by 4–5 percent points and 1–2 percent points for C- and L-band, respectively. For the FSG validation overall accuracies of 84.4%, 89.1%, and 90.0% were achieved for single frequency C- and L-band, and C/L-band combined, respectively. The resulting forest/non-forest (FNF) maps with accuracies of 90.3%, 92.2%, and 93.3%, respectively, are then compared to the Landsat-based Global Forest Change program’s and JAXA’s ALOS-1/2 based global FNF maps.
Highlights
Tropical forest represents the most important above-ground carbon pool and plays a crucial role in biodiversity, hydrological and biochemical cycles, and socio-economics for local communities
C-band seems to better distinguish lower biomass land covers, such as savannah, wet, and dry grassland up to a certain biomass level, above which C-band will saturate and classify as forest. It seems that C-band distinguishes inundated forest from forest better than L-band does. This could be due to this specific type of inundated forest, which might be a much lower biomass forest than the surrounding tropical forest
The accuracy assessment shows that C, L, and C/L-band combined all achieved FNF map accuracies above 90% and higher accuracies than global forest maps from both Landsat [6] and ALOS-2 [32]
Summary
Tropical forest represents the most important above-ground carbon pool and plays a crucial role in biodiversity, hydrological and biochemical cycles, and socio-economics for local communities. Deforestation and forest degradation are estimated to account for up to 17% of the global anthropogenic greenhouse gas emissions [1]. The forest sector is an important part in climate policies [2] and the negotiations of the United Nations Framework Convention on Climate Change (UNFCCC) as stated in Article 5 of the Paris Agreement [3]. A necessity for the implementation of REDD+ is the development of consistent and accurate national forest monitoring systems (NFMS) for monitoring, reporting, and verification (MRV) based on both remote sensing for activity data and in situ measurements for emission factors [5]. The World Bank generally encourages countries to base their forest emission reporting on GFC data
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