1 research outputs found
Predicting forest cover in distinct ecosystems: the potential of multi-source sentinel-1 and -2 data fusion
The fusion of microwave and optical data sets is expected to provide great potential for the
derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating
in twin mode, they can provide an unprecedented data source to build dense spatial and temporal
high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of
the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two
highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open
savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single
time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via
machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa
and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation
(CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of
87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave
infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed
Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic
aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover.
In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover
predictions in open savanna-like environments with heterogeneous regional features. The presented
approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high
spatial resolution