Abstract:
Savanna ecosystems and their woody vegetation provide valuable resources and ecosystem
services. Locally calibrated and cost effective estimates of these resources are required in order
to satisfy commitments to monitor and manage change within them. Baseline maps of woody
resources are important for analyzing change over time. Freely available, and highly repetitive,
C-band data has the potential to be a viable alternative to high-resolution commercial SAR imagery
(e.g., RADARSAT-2, ALOS2) in generating large-scale woody resources maps. Using airborne LiDAR
as calibration, we investigated the relationships between hyper-temporal C-band ASAR data and
woody structural parameters, namely total canopy cover (TCC) and total canopy volume (TCV), in a
deciduous savanna environment. Results showed that: the temporal filter reduced image variance;
the random forest model out-performed the linear model; while the TCV metric consistently showed
marginally higher accuracies than the TCC metric. Combinations of between 6 and 10 images could
produce results comparable to high resolution commercial (C- & L-band) SAR imagery. The approach
showed promise for producing a regional scale, locally calibrated, baseline maps for the management
of deciduous savanna resources, and lay a foundation for monitoring using time series of data from
newer C-band SAR sensors (e.g., Sentinel1).
