Abstract
We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed using an ensemble of Support Vector Machines (SVMs) trained on local radar backscatter features derived from superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Québec, Canada. We evaluate four spatial arrangements including (1) single pixels, (2) square grid cells, (3) superpixels based on segmentation of the radar images, and (4) superpixels derived from ancillary optical Landsat imagery. Detection of forest loss using superpixels outperforms single pixel and regular square grid cell approaches, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Québec forestry data and Hansen et al. forest loss products. Our results indicate that this approach can be applied to monitor forest loss across large study areas using L-band radar instruments such as ALOS/PALSAR, particularly when combined with superpixels generated from ancillary optical data.
Highlights
Accurate forest accounting is important for tracking the global carbon stock and ecological modeling
L-band synthetic aperture radar (SAR) images are not affected by clouds and aerosols, so SAR image stacks may be used for long-term forest studies in regions that are difficult to monitor at a high temporal resolution using optical sensors alone
We do not compare our methodology to techniques of more general change detection in part because we focus on a specific aspect of change detection in SAR images and our validation data is less reliable on the full ALOS/PALSAR time series
Summary
Accurate forest accounting is important for tracking the global carbon stock and ecological modeling. We do not compare our methodology to techniques of more general change detection in part because we focus on a specific aspect of change detection in SAR images (i.e., forest loss) and our validation data is less reliable on the full ALOS/PALSAR time series. We have validation data from the Quebec government [38] and the Hansen et al forest loss products, such data does not precisely align with our ALOS/PALSAR time-series in that a change in a particular year may have occurred before or after a particular image was retrieved. We apply our method to ALOS-1 and ALOS-2 time-series demonstrating the benefit of superpixels over pixels and square grid cells using Quebec forestry data [38] and Hansen et al forest loss products [35]. (a) Laurentides Wildlife Reserve (b) Training site (c) Detailed area (d) Superpixels populated with means (e) Square grid cells populated with means
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