An Error Model for Mapping Forest Cover and Forest Cover Change Using L-Band SAR

Abstract

We present an error model for forest cover mapping and change detection with L-band synthetic aperture radar (SAR), which considers measurement noise, forest height, number of images available, and imaging conditions. When applied to a multiseasonal set of Advanced Land Observing Satellite Phased-Array type L-band SAR images acquired over a forest site in southern Sweden, the error model, which is founded on a semiempirical model, suggests that a bitemporal set of cross-polarized L-band backscatter observations is sufficient to detect a forest cover loss of 50% at hectare scale for mature forests. The error probability increases when using co-polarization images, images acquired under adverse imaging conditions, or when detecting forest cover change in a forest of low height. The availability of multitemporal L-band observations is expected to improve forest cover retrieval and change detection, albeit highly correlated forest cover retrieval errors between images acquired within narrow time intervals (e.g., months) pose a limit on the improvements that can be achieved.