Derivation and assessment of forest-relevant polarimetric indices using RCM compact-pol data

Abstract

ABSTRACT Three radar polarimetric indices, Radar Vegetation Index (RVI), Canopy Structure Index (CSI) and Radar Forest Degradation Index (RFDI)—normally associated with quad-pol (QP) synthetic aperture radar (SAR) sensors—were derived using compact polarimetry (CP) data from the Radarsat Constellation Mission (RCM). Indices were generated over a 10,000-hectare temperate mixedwood forest containing a range of complex forest structures. For comparative purposes, the same indices were generated using Radarsat-2 QP data. Agreement between CP and QP indices were assessed across broad vegetated land cover types, at the forest stand-level wherein ground plots and airborne LiDAR data were available, and at the pixel level within validation stands representing different forest types. Agreement was consistently strong for the RVI and weak for the CSI, with agreement stronger when generalized to the stand level. Indices were more informative on differences between vegetation types (e.g. forest and open wetland) than between forest types with different structures. With a radar nominal off-nadir incidence angle at 38°, RCM CP indices had narrower dynamic ranges compared to Radarsat-2 QP indices, especially the CSI, contributing to a lower level agreement for the CSI. CP data enables derivation of RVI, CSI and RFDI indices simultaneously, and provides large spatial coverage and capacity for dense time-series collection, features which are unavailable from current QP sensors.