Building a Forward-Mode Three-Dimensional Reflectance Model for Topographic Normalization of High-Resolution (1–5 m) Imagery: Validation Phase in a Forested Environment

Abstract

The aim of the topographic normalization of remotely sensed imagery (TNRSI) is to reduce reflectance variability caused by steep terrain and, subsequently, to improve land-cover classification. Recently, multiple-forward-mode (FM) (MFM) reflectance models for topographic normalizations of medium-resolution (20-30 m) satellite imagery have improved the classification of forested covers with respect to more conventional topographic corrections. We propose an FM 3-D reflectance (FM3DR) model, based on the Discrete Anisotropic Radiative Transfer simulator, for the topographic normalization of high-resolution (1-5 m) imagery. The feasibility of this approach was first verified on real IKONOS imagery for three forest types within major biomes (oak, pine, and high tropical forest) in Mexico. Next, we formalized the topographic normalization performance index and variability as relevant criteria to test TNRSI across incident angles in terms of maximum likelihood classification effectiveness. The FM3DR model outperformed five previously published topographic corrections (cosine, Minnaert, sun-canopy-sensor (SCS), Civco two-stage, and slope matching corrections), and image-based statistical strategies (Civco two-stage and slope matching corrections) tended to perform better than more analytical strategies (cosine, Minnaert, and SCS corrections). An asset of this approach versus former models is the realistic account of terrain-related variation of understory and crown cover within a cover type. On top of that, once validated across forest types, the model is sufficient for the application of a full MFM 3-D reflectance-based topographic normalization without additional field measurement.