Improved topographic correction of forest image data using a 3‐D canopy reflectance model in multiple forward mode

Abstract

In most forestry remote sensing applications in steep terrain, simple photometric and empirical (PE) topographic corrections are confounded as a result of stand structure and species assemblages that vary with terrain and the anisotropic reflective properties of vegetated surfaces. To address these problems, we present MFM‐TOPO as a new physically‐based modelling (PBM) approach for normalising topographically induced signal variance as a function of forest stand structure and sub‐pixel scale components. MFM‐TOPO uses the Li‐Strahler geometric optical mutual shadowing (GOMS) canopy reflectance model in Multiple Forward Mode (MFM) to account for slope and aspect influences directly. MFM‐TOPO has an explicit physical‐basis and uses sun‐canopy‐sensor (SCS) geometry that is more appropriate than strictly terrain‐based corrections in forested areas since it preserves the geotropic nature of trees (vertical growth with respect to the geoid) regardless of terrain, view and illumination angles. MFM‐TOPO is compared against our recently developed SCS+C correction and a comprehensive set of other existing PE and SCS methods (cosine, C correction, Minnaert, statistical‐empirical, SCS, and b correction) for removing topographically induced variance and for improving SPOT image classification accuracy in a Rocky Mountain forest in Kananaskis, Alberta Canada. MFM‐TOPO removed the most terrain‐based variance and provided the greatest improvement in classification accuracy within a species and stand density based class structure. For example, pine class accuracy was increased by 62% over shaded slopes, and spruce class accuracy was increased by 13% over more moderate slopes. In addition to classification, MFM‐TOPO is suitable for retrieving biophysical parameters in mountainous terrain.

Acknowledgements

This research was supported in part by grants to Dr. Peddle and collaboration from the Natural Sciences and Engineering Research Council of Canada (NSERC), Alberta Ingenuity Centre for Water Research (AICWR), Prairie Adaptation Research Collaborative (PARC), Water Institute for Semiarid Ecosystems (WISE), Natural Resources Canada, NASA Goddard Space Flight Centre/University of Maryland, Alberta Research Excellence Program, Miistakis Institute of the Rockies (DEM), Center for Remote Sensing, Boston University (GOMS model) and the University of Lethbridge. Computing resources were provided through the Western Canada Research Grid (WestGrid NETERA c3.ca). SPOT imagery was acquired from Iunctus Geomatics Corporation and the Alberta Terrestrial Imaging Centre (ATIC), both of Lethbridge Alberta. We are grateful to Sam Lieff, Adam Minke and Kristin Yaehne for field assistance and the staff at the Kananaskis Field Stations for logistical support in the field.