Applications of the BIOPHYS Algorithm for Physically-Based Retrieval of Biophysical, Structural and Forest Disturbance Information

Canopy reflectance model inversion using look-up table approaches provides powerful and flexible options for deriving improved forest biophysical structural information (BSI) compared with traditional statistical empirical methods. The BIOPHYS algorithm is an improved, physically-based inversion approach for deriving BSI for independent use and validation and for monitoring, inventory and quantifying forest disturbance as well as input to ecosystem, climate and carbon models. Based on the multiple-forward mode (MFM) inversion approach, BIOPHYS results were summarised from different studies (Minnesota/NASA COVER; Virginia/LEDAPS; Saskatchewan/BOREAS), sensors (airborne MMR; Landsat; MODIS) and models (GeoSail; GOMS). Applications output included forest density, height, crown dimension, branch and green leaf area, canopy cover, disturbance estimates based on multi-temporal chronosequences, and structural change following recovery from forest fires over the last century. Good correspondences with validation field data were obtained. Integrated analyses of multiple solar and view angle imagery further improved retrievals compared with single pass data. Quantifying ecosystem dynamics such as the area and percent of forest disturbance, early regrowth and succession provide essential inputs to process-driven models of carbon flux. BIOPHYS is well suited for large-area, multi-temporal applications involving multiple image sets and mosaics for assessing vegetation disturbance and quantifying biophysical structural dynamics and change. It is also suitable for integration with forest inventory, monitoring, updating, and other programs.

[1]  Joan E. Luther,et al.  Estimation of forest cover type and structure from Landsat TM imagery using a canopy reflectance model for biomass mapping in western Newfoundland , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[2]  Michael A. Wulder,et al.  Landsat continuity: Issues and opportunities for land cover monitoring , 2008 .

[3]  Rasim Latifovic,et al.  From need to product: a methodology for completing a land cover map of Canada with Landsat data , 2003 .

[4]  A. Strahler,et al.  Recent advances in geometrical optical modelling and its applications , 2000 .

[5]  Craig A. Coburn,et al.  SCS+C: a modified Sun-canopy-sensor topographic correction in forested terrain , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Shunlin Liang,et al.  Recent developments in estimating land surface biogeophysical variables from optical remote sensing , 2007 .

[7]  Derek R. Peddle,et al.  Forest structure without ground data: Adaptive Full-Blind Multiple Forward-Mode reflectance model inversion in a mountain pine beetle damaged forest , 2010 .

[8]  F. Hall,et al.  Physically based classification and satellite mapping of biophysical characteristics in the southern boreal forest , 1997 .

[9]  K. Huemmrich The GeoSail model: a simple addition to the SAIL model to describe discontinuous canopy reflectance , 2001 .

[10]  Scott J. Goetz,et al.  Biophysical, morphological, canopy optical property, and productivity data from the Superior National Forest , 1992 .

[11]  Craig A. Coburn,et al.  Canopy Reflectance Model Inversion in Multiple Forward Mode: Forest Structural Information Retrieval from Solution Set Distributions , 2009 .

[12]  Mahta Moghaddam,et al.  Remote sensing in BOREAS: Lessons learned , 2004 .

[13]  R. Myneni,et al.  Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .

[14]  Yuri Knyazikhin,et al.  Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .

[15]  Alan H. Strahler,et al.  Vegetation canopy reflectance modeling—recent developments and remote sensing perspectives∗ , 1997 .

[16]  Joanne C. White,et al.  Monitoring Canada’s forests. Part 1: Completion of the EOSD land cover project , 2008 .

[17]  Sylvain G. Leblanc,et al.  Physically based inversion modeling for unsupervised cluster labeling, independent forest classification, and LAI estimation using MFM-5-Scale , 2007 .

[18]  Karl Fred Huemmrich,et al.  Remote Sensing of Forest Biophysical Structure Using Mixture Decomposition and Geometric Reflectance Models , 1995 .

[19]  Alexei Lyapustin,et al.  Green's function method in the radiative transfer problem. II. Spatially heterogeneous anisotropic surface. , 2002, Applied optics.

[20]  Craig A. Coburn,et al.  Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain , 2010 .

[21]  Alan H. Strahler,et al.  Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing , 1992, IEEE Trans. Geosci. Remote. Sens..

[22]  Michael A. Wulder,et al.  Structural change detection in a disturbed conifer forest using a geometric optical reflectance model in multiple-forward mode , 2003, IEEE Trans. Geosci. Remote. Sens..

[23]  Michael A. Wulder,et al.  Radiometric Image Processing , 2003 .

[24]  Christopher O. Justice,et al.  Special issue on the moderate resolution imaging spectroradiometer (MODIS): a new generation of land surface monitoring , 2002 .

[25]  Jeffrey G. Masek,et al.  Estimating forest carbon fluxes in a disturbed southeastern landscape: Integration of remote sensing, forest inventory, and biogeochemical modeling , 2006 .

[26]  R. Latifovic,et al.  Large area forest classification and biophysical parameter estimation using the 5-Scale canopy reflectance model in Multiple-Forward-Mode , 2004 .

[27]  F. Hall,et al.  Improved topographic correction of forest image data using a 3‐D canopy reflectance model in multiple forward mode , 2008 .

[28]  Christopher B. Field,et al.  Initiative to quantify terrestrial carbon sources and sinks , 2002 .

[29]  J. Privette,et al.  Inversion methods for physically‐based models , 2000 .

[30]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[31]  Shunlin Liang,et al.  Advances in Land Remote Sensing , 2008 .

[32]  C. Justice,et al.  An evaluation of the global 1-km AVHRR land dataset , 2000 .

[33]  W. Cohen,et al.  North American forest disturbance mapped from a decadal Landsat record , 2008 .