Forest structure and aboveground biomass in the southwestern United States from MODIS and MISR

Red band bidirectional reflectance factor data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) acquired over the southwestern United States were interpreted through a simple geometric–optical (GO) canopy reflectance model to provide maps of fractional crown cover (dimensionless), mean canopy height (m), and aboveground woody biomass (Mg ha−1) on a 250 m grid. Model adjustment was performed after dynamic injection of a background contribution predicted via the kernel weights of a bidirectional reflectance distribution function (BRDF) model. Accuracy was assessed with respect to similar maps obtained with data from the NASA Multiangle Imaging Spectroradiometer (MISR) and to contemporaneous US Forest Service (USFS) maps based partly on Forest Inventory and Analysis (FIA) data. MODIS and MISR retrievals of forest fractional cover and mean height both showed compatibility with the USFS maps, with MODIS mean absolute errors (MAE) of 0.09 and 8.4 m respectively, compared with MISR MAE of 0.10 and 2.2 m, respectively. The respective MAE for aboveground woody biomass was ~10 Mg ha−1, the same as that from MISR, although the MODIS retrievals showed a much weaker correlation, noting that these statistics do not represent evaluation with respect to ground survey data. Good height retrieval accuracies with respect to averages from high resolution discrete return lidar data and matches between mean crown aspect ratio and mean crown radius maps and known vegetation type distributions both support the contention that the GO model results are not spurious when adjusted against MISR bidirectional reflectance factor data. These results highlight an alternative to empirical methods for the exploitation of moderate resolution remote sensing data in the mapping of woody plant canopies and assessment of woody biomass loss and recovery from disturbance in the southwestern United States and in parts of the world where similar environmental conditions prevail

Canopy reflectance modeling of forest stand volume

xiii, 143 leaves : ill. (some col.) ; 29 cm.Three-dimensional canopy relectance models provide a physical-structural basis to satellite image analysis, representing a potentially more robust, objective and accurate approach for obtaining forest cover type and structural information with minimal ground truth data. The Geometric Optical Mutual Shadowing (GOMS) canopy relectance model was run in multiple-forward-mode (MFM) using digital multispectral IKONOS satellite imagery to estimate tree height and stand volume over 100m2 homogeneous forest plots in mountainous terrain, Kananaskis, Alberta. Height was computed within 2.7m for trembling aspen and 1.8m fr lodgepole pine, with basal area estimated within 0.05m2. Stand volume, estimated as the product of mean tree height and basal area, had an absolute mean difference from field measurements of 0.85m3/100m2 and 0.61m3/100m2 for aspen and pine, respectively

Remote sensing of montane forest structure and biomass : a canopy relectance model inversion approach

xvi, 156 leaves : ill. (some col.), maps ; 29 cm.The multiple-forward-mode (MFM) inversion procedure is a set of methods for indirect canopy relectance model inversion using look-up tables (LUT). This thesis refines the MFM technique with regard to: 1) model parameterization for the MFM canopy reflectance model executions and 2) methods for limiting or describing multiple solutions. Forest stand structure estimates from the inversion were evaluated using 40 field validation sites in the Canadian Rocky Mountains. Estimates of horizontal and vertical crown radius were within 0.5m and 0.9m RMSE for both conifer and deciduous species. Density estimates were within 590 stems/ha RMSE for conifer and 310 stems/ha RMSE for deciduous. The most effective inversion method used a variable spectral domain with constrained, fine increment LUTs. A biomass estimation method was also developed using empirical relationships with crown area. Biomass density estimates using the MFM method were similar to estimates produced using other multispectral analysis methods (RMSE=50t/ha)

Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes

International audienceSatellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals

Spectrodirectional investigation of a geometric-optical canopy reflectance model by laboratory simulation

Canopy reflectance models (CRMs) can accurately estimate vegetation canopy biophysical-structural information such as Leaf Area Index (LAI) inexpensively using satellite imagery. The strict physical basis which geometric-optical CRMs employ to mathematically link canopy bidirectional reflectance and structure allows for the tangible replication of a CRM's geometric abstraction of a canopy in the laboratory, enabling robust CRM validation studies. To this end, the ULGS-2 goniometer was used to obtain multiangle, hyperspectral (Spectrodirectional) measurements of a specially-designed tangible physical model forest, developed based upon the Geometric-Optical Mutual Shadowing (GOMS) CRM, at three different canopy cover densities. GOMS forwardmodelled reflectance values had high levels of agreement with ULGS-2 measurements, with obtained reflectance RMSE values ranging from 0.03% to 0.1%. Canopy structure modelled via GOMS Multiple-Forward-Mode (MFM) inversion had varying levels of success. The methods developed in this thesis can potentially be extended to more complex CRMs through the implementation of 3D printing

An unsupervised classification-based time series change detection approach for mapping forest disturbance

Unsupervised Classification to Change (UC-Change) is a new remote sensing approach for mapping areas affected by logging and wildfires. It addresses the main limitations of existing image time-series change detection techniques, such as limited multi-sensor capabilities, use of purely spectral-based forest recovery metrics, and poor detection of salvage harvesting. UC Change detects disturbances and tracks forest recovery by analyzing changes in the spatial distribution of spectral classes over time. The algorithm detected approximately 85% and 70% of reference cutblock and fire scar pixels at a ±2-year temporal agreement, respectively, consistently outperforming existing algorithms across different biogeoclimatic zones of British Columbia, Canada. The results indicate an upper estimate of 7.5 million ha of forest cleared between 1984 and 2014, which is above estimates based on existing maps and databases (6.3 – 6.7 million ha). Also presented is a new framework for using open-access data for validation of change detection results.Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) grant entitled Advanced Methods, Education and Training in Hyperspectral Science and Technology (AMETHYST). Financial code: KS-NSERC2 Staenz 40307-4185-800