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Benson_WE3T051.pdf Benson_WE3T051.pdf Presentation Transcript

  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and Conclusions Forest Structure Estimation in the Canadian Boreal forestMichael L. Benson Leland E.Pierce Kathleen M. Bergen Kamal Sarabandi Kailai Zhang Caitlin E. RyanThe University of Michigan, Radiation Lab & School of Natural Resources and the Environment Ann Arbor, MI 48109-2122 USA Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and Conclusions Goal: Accurate estimation of Forest Structure parameters using measured SAR, LIDAR, and Optical data.Motivation: Forest Structure is important ecologically for global climate estimation as well as biodiversity and other topics. This Talk: Use a set of simulators for each sensing modality as well as real remotely sensed data and presents an inversion algorithm capable of accurate forest parameter retrieval requiring a minimal amount of ancillary / ground truth data. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsOutline 1. Introduction 2. Background 3. Approach 4. Forward Models & Database Generation Forest Geometrical Model Optical Model SAR Model LIDAR Model 5. Application to BOREAS Remotely Sensed Data sets 6. Classification Algorithm 7. Results 8. Conclusions Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsIntroduction One possible mode of operation for DesDyni is to use LIDAR shots in a region in combination with the contiguous maps produced by SAR to better estimate forest structures everywhere. This talk explores one way of classifying the a large observation area and determining underlying forest height and biomass characteristics from areas where both SAR and LIDAR are available to areas where only SAR is available. We’ve previously presented results from our proof of concept (IGARSS ’09) using only simulated data and a small sample of real data (IGARSS ’ 10) we now present a working novel multi-step classification algorithm. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsApproach & High level algorithm Use simulators to estimate OPTICAL, LIDAR and SAR measurements from 3D forest descriptions Generate many pine and spruce tree stands with a variety of canopy heights and biomasses to generate a stand databse Co-register OPTICAL, SAR, and LIDAR measurements in a single image Compare each image pixel to the database and find the most similar database stand Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsBOREAS Southern Study Area The SSA is approximately 11,700 square kilometers centered on 53.87299◦ N latitude and 105.2875 ◦ W longitude. A a confluence of multi-modal remotely sensed data exists from 1994 - 1996. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsBoreas Southern Study Area Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsBOREAS Southern Study Area: SAR & LiDAR Coverage Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsAlgorithm Overview Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsFractal Tree Model Model developed in late 1990’s. Fractal pseudo-random trees. Use Lindenmayer System: string-rewriting rules are used to generate realistic branching structures, with needles and leaves. Each species of tree has its own set of rules so it looks realistic. Both coniferous and deciduous trees can be modeled. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and Conclusions Fractal Forest ModelForest Attributes: Biomass Tree SpeciesTree Attributes: Height Crown Diameter Height to live crown Trunk Diameter Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsSSA Jack Pine Stand Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsSSA Black Spruce Stand Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsOptical Model Use measured reflectance values for each canopy constituent: branches, trunks, leaves, needles, ground. Fractal geometry used with Pov-Ray ray-tracing code to generate realistic 7-channel optical dataset. Rays are traced for many bounces Sensor is placed far above the forest, looking down at a 45◦ angle. Values are averaged over one pixel to produce the simulated data. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsSAR Model Use Foldy’s approximation to obtain the mean field in a vertically-layered approximation to the canopy. Coherent simulation of each scattering mechanism: direct crown, direct ground, trunk-ground, crown-ground, crown-ground-crown, Fully-polarimetric. Use at L band (1.25GHz) All simulations at 20, 45, and 80 degrees incidence angle, 100 looks. Interpolated polynomial best fit to allow for incidence angle flexibility. Validated at L band with measured SAR data (from BOREAS and Raco, MI). Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsLIDAR Model Divide volume of stand into cubes. Each cube analyzed for what fraction of light is intercepted by the vegetation (cylinders and disks). Use vertical rays to estimate number of intersections per cube. Radiative Transfer from cube-to-cube to produce time-trace of LIDAR signal. Horizontal Gaussian pulse weighting across the stand, with a vertical Gaussian as well to obtain vertical resolution. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsRadiative Transfer for one cube Given power propagating from above and below: quantify how much transmitted and reflected in each direction. Update the power propagating to next cubes. Can use measurements from literature to determine value for %reflected for branches: 10%. Transmission through open areas is assumed 100%. Leaf transmission is assumed to be 50%. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsDatabase Overview Generated 4707 jack pine stands Generated 4364 black spruce stands All stands had a minimum of 10 types of trees and up to 2000 tree instances in an area of 625 m2 Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsDigital Elevation Model The BOREAS project generated a DEM in the 8th hydrological project with 100m resolution A higher resolution DEM was required for accurate orthorectification of the AirSAR images We created a 1315km by 1390km km DEM by reprojecting and mosaicing numerous DEMs from CDED. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsSAR: AirSAR Numerous AirSAR images exist in the Boreas SSA For this study, we selected two high resolution images with 6.66m range resolution and 9.26m azimuth resolution These images were orthorectified using a DEM from CDED to a sub-pixel accuracy of 6m Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsLiDAR: Scanning Lidar Imager of Canopies by EchoRecovery (SLICER) 37 Slicer flight paths were conducted in the BOREAS study areas in July 1996 yielding a total of 834,277 LiDAR waveforms For each measurement, we extracted the power at canopy height and the power ratio between the canopy height and the ground return Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsLiDAR: SLICER Based on the location of each measurement, a weighted average for both parameters was derived for each Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsOptical: LandSAT7 We used level 2T orthorectified Landsat data acquired in July 1994 Images were atmospherically corrected, cleaned of clouds and cloud shadows and reprojected into a single mosaic Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsGround Truth Three data products from the BOREAS project were used as ground truth for this study: Forest Species (Jack Pine or Black Spruce) Forest Biomass Forest Canopy Height Each ground truth data product was reprojected to 10m resolution cells (as needed) Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsGround Truth - Tree Representations DBHjp = 0.0066h3 − 0.1404h2 + Stem mapped measurements 1.8672h − 1.9917 were recorded in the Jack Pine stands as well as the Black CHgtjp = 0.0001h4 − 0.0001h3 − Spruce stand. 0.0205h2 + 0.4788h − 0.7479 Using these measurements, we have developed allometric DBHbs = equations to generate tree a −0.0073h3 + 0.1708h2 + 0.2413h given species’ height to live crown and diameter at breast CHgtbs = height as a function of the −0.0531h2 + 1.452h − 1.6152 desired tree height. R 2 = 0.9564, 0.8555, 0.9442, 0.7133 Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsAlgorithm Overview Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsLevel 0 Classification: Supervised Maximum LikilhoodClassification A simple two class classification scheme was used: Trees and other. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsLevel 1 Classification: Database Comparison Each pixel containing AirSAR, SLICER, and LandSAT data as well as ground truth data was examined Real remotely sensed values were compared to the 9000+ simulated stands in our database The stand that most likely resembled the pixel under examination was selected and that stand’s biomass and mean canopy height were assigned to the pixel Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsError Function Measure The error used is the weighted RMS error over the features: 1. 1.1 LIDAR mean power 1.2 LIDAR peak power / LIDAR ground power 1.3 SAR VV 1.4 SAR HH 2. Optical Ch. 6 3. Optical NDVI 4. SAR VH VV 5. SAR HH Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsIntroduction to Results Compare previous proof of concept to this study. Note that the proof of concept additionally used C-band SAR and IfSAR Note that the proof of concept used the same forward models to generate our database as well as for the inversion and classification. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsProof of Concept Results: Height Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsProof of Concept Results: Biomass Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsClassification Results Classified 9071 pixels Species retrieval was 76.94% accurate. Height retrieval was 50.48% accurate with an RMS error of 5.3m (to 7.3m). Biomass retrieval was 51.38% accurate with an RMS error of 155.53 Ton/Ha. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsClassification Results (2) If we know the target canopy will be small, under 13m, we can achieve even better results: Species retrieval was 76.94% accurate. Height retrieval was 67.16% accurate with an RMS error of 4.37m. Biomass retrieval was 50.03% accurate with an RMS error of 106.3 Ton/Ha. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da
  • Motivation Introduction Models Application to BOREAS Data Classification Algorithm Results and ConclusionsConclusions and Future Work We coregistered remotely sensed data from three different sensors collected over a two year period. We generated a database with over 9,000 stands that resemble those found in the BOREAS SSA. We created and implemented a multistep classification process which correctly identified the predominant tree species and was over 50% accurate in identifying the canopy height and biomass Future work includes introducing a recursive element to the L1 classification Future work includes the introduction of a multi-step error function (used to select the most similar database stand) Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da