cartus_TH4.T02.3.ppt

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cartus_TH4.T02.3.ppt

  1. 1. Ecosystem Structure Measurements from DESDynI: Technological options and data fusion using Small-Footprint Lidar and ALOS/PALSAR data over Central Chile Josef Kellndorfer Scott Goetz Wayne Walker Oliver Cartus The Woods Hole Research Center ( www.whrc.org ) Markus Rombach, Digimapas Chile Sergio Gonzalez, Arauco Timber Company Ralph Dubayah, University of Maryland
  2. 2. <ul><li>(1) For showcase scenario of Central Chile: Evaluate multi-sensor data-fusion strategies for canopy height (CH) and Growing Stock Volume (GSV) retrieval. </li></ul><ul><li>(2) Simulate DESDynI Radar (initially also Lidar) performance: Development of DESDynI Radar ecosystem structure retrieval algorithms </li></ul><ul><li>(3) Increasing availability of small-footprint Lidar for forest structure analysis  development of algorithms for up scaling in situ measurements to airborne samples (Lidar) and space-borne (SAR, optical data) wall-to-wall data </li></ul><ul><li>(4) Investigation of SAR saturation, forest structural differences, benfit of having multi-temporal SAR data, InSAR coherence </li></ul>Study Objectives
  3. 3. Chile- ALOS FBD Data Mosaic <ul><li>ALOS Processing </li></ul><ul><ul><li>189 SLC Products ( FBD ) ordered from ASF </li></ul></ul><ul><ul><li>Data for 2007,2009,2010 </li></ul></ul><ul><ul><li>Multi-temporal coverage: 1-3 images per year </li></ul></ul><ul><ul><li>GAMMA Speckle Filter </li></ul></ul><ul><ul><li>Terrain-corrected geocoding with SRTM-3 DEM </li></ul></ul><ul><ul><li>Resampled to 15x15 m grid </li></ul></ul><ul><ul><li>Topographic Normalization </li></ul></ul>First study area: Highly managed plantations of: radiata pine ( Pinus radiata ) bluegum eucalyptus ( Eucalyptus globulus )
  4. 4. Landsat ETM+ data Global Land Survey 2005 Mosaic 1. Dec. 2005/ 30. Jan. 2007 (Dry season) Downloaded from USGS Earth Explorer: http://earthexplorer.usgs.gov. Product Type: L1T, i.e. data is terrain corrected. SLC gaps filled for ~98% Metrics: Reflectances, Tasseled Cap Transformations and NDVI
  5. 5. <ul><li>Laser scanning system: Riegl LMS-Q560. </li></ul><ul><li>Range capture: Full waveform digitization. </li></ul><ul><li>Field of view: ± 22,5 degrees </li></ul><ul><li>Laser wavelength: 1550nm </li></ul><ul><li>Operating altitude: 30 m - 800 m AGL </li></ul><ul><li>Beam divergence: 0.5 mrad </li></ul><ul><li>Swath width: 80 % of op. altitude (45 degrees) </li></ul><ul><li>Range resolution: 0.020 m </li></ul><ul><li>Vertical accuracy: < 0.15 m </li></ul><ul><li>Horizontal accuracy: < 0.25 m </li></ul>DIGIMAPAS: Airborne Small Footprint Lidar data
  6. 6. 1m Canopy Height Model, CHM CHM (1 m pixels) = DSM (first return) – DTM (last return) DTM DSM CHM
  7. 7. <ul><li>ARAUCO Stand Level Inventory data for 2007 </li></ul>CHM Inventory data <ul><li>Database provides Canopy Height (100 highest trees), DBH, Growing Stock Volume (GSV m 3 /ha), Species, etc. for ~7000 stands </li></ul><ul><li>According to ARAUCO data is representative for 100 000 ha of plantation forest </li></ul><ul><li>For now: 440 stands (mainly radiata pine stands) </li></ul>
  8. 8. Extraction of Lidar Metrics for inventory stands <ul><li>Histogram-simulated Lidar waveforms from 1-meter small-footprint lidar data: </li></ul><ul><li>Examples show simulated waveforms (resp. canopy height profiles) at hectare scale stand level. </li></ul><ul><li>Extracted metrics: </li></ul><ul><li>Relative Heights 10-100 (percentiles of CHM distribution) </li></ul><ul><li>Canopy Cover (% pixels > 2 or 5 m) </li></ul><ul><li>Number of Gaussians </li></ul>
  9. 9. Lidar Metrics vs. in situ data <ul><li>In particular RH30 - RH90 well correlated to GSV and canopy height (Height 100) </li></ul><ul><li>RH100 differs noticeably from in situ tree height </li></ul><ul><li>Relative Heights highly correlated (do they provide different information) </li></ul>
  10. 10. Lidar Retrieval Modeling with RandomForest ensemble regression trees: -Straightforward approach for combining various types of data -Provides tools for analyzing the importance of certain predictors (e.g. RH) -Accuracy assessment: all presented numbers will be so-called out-of-bag estimates (bootstrap validation)
  11. 11. Lidar Retrieval (stands > 2ha) Topography did not affect retrieval performance. Retrieval did not improve when integrating canopy cover or number of Gaussians
  12. 12. Retrieval based on empirical relationship between: RH90 (best predictor for height), Height and GSV (25 % Training and 75 % test samples) <ul><li>Retrieval performance only slightly lower than in case of retrieval with RandomForest and various Lidar metrics </li></ul><ul><li>The high correlation between Height and GSV appears to be the main driver for the Lidar-based GSV retrieval </li></ul><ul><li>GSV/Height Allometry depends on … </li></ul>GSV/Height Allometry
  13. 13. Relative Stocking <ul><li>Relative stocking, RS: ratio of observed basal area at a certain stand age and an optimal basal area </li></ul><ul><li>Differences in RS because of different site or seedling quality </li></ul>GSV retrieval accuracy (with RandomForest) higher for high relative stocking stands, e.g.: RMSEr GSV = 18% (RS>75%) RMSEr GSV = 23 % (RS<60%)  RS overall high in test area (natural forests?)
  14. 14. Effect of Footprint Size Footprints of 100 - 20 000 m 2 extracted from CHM within in situ stands DESDYNI footprint Even with 10 m footprints Lidar metrics are representative for the entire hectare scale stand
  15. 15. Synergy: Lidar, ALOS PALSAR, Landsat ETM Approach: Lidar GSV/Height estimates are used as response variables to train RandomForest Models with ALOS/ETM data as predictors
  16. 16. Test for ~10 km large subset 3 x HH/HV intensity Landsat ETM+ <ul><li>1m Lidar Canopy Height Model </li></ul><ul><li>eCognition segmentation </li></ul><ul><li>GSV/Height retrieval </li></ul>
  17. 17. <ul><li>High Multi-temporal consistency of HH & HV backscatter </li></ul>ALOS Images: 05 July 2007 (dry) 05 Oct. 2007 (rainy) 21 Oct. 2007 (dry)
  18. 18. When using only 1 ALOS FBD HH/HV image pair (stands > 2 ha) <ul><li>- Predictors in RandomForest: HH & HV Intensity + Coefficient of Variation CV (texture) </li></ul><ul><li>Differences in Retrieval Accuracy between the three images only 2 % </li></ul><ul><li>although they were acquired under different weather conditions </li></ul>
  19. 19. <ul><li>3 ALOS HH/HV images (stands > 2 ha) </li></ul><ul><li>Two multi-temporal approaches were tested: </li></ul><ul><li>All images as predictors in one model (upper row) </li></ul><ul><li>Separate model for each FBD image  weighted multi-temporal combination (cf. Santoro et al., 2006) (lower left plot) </li></ul>Integration of multi-temporal data improves retrieval performance
  20. 20. <ul><li>3 ALOS HH/HV images + ETM+ (stands > 2 ha) </li></ul>Multi-temporal combination of single image estimates
  21. 21. <ul><li>3 ALOS HH/HV images + ETM+ (stands > 5 ha) </li></ul>GSV<300 m 3 /ha
  22. 22. <ul><ul><ul><li>Summary: </li></ul></ul></ul><ul><ul><ul><li> 1m Lidar CHMs allowed GSV/Height retrieval with relative errors of 23 and 7 % respectively (RMSE of 63 m 3 /ha and 1.7 m) for highly managed pine plantations. For high relative stocking stands accuracy even higher. </li></ul></ul></ul><ul><ul><ul><li> Use of Random Forest with various Canopy metrics hardly improves the retrieval performance compared to allometric relations based on single metrics </li></ul></ul></ul><ul><ul><ul><li> When using Lidar GSV/Height estimates to train models for ALOS/ETM data: </li></ul></ul></ul><ul><ul><ul><li> Availability of multi-temporal data improved the retrieval (5-10%) </li></ul></ul></ul><ul><ul><ul><li> Integration of ETM data improved the retrieval </li></ul></ul></ul><ul><ul><ul><li>Outlook: Extrapolation of ALOS/ETM+ models/retrieval to large areas / entire Chile </li></ul></ul></ul><ul><ul><ul><li> How many Lidar transects are required to capture the spatial variability of backscatter signatures over forest ? </li></ul></ul></ul><ul><ul><ul><li> Retrieval performance was assessed for highly managed plantation forests. Accuracy in other natural forests? </li></ul></ul></ul><ul><ul><ul><li> How much can repeat-pass coherence contribute (reliably?) to large area GSV/Height retrieval - repeat-pass coherence strongly depends on weather conditions and the baseline </li></ul></ul></ul>Summary and Outlook
  23. 23. Stands >2 ha ALOS HH& HV intensity vs. Lidar GSV estimates Images: 05 July 2007 (dry) 05 Oct. 2007 (rainy) 21 Oct. 2007 (dry)
  24. 24. ETM+ only (stands > 2 ha) Predictors: ETM bands 1-5 Tasseled Cap transformations NDVI Accuracy similar to that achieved when using 1 ALOS HH/HV image pair

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