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    cartus_TH4.T02.3.ppt cartus_TH4.T02.3.ppt Presentation Transcript

    • 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 ( ) Markus Rombach, Digimapas Chile Sergio Gonzalez, Arauco Timber Company Ralph Dubayah, University of Maryland
      • (1) For showcase scenario of Central Chile: Evaluate multi-sensor data-fusion strategies for canopy height (CH) and Growing Stock Volume (GSV) retrieval.
      • (2) Simulate DESDynI Radar (initially also Lidar) performance: Development of DESDynI Radar ecosystem structure retrieval algorithms
      • (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
      • (4) Investigation of SAR saturation, forest structural differences, benfit of having multi-temporal SAR data, InSAR coherence
      Study Objectives
    • Chile- ALOS FBD Data Mosaic
      • ALOS Processing
        • 189 SLC Products ( FBD ) ordered from ASF
        • Data for 2007,2009,2010
        • Multi-temporal coverage: 1-3 images per year
        • GAMMA Speckle Filter
        • Terrain-corrected geocoding with SRTM-3 DEM
        • Resampled to 15x15 m grid
        • Topographic Normalization
      First study area: Highly managed plantations of: radiata pine ( Pinus radiata ) bluegum eucalyptus ( Eucalyptus globulus )
    • Landsat ETM+ data Global Land Survey 2005 Mosaic 1. Dec. 2005/ 30. Jan. 2007 (Dry season) Downloaded from USGS Earth Explorer: Product Type: L1T, i.e. data is terrain corrected. SLC gaps filled for ~98% Metrics: Reflectances, Tasseled Cap Transformations and NDVI
      • Laser scanning system: Riegl LMS-Q560.
      • Range capture: Full waveform digitization.
      • Field of view: ± 22,5 degrees
      • Laser wavelength: 1550nm
      • Operating altitude: 30 m - 800 m AGL
      • Beam divergence: 0.5 mrad
      • Swath width: 80 % of op. altitude (45 degrees)
      • Range resolution: 0.020 m
      • Vertical accuracy: < 0.15 m
      • Horizontal accuracy: < 0.25 m
      DIGIMAPAS: Airborne Small Footprint Lidar data
    • 1m Canopy Height Model, CHM CHM (1 m pixels) = DSM (first return) – DTM (last return) DTM DSM CHM
      • ARAUCO Stand Level Inventory data for 2007
      CHM Inventory data
      • Database provides Canopy Height (100 highest trees), DBH, Growing Stock Volume (GSV m 3 /ha), Species, etc. for ~7000 stands
      • According to ARAUCO data is representative for 100 000 ha of plantation forest
      • For now: 440 stands (mainly radiata pine stands)
    • Extraction of Lidar Metrics for inventory stands
      • Histogram-simulated Lidar waveforms from 1-meter small-footprint lidar data:
      • Examples show simulated waveforms (resp. canopy height profiles) at hectare scale stand level.
      • Extracted metrics:
      • Relative Heights 10-100 (percentiles of CHM distribution)
      • Canopy Cover (% pixels > 2 or 5 m)
      • Number of Gaussians
    • Lidar Metrics vs. in situ data
      • In particular RH30 - RH90 well correlated to GSV and canopy height (Height 100)
      • RH100 differs noticeably from in situ tree height
      • Relative Heights highly correlated (do they provide different information)
    • 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)
    • Lidar Retrieval (stands > 2ha) Topography did not affect retrieval performance. Retrieval did not improve when integrating canopy cover or number of Gaussians
    • Retrieval based on empirical relationship between: RH90 (best predictor for height), Height and GSV (25 % Training and 75 % test samples)
      • Retrieval performance only slightly lower than in case of retrieval with RandomForest and various Lidar metrics
      • The high correlation between Height and GSV appears to be the main driver for the Lidar-based GSV retrieval
      • GSV/Height Allometry depends on …
      GSV/Height Allometry
    • Relative Stocking
      • Relative stocking, RS: ratio of observed basal area at a certain stand age and an optimal basal area
      • Differences in RS because of different site or seedling quality
      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?)
    • 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
    • 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
    • Test for ~10 km large subset 3 x HH/HV intensity Landsat ETM+
      • 1m Lidar Canopy Height Model
      • eCognition segmentation
      • GSV/Height retrieval
      • High Multi-temporal consistency of HH & HV backscatter
      ALOS Images: 05 July 2007 (dry) 05 Oct. 2007 (rainy) 21 Oct. 2007 (dry)
    • When using only 1 ALOS FBD HH/HV image pair (stands > 2 ha)
      • - Predictors in RandomForest: HH & HV Intensity + Coefficient of Variation CV (texture)
      • Differences in Retrieval Accuracy between the three images only 2 %
      • although they were acquired under different weather conditions
      • 3 ALOS HH/HV images (stands > 2 ha)
      • Two multi-temporal approaches were tested:
      • All images as predictors in one model (upper row)
      • Separate model for each FBD image  weighted multi-temporal combination (cf. Santoro et al., 2006) (lower left plot)
      Integration of multi-temporal data improves retrieval performance
      • 3 ALOS HH/HV images + ETM+ (stands > 2 ha)
      Multi-temporal combination of single image estimates
      • 3 ALOS HH/HV images + ETM+ (stands > 5 ha)
      GSV<300 m 3 /ha
          • Summary:
          •  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.
          •  Use of Random Forest with various Canopy metrics hardly improves the retrieval performance compared to allometric relations based on single metrics
          •  When using Lidar GSV/Height estimates to train models for ALOS/ETM data:
          •  Availability of multi-temporal data improved the retrieval (5-10%)
          •  Integration of ETM data improved the retrieval
          • Outlook: Extrapolation of ALOS/ETM+ models/retrieval to large areas / entire Chile
          •  How many Lidar transects are required to capture the spatial variability of backscatter signatures over forest ?
          •  Retrieval performance was assessed for highly managed plantation forests. Accuracy in other natural forests?
          •  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
      Summary and Outlook
    • Stands >2 ha ALOS HH& HV intensity vs. Lidar GSV estimates Images: 05 July 2007 (dry) 05 Oct. 2007 (rainy) 21 Oct. 2007 (dry)
    • 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