An evaluation of the JPL TOPSAR for extracting tree heights

Kobayashi, Yutaka; Sarabandi, Kamal; Pierce, L.E.; Dobson, M.C.

doi:10.1109/36.885193

Cited by 38 publications

(1 citation statement)

References 19 publications

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“…Tiles from each data set were mosaiced and an SRTM minus NED difference (SRTMDIFF) image was calculated ( Figure 3). The difference image provides an estimate of the interferometric ''scattering phase center height'' (h spc ), which has been shown to correlate highly with the amount and type of vegetation present [Brown, 2003;Brown and Sarabandi, 2003;Kellndorfer et al, 2004b;Kobayashi et al, 2000;Saich et al, 2001;Walker et al, 2007aWalker et al, , 2007b. Topographic slope (SLP) and aspect (ASP) layers were also generated from the NED DEM.…”

Section: Insar Scattering Phase Center Height From Srtm and Ned Datamentioning

confidence: 99%

Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional‐scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets

et al. 2010

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A method is presented to characterize forest stand heights in a 110,000 km2 region in the eastern United States surrounding the Chesapeake Bay area, driven by a statistical fusion model solely based on remote sensing data. The predicted map was tested against ground survey data from the Forest Inventory and Analysis (FIA) plot network. Input data to the model were 2003 medium footprint lidar data from the Laser Vegetation Imaging Sensor (LVIS) sensor, interferometric radar data from the 2000 Shuttle Radar Topography Mission (SRTM), 1999–2001 Landsat ETM+ data, and ancillary data sets of land cover and canopy density developed for the 2001 National Land Cover Database. In the presented approach, the interferometric synthetic aperture radar (InSAR), optical, and ancillary data sets were masked to the forested areas of the study region and used to segment the raster data stack. The generated image objects closely represented quasi‐homogenous forest stands. For a small region in the study area covered by an LVIS acquisition, LVIS lidar data were then used within the established segments to extract lidar‐based mean forest stand heights. Subsequently these LVIS mean stand heights were used as the response variable to the statistical prediction model (randomForest) which had segment‐based metrics like mean InSAR height (derived from SRTM minus ground digital elevation model data from the National Elevation Data set), mean optical reflectance (derived from Landsat ETM+ Tassled Cap Data), and ancillary metrics as predictive variables. The model developed over the area where LVIS data were available was then applied to map the entire study region. Independent validation of the model was performed in two ways. First, splitting of the model data stack into training and independent testing populations, i.e., testing on LVIS data. This test was deemed to describe the model performance within the LVIS swath. Second, predicted heights were compared to plot height metrics derived from FIA data in the entire study region, thus testing the validity of the model across the larger study area. Results, which are somewhat tampered by the time disconnect between the various data collections, showed the validity and usefulness of this approach. Independent LVIS testing resulted in a correlation coefficient r = 0.83 with an RMSE of 3.0 m (9% error), independent FIA data tested with r = 0.71 with an RMSE of 4.4 m (13% error).

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