Deriving forest characteristics using polarimetric InSAR measurements and models

Saich, P.; Balzter, Heiko; Luckman, Adrian

doi:10.1109/igarss.2001.978268

Cited by 4 publications

(5 citation statements)

References 3 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 , 2007a, 2007b]. Topographic slope (SLP) and aspect (ASP) layers were also generated from the NED DEM.…”

Section: Data Acquisition and Preprocessingmentioning

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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Section: Data Acquisition and Preprocessingmentioning

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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“…We use the Principal Investigator (PI) data for incidence angle and polarization information. References [20], [21], [22], [16] use a similar method to obtain estimates of SPC height from airborne INSAR and spaceborne SRTM data. The SRTM GDPS data for this tree height retrieval were obtained from [25].…”

Section: Srtm Scattering Phase Center Heightmentioning

confidence: 99%

“…In order to populate the plots with more stands, we add another red pine stand for which we have only height data, RP7, and use a site index curve [48] to provide true average heights for four other red pine stands RP8-11, similar to the approach used in [21], [22]. A site index curve predicts the height of trees in a stand based on height measurements in the past and the age of the stand.…”

Section: B Estimation Scenario Two: Tree Density and Moisture Are Romentioning

confidence: 99%

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Model-Based Estimation of Forest Canopy Height in Red and Austrian Pine Stands Using Shuttle Radar Topography Mission and Ancillary Data: A Proof-of-Concept Study

Brown

Sarabandi

Pierce

2010

IEEE Trans. Geosci. Remote Sensing

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In this paper accurate tree stand height retrieval is demonstrated using C-band Shuttle Radar Topography Mission (SRTM) height and ancillary data. The tree height retrieval algorithm is based on modeling uniform tree stands with a single layer of randomly-oriented vegetation particles. For such scattering media, the scattering phase center (SPC) height, as measured by SRTM, is a function of tree height, incidence angle, and the extinction coefficient of the medium. The extinction coefficient for uniform tree stands is calculated as a function of tree height and density using allometric equations and a fractal tree model. The accuracy of the proposed algorithm is demonstrated using SRTM and TOPSAR data for 15 red pine and Austrian pine stands. (TOPSAR is an airborne interferometric synthetic aperture radar.) The algorithm yields rms errors of 2.5 to 3.6m, which is substantial The first author is currently with Lawrence Livermore National Laboratory. Livermore, CA 94550.

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“…Kellogg Experimental Forest, near Battle Creek, Michigan. In order to populate the plots with more stands, we add in another red pine stand for which we have only height data and use a site index curve to provide true average heights for four other red pine stands, similar to the approach used in [8,9]. A site index curve predicts the height of trees in an stand based on height measurements in the past and the age of the stand.…”

Section: Test Results Using Actual Datamentioning

confidence: 99%

Estimation of red pine tree height using Shuttle Radar Topography Mission and ancillary data

Brown

Sarabandi

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)

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In this paper accurate tree height retrieval for red pine and Austrian pine is demonstrated using C-band Shuttle Radar Topography Mission (SRTM) height and ancillary data. The tree height retrieval algorithm is based on modeling uniform tree stands with a single layer of randomly-oriented vegetation particles. For such scattering media, the scattering phase center (SPC) height, as measured by SRTM, is a function of tree height, incidence angle, and the extinction coefficient of the medium. The extinction coefficient for uniform tree stands is calculated as a function of tree height and density using allometric equations and a fractal tree model. The algorithm outputs tree height estimates that are significantly closer to the true tree height than the raw SRTM SPC height values obtained from the height difference between the SRTM data and the National Elevation Dataset (NED). The accuracy of the proposed algorithm is demonstrated using SRTM and TOPSAR data for 15 red pine and Austrian pine stands.

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Deriving forest characteristics using polarimetric InSAR measurements and models

Cited by 4 publications

References 3 publications

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

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

Model-Based Estimation of Forest Canopy Height in Red and Austrian Pine Stands Using Shuttle Radar Topography Mission and Ancillary Data: A Proof-of-Concept Study

Estimation of red pine tree height using Shuttle Radar Topography Mission and ancillary data

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