Comparison of SRTM-NED data to LIDAR derived canopy metrics

Kenyi, L.W.; Dubayah, Ralph; Hofton, M. A.; Blair, J. B.; Schardt, Mathias

doi:10.1109/igarss.2007.4423431

Cited by 3 publications

(3 citation statements)

References 15 publications

(9 reference statements)

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“…Similarly, Li et al [25] reported that the error of ASTER GDEM in forests is 30.2 m. ASTER GDEM and AW3D30 are basically the "first return", i.e., the canopy, whereas SRTM can also reach the vegetation in the middle of the canopy due to its penetration capability. As a result, ASTER GDEM and AW3D30 only record the canopy, whereas SRTM could slightly penetrate into the canopy [13,25,36]. SRTM 1 and SRTM 3 have very similar quality in cultivated land and artificial surfaces, while SRTM 1 and AW3D30 are the best in all types of land among the four assessed DEMs.…”

Section: Accuracy Versus Land Covermentioning

confidence: 97%

Evaluation of Recently Released Open Global Digital Elevation Models of Hubei, China

Peng

Hou

et al. 2017

Remote Sensing

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Abstract:The recent release of worldwide SRTM 1 DEM and AW3D30 adds new members to the open global medium resolution (90-30 m ground spacing) digital elevation models. Together with the previously existing SRTM 3 and ASTER GDEM, their quality is of great interest to various scientific applications. This paper uses 1:50,000 DEM in Hubei Province of China as a reference to assess their vertical accuracy in terms of terrain types, slopes, and land cover. For ASTER GDEM and AW3D30, we further evaluate their accuracy in terms of the stack number, i.e., the number of scenes used to generate the DEM. It is found out that: (1) all of the DEMs have nearly the same horizontal offset due to the adoption of different datums; (2) the vertical accuracy varies in terms of terrain complexity, from~5 m for plains,~10 m for hills to~20 m for mountains; (3) the vertical accuracy is negatively related to the tangent of terrain slope exponentially in forest areas and linearly in cultivated lands; (4) forest areas have the lowest vertical accuracy, comparing to built-up areas, wetland, and cultivate land areas while SRTM 1 and AW3D30 have the highest accuracy in all land cover classes; (5) the large elevation differences over forest areas are likely due to canopy coverage; and (6) for ASTER GDEM and AW3D30, their accuracy is in general positively related to the stack number. This study provides a practically useful quality specification and comprehensive understanding for these four global DEMs, especially the recently released worldwide SRTM 1 DEM and AW3D30.

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Section: Accuracy Versus Land Covermentioning

confidence: 97%

Evaluation of Recently Released Open Global Digital Elevation Models of Hubei, China

Peng

Hou

et al. 2017

Remote Sensing

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“…The data sets upon which these studies are based are multitemporal, such as from ERS-1/2 and JERS, or are highly localized, like those produced by airborne INSAR or special spaceborne, multitemporal INSAR missions that were not global, unlike BROWN, SARABANDI, AND PIERCE: ESTIMATION OF TREE HEIGHT 3 SRTM. In the past several years, progress has been made in retrieving forest structural parameters using the SRTM data set [14], [15], [16], [17]. This paper reports the novel approach in [14], [15], which retrieves tree stand height from SRTM and ancillary data employing an algorithm based on an electromagnetic scattering model, not using an empirical regression model derived from ground truth measurements, as in [16].…”

Section: Introductionmentioning

confidence: 99%

“…The basic strategy used in the studies listed above to determine forest vertical structure parameters, with the exceptions of [16], [17], is to develop theory-based, forward-scattering models describing SAR, IN-SAR, and POLINSAR observables as a function of canopy parameters. The models are usually simplified to include only the most influential parameters of interest.…”

Section: Introductionmentioning

confidence: 99%

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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Estimation of Broadleaf Tree Canopy Height of Wolong Nature Reserve Based on InSAR and Machine Learning Methods

Liu

et al. 2022

Forests

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Tree height is an important parameter for calculating forest carbon sink and assessing forest carbon cycle. In order to obtain forest tree height over a large area both efficiently and at a low cost, this study proposed an Interferometric Synthetic Aperture Radar (InSAR) combined with a machine learning method to estimate the tree canopy height. The forest height in the study area was obtained using Unmanned Aerial Vehicle (UAV) photogrammetry, which was considered to be the true canopy height. Two machine learning methods (Random Forest, Multi-layer perceptron) were used to establish the relationship between phase center height calculated by InSAR DEM differential interference method and coherent amplitude method with true canopy height. The topographic factor, backward scattering coefficient and coherence coefficient were introduced into the relationship model. It was found that the accuracy of tree height estimation using random forest and two InSAR methods can reach 0.95 and 0.94. The root-mean-square error was 1.76 m, 1.86 m, respectively. The accuracy of tree height estimation using multi-layer perceptron and two InSAR methods was 0.25 and 0.2. The root-mean-square error was 3.96 m and 4.13 m. The results indicated that the combination of InSAR and machine learning can estimate canopy height efficiently and at a low cost. Moreover, the integrated learning algorithm random forest demonstrated better stability and higher accuracy than the single learning algorithm multi-layer perceptron.

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Comparison of SRTM-NED data to LIDAR derived canopy metrics

Cited by 3 publications

References 15 publications

Evaluation of Recently Released Open Global Digital Elevation Models of Hubei, China

Evaluation of Recently Released Open Global Digital Elevation Models of Hubei, China

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 Broadleaf Tree Canopy Height of Wolong Nature Reserve Based on InSAR and Machine Learning Methods

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