Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape

Hilbert, Claudia; Schmullius, Christiane

doi:10.3390/rs4082210

Cited by 90 publications

(92 citation statements)

References 31 publications

(49 reference statements)

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“…The trailing edge extent is determined as the height difference between the signal end and the last bin in which the signal intensity of the waveform is half the maximum intensity. However, when considering waveforms with high differences between canopy and ground signal amplitude, a modified leading and trailing edge extent were appropriate to better represent canopy surface and terrain topography [59]. For example, when regarding waveforms with high ground amplitude and low canopy amplitude, there may be no intersection between the extension of the location at the half maximum amplitude and waveforms of low canopy amplitude.…”

Section: Lidar Waveform Datamentioning

confidence: 99%

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

et al. 2015

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Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA's Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%.

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Section: Lidar Waveform Datamentioning

confidence: 99%

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

et al. 2015

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“…(2) The influence of slope on mean forest canopy height is not as strong as that for maximum forest canopy height because the leading edge, which is a measure of canopy surface variability and surface relief (Hilbert and Schmullius, 2012;Baghdadi et al, 2013), is not included in the peak distance. (3) The topographic correction required to estimate mean forest canopy height is often significantly lower than what is expected from the local slope because a large part of the ground surface is not recorded in the waveform (Lefsky et al, 2007;Hilbert and Schmullius, 2012).…”

Section: Height Retrieval Of Glas Datamentioning

confidence: 99%

“…For example, many studies have shown that forest canopy height is very difficult to determine when the surface slope is greater than 15°. Additionally, if the forest structure is highly complex, slopes greater than 10°c an be problematic (Hilbert and Schmullius, 2012). Slope correction will continue to be an open issue for large-footprint Lidar data processing (Lefsky et al, 2007;Simard et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

Wang

Ding

et al. 2016

Remote Sensing of Environment

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Mapping the global distribution of forest canopy height is important for estimating forest biomass and terrestrial carbon flux. In this study, we present a global map of mean forest canopy height at 500 m spatial resolution obtained by combining Geoscience Laser Altimeter System (GLAS) data acquired from 2005 to 2006 and 13 ancillary variables, including seven climatic variables and six remote sensing variables (nadir BRDF-adjusted reflectance at red and NIR bands, tree cover, anisotropic factor, accumulated Enhanced Vegetation Index, and elevation). The original contributions of this study include the following: (1) The wavelet method was applied to complement the GLA14 product to identify the ground peak and the top-canopy peak. We found that it was useful for dealing with waveforms with low reconstruction accuracy. (2) GLAS data from the leafless season were not used for nonevergreen forest because the height retrieval results exhibited underestimation and strong variations. (3) The anisotropic factor (ANIF), an indicator related to surface structure, was included as an ancillary variable for the first time and was determined to be important for height modeling in the Asian and North American regions. (4) The balanced random forest (BRF) algorithm was applied to register GLAS mean forest canopy height to a 500 m grid considering the small proportion of extreme height classes (tall and short trees), and it achieved good performance in terms of modeling accuracy (RMSE = 2.75 to 4.45 m) and preserving data variation. An inter-comparison among three global forest height maps [the present study, Lefsky (2010), andSimard et al. (2011)] was implemented in a pixel-by-pixel manner. High agreement (R 2 = 0.73, RMSE = 4.49 m) was determined between the present study and Simard et al., whereas the result from Lefsky was notably different from the other two results (R 2 = 0.14, RMSE = 8.92 m, compared with the present study; R 2 = 0.11, RMSE = 11.19 m, compared with Simard et al.). Large disparities were generally associated with evergreen broadleaf forests in South America, deciduous needleleaf forests in Europe and Russian North Asia, and evergreen needleleaf forests on the West Coast of North America. Differences in the height metric were a main factor affecting the disparities among the three results. Validation against field survey data acquired from the Distributed Active Archive Center indicated the accuracy of our mean forest canopy height map (R 2 = 0.63, RMSE = 4.68 m, n = 59).

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“…The range between the reference center on a satellite and the target surface is calculated by measuring the round-trip propagation time of short laser pulses, and other information such as surface roughness, slope, and reflectivity can also be measured through processing the received waveform. The Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) with small footprint and along track spacing provided more accurate surface elevation and higher horizontal resolution on Earth observation, and was widely used to monitor ice sheet, sea ice, vegetation, and other changing environments [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

The Waveform Model of Laser Altimeter System with Flattened Gaussian Laser

Wang

Yang

et al. 2015

Journal of the Optical Society of Korea

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The current waveform model of a laser altimeter is based on a Gaussian laser beam of fundamental mode, while the flattened Gaussian beam has many advantages such as nearly constant energy distribution on the center of the cross-section. Following the theory of the flattened Gaussian beam and the waveform theory of the laser altimeter, some of the primary parameters of the received waveform were derived, and a laser altimetry waveform simulator and waveform processing software were programmed and improved under the circumstance of a flattened Gaussian beam. The result showed that the bias between theoretical and simulated waveforms was less than 3% for every order mode, the waveform width and range error would increase as target slope or order number rose. Under higher order mode, the shapes of the received waveforms were no longer Gaussian, and could be fitted more precisely as a generalized Gaussian function with power bigger than 2. The flattened beam got much better performance for a multi-surface target, especially when the small surface is far from the center of the laser footprint. This article provides the waveform theoretical basis for the use of a flattened Gaussian beam in a laser altimeter.

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Influence of Surface Topography on ICESat/GLAS Forest Height Estimation and Waveform Shape

Cited by 90 publications

References 31 publications

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

The Waveform Model of Laser Altimeter System with Flattened Gaussian Laser

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