Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data

Sun, Guoqing; Ranson, K.J.; Kimes, D. S.; Blair, J. B.; Kovacs, K.

doi:10.1016/j.rse.2006.09.036

Cited by 259 publications

(182 citation statements)

References 27 publications

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“…GLA14 data provide surface elevation, latitude and longitude of footprints, laser range offsets for the signal beginning and end, and location, amplitude, and width of up to six Gaussian peaks. The six Gaussian distributions of a waveform correspond to different vertical structural features of vegetation cover and underlying topography [56,57]. With the record index and shot number, the location (latitude/longitude) of each GLAS footprint from GLA14 data was added to the individual waveform extracted from GLA01 data, as well as other parameters such as noise level and transmitted pulse waveform, which were used later in waveform processing.…”

Section: Lidar Waveform Datamentioning

confidence: 99%

“…We estimated the noise levels before the signal beginning and after the signal end from the raw waveform separately using a method based on the histogram (it is a graphical representation of the waveform bins) [56]. Cloudless waveforms were selected using the cloud detection flag (FRir_qaFlag = 15).…”

Section: Lidar Waveform Datamentioning

confidence: 99%

“…Saturated signals were identified using the GLAS flag (SatNdx > 0). Then, the waveforms were smoothed using a Gaussian filter with a width similar to the transmitted laser pulse, and the signal beginning and end were identified using a noise threshold [56]. The total waveform energy was calculated by summing all the return energy from the signal beginning to end.…”

Section: Lidar Waveform Datamentioning

confidence: 99%

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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%

Section: Lidar Waveform Datamentioning

confidence: 99%

Section: Lidar Waveform Datamentioning

confidence: 99%

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

et al. 2015

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“…This finding is not unexpected given the ground height-GLAS height comparisons reported in the literature. Sun et al (2008) compare various GLAS height metrics to coincident airborne LiDAR estimates and report RMSEs of 3-5.5 m (their table 2) in the temperate forests of the eastern US. Rosette et al (2008) report ground-GLAS height RMSEs of 2.86 m after correcting for topography.…”

Section: Digital Vegetation Zone Map Of Québec (Mrnfpq 2003) An Ecozmentioning

confidence: 99%

Model effects on GLAS-based regional estimates of forest biomass and carbon

Nelson

2010

International Journal of Remote Sensing

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Ice, Cloud, and land Elevation Satellite (ICESat) / Geosciences Laser Altimeter System (GLAS) waveform data are used to estimate biomass and carbon on a 1.27 Â 10 6 km 2 study area in the Province of Québec, Canada, below the tree line. The same input datasets and sampling design are used in conjunction with four different predictive models to estimate total aboveground dry forest biomass and forest carbon. The four models include non-stratified and stratified versions of a multiple linear model where either biomass or (biomass) 0.5 serves as the dependent variable. The use of different models in Québec introduces differences in Provincial dry biomass estimates of up to 0.35 G, with a range of 4.94 AE 0.28 Gt to 5.29 AE 0.36 Gt. The differences among model estimates are statistically non-significant, however, and the results demonstrate the degree to which carbon estimates vary strictly as a function of the model used to estimate regional biomass. Results also indicate that GLAS measurements become problematic with respect to height and biomass retrievals in the boreal forest when biomass values fall below 20 t ha -1 and when GLAS 75th percentile heights fall below 7 m.

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“…However, we may not need to estimate all five parameters independently as crown radius is related to canopy height through allometry, and crown radius and stem density are constrained by canopy cover. Moreover, recent advances in remote sensing both with high spatial resolution optical sensors to extract LAI and tree crown size [Song and Dickinson, 2008;Song, 2007;Clark et al, 2004;Leckie et al, 2003] and with Lidar to extract canopy height [Lefsky et al, 2002;Sun et al, 2008] are making it possible to extract these canopy structural parameters as input to GCR.…”

Section: Discussionmentioning

confidence: 99%

Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data

et al. 2009

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[1] This study investigates the impacts of canopy structure specification on modeling net radiation (R n ), latent heat flux (LE) and net photosynthesis (A n ) by coupling two contrasting radiation transfer models with a two-leaf photosynthesis model for a maturing loblolly pine stand near Durham, North Carolina, USA. The first radiation transfer model is based on a uniform canopy representation (UCR) that assumes leaves are randomly distributed within the canopy, and the second radiation transfer model is based on a gappy canopy representation (GCR) in which leaves are clumped into individual crowns, thereby forming gaps between the crowns. To isolate the effects of canopy structure on model results, we used identical model parameters taken from the literature for both models. Canopy structure has great impact on energy distribution between the canopy and the forest floor. Comparing the model results, UCR produced lower R n , higher LE and higher A n than GCR. UCR intercepted more shortwave radiation inside the canopy, thus producing less radiation absorption on the forest floor and in turn lower R n . There is a higher degree of nonlinearity between A n estimated by UCR and by GCR than for LE. Most of the difference for LE and A n between UCR and GCR occurred around noon, when gaps between crowns can be seen from the direction of the incident sunbeam. Comparing with eddy-covariance measurements in the same loblolly pine stand from May to September 2001, based on several measures GCR provided more accurate estimates for R n , LE and A n than UCR. The improvements when using GCR were much clearer when comparing the daytime trend of LE and A n for the growing season. Sensitivity analysis showed that UCR produces higher LE and A n estimates than GCR for canopy cover ranging from 0.2 to 0.8. There is a high degree of nonlinearity in the relationship between UCR estimates for A n and those of GCR, particularly when canopy cover is low, and suggests that simple scaling of UCR parameters cannot compensate for differences between the two models. LE from UCR and GCR is also nonlinearly related when canopy cover is low, but the nonlinearity quickly disappears as canopy cover increases, such that LE from UCR and GCR are linearly related and the relationship becomes stronger as canopy cover increases. These results suggest the uniform canopy assumption can lead to underestimation of R n , and overestimation of LE and A n . Given the potential in mapping regional scale forest canopy structure with high spatial resolution optical and Lidar remote sensing plotforms, it is possible to use GCR for up-scaling ecosystem processes from flux tower measurements to heterogeneous landscapes, provided the heterogeneity is not too extreme to modify the flow dynamics.

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Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data

Cited by 259 publications

References 27 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

Model effects on GLAS-based regional estimates of forest biomass and carbon

Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data

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