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

Nelson, Ross

doi:10.1080/01431160903380557

Cited by 58 publications

(29 citation statements)

References 15 publications

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“…Spaceborne SAR sensors estimate plot-level vegetation structure with high uncertainty due to the need to average many contiguous radar pixels, which causes conflicts of scale with ground data [39]. Spaceborne LiDAR from the Geoscience Laser Altimeter System (GLAS) on the ICESat-1 satellite has difficulty capturing canopy surface in sparse and short stature forests [40]. In the TTE, the uncertainty of these measurements alone may mask subtle yet significant plot-level vegetation height differences (e.g., 0.5 m-4 m) that may play a central role in the prediction of climate feedbacks in the high northern latitudes [9,41].…”

Section: Introductionmentioning

confidence: 99%

The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone

et al. 2014

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Satellite-based estimates of vegetation structure capture broad-scale vegetation characteristics as well as differences in vegetation structure at plot-scales. Active remote sensing from laser altimetry and radar systems is regularly used to measure vegetation height and infer vegetation structural attributes, however, the current uncertainty of their spaceborne measurements is likely to mask actual plot-scale differences in vertical structures in sparse forests. In the taiga (boreal forest)-tundra ecotone (TTE) the accumulated effect of subtle plot-scale differences in vegetation height across broad-scales may be significant. This paper examines the uncertainty of plot-scale forest canopy height measurements in northern Siberia Larix stands by combining complementary canopy surface elevations derived from satellite photogrammetry and ground elevations derived from the Geosciences Laser Altimeter System (GLAS) from the ICESat-1 satellite. With a linear model, spaceborne-derived canopy height measurements at the plot-scale predicted TTE stand height ~5 m-~10 m tall (R 2 = 0.55, bootstrapped 95% confidence interval of R 2 = 0.36-0.74) with an uncertainty ranging from ±0.86 m-1.37 m. A larger sample may mitigate the broad uncertainty of the model fit, however, the methodology provides a means for capturing plot-scale canopy height and its uncertainty from spaceborne data at GLAS footprints in

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Section: Introductionmentioning

confidence: 99%

The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone

et al. 2014

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“…As a result, it is likely that significantly different landscapes will require a different approach for refining GLAS GF estimates or an independent predictor sensitivity analysis. Furthermore, the effects of high latitudes with respect to GLAS measurements have been briefly documented for canopy heights only [51], but may apply to GLAS derivations of GF, as well. Whilst a latitudinal effect is suspected to influence GLAS GF and waveform scaling factors, the severity of such effects is unknown; a further sensitivity analysis may quantify such effects.…”

Section: Discussionmentioning

confidence: 99%

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

et al. 2017

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Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between canopy and ground surfaces resulting from differences in footprint size, energy thresholding, noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most important attributes were identified as: soil phosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields product and terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS data for all available data and an optimized subset, where the latter produced most encouraging results (R 2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates of GF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors. Large-scale active sensor estimates of GF can establish a baseline from which future monitoring investigations can be initiated via upcoming Earth Observation missions.

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“…Fourth, many studies have found that tall or short trees are challenging to map (Nelson, 2010;Simard et al, 2011). This phenomenon could be caused by low correlations between ancillary data and extreme tree height (Simard et al, 2011) and/or by the reduced capability of measuring extreme height with the GLAS waveform (Nelson, 2010).…”

Section: Objectives Of This Studymentioning

confidence: 99%

“…This phenomenon could be caused by low correlations between ancillary data and extreme tree height (Simard et al, 2011) and/or by the reduced capability of measuring extreme height with the GLAS waveform (Nelson, 2010). The training algorithm could also be a factor.…”

Section: Objectives Of This Studymentioning

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

Cited by 58 publications

References 15 publications

The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone

The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

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

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