Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Silva, Carlos Alberto; Duncanson, Laura; Hancock, Steven; Neuenschwander, A. L.; Thomas, Nathan; Hofton, M. A.; Fatoyinbo, Lola; Simard, Marc; Marshak, Charles Z.; Armston, John; Lutchke, S.; Dubayah, Ralph

doi:10.1016/j.rse.2020.112234

Cited by 147 publications

(73 citation statements)

References 41 publications

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“…The bias in the prediction biomass estimates using GEDI observations as independent data depends on the model structure and the predictor variables included. Despite the fact that some recent studies in the literature assessed the accuracy of real [10,25] or ALS-simulated GEDI data [20,21,43,44], our study is, to the best of our knowledge, the first one to evaluate the performance of on-orbit GEDI L2A and L2B (Version 1) products in obtaining AGB estimates at footprint level (GEDI-like Level4A) by comparing with spatially and temporally coincident, discrete-return ALS data across vast areas of diverse Mediterranean types of forests.…”

Section: Discussionmentioning

confidence: 99%

“…The Advanced Topographic Laser Altimeter System (ATLAS) on-board the ICESat-2 satellite and GEDI mission on-board the International Space Station (ISS) are currently generating dense along-track LiDAR information at large spatial coverages and can be used to support carbon monitoring and fuel mapping estimations at global level. GEDI and ICESat2 mission depends on worldwide, crowd-sourced in situ field inventories, and ALS datasets to develop representative pre-launch calibration equations for predicting AGB across the National Aeronautics and Space Administration (NASA) observation domain [19][20][21]. GEDI mission transports a full-waveform (FW) Light Detection and Ranging (LiDAR) instrument to monitor tropical and temperate forests ecosystems.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have assessed the accuracy of GEDI and ICESat-2-derived canopy heights [10,24,25,28] and AGB estimates [15,20,21,29] from different ecosystems around the world. However, canopy height was validated using non coincident temporal or simulated ALS data [10,24,25,28] and simulated GEDI and ICESat-2-data to calibrate global AGB models [15,20,21,29]. Hence, it is important, first, to evaluate the accuracies of post-launch GEDI data and products, since they might differ from the accuracies of the simulated GEDI data, and secondly, to calibrate/validate the local and regional spaceborne LiDAR-AGB model.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

et al. 2021

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Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

et al. 2021

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“…The emergence of GEDI, ICESAT-2 and NISAR multisource sensor data fusion methods will contribute to the evaluation of AGB maps. Compared with other prediction maps, these maps have higher accuracy, more spatial coverage cover and higher spatial resolution [46,47].…”

Section: Discussionmentioning

confidence: 99%

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

et al. 2021

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The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.

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“…In addition, the Global Ecosystem Dynamics Investigation (GEDI) can provide vertical canopy information between 52 • N and S latitudes, which adds another source of LiDAR data [19,58]. Integrating ICESat-2, GEDI, and LiDAR with other forms of optical data, as well as environmental variables, and using machine learning algorithms to estimate the forest canopy height, may have significant potential in the future [59][60][61][62][63][64][65].…”

Section: Uncertainty Limitations and Suggestionsmentioning

confidence: 99%

Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm

Jiang

Zhao

et al. 2021

Remote Sensing

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The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.

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Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Cited by 147 publications

References 41 publications

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm

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