First validation of GEDI canopy heights in African savannas

Li, Xiaoxuan; Wessels, Konrad J; Armston, John; Hancock, Steven; Mathieu, Renaud; Main, Russell; Naidoo, Laven; Erasmus, Barend; Scholes, Robert J.

doi:10.1016/j.rse.2022.113402

Cited by 23 publications

(7 citation statements)

References 63 publications

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“…Within our model assessments, we found moderate to high predictive performance for a set of eight GEDI structure metrics across our diverse study region. Much of the existing GEDI literature has focused on accuracy assessments of the GEDI waveform geolocations and structure measures (Adam et al, 2020;Li et al, 2023), developing footprint level biomass models (Duncanson et al, 2022), or leveraged simulated GEDI data in lieu of actual footprint samples (Burns et al, 2020;. Studies have begun to investigate GEDI footprint samples as the basis for scaling up forest structure measures by leveraging fusions of passive and active satellite earth observations, although the majority of those studies have been focused on elevation, canopy height, or biomass (Healey et al, 2020;Potapov et al, 2021;Shendryk, 2022).…”

Section: Discussionmentioning

confidence: 99%

Women in remote sensing: 2022

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Discussionmentioning

confidence: 99%

Women in remote sensing: 2022

2024

Frontiers Research Topics

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“…Within our model assessments, we found moderate to high predictive performance for a set of eight GEDI structure metrics across our diverse study region. Much of the existing GEDI literature has focused on accuracy assessments of the GEDI waveform geolocations and structure measures (Adam et al, 2020;Li et al, 2023), developing footprint level biomass models (Duncanson et al, 2022), or leveraged simulated GEDI data in lieu of actual footprint samples (Burns et al, 2020;Silva et al, 2021). Studies have begun to investigate GEDI footprint samples as the basis for scaling up forest structure measures by leveraging fusions of passive and active satellite earth observations, although the majority of those studies have been focused on elevation, canopy height, or biomass (Healey et al, 2020;Potapov et al, 2021;Shendryk, 2022).…”

Section: Discussionmentioning

confidence: 99%

Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat

Vogeler

Fekety

Elliott

et al. 2023

Front. Remote Sens.

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Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.

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“…With a spatial resolution of 30 m, this dataset provides high-quality topographic variables, such as elevation, slope, and aspect. These variables are essential for understanding vegetation growth and distribution, especially in relation to vegetation height analysis [33]. The SRTM data of the study area were retrieved and processed through the GEE platform using the terrain dataset ID "SRTMGL1_003", which was used to compute elements such as digital elevation model, slope, and slope orientation topography.…”

Section: Auxiliary Datamentioning

confidence: 99%

Upscaling Forest Canopy Height Estimation Using Waveform-Calibrated GEDI Spaceborne LiDAR and Sentinel-2 Data

Wang,

Shen,

Cao

2024

Remote Sensing

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Forest canopy height is a fundamental parameter of forest structure, and plays a pivotal role in understanding forest biomass allocation, carbon stock, forest productivity, and biodiversity. Spaceborne LiDAR (Light Detection and Ranging) systems, such as GEDI (Global Ecosystem Dynamics Investigation), provide large-scale estimation of ground elevation, canopy height, and other forest parameters. However, these measurements may have uncertainties influenced by topographic factors. This study focuses on the calibration of GEDI L2A and L1B data using an airborne LiDAR point cloud, and the combination of Sentinel-2 multispectral imagery, 1D convolutional neural network (CNN), artificial neural network (ANN), and random forest (RF) for upscaling estimated forest height in the Guangxi Gaofeng Forest Farm. First, various environmental (i.e., slope, solar elevation, etc.) and acquisition parameters (i.e., beam type, Solar elevation, etc.) were used to select and optimize the L2A footprint. Second, pseudo-waveforms were simulated from the airborne LiDAR point cloud and were combined with a 1D CNN model to calibrate the L1B waveform data. Third, the forest height extracted from the calibrated L1B waveforms and selected L2A footprints were compared and assessed, utilizing the CHM derived from the airborne LiDAR point cloud. Finally, the forest height data with higher accuracy were combined with Sentinel-2 multispectral imagery for an upscaling estimation of forest height. The results indicate that through optimization using environmental and acquisition parameters, the ground elevation and forest canopy height extracted from the L2A footprint are generally consistent with airborne LiDAR data (ground elevation: R2 = 0.99, RMSE = 4.99 m; canopy height: R2 = 0.42, RMSE = 5.16 m). Through optimizing, ground elevation extraction error was reduced by 45.5% (RMSE), and the canopy height extraction error was reduced by 30.3% (RMSE). After training a 1D CNN model to calibrate the forest height, the forest height information extracted using L1B has a high accuracy (R2 = 0.84, RMSE = 3.13 m). Compared to the optimized L2A data, the RMSE was reduced by 2.03 m. Combining the more accurate L1B forest height data with Sentinel-2 multispectral imagery and using RF and ANN for the upscaled estimation of the forest height, the RF model has the highest accuracy (R2 = 0.64, RMSE = 4.59 m). The results show that the extrapolation and inversion of GEDI, combined with multispectral remote sensing data, serve as effective tools for obtaining forest height distribution on a large scale.

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First validation of GEDI canopy heights in African savannas

Cited by 23 publications

References 63 publications

Women in remote sensing: 2022

Women in remote sensing: 2022

Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat

Upscaling Forest Canopy Height Estimation Using Waveform-Calibrated GEDI Spaceborne LiDAR and Sentinel-2 Data

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