N: 52 S: -52 E: 180 W: -180
Description
This Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 138 ending on 2021-08-04. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD. The gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) describes the hybrid model-based mode of inference used in the L4B product. Corresponding 1 km estimates of the standard error of the mean are also provided in the L4B product. Uncertainty is due to both GEDI's sampling of the 1 km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free.
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Publications Citing This Dataset
| Title | Authors | Year Sort ascending | Referenced Datasets |
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| Reduced Vegetation Uptake During the Extreme 2023 Drought Turns the Amazon Into a Weak Carbon Source |
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| Aboveground woody biomass regression and uncertainty estimation from spaceborne lidar and optical datasets |
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| A quantitative evaluation of forest aboveground biomass density map products in Oregon, USA |
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| GEDICorrect: A scalable python tool for orbit-, beam-, and footprint-level GEDI geolocation correction |
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| Canopy Rainfall Storage Capacity Quantified Across the Diversely |
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| Assessing Forest Fire Susceptibility in the Hindu Kush Himalaya: Implications for Biodiversity and Carbon Stock |
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| Disentangling the impact of climate and ecological restoration measures on ecosystem service: A case study in Yimeng mountains |
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| Cloud-Resilient Forest Monitoring for Sustainable Urban Land Management: L-Band SAR Assessment of Charcoal-Driven Peri-Urban Woodland Change in Lusaka, Zambia |
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| L. Duncanson a,* PM Montesano b,c,A. Neuenschwander d, A. |
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| Managed rainforests support higher carbon density and sequestration in the Congo Basin |
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| Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia |
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| A tridimensional framework for governance in the wildland-urban interface using Pyro-Socio-Ecological Zones |
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| Response of vegetation optical depth across multiple microwave frequencies to global vegetation dynamics |
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| State of the art and for remote sensing monitoring of carbon dynamics in African tropical forests |
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| Sub-hectare resolution forest biomass mapping from Copernicus DEM with low-dimensional models |
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| Spring phenology and productivity alter vegetation vulnerability under |
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| Machine Learning and Multisensor Data Fusion for Forest above Ground Biomass Estimation in Arkansas |
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| Mapping large-scale pantropical forest canopy height by integrating GEDI |
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| National-scale calibrated GEDI AGBD models for effective assessment of |
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| Using bi-temporal ALS and NFI-based time-series data to account for large-scale aboveground carbon dynamics: the showcase of mediterranean forests |
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| Unveiling spatial variations of high forest live biomass carbon stocks |
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| High-resolution mapping of forest structure and carbon stock using multi-source remote sensing data in Japan |
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| Characterizing the Accelerated Global Carbon Emissions from Forest Loss |
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| Biodiversity and Wetting of Climate Alleviate Vegetation Vulnerability |
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| Central African biomass carbon losses and gains during 20102019 |
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