A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Improved country-wide estimation of above-ground tropical forest biomass using locally calibrated GEDI spaceborne LiDAR data
NASA’s Global Ecosystem Dynamics Investigation (GEDI) presents an unprecedented opportunity for cost-effective estimations of above-ground biomass density (AGBD) using spaceborne Light Detection And Ranging technology. Current performance of the GEDI Level 4 A (L4A) AGBD product is, however, subject to model choice and availability of calibration data. Here, we identified biases in the current GEDI L4A AGBD product compared to National Forest Inventory (NFI) data for the Southeast Asian country of Laos, with absolute bias values ranging from −54.24 to 106.23 Mg Ha ^−1 across different forest types. We optimised the GEDI L4A AGBD model configurations for natural forests in Laos and calibrated them with ancillary variables. The biases were significantly reduced (average bias reduction for all forest types = 42.2 Mg Ha ^−1 ), with the greatest reduction for the evergreen (EG) forest type. The calibrated GEDI footprints were aggregated to produce a country-wide map of AGBD for natural forests. The approach also enabled the updating of national-level estimates of average AGBD stock for each forest class in Laos using a model-assisted estimator complementary to the existing NFI design-based estimator. Results highlight the importance of localised calibration in remote sensing applications used in estimating forest biomass, and offer a replicable framework for application in other regions with limited availability of ground data and/or extensive, remote areas of forest.
Improved country-wide estimation of above-ground tropical forest biomass using locally calibrated GEDI spaceborne LiDAR data
NASA’s Global Ecosystem Dynamics Investigation (GEDI) presents an unprecedented opportunity for cost-effective estimations of above-ground biomass density (AGBD) using spaceborne Light Detection And Ranging technology. Current performance of the GEDI Level 4 A (L4A) AGBD product is, however, subject to model choice and availability of calibration data. Here, we identified biases in the current GEDI L4A AGBD product compared to National Forest Inventory (NFI) data for the Southeast Asian country of Laos, with absolute bias values ranging from −54.24 to 106.23 Mg Ha ^−1 across different forest types. We optimised the GEDI L4A AGBD model configurations for natural forests in Laos and calibrated them with ancillary variables. The biases were significantly reduced (average bias reduction for all forest types = 42.2 Mg Ha ^−1 ), with the greatest reduction for the evergreen (EG) forest type. The calibrated GEDI footprints were aggregated to produce a country-wide map of AGBD for natural forests. The approach also enabled the updating of national-level estimates of average AGBD stock for each forest class in Laos using a model-assisted estimator complementary to the existing NFI design-based estimator. Results highlight the importance of localised calibration in remote sensing applications used in estimating forest biomass, and offer a replicable framework for application in other regions with limited availability of ground data and/or extensive, remote areas of forest.
Improved country-wide estimation of above-ground tropical forest biomass using locally calibrated GEDI spaceborne LiDAR data
Yuchuan Zhou (author) / David M Taylor (author) / Hao Tang (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data
| DOAJ | 2023
Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data
| Online Contents | 2014
Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest
| DOAJ | 2013
| DOAJ | 2022