Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests.
Details
Original language | English |
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Pages (from-to) | 3129-3148 |
Number of pages | 20 |
Journal | International journal of remote sensing |
Volume | 45 |
Issue number | 9 |
Publication status | Published - 2024 |
Peer-reviewed | Yes |
External IDs
ORCID | /0000-0003-1351-4214/work/161891515 |
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ORCID | /0000-0003-0363-9697/work/161891720 |
Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- GEDI, SAR Tomography, Tropical forest, Vertical structure