Challenges to aboveground biomass prediction from waveform lidar
Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ac3cec |
| _version_ | 1827869628517318656 |
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| author | Jamis M Bruening Rico Fischer Friedrich J Bohn John Armston Amanda H Armstrong Nikolai Knapp Hao Tang Andreas Huth Ralph Dubayah |
| author_facet | Jamis M Bruening Rico Fischer Friedrich J Bohn John Armston Amanda H Armstrong Nikolai Knapp Hao Tang Andreas Huth Ralph Dubayah |
| author_sort | Jamis M Bruening |
| collection | DOAJ |
| description | Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD—that forest stands with different AGBD may produce highly similar waveforms—and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non-uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7–411 Mg ha ^−1 , average of 167 Mg ha ^−1 ). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD. |
| first_indexed | 2024-03-12T15:46:50Z |
| format | Article |
| id | doaj.art-3e0f5dd6f47446c8b3dcdda3b03d389b |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:46:50Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-3e0f5dd6f47446c8b3dcdda3b03d389b2023-08-09T15:21:58ZengIOP PublishingEnvironmental Research Letters1748-93262021-01-01161212501310.1088/1748-9326/ac3cecChallenges to aboveground biomass prediction from waveform lidarJamis M Bruening0https://orcid.org/0000-0002-9750-7806Rico Fischer1https://orcid.org/0000-0002-0482-0095Friedrich J Bohn2https://orcid.org/0000-0002-7328-1187John Armston3https://orcid.org/0000-0003-1232-3424Amanda H Armstrong4https://orcid.org/0000-0002-9123-8924Nikolai Knapp5https://orcid.org/0000-0001-5065-9979Hao Tang6https://orcid.org/0000-0001-7935-5848Andreas Huth7Ralph Dubayah8https://orcid.org/0000-0003-1440-6346Department of Geographical Sciences, University of Maryland , College Park, MD 20740, United States of AmericaDepartment of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ) , 04318 Leipzig, GermanyDepartment of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ) , 04318 Leipzig, GermanyDepartment of Geographical Sciences, University of Maryland , College Park, MD 20740, United States of AmericaDepartment of Environmental Sciences, University of Virginia, Clark Hall , Charlottesville, VA 22902, United States of America; Universities Space Research Association, Goddard Earth Sciences Technology and Research Studies and Investigations , NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, United States of AmericaDepartment of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ) , 04318 Leipzig, GermanyDepartment of Geographical Sciences, University of Maryland , College Park, MD 20740, United States of America; Department of Geography, National University of Singapore , Kent Ridge 117570, SingaporeDepartment of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ) , 04318 Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv) , 04103 Halle-Leipzig-Jena, Germany; Institute of Environmental Systems Research, University Osnabrück , 49076 Osnabrück, GermanyDepartment of Geographical Sciences, University of Maryland , College Park, MD 20740, United States of AmericaAccurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD—that forest stands with different AGBD may produce highly similar waveforms—and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non-uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7–411 Mg ha ^−1 , average of 167 Mg ha ^−1 ). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.https://doi.org/10.1088/1748-9326/ac3cecGEDIwaveform lidarforest modelingaboveground biomassforest structure |
| spellingShingle | Jamis M Bruening Rico Fischer Friedrich J Bohn John Armston Amanda H Armstrong Nikolai Knapp Hao Tang Andreas Huth Ralph Dubayah Challenges to aboveground biomass prediction from waveform lidar Environmental Research Letters GEDI waveform lidar forest modeling aboveground biomass forest structure |
| title | Challenges to aboveground biomass prediction from waveform lidar |
| title_full | Challenges to aboveground biomass prediction from waveform lidar |
| title_fullStr | Challenges to aboveground biomass prediction from waveform lidar |
| title_full_unstemmed | Challenges to aboveground biomass prediction from waveform lidar |
| title_short | Challenges to aboveground biomass prediction from waveform lidar |
| title_sort | challenges to aboveground biomass prediction from waveform lidar |
| topic | GEDI waveform lidar forest modeling aboveground biomass forest structure |
| url | https://doi.org/10.1088/1748-9326/ac3cec |
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