Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process Article Swipe
David Morin
,
Milena Planells
,
Nicolas Baghdadi
,
Alexandre Bouvet
,
Ibrahim Fayad
,
Thuy Le Toan
,
Stéphane Mermoz
,
Ludovic Villard
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14092079
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14092079
Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest monitoring tools. Research studies have been carried out to assess the use of optical and radar images for producing forest height or biomass maps. The main limitations are the quantity, quality and representativeness of the reference data for model training. The Global Ecosystem Dynamics Investigation (GEDI) mission (full waveform LiDAR on board the International Space Station) has provided an unprecedented number of forest canopy height samples from 2019. These samples could be used to improve reference datasets. This paper aims to present and validate a method for estimating forest dominant height from open access optical and radar satellite images (Sentinel-1, Sentinel-2 and ALOS-2 PALSAR-2), and then to assess the use of GEDI samples to replace field height measurements in model calibration. Our approach combines satellite image features and dominant height measurements, or GEDI metrics, in a Support Vector Machine regression algorithm, with a feature selection process. The method is tested on mixed uneven-aged broadleaved and coniferous forests in France. Using dominant height measurements for model training, the cross-validation shows 7.3 to 11.6% relative Root Mean Square Error (RMSE) depending on the forest class. When using GEDI height metrics instead of field measurements for model training, errors increase to 12.8–16.7% relative RMSE. This level of error remains satisfactory; the use of GEDI could allow the production of dominant height maps on large areas with better sample representativeness. Future work will focus on confirming these results on new study sites, improving the filtering and processing of GEDI data, and producing height maps at regional or national scale. The resulting maps will help forest managers and public bodies to optimise forest resource inventories, as well as allow scientists to integrate these cartographic data into climate models.
Related Topics
Concepts
Remote sensing
Environmental science
Lidar
Mean squared error
Forest inventory
Representativeness heuristic
Basal area
Forest management
Sustainable forest management
Computer science
Forest ecology
Satellite
Multispectral image
Forestry
Geography
Mathematics
Statistics
Ecology
Agroforestry
Ecosystem
Biology
Aerospace engineering
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14092079
- https://www.mdpi.com/2072-4292/14/9/2079/pdf?version=1650969183
- OA Status
- gold
- Cited By
- 35
- References
- 84
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224945137
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224945137Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14092079Digital Object Identifier
- Title
-
Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping ProcessWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-26Full publication date if available
- Authors
-
David Morin, Milena Planells, Nicolas Baghdadi, Alexandre Bouvet, Ibrahim Fayad, Thuy Le Toan, Stéphane Mermoz, Ludovic VillardList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14092079Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/9/2079/pdf?version=1650969183Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/14/9/2079/pdf?version=1650969183Direct OA link when available
- Concepts
-
Remote sensing, Environmental science, Lidar, Mean squared error, Forest inventory, Representativeness heuristic, Basal area, Forest management, Sustainable forest management, Computer science, Forest ecology, Satellite, Multispectral image, Forestry, Geography, Mathematics, Statistics, Ecology, Agroforestry, Ecosystem, Biology, Aerospace engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
35Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 14, 2023: 11, 2022: 4Per-year citation counts (last 5 years)
- References (count)
-
84Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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