The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Tree‐centric mapping of forest carbon density from airborne laser scanning and hyperspectral data
Open accessAbstract
Summary: Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high‐fidelity mapping of carbon stocks at regional scales. We develop a tree‐centric approach to carbon mapping, based on identifying individual tree crowns (ITC s) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITC s from the laser scanning point cloud using a region‐growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown‐width estimate. From that point on, we use well‐established approaches developed for field‐based inventories: above‐ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density. We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field‐ and ALS‐based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect. An advantage of the tree‐centric approach over existing area‐based methods is that it can produce maps at any scale and is fundamentally based on field‐based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.
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Cites
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Improved allometric models to estimate the aboveground biomass of tropical trees
Chave, J., Réjou‐Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B. C., Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., et al. (2014) · Global Change Biology · doi:10.1111/gcb.12629 · Cited by 3,131
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Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate
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The Structure, Distribution, and Biomass of the World's Forests
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Cited by
Allometric equations for integrating remote sensing imagery into forest monitoring programmes
Jucker, T., Caspersen, J., Chave, J., Antin, C., Barbier, N., Bongers, F., Dalponte, M., van Ewijk, K. Y., Forrester, D. I., Haeni, M., et al. (2017) · Global Change Biology · doi:10.1111/gcb.13388 · Cited by 405
Self-citationIndividual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks
Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., & White, E. (2019) · Remote Sensing · doi:10.3390/rs11111309 · Cited by 382
Metadata
- Authors
- Dalponte, M., & Coomes, D. A.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2016-05-14
- Volume
- 7
- Issue
- 10
- Pages
- 1236-1245
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2315994349
- Cited by
- 379
- References
- 38
- FWCI
- 10.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
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