期刊
ELIFE
卷 10, 期 -, 页码 -出版社
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.62922
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资金
- Gordon and Betty Moore Foundation [GBMF4563]
- National Institute of Food and Agriculture McIntire Stennis projects [1007080]
- National Science Foundation [1926542]
- Direct For Biological Sciences
- Division Of Environmental Biology [1926542] Funding Source: National Science Foundation
By utilizing remote sensing techniques and deep learning methods, researchers conducted individual-level crown estimates for 100 million trees at 37 sites across the United States. This open-source dataset has the potential to significantly expand individual-level research on trees in the United States.
Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote sensing techniques allow surveys of individual trees at unprecedented extents, there remain technical challenges in turning sensor data into tangible information. Using deep learning methods, we produced an open-source data set of individual-level crown estimates for 100 million trees at 37 sites across the United States surveyed by the National Ecological Observatory Network's Airborne Observation Platform. Each canopy tree crown is represented by a rectangular bounding box and includes information on the height, crown area, and spatial location of the tree. These data have the potential to drive significant expansion of individual-level research on trees by facilitating both regional analyses and cross-region comparisons encompassing forest types from most of the United States.
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