A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network.
Elife
; 102021 02 19.
Article
em En
| MEDLINE
| ID: mdl-33605211
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Árvores
/
Ecologia
/
Tecnologia de Sensoriamento Remoto
/
Aprendizado Profundo
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Elife
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos