Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes (2020)
- Authors:
- Corte, Ana Paula Dalla
- Souza, Deivison Venicio
- Rex, Franciel Eduardo
- Sanquetta, Carlos Roberto
- Mohan, Midhun
- Silva, Carlos Alberto
- Zambrano, Angelica Maria Almeyda
- Prata, Gabriel
- Almeida, Danilo Roberti Alves de
- Trautenmüller, Jonathan William
- Klauberg, Carine
- Moraes, Anibal de
- Sanquetta, Mateus N
- Wilkinson, Ben
- Broadbent, Eben North
- Autor USP: ALMEIDA, DANILO ROBERTI ALVES DE - ESALQ
- Unidade: ESALQ
- DOI: 10.1016/j.compag.2020.105815
- Subjects: AERONAVES NÃO TRIPULADAS; APRENDIZADO COMPUTACIONAL; ÁRVORES FLORESTAIS; FLORESTAS; INVENTÁRIO FLORESTAL; REDES NEURAIS; SENSORIAMENTO REMOTO; TECNOLOGIA LIDAR
- Agências de fomento:
- Financiamento CAPES
- Financiamento CNPq
- Financiamento FAPESP
- Financiamento Spatial Ecology and Conservation Lab. University of Florida (SPEC-UF), Gainesville, FL, EUA
- Financiamento GatorEye Unmanned Flying Laboratory. Spatial Ecology & Conservation (SPEC-LAB), Gainesville, FL, EUA
- Financiamento National Institute of Food and Agriculture. McIntire-Stennis Program (USDA-McIntire-Stennis), Washington, D.C., EUA
- Financiamento Federal University of Parana (UFPR)
- Financiamento NITA Working Group (NITA), Boulder, CO, EUA
- Language: Inglês
- Imprenta:
- Source:
- Título: Computers and Electronics in Agriculture
- ISSN: 0168-1699
- Volume/Número/Paginação/Ano: v. 179, art. 105815, p. 1-14, September 2020
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
CORTE, Ana Paula Dalla et al. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, v. 179, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.compag.2020.105815. Acesso em: 23 fev. 2026. -
APA
Corte, A. P. D., Souza, D. V., Rex, F. E., Sanquetta, C. R., Mohan, M., Silva, C. A., et al. (2020). Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, 179, 1-14. doi:10.1016/j.compag.2020.105815 -
NLM
Corte APD, Souza DV, Rex FE, Sanquetta CR, Mohan M, Silva CA, Zambrano AMA, Prata G, Almeida DRA de, Trautenmüller JW, Klauberg C, Moraes A de, Sanquetta MN, Wilkinson B, Broadbent EN. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes [Internet]. Computers and Electronics in Agriculture. 2020 ; 179 1-14.[citado 2026 fev. 23 ] Available from: https://doi.org/10.1016/j.compag.2020.105815 -
Vancouver
Corte APD, Souza DV, Rex FE, Sanquetta CR, Mohan M, Silva CA, Zambrano AMA, Prata G, Almeida DRA de, Trautenmüller JW, Klauberg C, Moraes A de, Sanquetta MN, Wilkinson B, Broadbent EN. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes [Internet]. Computers and Electronics in Agriculture. 2020 ; 179 1-14.[citado 2026 fev. 23 ] Available from: https://doi.org/10.1016/j.compag.2020.105815 - Reframing tropical savannization: linking changes in canopy structure to energy balance alterations that impact climate
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Informações sobre o DOI: 10.1016/j.compag.2020.105815 (Fonte: oaDOI API)
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