The use of machine learning methods to estimate aboveground biomass of grasslands: A review
The study of grasslands using machine learning (ML) methods combined with proximal/remote sensing data (RS) has been steadily increasing in the last decades. Available algorithms range from a primarily academic use to more widespread practical applications intended at helping farm management. Here,...
Guardado en:
Autores principales: | Tiago G. Morais, Ricardo F.M. Teixeira, Mario Figueiredo, Tiago Domingos |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Elsevier
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/9a1af780b7e84abdad18462459b2bec5 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Hyperspectral retrieval of leaf physiological traits and their links to ecosystem productivity in grassland monocultures
por: Yujin Zhao, et al.
Publicado: (2021) -
Estimation of Maize Leaf Area Index and Aboveground Biomass Based on Hyperspectral Data
por: SHU Meiyan, et al.
Publicado: (2021) -
Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data
por: Wanxue Zhu, et al.
Publicado: (2021) -
The retrieval of plant functional traits from canopy spectra through RTM-inversions and statistical models are both critically affected by plant phenology
por: Felix Schiefer, et al.
Publicado: (2021) -
Estimation of aboveground biomass using aerial photogrammetry from unmanned aerial vehicle in teak (Tectona grandis) plantation in Thailand
por: SASIWIMOL RINNAMANG, et al.
Publicado: (2020)