Integration of UAV-sensed features using machine learning methods to assess species richness in wet grassland ecosystems-Reference-Cited by-同舟云学术

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Integration of UAV-sensed features using machine learning methods to assess species richness in wet grassland ecosystems

Published:2024-11 Issue: Volume:83 Page:102813
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Bazzo Clara Oliva Gonçalves,Kamali Bahareh,dos Santos Vianna Murilo,Behrend Dominik,Hueging Hubert,Schleip Inga,Mosebach Paul,Haub Almut,Behrendt Axel,Gaiser Thomas

Funder

BMBF Bonn

Publisher

Elsevier BV

Reference94 articles.

1. Correlating species and spectral diversities using hyperspectral remote sensing in early-successional fields;Aneece;Ecol. Evol.,2017

2. Coincident detection of crop water stress, nitrogen status, and canopy density using ground based multispectral data;Barnes,2000

3. Estimating pasture biomass and canopy height in Brazilian savanna using UAV photogrammetry;Batistoti;Remote Sens.,2019

4. A review of estimation methods for aboveground biomass in grasslands using UAV;Bazzo;Remote Sens.,2023

5. Random forest in remote sensing: a review of applications and future directions;Belgiu;ISPRS J. Photogramm. Remote Sens.,2016

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Systematic review of remote sensing technology for grassland biodiversity monitoring: Current status and challenges;Global Ecology and Conservation;2024-10

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