加强野外植被监测：一种基于地面图像的物种覆盖估计的深度学习方法

IF 6.3 2区环境科学与生态学 Q1 ECOLOGY

Methods in Ecology and Evolution Pub Date : 2025-03-24 DOI:10.1111/2041-210X.70024

Pauline Müller, Stefano Puliti, Johannes Breidenbach

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Towards enhancing field-based vegetation monitoring: A deep learning approach for species coverage estimation from ground-level imagery

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Towards enhancing field-based vegetation monitoring: A deep learning approach for species coverage estimation from ground-level imagery

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来源期刊

Methods in Ecology and Evolution ECOLOGY-

CiteScore

11.60

自引率

3.00%

发文量

236

审稿时长

4-8 weeks

期刊介绍： A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.