Evidence map›Paper›PMID 42801206›Full record

ArticlePlant diversity2026

Assessing the performance of remotely sensed functional traits and spectral metrics for estimating multidimensional plant diversity in wetlands using UAV data.

Long Ren, Zhaoju Zheng, Paolo Villa, Ping Zhao, Cong Xu, Ying Fu, Zhaofu Zhou, Jinchen Wu, Xinsheng Zhao, Wei Li and 4 more

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In one paragraph

Article in Plant diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Long RenState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Zhaoju ZhengState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Paolo VillaInstitute for Electromagnetic Sensing of the Environment, National Research Council of Italy, Milan 20133, Italy.
Ping ZhaoState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Cong XuState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Ying FuState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Zhaofu ZhouState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Jinchen WuState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Xinsheng ZhaoInstitute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China.
Wei LiInstitute of Wetland Research, Chinese Academy of Forestry, Beijing 100091, China.
Hui LuAdministration of Natural Protected Areas, Yanqing, Beijing 102101, China.
Sufan LiuAdministration of Natural Protected Areas, Yanqing, Beijing 102101, China.
Dan ZhaoState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
Yuan ZengState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plant diversity underpins wetland ecosystem stability and functional sustainability, and its reliable assessment is vital for effective conservation and management. Remote sensing provides an efficient mean of plant diversity monitoring, however the potential of remotely sensed functional traits (RS-traits) and spectral metrics for plant diversity estimation in wetland ecosystems has not been fully investigated. In this study, we integrated UAV hyperspectral and LiDAR data to extract spectral band metrics, Rao's quadratic entropy and principal components, texture features, and retrieve physiological and morphological RS-traits. We then used these features to predict multi-dimensional (species, functional, and phylogenetic) plant diversity using multiple stepwise regression (MSR), generalized additive models (GAMs), and random forest regression (RF). The results demonstrated that the retrieved RS-traits were generally consistent with field measurements (R

Indexed as

Empirical modelsFunctional diversityPhylogenetic diversityRemote sensingSpecies diversityWetlands

Identifiers

PMID42801206
PMCPMC13615782

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.