Capturing plant functional traits in coastal dunes using close-range remote sensing

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Giacomo Trotta , Marco Vuerich , Elisa Petrussa , Edoardo Asquini , Paolo Cingano , Francesco Boscutti
{"title":"Capturing plant functional traits in coastal dunes using close-range remote sensing","authors":"Giacomo Trotta ,&nbsp;Marco Vuerich ,&nbsp;Elisa Petrussa ,&nbsp;Edoardo Asquini ,&nbsp;Paolo Cingano ,&nbsp;Francesco Boscutti","doi":"10.1016/j.ecoinf.2025.103159","DOIUrl":null,"url":null,"abstract":"<div><div>Coastal dunes are dynamic ecosystems characterized by steep environmental gradients that impose significant stress on plant communities. These stressors, such as salinity, drought, and nutrient-poor soils, create a mosaic of plant communities with strong functional trait identity. Several studies have focused on plant functional responses to environmental conditions, but a gap remains in connecting plant functional traits to large-scale ecological processes through remote sensing. We studied a dune plant community (a total of 17 species) and the ecosystem key species <em>Cakile maritima</em> Scop. to explore how remote sensing-derived vegetation indices correlate with plant growth and specific physiological and morphological leaf traits, including specific leaf area, leaf dry matter content, and flavonoid concentration. We introduced a close-range approach using multispectral imaging to capture high-resolution (1.3 mm/px) data on plant functional traits in coastal dune ecosystems overcoming the limitations of broader-scale remote sensing methods which often suffer from lower spatial resolution and interference from non-vegetated areas. By semi-automatically identifying regions of interest for each species and eliminating background noise, we acquired accurate multispectral signatures that represent plant responses and highlight ecological processes of the key species and the broader community. We observed traits to be stronger than plant growth in explaining the variance of multispectral indices, with leaf flavonoids showing the highest contribution to plant spectral signature.</div><div>We demonstrated the effectiveness of close-range multispectral imaging in linking plant traits to ecological processes, with significant implications for upscaling plant responses to environmental variable across larger spatial scales. Furthermore, the research outlines practical guidelines for collecting and processing close-range multispectral data, offering a valuable new tool for and accurate field monitoring of ecosystem processes and plant functions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103159"},"PeriodicalIF":5.8000,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574954125001682","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Coastal dunes are dynamic ecosystems characterized by steep environmental gradients that impose significant stress on plant communities. These stressors, such as salinity, drought, and nutrient-poor soils, create a mosaic of plant communities with strong functional trait identity. Several studies have focused on plant functional responses to environmental conditions, but a gap remains in connecting plant functional traits to large-scale ecological processes through remote sensing. We studied a dune plant community (a total of 17 species) and the ecosystem key species Cakile maritima Scop. to explore how remote sensing-derived vegetation indices correlate with plant growth and specific physiological and morphological leaf traits, including specific leaf area, leaf dry matter content, and flavonoid concentration. We introduced a close-range approach using multispectral imaging to capture high-resolution (1.3 mm/px) data on plant functional traits in coastal dune ecosystems overcoming the limitations of broader-scale remote sensing methods which often suffer from lower spatial resolution and interference from non-vegetated areas. By semi-automatically identifying regions of interest for each species and eliminating background noise, we acquired accurate multispectral signatures that represent plant responses and highlight ecological processes of the key species and the broader community. We observed traits to be stronger than plant growth in explaining the variance of multispectral indices, with leaf flavonoids showing the highest contribution to plant spectral signature.
We demonstrated the effectiveness of close-range multispectral imaging in linking plant traits to ecological processes, with significant implications for upscaling plant responses to environmental variable across larger spatial scales. Furthermore, the research outlines practical guidelines for collecting and processing close-range multispectral data, offering a valuable new tool for and accurate field monitoring of ecosystem processes and plant functions.

Abstract Image

海岸带沙丘植物功能性状近景遥感研究
海岸带沙丘是一种动态生态系统,其特征是陡峭的环境梯度,对植物群落施加了巨大的压力。这些压力源,如盐度、干旱和营养贫乏的土壤,创造了具有强烈功能特征同一性的植物群落的马赛克。一些研究主要集中在植物对环境条件的功能响应上,但在通过遥感将植物功能性状与大尺度生态过程联系起来方面仍然存在空白。研究了一个沙丘植物群落(共17种)和生态系统的关键物种海螺(Cakile marima Scop)。探讨遥感植被指数与植物生长和叶片特定生理形态性状(包括比叶面积、叶片干物质含量和类黄酮浓度)之间的关系。本文采用近距离多光谱成像技术捕获高分辨率(1.3 mm/px)的海岸带沙丘生态系统植物功能性状数据,克服了大尺度遥感方法空间分辨率较低和受非植被区域干扰的限制。通过半自动识别每个物种感兴趣的区域并消除背景噪声,我们获得了准确的多光谱特征,这些特征代表了植物的响应,并突出了关键物种和更广泛的群落的生态过程。我们发现性状比植物生长更能解释多光谱指标的差异,其中叶片黄酮类化合物对植物光谱特征的贡献最大。我们证明了近距离多光谱成像在将植物性状与生态过程联系起来方面的有效性,这对在更大的空间尺度上提升植物对环境变量的响应具有重要意义。此外,该研究还概述了近距离多光谱数据收集和处理的实用指南,为生态系统过程和植物功能的精确野外监测提供了有价值的新工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信