Giacomo Trotta , Marco Vuerich , Elisa Petrussa , Edoardo Asquini , Paolo Cingano , Francesco Boscutti
{"title":"海岸带沙丘植物功能性状近景遥感研究","authors":"Giacomo Trotta , Marco Vuerich , Elisa Petrussa , Edoardo Asquini , Paolo Cingano , 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":"{\"title\":\"Capturing plant functional traits in coastal dunes using close-range remote sensing\",\"authors\":\"Giacomo Trotta , Marco Vuerich , Elisa Petrussa , Edoardo Asquini , Paolo Cingano , 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}","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}
Capturing plant functional traits in coastal dunes using close-range remote sensing
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.
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.