Ecological Informatics最新文献

{"title":"Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf","authors":"Bonyad Ahmadi ,&nbsp;Mehdi Gholamalifard ,&nbsp;Seyed Mahmoud Ghasempouri ,&nbsp;Tiit Kutser","doi":"10.1016/j.ecoinf.2025.103171","DOIUrl":"10.1016/j.ecoinf.2025.103171","url":null,"abstract":"<div><div>Colored Dissolved Organic Matter, a pivotal component of aquatic biogeochemical cycles, plays a critical role in regulating water quality and ecosystem functionality. This study provides the first comprehensive assessment of CDOM dynamics in the Persian Gulf's industrialized coastal waters, focusing on the Pars Special Economic Energy Zone (PSEEZ)—a global energy epicenter and the world's largest natural gas reserve. Seasonal field campaigns conducted in 2023 acquired 199 in situ samples stratified across four seasons (Spring: <em>n</em> = 62, Summer: <em>n</em> = 18, Fall: <em>n</em> = 55, Winter: <em>n</em> = 64) using a CTD-integrated Cyclops-7 fluorometer. Sampling intervals were methodologically synchronized with satellite overpasses (±3 h) to minimize temporal discrepancies between ground-truth measurements and remotely sensed data, thereby ensuring spatiotemporal coherence essential for robust algorithm calibration and validation. Contrary to expectations, CDOM concentrations in petrochemical-influenced areas (e.g., stations P7: 0.29 ppb, P13: 0.35 ppb) were markedly lower than in natural mangrove ecosystems (stations N13: 19.61 ppb, NA2: 12.91 ppb), underscoring the antagonistic effects of industrial pollutants on organic matter stability. Initial CDOM retrieval algorithms yielded suboptimal accuracy (MAE = 1.16, RMSLE = 1.2). A regionally tuned band ratio algorithm improved performance by 27 % (MAE = 0.85) and 22 % (RMSLE = 0.94). Machine learning models further enhanced retrievals, with the Mixture Density Network (MDN) emerging as the superior framework. The MDN achieved an RMSLE of 0.47 (17.5 % improvement over MLP, 14.5 % over SVM) and reduced systematic bias (SSPB) by 26.12 units compared to Bayesian Ridge Regression (BRR), outperforming conventional models like SVM (MAE = 0.61, RMSLE = 0.55). While the MDN exhibited marginally higher absolute error (MAE = 0.53) than deterministic models, its probabilistic architecture uniquely addressed the Persian Gulf's optical complexity, characterized by overlapping signals from SGD-driven organics, hydrocarbon plumes, and sediment resuspension. This study establishes MDN as a transformative tool for CDOM retrieval in optically heterogeneous, anthropogenically stressed waters, while advocating for regionally adaptive frameworks to advance precision water quality monitoring in critical marine ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103171"},"PeriodicalIF":5.8,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From underwater to drone: A novel multi-scale knowledge distillation approach for coral reef monitoring","authors":"Matteo Contini ,&nbsp;Victor Illien ,&nbsp;Julien Barde ,&nbsp;Sylvain Poulain ,&nbsp;Serge Bernard ,&nbsp;Alexis Joly ,&nbsp;Sylvain Bonhommeau","doi":"10.1016/j.ecoinf.2025.103149","DOIUrl":"10.1016/j.ecoinf.2025.103149","url":null,"abstract":"<div><div>Drone-based remote sensing combined with AI-driven methodologies has shown great potential for accurate mapping and monitoring of coral reef ecosystems. This study presents a novel multi-scale approach to coral reef monitoring, integrating fine-scale underwater imagery with medium-scale aerial imagery. Underwater images are captured using an Autonomous Surface Vehicle (ASV), while aerial images are acquired with an aerial drone. A transformer-based deep-learning model is trained on underwater images to detect the presence of 31 classes covering various coral morphotypes, associated fauna, and habitats. For aerial analysis these predictions are refined (some classes are merged, others are retained, while some are removed) resulting in a final set of 12 ecological categories that serve as annotations for training a second model applied to aerial images. The transfer of information across scales is achieved through a weighted footprint method that accounts for partial overlaps between underwater image footprints and aerial image tiles. The results show that the multi-scale methodology successfully extends fine-scale classification to larger reef areas, achieving a high degree of accuracy in predicting coral morphotypes and associated habitats. The method showed a strong alignment between underwater-derived annotations and ground truth data, reflected by an AUC (Area Under the Curve) score of 0.9251. This shows that the integration of underwater and aerial imagery, supported by deep-learning models, can facilitate scalable and accurate reef assessments. This study combines multi-scale imaging and AI to provide scientific information on coral reef monitoring and conservation. Our approach leverages underwater and aerial imagery, aiming for the precision of fine-scale analysis while extending it to cover a broader reef area.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103149"},"PeriodicalIF":5.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal dynamics of malaria vector niche overlaps in Africa","authors":"Eric Ali Ibrahim ,&nbsp;John Odindi ,&nbsp;Mark Wamalwa ,&nbsp;Henri E.Z. Tonnang","doi":"10.1016/j.ecoinf.2025.103151","DOIUrl":"10.1016/j.ecoinf.2025.103151","url":null,"abstract":"<div><div>Malaria remains a significant public health challenge in sub-Saharan Africa, with transmission heightened by the dynamics of primary and secondary mosquitoes infected with <em>Plasmodium</em> parasites<em>.</em> Regions where both vector types co-exist face heightened likelihood of intensified malaria transmission. Hence, understanding vectors' ecological interactions, especially their niche overlaps in geographic or environmental space, is crucial for targeted malaria control and elimination strategies. We employed a dynamic cellular automata (CA) model to map niche overlaps among primary (<em>Anopheles gambiae</em> complex, <em>An. funestus</em> group) and secondary (<em>An. pharoensis</em>, <em>An. coustani</em>) malaria vectors across African, using open-access environmental and vector occurrence datasets sourced from open-access geospatial portals, and spanning 1985 to 2021. Prior to modeling, we conducted exploratory data analysis (EDA) involving descriptive statistics, correlation and cluster analysis to glean insights into the relationships between the variables. Spearman correlation analysis revealed weak significant correlations (|r| &lt; 0.3, <em>p</em>-value &lt;0.001) between environmental variables and vectors occurrence, while environmental variables exhibited strong intercorrelations. Furthermore, <em>An. gambiae</em> complex prevailed at higher elevations with a minimum relative humidity of 22 %, while secondary vectors prevailed at lower elevations with humidity &gt;38 % and temperatures above 20 °C. Our model, with accuracy exceeding 0.9 following validation, revealed expanding malaria vector niche overlaps across Africa, attributed to vectors expansion beyond their native regions. Such expanding vector niche overlaps predisposes numerous areas at risk of sustained and prolonged malaria transmission, underscoring the need for targeted malaria vector control interventions. Furthermore, dynamic modeling approaches, incorporating continuous data updates, captured ecological interactions accurately.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103151"},"PeriodicalIF":5.8,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Empirical Dynamic Modeling for forecasting: The role of variation among time series replicates","authors":"Fleur Slegers ,&nbsp;Robbin Bastiaansen ,&nbsp;Edwin Pos","doi":"10.1016/j.ecoinf.2025.103139","DOIUrl":"10.1016/j.ecoinf.2025.103139","url":null,"abstract":"<div><div>Accurate forecasting of ecological systems is essential for effective environmental management but remains challenging. One tool for this purpose is Empirical Dynamic Modeling (EDM). EDM typically requires long time series as input. To overcome data limitations, time series from similar sources (replicates) are often combined. Although EDM with replicates has been evaluated using simulated data, the impact of adding time series remains not fully understood. In this study, we use simulated data from the Lorenz-63 system, a three-species food chain, and a four-species Lotka–Volterra model of competition to evaluate the performance of EDM’s S-Map algorithm across various scenarios, employing three different approaches to generate time series replicates, each with a different type of variation between the replicates: varying initial conditions (Scenario A), sampling distinct sections of the attractor (Scenario B), and varying the system’s parameter controlling chaotic behavior (Scenario C). Our findings demonstrate that EDM performs better with longer time series, but that combining replicates can often compensate for short time series length, in line with expectations from previous results. However, both the type and level of variation among the combined replicates affect forecasting accuracy. Adding replicates in Scenario B consistently improves outcomes. However, in Scenarios A and C (involving different long-term behaviors or transient phases), combining replicates may negate these benefits, particularly for periodic and chaotic systems and large inter-replicate variations. Our results show that not all time series replicates are equally suitable for improving EDM forecasts, highlighting the importance of careful selection and combination of replicates.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103139"},"PeriodicalIF":5.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adapting a global plant identification model to detect invasive alien plant species in high-resolution road side images","authors":"Vincent Espitalier ,&nbsp;Jean-Christophe Lombardo ,&nbsp;Hervé Goëau ,&nbsp;Christophe Botella ,&nbsp;Toke Thomas Høye ,&nbsp;Mads Dyrmann ,&nbsp;Pierre Bonnet ,&nbsp;Alexis Joly","doi":"10.1016/j.ecoinf.2025.103129","DOIUrl":"10.1016/j.ecoinf.2025.103129","url":null,"abstract":"<div><div>Early detection of invasive alien plant species is crucial for addressing their environmental impact. Recent advancements in vehicle-mounted equipment enable automatic analysis of high-resolution images to detect invasive plants along roadsides, a primary vector for their spread. Deep learning technologies show promise for processing this data efficiently, but the choice of approach significantly affects both computational and human resource costs. Object detection and segmentation methods require costly annotations, making them impractical for scaling to the thousands of invasive species worldwide. In contrast, multi-label classification, i.e. to predict all species present in the image, is less demanding but still challenging to implement without many annotated images for numerous species. However, large datasets from citizen science platforms such as Pl@ntNet or iNaturalist offer rich visual data for classifying individual plant species. In this article, we assess whether large plant identification models trained on such data can be leveraged for species detection in high-resolution images. Specifically, we explore two approaches: a multi-label classification model and a tiling-based model, using a vision transformer from the Pl@ntNet platform. We evaluate these models on high-resolution roadside images, both using a pre-trained model without fine-tuning and after applying fine-tuning. Our findings indicate that the tiling approach significantly outperforms other methods without fine-tuning and shows a slight advantage when fine-tuning is applied, demonstrating significant potential for detecting thousands of species without task-specific adaptation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103129"},"PeriodicalIF":5.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping individual tree crowns to extract morphological attributes in urban areas using unmanned aerial vehicle-based LiDAR and RGB data","authors":"Geonung Park ,&nbsp;Bonggeun Song ,&nbsp;Kyunghun Park","doi":"10.1016/j.ecoinf.2025.103165","DOIUrl":"10.1016/j.ecoinf.2025.103165","url":null,"abstract":"<div><div>Mapping individual tree crowns (ITCs) along with their morphological attributes provides foundational variables for estimating functions, such as thermal stress and carbon emissions, within the urban environment. However, to calculate morphological attributes, it is necessary to delineate ITCs, and applying the watershed segmentation (WS) algorithm, commonly used in forests, to urban environments presents challenges due to the reliance on single-band data and complexity of heterogeneous urban elements. Additionally, deep learning (DL) models, which excel in image analysis, are constrained by the labor-intensive label generation process. This study introduces a novel framework integrating machine learning (ML)- and DL-based approaches to map ITCs and extract the morphological attributes in urban areas using unmanned aerial vehicles (UAVs). Using ML-based approaches, we conducted object-based image analysis to optimize the use of the WS algorithm because applying WS directly to urban environments, as in forests, overestimated the ITC size by 151.37 %. This approach also improved the effectiveness of the DL model, Mask R-CNN, by resolving challenges in label generation. Mask R-CNN delineated ITCs with an accuracy of 0.942, suggesting its robustness in handling the heterogeneity of tree arrangements. The results demonstrate that the proposed framework is applicable for use in urban areas globally that have similar ecological conditions. ITCs with morphological attributes provide fundamental variables for evaluating ecological functions, which are scalable for improving urban environmental planning. However, UAV data may face time and cost limitations as the monitoring coverage expands, which should be considered when applying this framework.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103165"},"PeriodicalIF":5.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of mangrove heights and aboveground biomass using UAV-LiDAR, Sentinel-1 and ZY-3 stereo images","authors":"Bolin Fu ,&nbsp;Yingying Wei ,&nbsp;Linhang Jiang ,&nbsp;Hang Yao ,&nbsp;Xiaomin Li ,&nbsp;Yanli Yang ,&nbsp;Mingming Jia ,&nbsp;Weiwei Sun","doi":"10.1016/j.ecoinf.2025.103160","DOIUrl":"10.1016/j.ecoinf.2025.103160","url":null,"abstract":"<div><div>Mangroves are crucial blue carbon ecosystems that are essential for promoting sustainable global development. Tree height is a key indicator of mangrove health; however, accurately estimating mangrove height in complex coastal environments is challenging. In this study, we constructed mangrove height inversion models using multiple types of remote sensing data and machine learning algorithms (partial least squares regression (PLSR), random forest (RF), and mixture density network (MDN)). We evaluated the performance of UAV-LiDAR point clouds, ZY-3 stereo images, and Sentinel-1 polarimetric and interferometric data in mangrove height inversion, and explored the accuracy differences among the dominant species. We also estimated the aboveground biomass of different dominant mangrove species to better understand their ecological functions and health conditions. The results showed the following: (1) The canopy height model and height variables of the LiDAR point clouds, DVI and near-infrared bands of the ZY-3 stereo images, and polarimetric decomposition parameters of the Sentinel-1 SAR images were more sensitive to mangrove heights. (2) The LiDAR point clouds and Sentinel-1 SAR images achieved the highest inversion accuracy when using the RF algorithm, with R² values of 0.875 and 0.685, respectively. The ZY-3 stereo images based on MDN obtained the optimal inversion results (R² = 0.719), with an improvement ranging from 0.143 to 0.198 when compared to the PLSR and RF algorithms. (3) <em>Avicennia marina</em> was associated with the highest estimation accuracy (R² = 0.897) compared to the other dominant mangrove species. <em>Aegiceras corniculatum</em> and <em>Avicennia marina</em> were associated with the highest inversion accuracy within the height range of 2–3 m (R² = 0.925, R² = 0.814, respectively), whereas <em>Kandelia candel</em> yielded the optimal inversion results at the height range of 1–2 m (R² = 0.652). (4) The aboveground biomass of <em>Aegiceras cornicatum</em> and <em>Kandelia candel</em> ranged from 20.176 to 103.164 Mg/ha and 132.019 to 719.226 Mg/ha, respectively, and the aboveground biomass of <em>Avicennia marina</em> was mainly distributed within the range of 169.916 to 803.204 Mg/ha. Our study provides a reference for monitoring the heights and health of mangroves, as well as their protection and development.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103160"},"PeriodicalIF":5.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early summer warming amplification threats towards sustainable development goals (SDGs) in India","authors":"Susanta Mahato ,&nbsp;Swades Pal ,&nbsp;P.K. Joshi ,&nbsp;Andreas Matzarakis ,&nbsp;Paolo Tarolli ,&nbsp;Vicky Anand","doi":"10.1016/j.ecoinf.2025.103156","DOIUrl":"10.1016/j.ecoinf.2025.103156","url":null,"abstract":"<div><div>This study investigates the anomalous surge in early summer Land Surface Temperature (LST) in India and its potential repercussions on various sectors, such as food security, energy resources, and public health. The research also assesses the implications of the accomplishment of the Sustainable Development Goals (SDGs) throughout the early summer. Analyzing data from 2001 to 2022, the findings reveal that early summer LST was notably increased, with daytime temperatures exceeding mean LST by 3.5–4.14 °C and nighttime temperatures by 0.83 to 2.41 °C. Anomalous positive Standard Anomaly (StA) deviations were prevalent in north-west, central northeast, west-central, and hilly regions during the day. Trend analysis indicated varying StA responses across six homogeneous monsoon regions, with an overall positive trend observed in most areas. Surprisingly, Sea Surface Temperature (SST), which typically influences summer heating, was not the primary driver in 2022. Instead, a prolonged rain deficit in significant parts of India was identified as the cause. Regression analysis between StA and crop yields showed statistically insignificant associations for most production regions, except for a detrimental impact on winter crop yields. Energy deficits of up to 15 % were recorded in heat-affected states. The study also considered potential health issues arising from summer warming. These cumulative effects pose significant challenges to India's economic growth. The study assesses mitigation strategies discussed at the COP27 summit to address early summer warming. The findings provide valuable insights for developing preparedness and resilience plans to mitigate these issues.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103156"},"PeriodicalIF":5.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of phytoplankton community composition from satellite data using a fuzzy and probabilistic combination model in mountainous reservoirs: A case of Huating Lake in spring and summer","authors":"Yixuan Qiu ,&nbsp;Zhongya Fan ,&nbsp;Huiyun Feng ,&nbsp;Yutao Wang ,&nbsp;Dan Li ,&nbsp;Wencai Wang ,&nbsp;Ruting Huang ,&nbsp;Jingang Jiang","doi":"10.1016/j.ecoinf.2025.103153","DOIUrl":"10.1016/j.ecoinf.2025.103153","url":null,"abstract":"<div><div>Although remote sensing has become a common tool for monitoring mountainous reservoirs, studies on the detection of phytoplankton community compositions (PCCs) remain insufficient. Based on satellite and field data, we developed a mathematical model incorporating fuzzy logic and probabilistic methods to directly estimate the biomass of seven different phytoplankton species in Huating Lake. Water surface temperature (WST) and chlorophyll-a concentration ([Chl-a]) were selected as input parameters for this model. The WST data were processed using a single-channel algorithm that combined the brightness temperature conversion model and land surface emissivity algorithm. Inversion of [Chl-a] was conducted using an empirical approach to compare the four models developed for the two sensitive reflectance bands. The [Chl-a] values obtained from these models were significantly correlated with the field data (<em>R</em> &gt; 0.8). The optimal model was selected based on validation results. After obtaining the inversion results for the WST and [Chl-a], we applied a fuzzy probabilistic model to determine the PCCs in Huating Lake from 2013 to 2023. A comparison with the measured data confirmed that this method reliably estimated PCC biomass (<em>R</em> &gt; 0.65). However, the modeling accuracy was not particularly high for Bacillariophyta and Euglenophyta with high biomass. We analyzed the spatial and temporal distribution of PCCs in Huating Lake over 10 years from 2013 to 2023 and found that the results were reasonable. The results demonstrate that the fuzzy probabilistic approach offers a novel methodology for estimating the biomass of seven phytoplankton species. This method facilitates the expansion of remote-sensing technology for monitoring PCC changes in mountainous reservoirs and provides scientific data support for understanding algal bloom mechanisms and developing prevention strategies.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103153"},"PeriodicalIF":5.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"10.1016/j.ecoinf.2025.103159","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":5.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}