Ecological Informatics最新文献

{"title":"Land-Unet: A deep learning network for precise segmentation and identification of non-structured land use types in rural areas for green urban space analysis","authors":"Yan Zhao ,&nbsp;Junru Xie ,&nbsp;Huiru Zhu ,&nbsp;Taige Luo ,&nbsp;Yao Xiong ,&nbsp;Chenyang Fan ,&nbsp;Haoxiang Xia ,&nbsp;Yuheng Chen ,&nbsp;Fuquan Zhang","doi":"10.1016/j.ecoinf.2025.103078","DOIUrl":"10.1016/j.ecoinf.2025.103078","url":null,"abstract":"<div><div>Land Use and Land Cover Change (LUCC) have become popular research topics in the environmental field. With the development of artificial intelligence technology, many downstream applications based on intelligent urban–rural semantic analysis have emerged. Scholars have made significant progress in the intelligent analysis of urban imagery, but exploration of unstructured rural remote sensing data has been limited. This paper addresses the existing pixel-level semantic ambiguity issues and proposes a new deep learning model, Land-Unet. The network features a dual-branch Edge-Sensing Block (ESB) structure, including a Spatial and Channel Synergistic Attention (SCSA) branch and a Dynamic Upsampling (DYU) technique, which effectively resolves contour ambiguity in edge semantic information in rural images. Experiments on multiple datasets using various deep learning methods show that compared with the original structure, the proposed method increases <span><math><mrow><mi>m</mi><mi>I</mi><mi>o</mi><mi>U</mi></mrow></math></span> by 9.7%, <span><math><mrow><mi>m</mi><mi>D</mi><mi>i</mi><mi>c</mi><mi>e</mi></mrow></math></span> by 5.9%, and <span><math><mrow><mi>m</mi><mi>A</mi><mi>c</mi><mi>c</mi></mrow></math></span> by 12.2%. Compared to transformer-based methods, proposed method also demonstrated improved performance. Additionally, a new rural satellite imagery dataset, RuralUse, has been open-sourced for semantic segmentation research.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103078"},"PeriodicalIF":5.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of chlorophyll-a in inland waters by remote sensing algorithm based on modified equivalent spectra of Sentinel-2","authors":"Wenbin Pan ,&nbsp;Fei Yu ,&nbsp;Jialin Li ,&nbsp;Chunqiang Li ,&nbsp;Ming Ye","doi":"10.1016/j.ecoinf.2025.103061","DOIUrl":"10.1016/j.ecoinf.2025.103061","url":null,"abstract":"<div><div>The development of remote sensing algorithms has traditionally relied on satellite spectra or simulated equivalents derived from in-situ spectra to monitor inland water quality. However, such equivalent spectra often result in significant errors when retrieving chlorophyll-<em>a</em> (Chl-<em>a</em>) concentrations due to discrepancies between in-situ and satellite-derived spectra. In this research, the authors innovatively adjusted the red-light component of in-situ spectra for application in two inland waters, Dongzhang Reservoir and Jie Zhukou Reservoir. Sentinel-2 multispectral images (MSI), standard equivalent spectra (ES), and modified equivalent spectra (MES) were utilized as input data to assess models' effectiveness in terms of accuracy, robustness, and generalizability. The research applied Chl-<em>a</em> retrieval models including deep neural networks (DNN), extreme gradient boosting (XGB), and conventional statistical approaches with various spectral indices, such as the red-NIR method, the three-band method, and the normalized difference chlorophyll index (NDCI). The results revealed that the MES-based model achieved best results in Chl-<em>a</em> retrieval (RMSE = 2.04 mg/m³) comparable to MSI-based model (RMSE = 2.07 mg/m³) and ES-based model (RMSE = 7.71 mg/m³). Moreover, MES-based model behaved robustness and precision within selected water bodies and temporal periods. Notably, the integration of the red-NIR method with DNN was particularly effective in retrieving Chl-<em>a</em> with higher accuracy, robustness, and generalizability. Enhancement method to the equivalent spectra methodology provided by the research have reduced retrieval errors in retrieving Chl-<em>a</em>, and providing a valuable reference for future model development in this domain.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103061"},"PeriodicalIF":5.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vegetation coverage patterns in the “mountain–basin” system of arid regions: Driving force contribution, non-stationarity, and threshold effects","authors":"Rou Ma ,&nbsp;Zhengyong Zhang ,&nbsp;Lin Liu ,&nbsp;Mingyu Zhang ,&nbsp;Chen Ma ,&nbsp;Yu Cao ,&nbsp;Yu Gao ,&nbsp;Xueying Zhang ,&nbsp;Xinyi Liu ,&nbsp;Jiayi Zhang ,&nbsp;Zifan Yuan","doi":"10.1016/j.ecoinf.2025.103084","DOIUrl":"10.1016/j.ecoinf.2025.103084","url":null,"abstract":"<div><div>The spatiotemporal pattern and asymmetry characteristics of the normalized difference vegetation index (NDVI) in Xinjiang were analyzed on multiple scales. A multi-model attribution analysis framework that combined a geodetector model (GD), geographically weighted regression (GWR), and random forest (RF) was constructed, since previous efforts using these approaches individually were not able to capture both linear and nonlinear effects. The action laws of contribution degree identification, spatial non-stationarity analysis, and response threshold exploration of NDVI driving factors were also analyzed. The results showed that: (1) the annual mean NDVI in Xinjiang from 2000 to 2021 was 0.106, and overall macroscopic pattern was high in mountainous areas and low in basins. The interannual NDVI exhibited a fluctuating and slightly increasing trend, while the summer NDVI increased the fastest. The asymmetric change trend of the NDVI between seasons was the strongest in the Altay Mountains and Yili River Valley. (2) The NDVI first increased and then decreased with increasing elevation, reaching a peak at a height ranging 2–3 km. The NDVI was highly heterogeneous in mountainous and oasis areas and relatively homogeneous in basins. (3) A scale effect was observed. The detection results of the GD model differed between the Xinjiang and mountain scales. (4) Temperature (Tem), relative humidity (Rh), and precipitation (Pre) had positive effects on NDVI changes, whereas land surface temperature (LST) and summer temperature had negative effects. The threshold of LST was 9 °C in summer, and the temperature threshold was 25 °C. Our results provide guidance for analyzing the causes and ecological effects of vegetation growth.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103084"},"PeriodicalIF":5.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing eco-physiological patterns of Ailanthus altissima (Mill.) Swingle and differences with native vegetation using Copernicus satellite data on a Mediterranean Island","authors":"Flavio Marzialetti ,&nbsp;Vanessa Lozano ,&nbsp;André Große-Stoltenberg ,&nbsp;Maria Laura Carranza ,&nbsp;Michele Innangi ,&nbsp;Greta La Bella ,&nbsp;Simonetta Bagella ,&nbsp;Giovanni Rivieccio ,&nbsp;Gianluigi Bacchetta ,&nbsp;Lina Podda ,&nbsp;Giuseppe Brundu","doi":"10.1016/j.ecoinf.2025.103080","DOIUrl":"10.1016/j.ecoinf.2025.103080","url":null,"abstract":"<div><div>Biological invasions, one of the most pervasive components of global change, can cause irreversible alterations in the composition and functioning of ecosystems. This includes changes of eco-physiological traits of plant communities. Satellite remote sensing provides the means to map surrogates of ecosystem composition and functioning such as eco-physiological traits over large spatial extents. In this study, Sentinel-2 and Sentinel-3 Copernicus satellite data resampled to 20 m² spatial resolution was used to characterize the annual cycle of spectral eco-physiological traits in 176 patches invaded by <em>Ailanthus altissima</em> (Mill.) Swingle in Sardinia (Italy) and in their corresponding and surrounding non-invaded areas. The overall aim was to examine if and how eco-physiological traits differed between <em>A. altissima</em> and native vegetation classes. A set of spectral eco-physiological indices proxies related to leaf chlorophyll <em>a</em>nd carotenoid content (Chlorophyll Vegetation Index − CVI, Structure Intensive Pigment Index 3 − SIPI3), productivity and canopy biomass (Enhanced Vegetation Index − EVI, Leaf Area Index − LAI), leaf water content (Normalized Multi-band Drought Index − NMDI, Moisture Stress Index − MSI), daily evapotranspiration (ET), and soil features (Coloration Index − CI) were calculated. The monthly trends of these indices in invaded patches and the seasonal differences between invaded and non-invaded cells were analyzed using linear mixed models (LMMs). One-way Analysis of Variance (ANOVA), and Estimated Marginal Means (EMMs) were used to test the differences between invaded and non-invaded cells. Our results highlighted the effectiveness of Sentinel-2 and -3 data in capturing the temporal trends of spectral eco-physiological traits. Most significant differences between invaded and non-invaded cells were observed during summer, with invaded cells featuring higher productivity, canopy biomass, and leaf water content, while leaf carotenoid content and bare soil cover was lower. Overall, our findings based on satellite-based remote sensing analysis provide further evidence of the competitive advantages <em>A. altissima</em> has over native vegetation, particularly in summer, when vegetation in Mediterranean-type ecosystems faces an intense drought stress. Such spatially explicit information facilitates the identification and selection of field sites for proximal measurements of the alterations and potential impacts attributed to <em>A. altissima</em> invasion.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103080"},"PeriodicalIF":5.8,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LEAVES: An open-source web-based tool for the scalable annotation and visualisation of large-scale ecoacoustic datasets using cluster analysis","authors":"Thomas Napier ,&nbsp;Euijoon Ahn ,&nbsp;Slade Allen-Ankins ,&nbsp;Lin Schwarzkopf ,&nbsp;Ickjai Lee","doi":"10.1016/j.ecoinf.2025.103026","DOIUrl":"10.1016/j.ecoinf.2025.103026","url":null,"abstract":"<div><div>Ecoacoustics has emerged as a pivotal discipline in the conservation and monitoring of ecosystems, offering insights into species’ behaviour and ecosystem health through soundscape analysis. Central to this is the need for accurate annotations of environmental audio recordings, which underpin the computational models used in ecological monitoring. However, due to the increasingly large scale of datasets, annotation using existing tools and techniques cannot be performed at feasible speeds or with the necessary accuracy required for real-world application. The LEAVES (Large-scale Ecoacoustics Annotation and Visualisation with Efficient Segmentation) platform addresses this gap by leveraging unsupervised clustering techniques optimised for the high-throughput annotation of large-scale ecoacoustics datasets. Our evaluation across six real-world datasets shows that LEAVES improves annotation efficiency by up to 7.12 times compared to manual annotation while maintaining 79%–90% label similarity to validated data. We expect that our proposed tool will greatly accelerate the annotation process when generating high-quality labelled datasets, supporting larger-scale studies with broader community engagement in ecoacoustics research.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103026"},"PeriodicalIF":5.8,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating effects of data quality and variable weighting on habitat suitability modelling","authors":"Stephanie Arsenault ,&nbsp;Robyn Linner ,&nbsp;Yong Chen","doi":"10.1016/j.ecoinf.2025.103086","DOIUrl":"10.1016/j.ecoinf.2025.103086","url":null,"abstract":"<div><div>Habitat modelling is important in the conservation and management of fishes and can be sensitive to data inputs and model configuration. Survey data used in Habitat Suitability Index (HSI) models may undergo changing sampling protocols over time, and these inconsistencies may impact results. Additionally, the various habitat variables included in HSI models are typically given equal weights, even though some variables may have greater influence over distribution than others. The Long River Survey, part of the Hudson River Biological Monitoring Program, in the Hudson River Estuary (HRE), has undergone considerable protocol changes, and was calibrated to address these issues in 2023. This survey and region are an excellent case study to compare two approaches in constructing HSI models: using calibrated versus uncalibrated abundance data and weighting all environmental variables equally or using a model-based weighting method. The results of this study suggest that using calibrated abundance data with unweighted habitat variables provide the most robust estimates for bay anchovy suitable spawning habitat in the HRE, which indicates that in cases when sampling has not been consistent over time, using calibrated abundance data in habitat suitability modelling may lead to improved models. Some model configurations were unable to identify a significant trend in suitable habitat over time and overestimated habitat quality illustrating the importance of carefully considering data inputs and model configuration when building habitat models to properly quantify suitable habitat and contribute to ecosystem-based fisheries management in the wake of climate change.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103086"},"PeriodicalIF":5.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved digital mapping of soil texture using the kernel temperature–vegetation dryness index and adaptive boosting","authors":"Xu Zhai ,&nbsp;Yuzhong Liu ,&nbsp;Yuanyuan Hong ,&nbsp;Yunjie Yang ,&nbsp;Pengju Wang ,&nbsp;Zhicheng Ye ,&nbsp;Xiaoyan Liu ,&nbsp;Tianlong She ,&nbsp;Lihui Wang ,&nbsp;Chen Xu ,&nbsp;Lili Zhang ,&nbsp;Qiang Wang","doi":"10.1016/j.ecoinf.2025.103083","DOIUrl":"10.1016/j.ecoinf.2025.103083","url":null,"abstract":"<div><div>Existing soil texture mapping methods cannot accurately predict soil texture in complex geographical environments. To address this challenge, we propose a method that combines a kernel temperature–vegetation dryness index (kTVDI) with a gradient boosting algorithm to accurately predict the spatial distribution of soil texture. In this study, we collected 399 soil samples collected from Mingguang City in southeast China and made spatial predictions of soil texture based on remote sensing indices such as the kernel normalized difference vegetation index computed from Landsat8 data and topographic attributes computed via digital elevation model as environmental covariates. We validated model performance by mapping the spatial distributions of sand, silt, and clay particle fractions in the city (30-m resolution), using the boosting algorithms adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). Among the environmental covariates, the kTVDI, digital elevation index, and salinity index have the highest importance values for soil texture prediction. The kTVDI is better for sand and silt prediction (especially sand). When combined with AdaBoost, the kTVDI can effectively improve the accuracy and consistency of the prediction model. Uncertainty analyses showed that the kTVDI was more effective at modeling soil texture in the plains. In summary, we present a new approach for accurately predicting the spatial distribution of soil texture and empirically validate its effectiveness and advantages for practical applications.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103083"},"PeriodicalIF":5.8,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A theoretical model of plant species competition: The case of invasive Carpobrotus sp. pl. and native Mediterranean coastal species","authors":"Simonetta Bagella,&nbsp;Iulia Martina Bulai ,&nbsp;Marco Malavasi,&nbsp;Giulia Orrù","doi":"10.1016/j.ecoinf.2025.103070","DOIUrl":"10.1016/j.ecoinf.2025.103070","url":null,"abstract":"<div><div>In this paper we introduce and study a new mathematical model that describes the interaction between invasive and native plants in competition for resources and space. The first are represented by <em>Carpobrotus</em> sp. pl. while the second by the native coastal area species. Following the terminology used in modeling, we have used the term “population” to refer to the set of plants, i.e. native and alien invasive. Differently than in a classical Lotka–Volterra competition model, here we assume that the invasive species grow following a generalized logistic growth law while the native population a logistic one. Moreover, in the competition term we take into account the fact that the two populations of plant interact in a 3D space and the interaction occurs on the borders of the vegetation patch and/or volume, meaning that both densities will be characterized also by two exponents. A complete mathematical analysis of the proposed model was done by studying also some particular cases of it. The model is characterized by the presence of different equilibrium points such as the trivial equilibrium point, where both populations get extinct, the Carpobrotus-free equilibrium, the native-plant-free equilibrium and the coexistence, where both plant populations coexists. Computational simulations show that different bistability scenarios exist but also the tristability of the last three equilibrium introduced above was studied. The obtained results suggest that external factors, due to human intervention, that leads to a decrease in the initial population of <em>Carpobrotus</em> sp. pl. and/or increase of the native population might help in having the stability of the Carpobrotus-free equilibrium or the coexistence equilibrium.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103070"},"PeriodicalIF":5.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suitability of the Amazonas region for beekeeping and its future distribution under climate change scenarios","authors":"Darwin Gómez-Fernández ,&nbsp;Ligia García ,&nbsp;Jhonsy O. Silva-López ,&nbsp;Jaris Veneros Guevara ,&nbsp;Erick Arellanos Carrión ,&nbsp;Rolando Salas-Lopez ,&nbsp;Malluri Goñas ,&nbsp;Nilton Atalaya-Marin ,&nbsp;Manuel Oliva-Cruz ,&nbsp;Nilton B. Rojas-Briceño","doi":"10.1016/j.ecoinf.2025.103082","DOIUrl":"10.1016/j.ecoinf.2025.103082","url":null,"abstract":"<div><div>Beekeeping plays an important role in global food production and the conservation of wild species. However, determining territorial suitability and future distribution under climate change scenarios is a relatively understudied area in Peru. This study assessed the beekeeping suitability of the Amazonas region and its variation under climate change scenarios in two projected periods (2041–2060 and 2081–2100), according to Shared Socioeconomic Pathways (SSP). The methodological framework integrated the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), and the Hadley Centre Global Earth Model - Global Coupled configuration 3.1 (HadGEM3-GC31-LL) was used for future climate analysis. The beekeeping suitability of the region was determined based on eleven criteria: four climatic, three topographic, and four environmental. The results indicate that beekeeping suitability is distributed as follows: 3.4 % (1417.90 km²) with ‘High’ suitability, 79.2 % (33,318.61 km²) with ‘Moderate’ suitability, 17.2 % (7230.26 km²) with ‘Marginal’ suitability, and 0.2 % (83.64 km²) as ‘Not suitable’. Moreover, the average temperature across the region is projected to increase by approximately 3 °C under the SSP2–4.5 scenario and between 6 °C and 8 °C under the SSP5–8.5 scenario during the projected periods. Precipitation will decrease in the northern part of the region, while the southwestern part will experience an increase. In the highly suitable beekeeping area, a temperature increases up to 10.8 °C is expected, with frequent variations around 3 °C to 8 °C, affecting more than 500 km². Additionally, a reduction in precipitation up to 311 mm/year is projected, with predominant variations ranging from −49.5 to −32.8 mm/year over approximately 600 km².Therefore, it is suggested to implement strategies to mitigate these upcoming challenges, particularly if the modeled economic development under the SSPs continues. This study modeled and mapped areas with present conditions suitable for beekeeping and future climate behavior. The modeling aims to guide beekeepers and local authorities in developing sustainable practices and implementing preventive measures to address future climatic challenges.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103082"},"PeriodicalIF":5.8,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grassland management and phenology affect trait retrieval accuracy from remote sensing observations","authors":"Maksim Iakunin ,&nbsp;Franziska Taubert ,&nbsp;Reimund Goss ,&nbsp;Severin Sasso ,&nbsp;Hannes Feilhauer ,&nbsp;Daniel Doktor","doi":"10.1016/j.ecoinf.2025.103068","DOIUrl":"10.1016/j.ecoinf.2025.103068","url":null,"abstract":"<div><div>Grasslands, the most widespread terrestrial biome, are subject to various management practices that influence their biodiversity and ecological functions. Remote sensing offers a promising tool for monitoring these impacts, but challenges persist in heterogeneous grassland systems. This study combines radiative transfer model (RTM) and machine learning algorithms to assess the efficacy of the model inversion in predicting plant functional traits under different grassland management regimes. The model was applied to intensively and extensively managed grasslands using field-collected hyperspectral data. Results show that while RTM inversion effectively predicts traits such as leaf area index (LAI) and pigment concentrations in homogeneous, intensively managed systems, its accuracy diminishes in diverse, extensively managed grasslands, particularly for traits like leaf mass per area (LMA) and pigment content. These limitations stem from the model’s assumption of homogeneous canopy scattering, which fails to account for the heterogeneity in mixed green and brown vegetation, especially at senescence. Despite these challenges, the study highlights the potential of hyperspectral remote sensing to capture grassland management based on a solely empirical approach. Future research should focus on refining models to better account for canopy heterogeneity and integrating in-situ data to improve trait prediction in complex ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"87 ","pages":"Article 103068"},"PeriodicalIF":5.8,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}