Ecological Informatics最新文献

{"title":"A weather-driven mathematical model of Culex population abundance and the impact of vector control interventions","authors":"Suman Bhowmick ,&nbsp;Patrick Irwin ,&nbsp;Kristina Lopez ,&nbsp;Megan Lindsay Fritz ,&nbsp;Rebecca Lee Smith","doi":"10.1016/j.ecoinf.2025.103163","DOIUrl":"10.1016/j.ecoinf.2025.103163","url":null,"abstract":"<div><div>Even as the incidence of mosquito-borne diseases like West Nile Virus (WNV) in North America has risen over the past several decades, effectively modelling mosquito population density or abundance has proven to be a persistent challenge. It is critical to capture the fluctuations in mosquito abundance across seasons in order to forecast the varying risk of pathogen transmission from one year to the next. We develop a process-based mechanistic weather-driven Ordinary Differential Equation (ODE) model to study the population biology of both aquatic and terrestrial stages of mosquito population. The progression of mosquito lifecycle through these stages is influenced by different factors, including temperature, daylight hours, intra-species competition and the availability of aquatic habitats. In our work, weather-driven parameters are derived from a combination of laboratory research and data from the literature. In our model, we include precipitation data as a substitute for evaluating additional mortality in the mosquito population. We compute the <em>Basic offspring number</em> of the associated model and perform sensitivity analysis. Finally, we employ our model to assess the effectiveness of various adulticides strategies to predict the reduction in mosquito population. This enhancement in modelling of mosquito abundance can be instrumental in guiding interventions aimed at reducing mosquito populations and mitigating mosquito-borne diseases such as the WNV. This model could help optimise the timing of adulticide applications and evaluate the impact of multiple spray events within a short period.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103163"},"PeriodicalIF":5.8,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098353","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":"Temporal generalization in evergreen leaf type classification using tailored Sentinel-2 composites","authors":"Peter Hofinger,&nbsp;Jan Dempewolf,&nbsp;Simon Ecke,&nbsp;Hans-Joachim Klemmt","doi":"10.1016/j.ecoinf.2025.103167","DOIUrl":"10.1016/j.ecoinf.2025.103167","url":null,"abstract":"<div><div>Large-scale forest ecosystem mapping relies critically on distinguishing deciduous and non-deciduous tree cover through advanced remote sensing technologies. Existing mapping approaches frequently suffer from spatial resolution limitations and temporal constraints. However, precise, high-fidelity forest cover characterizations are essential for forest management, ecological monitoring, and conservation planning. In this study we applied a novel methodology for classifying leaf types – evergreen versus deciduous – using Sentinel-2 multispectral satellite imagery at 10 meter resolution and machine learning, with the aim of strengthening the robustness of predictions and eliminating the need for retraining for unseen years when training on multi-year data. Key contributions include recursive feature elimination to identify the most relevant spectral bands and indices, and optimizing compositing methods to boost classification accuracy, balancing robustness and temporal detail. Eight machine learning models were tuned and trained on 16,162 tree crowns across 48 areas in Bavarian strict forest reserves (2019 to 2023) and validated with ForestGEO Traunstein Forest Dynamics Plot ground truth data (2018). We achieved an F1 score of 0.863 and an accuracy of 0.839 on the test area. Importantly, we found that model performance improved markedly with tree height, leading us to recommend our methodology for trees taller than 20 m. Results were benchmarked against the Copernicus High Resolution Layer Dominant Leaf Type product, with our top-performing model surpassing the Copernicus product in both metrics. This data-driven approach provides a scalable solution with temporal generalization, leveraging freely available satellite imagery and cloud-compute, aiding more effective forest management and environmental monitoring.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103167"},"PeriodicalIF":5.8,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089605","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":"Association of IUCN-threatened Indian mangroves: A novel data-driven rule filtering approach for restoration strategy","authors":"Moumita Ghosh ,&nbsp;Sourav Mondal ,&nbsp;Rohmatul Fajriyah ,&nbsp;Kartick Chandra Mondal ,&nbsp;Anirban Roy","doi":"10.1016/j.ecoinf.2025.103164","DOIUrl":"10.1016/j.ecoinf.2025.103164","url":null,"abstract":"<div><div>Restoring biodiversity is crucial for ecological sustainability. This study introduces a novel data-driven rule-filtering framework that adopts domain knowledge of taxonomic distinctness and proposes a new metric, total taxonomic distinctness, to prioritize species selection for targeted restoration efforts. We extract and validate association rules to identify frequently co-occurring species and rank them based on total taxonomic distinctness. This structured approach ensures the selection of ecologically significant species that enhance biodiversity and ecosystem resilience. We apply this three-stage framework to Indian mangrove ecosystems, focusing on four IUCN Red List species: <em>Heritiera fomes</em>, <em>Sonneratia griffithii</em>, <em>Ceriops decandra</em>, and <em>Phoenix paludosa</em>. Our results indicate that taxonomically distinct species tend to co-occur more frequently, enhancing ecosystem resilience. Statistical validation using multiple hypothesis testing ensures the robustness of our findings. To assess the framework’s broader applicability, we extend our analysis to species presence-absence data from sacred groves in the laterite regions of eastern India. The results reinforce our previous findings, demonstrating frequent association patterns among taxonomically distinct species. This study provides actionable insights for ecological restoration, guiding species selection and co-planting strategies. The framework is adaptable across ecosystems, offering a scalable approach to biodiversity conservation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103164"},"PeriodicalIF":5.8,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069088","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":"Regression analysis and artificial neural networks for predicting pine species volume in community forests","authors":"Wenceslao Santiago-García","doi":"10.1016/j.ecoinf.2025.103203","DOIUrl":"10.1016/j.ecoinf.2025.103203","url":null,"abstract":"<div><div>Volume prediction models are fundamental in forestry, as they support forest inventories, sustainable forest management strategies, and comprehensive environmental planning. The main objective of this study was to implement and compare two prominent approaches—regression and machine learning—for modeling whole-tree volume and stem volume in two <em>Pinus</em> species in community forests of southern Mexico. Destructive sampling provided data from 56 <em>P. patula</em> and 51 <em>P. pseudostrobus</em> trees, covering a wide range of diameters and heights. The regression approach employed seemingly unrelated nonlinear regression (NSUR) to fit simultaneous additive volume systems using both one- and two-variable models. In this approach, volume was modeled as a function of diameter at breast height (<em>d</em>) alone and as a function of both <em>d</em> and total tree height (<em>h</em>). Species and volume type were implicitly accounted for within the structure of the additive systems structure. For the machine learning approach, multilayer perceptron (MLP) artificial neural networks (ANNs) were trained using four input variables: diameter at breast height, total tree height, species, and volume type. These variables were explicitly incorporated into the ANN structure, enabling the model to learn complex, non-linear interactions. The ANN was optimized using L1 regularization and the Adam optimizer. The quantitative variables were diameter at breast height and total tree height, while the qualitative variables were species (<em>P. patula</em> and <em>P. pseudostrobus</em>) and volume type (whole-tree volume and stem volume), both coded as 1 and 0, respectively. The relative rank method was used to identify the most effective models based on goodness-of-fit statistics, including the coefficient of determination (R²), average absolute error (AAE), total relative error (TRE), average systematic error (ASE), and mean percent standard error (MPSE). The ANN approach consistently outperformed the regression model, achieving higher R² values and lower error metrics across all evaluations. Specifically, the ANN model reduced AAE, TRE, and ASE while maintaining biologically consistent predictions. This proposed ANN model represents a significant advancement in modeling both whole-tree and stem volume simultaneously and independently across different species, providing reliable and precise estimates. Given its ability to handle complex, non-linear relationships and its superior accuracy, we recommend the use of ANN as a practical tool in forestry applications, including forest resource evaluation and the development of sustainable forest management plans.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103203"},"PeriodicalIF":5.8,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089606","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":"Improved lightweight DeepLabV3+ for bare rock extraction from high-resolution UAV imagery","authors":"Pengde Lai ,&nbsp;Chao Lv ,&nbsp;Lv Zhou ,&nbsp;Shengxiong Yang ,&nbsp;Jiao Xu ,&nbsp;Qiulin Dong ,&nbsp;Meilin He","doi":"10.1016/j.ecoinf.2025.103204","DOIUrl":"10.1016/j.ecoinf.2025.103204","url":null,"abstract":"<div><div>Bare rock information extraction in karst regions is crucial for geological hazard monitoring and ecological assessment. However, in sparsely vegetated areas, bare rock exhibits similar spectral characteristics to surrounding land cover, and the boundaries are often indistinct, making it challenging for traditional classification methods to distinguish these transitional zones accurately. To address these challenges, this study proposes a bare rock extraction method based on an improved lightweight DeepLabV3+ model. MobileNetV2 is used as the backbone network, and the Channel Attention Module (CAM) and Spatial Attention Module (SAM) are introduced to enhance feature extraction capability. Results show the following: (1) When MobileNetV2 is used as the backbone of DeepLabV3+, the Accuracy, F1 score, and MIoU reach 97.39 %, 78.91 %, and 82.11 %, respectively, outperforming VGG16, Xception, SqueezeNet, and traditional segmentation models. (2) Applying the lightweight DeepLabV3+ model to bare rock identification in orthophoto imagery of the study area results in a bare rock rate error of approximately 5 %, demonstrating the practical applicability of the model. (3) After the introduction of the attention mechanism, the model's Recall, F1 score, and MIoU increased by 14.00 %, 8.37 %, and 5.62 %, respectively, remarkably enhancing identification completeness and boundary accuracy. Meanwhile, the improved model had a parameter count of 6.98 M and a computational complexity of 7.24G, achieving enhanced accuracy while maintaining computational efficiency. The research results can provide accurate bare rock information to support geological hazard monitoring and early warning, and offer new technical solutions for ecological restoration and risk assessment. (Data sets and code links: <span><span>https://figshare.com/articles/dataset/Bare_rock_dataset/28143443?file=53186633</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103204"},"PeriodicalIF":5.8,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089607","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":"Marine soundscape forecasting: A deep learning-based approach","authors":"Shashidhar Siddagangaiah","doi":"10.1016/j.ecoinf.2025.103189","DOIUrl":"10.1016/j.ecoinf.2025.103189","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Advancements in autonomous monitoring technology over the past decade have led to the widespread use of marine soundscape monitoring to assess marine environments. These environments are rapidly changing and exhibit complex temporal patterns and trends across different frequencies, influenced by biotic and abiotic factors as well as extreme events. This variability introduces a high degree of unpredictability. Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. This study investigates the use of the unsupervised learning-based isolation forest (iForest) technique to detect anomalous events in marine soundscapes that cause sudden changes in sound levels. Additionally, it evaluates the potential of deep-learning models for estimating trends and forecasting soundscapes while identifying the factors that influence their accuracy. To address these questions, I used marine passive acoustic monitoring data collected from the Taiwan Strait in 2017. The iForest method identified a higher number of anomalies (∼17) in the lower frequency range (10–500 Hz) with a precision of 75 %, primarily due to typhoons, cold bursts, and flooding. In contrast, precision was around 50 % in the mid (500–3000 Hz) and high (3000–24,000 Hz) frequency ranges, where most anomalies resulted from sudden changes in the acoustic behaviors of fish and shrimp, respectively. To analyze trends in marine soundscapes at different temporal scales—annual, seasonal, and diurnal—the anomaly-informed NeuralProphet model was employed. Results showed that NeuralProphet effectively captured annual and seasonal trend changes compared to the traditional singular spectrum analysis method. Beyond NeuralProphet, I also tested two recently developed state-of-the-art forecasting models—time-series dense encoder (TiDE) and neural hierarchical interpolation for time series (NHiTS)—to predict marine soundscapes. In the seven-day ahead seasonal forecasting task, the NHiTS model outperformed both TiDE and NeuralProphet. The deep-learning forecasting models produced more accurate predictions in the mid (500–3000 Hz) (MAE ∼0.4–1) and high (3000–24,000 Hz) (MAE ∼1.5–3) frequency ranges, where seasonal acoustic activity from fish and shrimp strongly influenced sound levels. In contrast, forecast accuracy declined in the lower frequency range (10–500 Hz) (MAE ∼4–8), where sound levels are more stochastic due to anthropogenic and meteorological influences. The findings of this study highlight the potential of deep-learning models for forecasting and trend estimation in marine soundscapes. These models not only improve our understanding of the conditions under which trends change but also enhance our ability to anticipate anomalies and forecast failures. This capability could provide researchers and policymakers with a powerful tool for monitoring transitions and deviations across different tem","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103189"},"PeriodicalIF":5.8,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098352","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":"Bayesian networks for causal analysis in socioecological systems","authors":"Rafael Cabañas ,&nbsp;Ana D. Maldonado ,&nbsp;María Morales ,&nbsp;Pedro A. Aguilera ,&nbsp;Antonio Salmerón","doi":"10.1016/j.ecoinf.2025.103173","DOIUrl":"10.1016/j.ecoinf.2025.103173","url":null,"abstract":"<div><div>Analyzing the influence of socioeconomy on land use is an important task, as socioeconomic factors can drive changes in land use that may ultimately affect human well-being. Recognizing the key factors that induce these changes may help policymakers design more effective strategies for addressing socioeconomic alterations on land-use planning, anticipate potential challenges, and mitigate negative impacts on both the environment and society. While probabilistic graphical models have been employed for this purpose in the past, this paper proposes the application of counterfactual reasoning to enhance the analysis by quantifying the degrees of necessity and sufficiency of various socioeconomic factors influencing land uses and population growth. Specifically, we present a case study using non-experimental data from southern Spain. For this, we propose the use of structural causal models, which are kind probabilistic models for causal analysis that simplify this kind of reasoning due to their graphical representation. They can be regarded as extensions of the so-called Bayesian networks, a well known modeling tool commonly used in environmental and ecological problems. This proposed approach is particularly effective for the identification of social and ecological variables that can be used in environmental monitoring and planning, offering key advantages including enhanced interpretability, and ease of adoption by environmental researchers. Our study reveals that immigration is both necessary and sufficient for population growth. In addition, built-up areas and herbaceous crops are favored by non-mountainous terrain and by high population density, whereas natural areas and mixed crops are supported by mountainous terrain and by low population density.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103173"},"PeriodicalIF":5.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083781","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":"Strategic landfill site selection for sustainable waste management in Phu Yen Province, Vietnam using geospatial technologies","authors":"Diem-My Thi Nguyen ,&nbsp;Dorian Tosi Robinson ,&nbsp;Christian Zurbrügg ,&nbsp;Thi Hanh Tien Nguyen ,&nbsp;Huu-Lieu Dang ,&nbsp;Van-Manh Pham","doi":"10.1016/j.ecoinf.2025.103198","DOIUrl":"10.1016/j.ecoinf.2025.103198","url":null,"abstract":"<div><div>Solid waste management is a growing global challenge, especially in developing countries such as Vietnam, where rapid urbanisation and inadequate infrastructure intensify environmental and public health risks. Landfilling is one of the most environmentally harmful waste disposal methods. However, it remains widely used in many countries because of its cost-effectiveness. Proper disposal of solid waste is a significant priority for reducing environmental pollution. Selecting suitable landfill sites requires consideration not only of physical and environmental aspects but also of economic and social factors. In Phu Yen Province, located in south central Vietnam, solid waste management faces growing challenges in solid waste management. Limited landfill infrastructure and poor operational standards are already impacting public health and the environment. Moreover, with existing landfills approaching the end of their usable lifespans, identifying new, appropriate sites has become an urgent priority. This study introduces a novel approach that integrates a geographic information system (GIS)-based multi-criteria decision analysis (MCDA) with a fuzzy analytic hierarchy process (Fuzzy AHP) to enhance landfill site suitability assessments. This study's approach enables a holistic evaluation of economic, environmental, topographical, and social factors, thereby ensuring a more comprehensive decision-making process. The findings reveal that 45 % of the study area is very highly or highly potential for landfill sites, 28 % is of medium potential, 27 % is of low or very low potential, and 25.7 % of the existing landfill locations pose significant environmental and human health risks. A spatial distribution map obtained from a comprehensive analysis incorporating economic, social, environmental, and topographical factors helped identify potential future sites for solid waste disposal in Phu Yen Province. The methodology demonstrated in this study is highly transferable and can be applied to other low- and middle-income countries that face similar waste management challenges.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103198"},"PeriodicalIF":5.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068987","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":"Spatiotemporal patterns of desertification sensitivity and influencing factors across the Western Inner Mongolia Plateau, China","authors":"Yang Chen ,&nbsp;Long Ma ,&nbsp;Xixi Wang ,&nbsp;Tingxi Liu ,&nbsp;Zixu Qiao","doi":"10.1016/j.ecoinf.2025.103190","DOIUrl":"10.1016/j.ecoinf.2025.103190","url":null,"abstract":"<div><div>Desertification remains a critical global ecological and environmental challenge that threatens sustainable development. Although our understanding of desertification dynamics and their underlying drivers has improved, continued research is needed due to the region-specific nature of these processes. This study focuses on the Western Inner Mongolia Plateau in China as a case study to examine the evolution of desertification and its driving factors using a multifaceted approach, including the Mediterranean Desertification and Land Use (MEDALUS) model. Results show that the desertification sensitivity index (DSI) across the plateau ranged from 1.12 in prairie regions to 1.87 in desert areas, with a spatial gradient decreasing from west to east. Overall, the DSI exhibited a declining trend over the study period, though some areas showed localized degradation. Between 2001 and 2020, the DSI decreased across approximately 64 % of the plateau, with approximately 23 % (primarily desert regions) experiencing a significant reduction. In contrast, 36 % of the area, particularly the southeastern grasslands, saw an increase in DSI. Among the examined factors, seven—precipitation, normalized difference vegetation index (NDVI), leaf area index(LAI), drought resistance, erosion protection, fire risk, and land-use intensity—demonstrated high explanatory power greater than 0.6, highlighting their significant positive or negative impact on desertification. Additional factors such as temperature, sunshine duration, and potential evapotranspiration also influenced desertification, albeit to a lesser extent. Notably, interactions among these variables played a crucial role in shaping desertification trends. Addressing desertification, therefore, requires integrated strategies that account for the complex interplay of soil, climate, vegetation, and land management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103190"},"PeriodicalIF":5.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098354","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":"Integrating UAV and Landsat data: A two-scale approach to topsoil moisture mapping in coastal wetlands","authors":"Ricardo Martínez Prentice ,&nbsp;Miguel Villoslada ,&nbsp;Raymond D. Ward ,&nbsp;Kalev Sepp","doi":"10.1016/j.ecoinf.2025.103197","DOIUrl":"10.1016/j.ecoinf.2025.103197","url":null,"abstract":"<div><div>Surface soil moisture (SSM) is a key variable influencing ecosystem dynamics, particularly in wetland systems, highlighting its importance for management. This study integrates UAV-derived high-resolution SSM maps with Landsat-based predictions to enable multiscale SSM monitoring in wetland ecosystems. UAV multispectral and thermal imagery were used to estimate the Temperature Vegetation Dryness Index (TVDI), which was calibrated with in-situ measurements of volumetric water content percentage (VWC%) to produce fine-scale SSM maps. These maps were aggregated to train and test XGBoost models using Landsat-derived predictors.</div><div>While UAV data captured fine-scale SSM variability, Landsat-based predictions provided consistency at lower spatial scales (30 m of spatial resolution from Collection-2 Level-2), with RMSE values below 10 %. Among all surveyed periods, August yielded the most reliable results. During this month—the warmest and most hydrologically dynamic—TVDI and Land Surface Temperature (LST) emerged as the strongest predictors. This also demonstrates that XGBoost model to better represent the full range of moisture conditions.</div><div>This framework addresses challenges like cloud cover in high-latitude regions and offers scalable solutions for SSM monitoring. Results contribute to the understanding of essential climate variables and support the restoration and management of coastal meadows. By bridging UAV and satellite observations, this approach provides a reliable and scalable tool for SSM assessment across diverse ecosystems. Future efforts should prioritize surveys during ecologically responsive periods, such as August, and explore the methodology's applicability in other wetland systems and long-term monitoring schemes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103197"},"PeriodicalIF":5.8,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941296","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}