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Ecological Informatics
Scopus
ISSN 1574-9541 · Elsevier · 1.182 artikel di indeks
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Artikel Jurnal
Explainable Hybrid Physics-Guided Neural Network (HPGNN) for diverse Inland Lakes water quality inversion
Accurate remote sensing of optically active water quality parameters (OAWQPs) in optically complex lakes remains challenging due to similar reflectance signatures, spectral overlap, and adjacency effects, leading to non-uniqueness and sensitivity to sparse, noisy in situ observations. While Machine…
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CNN-based wheat yield prediction using multi-source and multi-stage data integration from UAV imagery and sensors
In this study, a convolutional neural network (CNN)-based deep learning model was developed to predict durum wheat yield by integrating RGB and multispectral UAV imagery, ground-based sensor measurements (SPAD, NDVI, LAI, and plant height), and climatic parameters under semi-arid conditions. The…
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DCMF: Deep Counterfactual Metric Framework for limited data plant disease recognition
Deep learning methods have achieved remarkable success in plant disease recognition. However, these methods rely on large-scale labeled datasets for training to ensure the reliability of empirical risk minimization. In the real world, obtaining such extensive disease data remains challenging. With…
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Mapping alpine grassland species with the VI-MDACvT model based on UAV hyperspectral imagery
Hyperspectral imagery acquired by Unmanned Aerial Vehicles (UAVs) provides high spatial and spectral resolution, making it essential for accurate vegetation analysis. However, the reliance on a single deep learning model often limits the performance of vegetation classification in complex alpine…
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Artikel Jurnal
AviFluMap: An interactive tool to assess H5N1 avian influenza incursion risk in Australia via migratory birds
The current panzootic of clade 2.3.4.4b H5Nx high pathogenicity avian influenza (H5 HPAI bird flu) has resulted in unprecedented global impacts on both wild bird populations and poultry industries. Despite the virus' near-global circulation, Australia remains free of this strain. In response to the…
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Spatiotemporal statistical evaluation of recent active and passive satellite-derived soil moisture products across Central Asia under multiple scenarios
Algorithms for retrieving Surface Soil Moisture (SSM) from microwave remote sensing are continually refined, necessitating the evaluation of newly released products to identify their optimal applications and potential for improvement. This study evaluates ASCAT H119, SMAP DCA, SMAP MTDCA, SMAP-IB…
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Advancing provenance assignment using machine learning and time series analysis of chemical chronologies in archival tissues
Accurate provenance assignment is critical for understanding ecological connectivity and guiding conservation and management. Natural chemical chronologies stored in metabolically inert, incrementally growing tissues (e.g., otoliths) provide a powerful tool for this purpose. However, traditional…
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Artikel Jurnal
Dual-branch time–frequency network with channel masking for automated insect sound monitoring toward field environments
Insect acoustic signals convey vital ecological information, supporting behaviors such as mating and territory defense. Monitoring these sounds toward field environments offers a non-invasive way to assess biodiversity and ecosystem health, yet automatic classification is hindered by short signal…
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Predicting phytoplankton community in a large floodplain lake using interpretable machine learning
Floodplain lakes are highly dynamic ecosystems where hydrological fluctuations drive complex phytoplankton responses that are challenging to predict. In this study, a random forest (RF) framework was developed to predict phytoplankton succession in Poyang Lake, the largest freshwater lake in China…
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Artikel Jurnal
BT-YOLO: An attention-enhanced framework for monitoring bioluminescent algal blooms in coastal tourism zones
Monitoring of bioluminescent algae blooms, referred to in China as “Blue Tears”, is essential for managing ecological risks, ensuring public safety, and accommodating the growing tourist attention they attract in coastal regions. However, this task remains challenging due to the nocturnal nature of…
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Artikel Jurnal
Assessing the impact of land use and land cover on predicting in-stream total phosphorus using GIS and machine learning models
This study presents a spatially explicit framework for predicting in-stream total phosphorus (TP) concentrations by combining a convolutional neural network (CNN) with explainable AI techniques. A CNN–dense neural network (CNN–DNN) model was trained on five-year average in-stream TP data from 608…
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Artikel Jurnal
Calibrated general-purpose smartphone augmented reality for urban street-tree structural data acquisition
Urban street trees are key to urban climate regulation, carbon storage and habitat provision, yet many cities still lack fine-scale, up-to-date field data on their structure and site conditions. Conventional tape and laser measurements are accurate but slow, labour-intensive and often impractical…
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Artikel Jurnal
Bird diversity responses to mega-events influence: A case study of the Beijing 2022 Winter Olympics
Mega-events such as the Olympic Games are catalysts for rapid regional transformation, but their ecological impacts remain underexamined, particularly with respect to biodiversity. Based on over 44,000 citizen science records from 2018 to 2024 across Chongli and Yanqing Districts, this study…
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Artikel Jurnal
Informer-based cross-site transfer learning for water demand forecasting via domain adaptation and meta-learning
Accurate forecasting of water demand is essential for reliable and efficient operation of distribution networks. However, existing forecasting approaches are usually restricted to single-site closed settings and fail under distributional shifts across pumping stations, leaving the gap of cross-site…
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Artikel Jurnal
SALMA: A machine learning tool for precise leaf morphology measurements
Leaf area is a critical plant functional trait, widely used for understanding plant responses to climate change, ecosystem productivity, and species' adaptive strategies. Inaccurate leaf area measurements compromise the accuracy of other traits normalised by area, such as foliar chemical traits…
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Deconstructing niche shifts: Using native and non-native records to assess the invasion potential of geographically constrained species
Geographically-constrained species typically occupy small climatic niches in their native range, and are therefore often assumed to pose a low risk of becoming invasive. However, the association between range size and climatic niche is correlative, and the cause of the constraint, whether it is…
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Artikel Jurnal
Diagnosis of pine wilt disease using unattended UAV hyperspectral imager: A comparison of discrete bands and continuous spectrum -based methods
Unattended UAV systems offer an effective solution for high-frequency and high-resolution monitoring of forest disease with minimal human intervention. Deploying such systems, however, requires classification methods that achieve high accuracy while remaining robust to spectral interference and…
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Artikel Jurnal
A lightweight Mamba-frequency fusion algorithm for underwater image enhancement under physical model constraints
Underwater optical images are crucial data for ecological environment monitoring and marine biology research. However, due to the absorption and scattering of light by water, the image quality often suffers severe degradation, directly affecting the accuracy of species identification and habitat…
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Artikel Jurnal
Automated crocodile detection using deep learning and synthetic data
Image object detectors are a powerful tool that allows researchers and farmers to automatically detect and analyse animals in aerial images using unmanned aerial vehicles. This technology reduces the cost and time required to do animal surveys. However, one major challenge of object detectors is…
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Artikel Jurnal
Influence of seasonal canopy conditions on ICESat-2-based aboveground biomass estimation in deciduous forests
Forest aboveground biomass (AGB) is a key indicator of terrestrial carbon storage, and its accurate estimation is essential for effective carbon stock monitoring. However, how seasonal canopy conditions (leaf-on vs. leaf-off) affect the accuracy of ICESat-2-based AGB estimation in deciduous forests…
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