Mapping and classification of Liao River Delta coastal wetland based on time series and multi-source GaoFen images using stacking ensemble model

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102488 - 102488

Опубликована: Янв. 20, 2024

Язык: Английский

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Vegetation Classification and Evaluation of Yancheng Coastal Wetlands Based on Random Forest Algorithm from Sentinel-2 Images

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1124 - 1124

Опубликована: Март 22, 2024

The identification of wetland vegetation is essential for environmental protection and management as well monitoring wetlands’ health assessing ecosystem services. However, some limitations on classification may be related to remote sensing technology, confusion between plant species, challenges inadequate data accuracy. In this paper, in the Yancheng Coastal Wetlands studied evaluated from Sentinel-2 images based a random forest algorithm. Based consistent time series observations, characteristic patterns were better captured. Firstly, spectral features, indices, phenological characteristics extracted images, products obtained by constructing dense using dataset Google Earth Engine (GEE). Then, machine learning algorithm obtained, with an overall accuracy 95.64% kappa coefficient 0.94. Four indicators (POP, SOS, NDVIre, B12) main contributors importance weight analysis all features. Comparative experiments conducted different results show that method proposed paper has classification.

Язык: Английский

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Ensemble and transfer learning of soil inorganic carbon with visible near-infrared spectra

Geoderma, Год журнала: 2025, Номер 456, С. 117257 - 117257

Опубликована: Март 15, 2025

Язык: Английский

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Enhancing Land Cover/Land Use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN)

Ecological Informatics, Год журнала: 2023, Номер 78, С. 102333 - 102333

Опубликована: Окт. 11, 2023

Sustainable natural resources management relies on effective and timely assessment of conservation land practices. Using satellite imagery for Earth observation has become essential monitoring cover/land use (LCLU) changes identifying critical areas conserving biodiversity. Remote Sensing (RS) datasets are often quite large require tremendous computing power to process. The emergence cloud-based techniques presents a powerful avenue overcome limitations by allowing machine-learning algorithms process analyze RS the cloud. Our study aimed classify LCLU Talassemtane National Park (TNP) using Deep Neural Network (DNN) model incorporating five spectral indices differentiate six classes Sentinel-2 imagery. Optimization DNN was conducted comparative analysis three optimization algorithms: Random Search, Hyperband, Bayesian optimization. Results indicated that improved classification between with similar reflectance. Hyperband method had best performance, improving accuracy 12.5% achieving an overall 94.5% kappa coefficient 93.4%. dropout regularization prevented overfitting mitigated over-activation hidden nodes. initial results show machine learning (ML) applications can be tools management.

Язык: Английский

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Flood-drought shifts monitoring on arid Xinjiang, China using a novel machine learning based algorithm

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103030 - 103030

Опубликована: Янв. 1, 2025

Язык: Английский

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Integration of multi-temporal SAR data and robust machine learning models for improvement of flood susceptibility assessment in the southwest coast of India

Applied Computing and Geosciences, Год журнала: 2024, Номер 24, С. 100189 - 100189

Опубликована: Сен. 4, 2024

Язык: Английский

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Integration Sentinel-1 SAR data and machine learning for land subsidence in-depth analysis in the North Coast of Central Java, Indonesia

Earth Science Informatics, Год журнала: 2024, Номер 17(5), С. 4707 - 4738

Опубликована: Июль 22, 2024

Язык: Английский

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Mapping landslide susceptibility in the Eastern Mediterranean mountainous region: a machine learning perspective

Environmental Earth Sciences, Год журнала: 2025, Номер 84(9)

Опубликована: Апрель 30, 2025

Язык: Английский

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Artificial Intelligence (AI) approach for the quantification of C-phycocyanin in Spirulina platensis: Hybrid stacking-ensemble model based on machine learning and deep learning

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103186 - 103186

Опубликована: Май 1, 2025

Язык: Английский

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Evolution of ensemble machine learning approaches in water resources management: a review

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Май 24, 2025

Язык: Английский

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