An Innovative Tool for Monitoring Mangrove Forest Dynamics in Cuba Using Remote Sensing and WebGIS Technologies: SIGMEM DOI Creative Commons
Alexey Valero-Jorge,

Raúl González-Lozano,

Roberto González–De Zayas

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(20), P. 3802 - 3802

Published: Oct. 12, 2024

The main objective of this work was to develop a viewer with web output, through which the changes experienced by mangroves Gran Humedal del Norte de Ciego Avila (GHNCA) can be evaluated from remote sensors, contributing understanding spatiotemporal variability their vegetative dynamics. achievement is supported use open-source technologies such as MapStore, GeoServer and Django, well Google Earth Engine, combine offer robust technologically independent solution problem. In context, it decided adopt an action model aimed at automating workflow steps related data preprocessing, downloading, publishing. A visualizer output (Geospatial System for Monitoring Mangrove Ecosystems or SIGMEM) developed first time, evaluating in area central Cuba different vegetation indices. evaluation machine learning classifiers Random Forest Naive Bayes automated mapping highlighted ability discriminate between areas occupied other coverages Overall Accuracy (OA) 94.11%, surpassing 89.85% Bayes. estimated net change based on year 2020 determined during classification process showed decrease 5138.17 ha 2023 2831.76 2022. This tool will fundamental researchers, decision makers, students, new research proposals sustainable management Caribbean.

Language: Английский

Construction and optimization of ecological security patterns based on ecosystem service function and ecosystem sensitivity in the important ecological functional area — A case study in the Yellow River Basin DOI
Wei Wei, Yali Zhang,

Xiaoxu Wei

et al.

Ecological Engineering, Journal Year: 2025, Volume and Issue: 215, P. 107609 - 107609

Published: March 23, 2025

Language: Английский

Citations

1
Coupling eco-environmental quality and ecosystem services to delineate priority ecological reserves—A case study in the Yellow River Basin DOI

Yangjing Xu,

Xiuchun Yang,

Xiaoyu Xing

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 365, P. 121645 - 121645

Published: July 2, 2024

Language: Английский

Citations

8
The shifts of precipitation phases and their impacts DOI
Xuemei Li, Tao Che, Yuqian Tang

et al.

Science China Earth Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

0
SACNet: A Novel Self-Supervised Learning Method for Shadow Detection from High-Resolution Remote Sensing Images DOI
Dehai Chen, Jian Kang, Lanying Wang

et al.

Journal of Geovisualization and Spatial Analysis, Journal Year: 2025, Volume and Issue: 9(1)

Published: Feb. 24, 2025

Language: Английский

Citations

0
State-of-the-Art Status of Google Earth Engine (GEE) Application in Land and Water Resource Management: A Scientometric Analysis DOI

Nishtha Sharnagat,

A.K. Nema,

P. K. Mishra

et al.

Journal of Geovisualization and Spatial Analysis, Journal Year: 2025, Volume and Issue: 9(1)

Published: Feb. 26, 2025

Language: Английский

Citations

0
Reduced zero-curtain duration in freezing periods in the Headwater Area of the Yellow River, 2011‒2024 DOI Creative Commons
Dongliang Luo, Shuo Li, Yanlin Zhang

et al.

Advances in Climate Change Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Land Use/Land Cover Change Model for Mapping, Monitoring and Modeling Environmental Changes in Segara Anakan Due to Heavy Sedimentation in the Downstream of Citanduy River-Indonesia DOI
Bayu Prayudha, Yaya Ihya Ulumuddin, Vincentius Siregar

et al.

Environmental science and engineering, Journal Year: 2025, Volume and Issue: unknown, P. 307 - 329

Published: Jan. 1, 2025

Language: Английский

Citations

0
Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i DOI Creative Commons
Francisco Rodríguez, Ryan L. Perroy, C. Barrera

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4791 - 4791

Published: Dec. 22, 2024

The generation of cloud-free satellite mosaics is essential for a range remote sensing applications, including land use mapping, ecosystem monitoring, and resource management. This study focuses on across the climatic diversity Hawai’i Island, which encompasses ten Köppen climate zones from tropical to Arctic: periglacial. presents unique challenges image generation. We conducted comparative analysis three cloud-masking methods: two Google Earth Engine algorithms (CloudScore+ s2cloudless) new proprietary deep learning-based algorithm (L3) applied Sentinel-2 imagery. These methods were evaluated against best monthly composite selected high-frequency Planet imagery, acquires daily images. All bands enhanced 10 m resolution, an advanced weather mask was generate 2019 2023. stratified by cloud cover frequency (low, moderate, high, very high), applying one-way two-way ANOVAs assess pixel success rates. Results indicate that CloudScore+ achieved highest rate at 89.4% pixels, followed L3 s2cloudless 79.3% 80.8%, respectively. Cloud removal effectiveness decreased as increased, with clear rates ranging 94.6% under low high cover. Additionally, seasonality effects showed higher in wet season (88.6%), while no significant year-to-year differences observed advances current methodologies generating reliable subtropical regions, potential applications other cloud-dense environments.

Language: Английский

Citations

1
Unsupervised Noise-Resistant Remote-Sensing Image Change Detection: A Self-Supervised Denoising Network-, FCM_SICM-, and EMD Metric-Based Approach DOI Creative Commons

Jiangling Xie,

Yikun Li, Shuwen Yang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(17), P. 3209 - 3209

Published: Aug. 30, 2024

The detection of change in remote-sensing images is broadly applicable to many fields. In recent years, both supervised and unsupervised methods have demonstrated excellent capacity detect changes high-resolution images. However, most these are sensitive noise, their performance significantly deteriorates when dealing with that been contaminated by mixed random noises. Moreover, require samples manually labeled for training, which time-consuming labor-intensive. This study proposes a new change-detection (CD) framework resilient noise called self-supervised denoising network-based coupling FCM_SICM EMD (SSDNet-FSE). It consists two components, namely module CD module. proposed method first utilizes network real 3D weight attention mechanisms reconstruct noisy Then, noise-resistant fuzzy C-means clustering algorithm (FCM_SICM) used decompose the pixels reconstructed into multiple signal classes exploiting local spatial information, spectral membership linkage. Next, Earth mover’s distance (EMD) calculate between signal-class centers corresponding memberships bitemporal generate map magnitude change. Finally, automatic thresholding undertaken binarize change-magnitude final map. results experiments conducted on five public datasets prove superior over six state-of-the-art competitors confirm its effectiveness potential practical application.

Language: Английский

Citations

0
An Innovative Tool for Monitoring Mangrove Forest Dynamics in Cuba Using Remote Sensing and WebGIS Technologies: SIGMEM DOI Creative Commons
Alexey Valero-Jorge,

Raúl González-Lozano,

Roberto González–De Zayas

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(20), P. 3802 - 3802

Published: Oct. 12, 2024

The main objective of this work was to develop a viewer with web output, through which the changes experienced by mangroves Gran Humedal del Norte de Ciego Avila (GHNCA) can be evaluated from remote sensors, contributing understanding spatiotemporal variability their vegetative dynamics. achievement is supported use open-source technologies such as MapStore, GeoServer and Django, well Google Earth Engine, combine offer robust technologically independent solution problem. In context, it decided adopt an action model aimed at automating workflow steps related data preprocessing, downloading, publishing. A visualizer output (Geospatial System for Monitoring Mangrove Ecosystems or SIGMEM) developed first time, evaluating in area central Cuba different vegetation indices. evaluation machine learning classifiers Random Forest Naive Bayes automated mapping highlighted ability discriminate between areas occupied other coverages Overall Accuracy (OA) 94.11%, surpassing 89.85% Bayes. estimated net change based on year 2020 determined during classification process showed decrease 5138.17 ha 2023 2831.76 2022. This tool will fundamental researchers, decision makers, students, new research proposals sustainable management Caribbean.

Language: Английский

Citations

0