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GIS Solutions and Remote Sensing Applications in Monitoring, Assessing and Managing Different Aquatic and Glaciated Environments

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 6338

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Special Issue Editors

Dr. Hao Zhang

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Guest Editor

Laboratory for Applied Earth Observation and Spatial Analysis (LAEOSA), Department of Environmental Science and Engineering, Fudan University, Shanghai, China
Interests: climate change; remote sensing; spatial analysis; statistical modeling; machine learning; urbanization; sustainable development; urban planning
Special Issues, Collections and Topics in MDPI journals

Dr. Rui Zhou

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Guest Editor

School of Environmental and Geographical Sciences, Fudan University, Shanghai, China
Interests: landscape ecology; landscape pattern and ecological processes; urban planning and management; urban heat islands; sustainable development

Dr. Yuanbin Cai

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Guest Editor

College of Environment and Safety Engineering, Fuzhou University, Fuzhou, China
Interests: water quality model; urban planning and management; hydrology and water resources; remote sensing; dynamics analysis; sustainable development

Special Issue Information

Dear Colleagues,

The dynamics of different aquatic and glaciated environments determine the status of water supply and related risks (e.g., drought, fire, flood, and hill slide) worldwide, and profoundly influence the functionality and health of ecosystems and the sustainability of human life. In recent years, we have witnessed a rapid increase in extreme events driven by climate change, such as wild and forest fires, glacial ablation, pollution and water shortage, heat waves, and scorched cities. Unfortunately, intensive human activities amplify the impacts of climate change. Across local, regional, and global scales, the science and technology of GIS and remote sensing can provide useful tools for mapping, monitoring, and assessing the combined effects of climate change and human activities on aquatic and glaciated environments. However, given the complexity and uncertainty of human–nature interactions, the routine theories and methods reported in previous studies may not be sufficient to understand the changing world. Therefore, in this Special Issue, state-of-the-art GIS and remote sensing theories and technologies geared towards monitoring, assessing, and managing different aquatic and glaciated environments, particularly multidisciplinary collaborative simulations, machine learning algorithms, multiple dataset combinations, and data assimilation are welcome.

Dr. Hao Zhang
Dr. Rui Zhou
Dr. Yuanbin Cai
Guest Editors

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Keywords

climate change
extreme events
aquatic environment
glaciated environment
GIS
remote sensing
climatic adaptation
hydrology and water resources
water quality model

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Published Papers (4 papers)

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Research

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28 pages, 10038 KiB

Open AccessArticle

Mapping Water Bodies and Wetlands from Multispectral and SAR Data for the Cross-Border River Basins of the Polish–Ukrainian Border

by Tetiana Melnychenko and Tatiana Solovey

Water 2024, 16(3), 407; https://doi.org/10.3390/w16030407 - 26 Jan 2024

Cited by 1 | Viewed by 1344

Abstract

Using remote sensing data to accurately record water surface changes over large areas is crucial in monitoring water resources. However, mapping water surfaces from remote sensing data has its advantages and disadvantages. This study presents a method for mapping water surfaces and wetlands based on Sentinel-1/-2 data over a study area of more than 26,000 km² in three river basins, the Bug, Dniester, and San, located along the Polish–Ukrainian border. To achieve this goal, an image processing algorithm with additional options was developed (special filters, type classification, and post-classification), which minimized the shortcomings and increased the accuracy of the method. As a result, by using optical and radar data, it was possible to create maps of water bodies in the study area in the driest month of the year from 2018 to 2021. The results were evaluated numerically and graphically. The accuracy of the method was assessed using the Kappa coefficient. For optical data, the lowest value was 76.28% and the highest was 88.65%; for radar data, these values were 87.61% and 97.18%, respectively. When assessing accuracy, the highest values were achieved for overall accuracy (OA), with a maximum of 0.95 (for SAR) and 0.91 (for optical data). The highest values were in user accuracy (UA), with a maximum value of 1 for both SAR and optical data. Full article

(This article belongs to the Special Issue GIS Solutions and Remote Sensing Applications in Monitoring, Assessing and Managing Different Aquatic and Glaciated Environments)

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21 pages, 5746 KiB

Open AccessArticle

A Comparative Analysis of Machine Learning Techniques for National Glacier Mapping: Evaluating Performance through Spatial Cross-Validation in Perú

by Marcelo Bueno, Briggitte Macera and Nilton Montoya

Water 2023, 15(24), 4214; https://doi.org/10.3390/w15244214 - 7 Dec 2023

Cited by 1 | Viewed by 1729

Abstract

Accurate glacier mapping is crucial for assessing future water security in Andean ecosystems. Traditional accuracy assessment may be biased due to overlooking spatial autocorrelation during map validation. In recent years, spatial cross-validation (CV) strategies have been proposed in environmental and ecological modeling to reduce bias in predictive accuracy. In this study, we demonstrate the influence of spatial autocorrelation on the accuracy assessment of glacier surface predictive models. This is achieved by comparing the performance of several widely used machine learning algorithms including the gradient-boosting machines (GBM), k-nearest neighbors (KNN), random forest (RF), and logistic regression (LR) for mapping nine main Peruvian glacier regions. Spatial and non-spatial cross-validation methods were used to evaluate the model’s classification errors in terms of the Matthews correlation coefficient. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results. Regarding only spatial CV, the k-nearest neighbors were the overall best model across Huallanca (0.90), Huayhuasha (0.78), Huaytapallana (0.96), Raura (0.93), Urubamba (0.96), Vilcabamba (0.93), and Vilcanota (0.92) regions, consistently demonstrating the highest performance followed by logistic regression at Blanca (0.95) and Central (0.97) regions. Our validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives. Full article

(This article belongs to the Special Issue GIS Solutions and Remote Sensing Applications in Monitoring, Assessing and Managing Different Aquatic and Glaciated Environments)

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16 pages, 6218 KiB

Open AccessArticle

Using RS and GIS Techniques to Assess and Monitor Coastal Changes of Coastal Islands in the Marine Environment of a Humid Tropical Region

by Muhamed Fasil Chettiyam Thodi, Girish Gopinath, Udayar Pillai Surendran, Pranav Prem, Nadhir Al-Ansari and Mohamed A. Mattar

Water 2023, 15(21), 3819; https://doi.org/10.3390/w15213819 - 1 Nov 2023

Cited by 3 | Viewed by 2148

Abstract

Vypin, Vallarpadam, and Bolgatty are significant tropical coastal islands situated in the humid tropical Kerala region of India, notable for their environmental sensitivity. This study conducted a comprehensive assessment of shoreline alterations on these islands by integrating Remote Sensing (RS) and Geographic Information Systems (GIS) techniques. Utilizing satellite imagery from the LANDSAT series with a spatial resolution of 30 m, the analysis spanned the years from 1973 to 2019. The Digital Shoreline Analysis System (DSAS) tool, integrated into the ArcGIS software, was employed to monitor and analyze shoreline shifts, encompassing erosion and accretion. Various statistical parameters, including Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), were utilized to evaluate these changes. Additionally, the study aimed to discern the root causes of shoreline modifications in the study area, encompassing disturbances and the construction of new structures on these islands. The results conclusively demonstrated the substantial impact endured by these coastal islands, with accretion on both sides leading to the creation of new landmasses. This manuscript effectively illustrates that these islands have experienced marine transgression, notably evidenced by accretion. Anthropogenic activities were identified as the primary drivers behind the observed shoreline changes, underscoring the need for careful management and sustainable practices in these fragile coastal ecosystems. Full article

(This article belongs to the Special Issue GIS Solutions and Remote Sensing Applications in Monitoring, Assessing and Managing Different Aquatic and Glaciated Environments)

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Review

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39 pages, 17797 KiB

Open AccessReview

Application of Artificial Intelligence in Glacier Studies: A State-of-the-Art Review

by Serik Nurakynov, Aibek Merekeyev, Zhaksybek Baygurin, Nurmakhambet Sydyk and Bakytzhan Akhmetov

Water 2024, 16(16), 2272; https://doi.org/10.3390/w16162272 - 12 Aug 2024

Viewed by 711

Abstract

Assessing glaciers using recent and historical data and predicting the future impacts on them due to climate change are crucial for understanding global glacier mass balance, regional water resources, and downstream hydrology. Computational methods are crucial for analyzing current conditions and forecasting glacier changes using remote sensing and other data sources. Due to the complexity and large data volumes, there is a strong demand for accelerated computing. AI-based approaches are increasingly being adopted for their efficiency and accuracy in these tasks. Thus, in the current state-of-the-art review work, available research results on the application of AI methods for glacier studies are addressed. Using selected search terms, AI-based publications are collected from research databases. They are further classified in terms of their geographical locations and glacier-related research purposes. It was found that the majority of AI-based glacier studies focused on inventorying and mapping glaciers worldwide. AI techniques like U-Net, Random forest, CNN, and DeepLab are mostly utilized in glacier mapping, demonstrating their adaptability and scalability. Other AI-based glacier studies such as glacier evolution, snow/ice differentiation, and ice dynamic modeling are reviewed and classified, Overall, AI methods are predominantly based on supervised learning and deep learning approaches, and these methods have been used almost evenly in glacier publications over the years since the beginning of this research area. Thus, the integration of AI in glacier research is advancing, promising to enhance our comprehension of glaciers amid climate change and aiding environmental conservation and resource management. Full article

(This article belongs to the Special Issue GIS Solutions and Remote Sensing Applications in Monitoring, Assessing and Managing Different Aquatic and Glaciated Environments)

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