Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

Journal Browser

Advances in Remote Sensing of Watershed Ecology and Pollution

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4898

Share This Special Issue

Special Issue Editors

Prof. Dr. Yongnian Gao

E-Mail Website
Guest Editor

School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
Interests: watershed remote sensing; wetland/water remote sensing; watershed big data; information watershed

Prof. Dr. Taixia Wu

E-Mail Website
Guest Editor

School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
Interests: remote sensing; polarization; water extraction; water resource
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is believed that watersheds are the best natural division unit for water-related ecological research, the management of water resources and the ecological environment, and the quality of ecological conditions and the severity of pollution, which are important aspects affecting the sustainability of watersheds. Moreover, research on watershed ecology, watershed environmental science and watershed geography mainly on watersheds. Remote sensing technology has increasingly become an important means of watershed ecology and pollution investigation and monitoring. Watershed ecology and pollution remote sensing has become an important aspect of watershed remote sensing science. For watershed ecology and pollution, a series of remote sensing studies on different types of watersheds around the underlying surface, element composition, the model, data source and temporal and spatial scales have important application value for problem discovery and management in the field of watershed sustainability.

In this Special Issue, original research articles and reviews are welcome. The topics of primary interest include, but are not limited to:

Different underlying surfaces: Remote sensing of ecology or pollution in land area, water, wetland, construction land, industrial land, living land, agricultural land, grassland, forest land, etc.;
Different elements: Remote sensing of ecological or pollution indicators of vegetation, biodiversity, landscape ecology, land cover/use, non-point source pollution, point source pollution, aquatic organisms, water physics, water chemistry, etc.;
Different methods: Empirical statistical models, machine learning models or physical mechanism models for remote sensing quantification of watershed ecological or pollution indicators;
Different remote sensing data sources: Satellite, aviation, Unmanned Aerial Vehicles (UAV), optics, microwave or lidar, used for the remote sensing of watershed ecology or pollution;
Different watershed types: Remote sensing of ecology or pollution in lake watersheds, river watersheds or reservoir watersheds;
Different scales: Remote sensing of watershed ecology or pollution at different spatial scales such as small watersheds, medium watersheds or large watersheds, and at different time scales, such as specific time, monthly or interannual changes;
Different cases: Research on remote sensing methods, data products, and the application analysis of watershed ecology or pollution.

All manuscripts should focus on watershed ecology or pollution, remote sensing and sustainability. The relationship between the research theme and sustainability should be discussed in the introduction or discussion of the manuscript.

Prof. Dr. Yongnian Gao
Prof. Dr. Taixia Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

vegetation/landscape
water quality
non-point source pollution
wetland/water resource
watershed
machine learning
remote sensing
SDGs

Published Papers (3 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

15 pages, 3678 KiB

Open AccessArticle

Modeling and Predicting Land Use/Land Cover Change Using the Land Change Modeler in the Suluh River Basin, Northern Highlands of Ethiopia

by Hailay Hagos Entahabu, Amare Sewnet Minale and Emiru Birhane

Sustainability 2023, 15(10), 8202; https://doi.org/10.3390/su15108202 - 18 May 2023

Cited by 1 | Viewed by 1390

Abstract

Land use and land cover change are among the drivers of environmental change. The Suluh River Basin’s land use and land cover are modeled in this study using a land change modeler. To accomplish the goals of this study, Landsat images and ancillary data sources were utilized. In eCognition Developer 9.2 software, nearest neighbor fuzzy classification was used to classify Landsat images. With the IDRISI Selva 17.3 software, change detection and modeling were carried out. Both qualitative and quantitative analyses of the data were conducted. The results showed that, despite a drop in forest land of 97.2%, grazing land of 89.8%, plantation land of 89.1%, shrub-bush land of 1.5%, and water bodies of 84.8% from 1990 to 2002, bare land increased by 10.6%, built-up land by 29.4%, and cultivated land by 65.4%. The model projects, bare, built-up, and cultivated land will increase at the cost of water bodies, grazing, forest, shrub-bush, and plantation land between the years 2028 and 2048. Rainfall, slope, height, distance to rivers, distance to highways, distance from towns, and population density were the main determinants of LULC change in the study area. Therefore, in order to promote sustainable development, safeguard the river basin, and lessen the severity of the changes, appropriate management and timely action must be taken by policymakers and decision makers. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Watershed Ecology and Pollution)

► Show Figures

Figure 1

23 pages, 5016 KiB

Open AccessArticle

Optimization of State of the Art Fuzzy-Based Machine Learning Techniques for Total Dissolved Solids Prediction

by Mohammad Hijji, Tzu-Chia Chen, Muhammad Ayaz, Ali S. Abosinnee, Iskandar Muda, Yury Razoumny and Javad Hatamiafkoueieh

Sustainability 2023, 15(8), 7016; https://doi.org/10.3390/su15087016 - 21 Apr 2023

Cited by 2 | Viewed by 1190

Abstract

Total dissolved solid prediction is an important factor which can support the early warning of water pollution, especially in the areas exposed to a mixture of pollutants. In this study, a new fuzzy-based intelligent system was developed, due to the uncertainty of the TDS time series data, by integrating optimization algorithms. Monthly-timescale water quality parameters data from nearly four decades (1974–2016), recorded over two gaging stations in coastal Iran, were used for the analysis. For model implementation, the current research aims to model the TDS parameter in a river system by using relevant biochemical parameters such as Ca, Mg, Na, and HCO₃. To produce more compact networks along with the model’s generalization, a hybrid model which integrates a fuzzy-based intelligent system with the grasshopper optimization algorithm, NF-GMDH-GOA, is proposed for the prediction of the monthly TDS, and the prediction results are compared with five standalone and hybrid machine learning techniques. Results show that the proposed integrated NF-GMDH-GOA was able to provide an algorithmically informed simulation (NSE = 0.970 for Rig-Cheshmeh and NSE = 0.94 Soleyman Tangeh) of the dynamics of TDS records comparable to the artificial neural network, extreme learning machine, adaptive neuro fuzzy inference system, GMDH, and NF-GMDH-PSO models. According to the results of sensitivity analysis, Sodium in natural bodies of water with maximum value of error (RMSE = 56.4) had the highest influence on the TDS prediction for both stations, and Mg with RMSE = 43.251 stood second. The results of the Wilcoxon signed rank tests also indicated that the model’s prediction means were different, as the p value calculated for the models was less than the standard significance level (

α = 0.05

). Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Watershed Ecology and Pollution)

► Show Figures

Figure 1

15 pages, 3897 KiB

Open AccessArticle

Sustainability of Groundwater Potential Zones in Coastal Areas of Cuddalore District, Tamil Nadu, South India Using Integrated Approach of Remote Sensing, GIS and AHP Techniques

by Mahenthiran Sathiyamoorthy, Uma Shankar Masilamani, Aaron Anil Chadee, Sreelakhmi Devi Golla, Mohammed Aldagheiri, Parveen Sihag, Upaka Rathnayake, Jyotendra Patidar, Shivansh Shukla, Aryan Kumar Singh, Bimlesh Kumar and Hector Martin

Sustainability 2023, 15(6), 5339; https://doi.org/10.3390/su15065339 - 17 Mar 2023

Cited by 2 | Viewed by 1793

Abstract

This paper aims to identify the groundwater-potential zones in coastal zones of the Cuddalore district by integrating remote sensing, Geographic Information System (GIS), and Analytical Hierarchy Process (AHP) techniques. The thematic layers such as geomorphology, landuse/land cover, lineament density, geology, soil, drainage density, rainfall, and slope are considered for the identification of groundwater-potential zones. The groundwater-potential zones are categorized into five zones as ‘very good’, ‘good’, ‘moderate’, ‘poor’, and ‘very poor’. From the study, it is observed that the rainwater/surface water infiltration to the aquifer is high in the eastern region due to flat topography, and thus, these zones possess ‘very good’ and ‘good’ groundwater-potential zones. The groundwater potential in the central region of the study area possesses moderate infiltration capacity, which is suitable for agricultural practices. Moreover, it is also observed that the groundwater potential is ‘poor’ and ‘very poor’ in the northwest region due to steep slopes in which suitable recharge structures should be constructed in these zones to harvest the rainwater. Eventually, the obtained results are validated with existing bore wells in the study area, and it reveals that a GIS-based integrated method is an effective tool for the exploration of groundwater resources with high accuracy. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Watershed Ecology and Pollution)

► Show Figures

Journal Menu

Journal Browser

Advances in Remote Sensing of Watershed Ecology and Pollution

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (3 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI