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Application of Remote Sensing to the Monitoring of Land Cover and Water Environment

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Resources and Sustainable Utilization".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 6327

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Special Issue Editors

Dr. Xiaoshuang Ma

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Guest Editor

1. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China
Interests: remote sensing image processing and interpretation; remote sensing of environment; synthetic aperture radar; target detection on remote sensing images; image denoising; deep learning; computer vision
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Dr. Chen Wang

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Guest Editor

School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Interests: SAR image processing; 3D mapping; cadastre

Dr. Zhixiang Yin

E-Mail Website
Guest Editor

School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Interests: super-resolution mapping, spatio-temporal fusion of remote sensing images, deep learning

Special Issue Information

Dear Colleagues,

Urbanization, dense population and the utilization of natural resources continually exert pressures on the Earth, resulting in increasingly prominent environmental problems. Mountains, fields, rivers, lakes and oceans are the “life community” in which human beings share weal and woe. Monitoring and evaluating the environmental issues related to the above elements is crucial for scientific formulation of regional development strategies, and is also a basic requirement for the concept of sustainable development. Remote sensing technology can obtain information on the Earth's surface over a large area in a short time, and has been widely used in environmental monitoring and assessment. The development of environmental remote sensing is changing quickly in terms of application areas and technical methods. Papers addressing these topics are invited for this Special Issue, especially those combining a high academic standard and a practical focus on providing monitoring solutions on land cover and water environments based on remote sensing technology.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

(1) Reviews on remote sensing applications to land cover and water environment monitoring.

(2) Understanding land cover in urban and rural areas with high-resolution remote sensing images;

(3) Change detection and analysis of land cover types and water in urban and rural areas by remote sensing techniques;

(4) Developing remote sensing methods of monitoring the environmental conditions of water;

(5) Identification, assessment and response to disasters associated with land and water (e.g., earthquake, earth surface subsidence, red tide, oil spills in marine environments).

Dr. Xiaoshuang Ma
Dr. Chen Wang
Dr. Zhixiang Yin
Guest Editors

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Keywords

land cover monitoring
environmental monitoring
environmental remote sensing
remote sensing of water quality
soil and water conservation
geological hazard monitoring
water environments

Published Papers (4 papers)

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Research

13 pages, 3225 KiB

Open AccessArticle

Monitoring of Oil Spill Risk in Coastal Areas Based on Polarimetric SAR Satellite Images and Deep Learning Theory

by Lu Liao, Qing Zhao and Wenyue Song

Sustainability 2023, 15(19), 14504; https://doi.org/10.3390/su151914504 - 05 Oct 2023

Viewed by 877

Abstract

Healthy coasts have a high ecological service value. However, many coastal areas are faced with oil spill risks. The Synthetic Aperture Radar (SAR) remote sensing technique has become an effective tool for monitoring the oil spill risk in coastal areas. In this study, taking Jiaozhou Bay in China as the study area, an innovative oil spill monitoring framework was established based on Polarimetric SAR (PolSAR) images and deep learning theory. Specifically, a DeepLabv3+-based semantic segmentation model was trained using 35 Sentinel-1 satellite images of oil films on the sea surface from maritime sectors in different regions all over the world, which not only considered the information from the PolSAR images but also meteorological conditions; then, the well-trained framework was deployed to identify the oil films in the Sentinel-1 images of Jiaozhou Bay from 2017 to 2019. The experimental results show that the detection accuracies of the proposed oil spill detection model were higher than 0.95. It was found that the oil films in Jiaozhou Bay were mainly concentrated in the vicinity of the waterways and coastal port terminals, that the occurrence frequency of oil spills in Jiaozhou Bay decreased from 2017 to 2019, and that more than 80 percent of the oil spill events occurred at night, mainly coming from the illegal discharge of waste oil from ships. These data indicate that, in the future, the PolSAR technique will play a more important role in oil spill monitoring for Jiaozhou Bay due to its capability to capture images at night. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Monitoring of Land Cover and Water Environment)

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19 pages, 10525 KiB

Open AccessArticle

Spatial Distribution of Soil Heavy Metal Concentrations in Road-Neighboring Areas Using UAV-Based Hyperspectral Remote Sensing and GIS Technology

by Wenxia Gan, Yuxuan Zhang, Jinying Xu, Ruqin Yang, Anna Xiao and Xiaodi Hu

Sustainability 2023, 15(13), 10043; https://doi.org/10.3390/su151310043 - 25 Jun 2023

Cited by 2 | Viewed by 1306

Abstract

Monitoring and restoring soil quality in areas neighboring roads affected by traffic activities require a thorough investigation of heavy metal concentrations. This study examines the spatial heterogeneity of copper (Cu) and chromium (Cr) concentrations in a 0.113 km² area adjacent to Jin-Long Avenue in Wuhan, China, using Unmanned Aerial Vehicle (UAV)-based hyperspectral remote sensing technology. Through this UAV-based remote sensing technology, we innovatively achieve a small-scale and fine-grained analysis of soil heavy metal pollution related with traffic activities, which represents a major contribution of this research study. In our approach, we generated 4375 spectral variates by transforming the original spectrum. To enhance result accuracy, we applied the Boruta algorithm and correlation analysis to select optimal spectral variates. We developed the retrieval model using the Gradient Boosting Decision Tree (GBDT) regression method, selected from a set of four regression methods using the LOOCV method. The resulting model yielded R-square values of 0.325 and 0.351 for Cu and Cr, respectively, providing valuable insights into the heavy metal concentrations. Based on the retrieved heavy metal concentrations from bare soil pixels (17,420 points), we analyzed the relationship between heavy metal concentrations and the perpendicular distance from the road. Additionally, we employed the universal kriging interpolation method to map heavy metal concentrations across the entire area. Our findings reveal that the concentration of heavy metals in this area exceeds background values and decreases as the distance from the road increases. This research significantly contributes to the understanding of spatial distribution characteristics and pollution caused by heavy metal concentrations resulting from traffic activities. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Monitoring of Land Cover and Water Environment)

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14 pages, 4567 KiB

Open AccessArticle

Deep Learning-Based Algal Bloom Identification Method from Remote Sensing Images—Take China’s Chaohu Lake as an Example

by Shengyuan Zhu, Yinglei Wu and Xiaoshuang Ma

Sustainability 2023, 15(5), 4545; https://doi.org/10.3390/su15054545 - 03 Mar 2023

Cited by 3 | Viewed by 1612

Abstract

Rapid and accurate monitoring of algal blooms using remote sensing techniques is an effective means for the prevention and control of algal blooms. Traditional methods often have difficulty achieving the balance between interpretative accuracy and efficiency. The advantages of a deep learning method bring new possibilities to the rapid and precise identification of algal blooms using images. In this paper, taking Chaohu Lake as the study area, a dual U-Net model (including a U-Net network for spring and winter and a U-Net network for summer and autumn) is proposed for the identification of algal blooms using remote sensing images according to the different traits of the algae in different seasons. First, the spectral reflection characteristics of the algae in Chaohu Lake in different seasons are analyzed, and sufficient samples are selected for the training of the proposed model. Then, by adding an attention gate architecture to the classical U-Net framework, which can enhance the capability of the network on feature extraction, the dual U-Net model is constructed and trained for the identification of algal blooms in different seasons. Finally, the identification results are obtained by inputting remote sensing data into the model. The experimental results show that the interpretation accuracy of the proposed deep learning model is higher than 90% in most cases with the fastest processing time being less than 10 s, which achieves much better performance than the traditional supervised classification method and also outperforms the single U-Net model using data of whole year as the training samples. Furthermore, the profiles of algal blooms are well-captured. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Monitoring of Land Cover and Water Environment)

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17 pages, 14276 KiB

Open AccessArticle

The Assessment of the Spatiotemporal Characteristics of the Eco-Environmental Quality in the Chishui River Basin from 2000 to 2020

by Songlin Zhou, Wei Li, Wei Zhang and Ziyuan Wang

Sustainability 2023, 15(4), 3695; https://doi.org/10.3390/su15043695 - 17 Feb 2023

Cited by 5 | Viewed by 1819

Abstract

The Chishui River Basin is located in the bordering area of Yunnan, Guizhou and Sichuan provinces, which serves as an important ecological barrier in the upper reaches of the Yangtze River, and plays a leading role in preserving natural environments, protecting water resources, and maintaining soil functions. However, the eco-environmental quality in the basin has encountered serious challenges in recent years, and the conflict between eco-environmental protection and economic development becomes increasingly prominent. Therefore, it is particularly important to quantitatively assess the extent of the eco-environmental changes in this basin. The present study acquired Landsat series remote sensing images based on the Google Earth Engine (GEE) platform, constructed a remote sensing ecological index (RSEI) as the assessment index that reflects the eco-environmental quality using principal component analysis, studied the changing trend in the eco-environmental quality using the Sen–Mann–Kendall trend test, analyzed the spatial clustering distribution patterns of the eco-environmental quality, based on spatial autocorrelation analysis, and applied the geographical detector model to determine the impacts of natural and anthropogenic factors on the eco-environmental quality. We further applied the CA–Markov model to simulate and predict the eco-environmental quality of the basin in 2025. The results showed the following: (1) between 2000 and 2020, the eco-environmental quality of the Chishui River Basin had been greatly improved. The average RSEI value increased from 0.526 in 2000 to 0.668 in 2020, and the percentage of areas belonging to the good or excellent quality category increased from 42.65% to 68.48%. (2) The main drivers of the eco-environmental quality included population density, mean annual temperature, land use type and elevation. The interactive effect between these drivers was significantly higher than that of individual drivers, and thus possessed stronger explanatory power for quality differences. (3) It is predicted that in 2025, the eco-environmental quality of the basin will continue to improve, and the proportion of land areas with good or excellent quality will continuously increase. The present study can provide reference value for local environmental protection and regional planning. Full article

(This article belongs to the Special Issue Application of Remote Sensing to the Monitoring of Land Cover and Water Environment)

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