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Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds III

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 4474

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Special Issue Editors

Prof. Dr. Jiayi Pan

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Guest Editor

1. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
2. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
Interests: coastal and lake remote sensing; coastal ocean dynamics; marine remote sensing physics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bo Huang

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Guest Editor

Department of Geography and Resource Management, Chinese University of Hong Kong, Hong Kong, China
Interests: spatio-temporal data analytics; unified satellite image fusion; spatial statistics for land use/land cover change modeling; multi-objective optimization for sustainable land use planning
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Dr. Hongsheng Zhang

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Guest Editor

Department of Geography, The University of Hong Kong, Hong Kong 999077, China
Interests: coastal ecosystems and sustainability; coastal remote sensing; land use/land cover change; multi-sensor data fusion
Special Issues, Collections and Topics in MDPI journals

Dr. Adam T. Devlin

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Guest Editor

1. Cooperative Institute for Marine and Atmospheric Research, School of Ocean and Earth Science and Technology, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, USA
2. Department of Oceanography, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, USA
Interests: sea level change; lake study; climate and earth system; hydrodynamic simulation; coastal oceanography; remote sensing image processing

Special Issue Information

Dear Colleagues,

Coastal regions, lands, lakes, rivers, and watersheds are critical components of the Earth’s environmental system. The use of remote sensing to monitor these elements is vital for effective environmental management. Although each component individually contributes to environmental change, examining their interactions is crucial for a comprehensive understanding of the mechanisms behind these changes. As remote sensing technology advances, increasingly accurate observational data for coastal regions, lands, lakes, rivers, and watersheds become available, offering effective approaches to monitor the Earth’s environmental system in real-time with high precision. Progress in data fusion technology enables the efficient integration of remote sensing data from multiple sensors and platforms. Remote sensing is growing more important for enhancing our understandings of environmental change processes and the underlying mechanisms involved in them. This Special Issue seeks to compile innovative studies that employ advanced remote sensing technology and apply these methods to coastal regions, lands, lakes, rivers, and watersheds, as well as to the interactions between these ecosystem components. The objective here is to expand our knowledge of environmental change processes and mechanisms with the aim of ultimately contributing to a more comprehensive understanding of our planet’s complex environmental dynamics.

We welcome contributions to the third volume of the Special Issue of Remote Sensing on “Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watershed”.

Prof. Dr. Jiayi Pan
Prof. Dr. Bo Huang
Dr. Hongsheng Zhang
Dr. Adam T. Devlin
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. Remote Sensing 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 2700 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

remote sensing of coastal waters and zones
remote sensing data fusion technology
land cover/use
remote sensing of lakes, rivers, and watersheds
remote sensing and GIS technology
marine remote sensing

Related Special Issue

Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds II in Remote Sensing (23 articles)

Published Papers (4 papers)

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Research

16 pages, 4205 KiB

Open AccessArticle

Ice Thickness Assessment of Non-Freshwater Lakes in the Qinghai–Tibetan Plateau Based on Unmanned Aerial Vehicle-Borne Ice-Penetrating Radar: A Case Study of Qinghai Lake and Gahai Lake

by Huian Jin, Xiaojun Yao, Qixin Wei, Sugang Zhou, Yuan Zhang, Jie Chen and Zhipeng Yu

Remote Sens. 2024, 16(6), 959; https://doi.org/10.3390/rs16060959 - 09 Mar 2024

Viewed by 460

Abstract

Ice thickness has a significant effect on the physical and biogeochemical processes of a lake, and it is an integral focus of research in the field of ice engineering. The Qinghai–Tibetan Plateau, known as the Third Pole of the world, contains numerous lakes. Compared with some information, such as the area, water level, and ice phenology of its lakes, the ice thickness of these lakes remains poorly understood. In this study, we used an unmanned aerial vehicle (UAV) with a 400/900 MHz ice-penetrating radar to detect the ice thickness of Qinghai Lake and Gahai Lake. Two observation fields were established on the western side of Qinghai Lake and Gahai Lake in January 2019 and January 2021, respectively. Based on the in situ ice thickness and the propagation time of the radar, the accuracy of the ice thickness measurements of these two non-freshwater lakes was comprehensively assessed. The results indicate that pre-processed echo images from the UAV-borne ice-penetrating radar identified non-freshwater lake ice, and we were thus able to accurately calculate the propagation time of radar waves through the ice. The average dielectric constants of Qinghai Lake and Gahai Lake were 4.3 and 4.6, respectively. This means that the speed of the radar waves that propagated through the ice of the non-freshwater lake was lower than that of the radio waves that propagated through the freshwater lake. The antenna frequency of the radar also had an impact on the accuracy of ice thickness modeling. The RMSEs were 0.034 m using the 400 MHz radar and 0.010 m using the 900 MHz radar. The radar with a higher antenna frequency was shown to provide greater accuracy in ice thickness monitoring, but the control of the UAV’s altitude and speed should be addressed. Full article

(This article belongs to the Special Issue Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds III)

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20 pages, 6689 KiB

Open AccessArticle

Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

by Yuewan Wu and Jiayi Pan

Remote Sens. 2023, 15(22), 5387; https://doi.org/10.3390/rs15225387 - 16 Nov 2023

Cited by 3 | Viewed by 888

Abstract

This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture Radar (SAR) image (Sentinel-1) enhances detection accuracy. The overall accuracy (OA) and kappa coefficients increased from 84.3% to 88.3% and from 89.21% to 92.55%, respectively, compared to the exclusive use of the Landsat-8 image. Notably, the Random Forest algorithm, with its unique dual-random sampling technique for fusing multi-sensor satellite data, outperforms other machine learning methods like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), Maximum Likelihood Classification (Max-Likelihood), and Minimum Distance Classification (Min-Distance) in impervious surface extraction efficiency. With additional satellite images from 2015, 2017, and 2020, the impervious surface changes are tracked in the Nanchang metropolitan region. From 2015 to 2021, they record a notable increase in impervious surfaces, signaling a quickened urban expansion. This study observes several impervious surface growth patterns, such as a tendency to concentrate near rivers, and larger areas in the east of Nanchang. While the expansion was mainly southward from 2015 to 2021, by 2021, the growth began spreading northward around the Gan River basin. Full article

(This article belongs to the Special Issue Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds III)

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19 pages, 3960 KiB

Open AccessArticle

Analysis of Land Use/Cover Changes and Driving Forces in a Typical Subtropical Region of South Africa

by Sikai Wang, Suling He, Jinliang Wang, Jie Li, Xuzhen Zhong, Janine Cole, Eldar Kurbanov and Jinming Sha

Remote Sens. 2023, 15(19), 4823; https://doi.org/10.3390/rs15194823 - 04 Oct 2023

Viewed by 1167

Abstract

Land use/cover change (LULCC) is an integral part of global environmental change and is influenced by both natural and socioeconomic factors. This study aims to comprehensively analyze land use and land cover (LULC) in Kwazulu-Natal and Mpumalanga provinces in eastern South Africa from 1995 to 2020 and to identify the driving force behind LULCC. Utilizing Landsat series satellite imagery as a data source and based on the Google Earth Engine (GEE) platform and eCognition software 9.0, two different classification methods, pixel-based classification and object-oriented classification, were adopted to gather LULC data every five years. The spatiotemporal characteristics of the data were then analyzed. Using an optimal parameter-based geodetector (OPGD), this study explored the driving factors of LULCC in this region. The results show the following: (1) Of the two classification methods examined, the object-oriented classification had higher accuracy, with an overall accuracy of 80–90%. The pixel-based classification had lower accuracy, with an overall accuracy of 62.33–72.14%. (2) From 1995 to 2020, the area of farmland in the study area showed a fluctuating increase, while the areas of forest and grassland declined annually. The area of constructed land increased annually, whereas the areas of water and unused land fluctuated over time. (3) Socioeconomic factors generally had greater explanatory power than natural factors, with population growth and economic development being the main drivers of LULCC in the region. This study provides a reliable scientific basis for the formulation of sustainable land resource development strategies in the area, as well as for the management and implementation of urban and rural planning, ecological protection, and environmental governance by relevant departments. Full article

(This article belongs to the Special Issue Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds III)

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18 pages, 3599 KiB

Open AccessArticle

A Comprehensive Multi-Metric Index for Health Assessment of the Poyang Lake Wetland

by Wenjing Yang, Jie Zhong, Ying Xia, Qiwu Hu, Chaoyang Fang, Mingyang Cong, Bo Yao and Qinghui You

Remote Sens. 2023, 15(16), 4061; https://doi.org/10.3390/rs15164061 - 17 Aug 2023

Cited by 2 | Viewed by 1199

Abstract

The Poyang Lake wetland is home to many unique and threatened species. However, it has been severely degraded in recent decades due to the joint effects of human influence and climate change. Here we establish a wetland health index (WHI) for Poyang Lake, which considers five types of attributes (biological, water quality, sediment, land use and remote sensing, and socio-economic attributes) of the wetland to evaluate wetland conditions. Forty-nine variables across five categories were assembled as candidate metrics for the WHI through field surveys conducted in 2019 at 30 sample sites. Principal component analyses were performed to identify the most important variables in each of the five categories as the primary metrics of each index category (e.g., biological index). Eighteen variables were finally selected from the five categories to construct the WHI. The WHI scores varied from 0.34 to 0.80 at the 30 sample sites, with a mean of 0.55. The Poyang Lake wetland is generally in fair condition according to our WHI scores. Sample sites where connected rivers flow into the lake were assessed to be in a poor condition, highlighting the importance of reducing pollution input from rivers for wetland conservation. Scores of individual indices of the five categories were not highly correlated (0.29 ≤ pairwise Spearman’s r ≤ 0.69), suggesting that information provided by each index is different and might be complementary. The composite WHI as well as the individual category indices can provide comprehensive information on wetland conditions that would facilitate the development of more targeted and effective strategies for wetland management. Full article

(This article belongs to the Special Issue Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds III)

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