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Multisource Remote Sensing for Coastal Mapping, Monitoring, and Applications

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 17128

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Special Issue Editors

Prof. Dr. Weiwei Sun

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Department of Geography and Spatial Information Techniques, Ningbo University Beijing, Ningbo 315211, China
Interests: remote sensing; hyperspectral image processing; artificial intelligence
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Dr. Xiangchao Meng

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Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315201, China
Interests: coastal remote sensing; hyperspectral image processing with machine learning
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Prof. Dr. Jiangtao Peng

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Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan 430072, China
Interests: machine learning; hyperspectral image processing
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Prof. Dr. Xudong Zhu

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College of the Environment and Ecology/Coastal and Ocean Management Institute, Xiamen University, Xiamen 361102, China
Interests: coastal wetlands; carbon cycle; climate change; remote sensing; ecological modeling

Prof. Dr. Xiyong Hou

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Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
Interests: remote sensing of resources and environment in coastal zone; coastal risk assessment; Integrated Coastal Zone Management (ICZM); landscape ecology; landscape diversity; habitat quality assessment
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Dr. Gang Yang

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Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315201, China
Interests: coastal remote sensing; remote sensing time-series products temporal reconstruction
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Special Issue Information

Dear Colleagues,

(1) Introduction, including scientific background and highlighting the importance of this research area.

Coastal zones are regions of high human activity with rich biodiversity and significant ecological service value. They are also typical sensitive areas with fragile ecological environment and have been increasingly threatened by human activities and climate change. Multisource remote sensing, including satellites, unmanned aerial vehicle (UAV), and ground observations, provide an effective measure for coastal processes.

(2) Aim of the Special Issue and how the subject relates to the journal scope.

This Special Issue focuses on multisource remote sensing in coastal monitoring, mapping, change detection, and applications, it will foster newly advanced technology for remote sensing of coastal zones. The theme of this Special Issue mainly includes multisource remote sensing, including satellite, UAV, ground platforms with panchromatic, multispectral, hyperspectral, synthetic aperture radar (SAR), LIDAR, and so forth, for coastal processing and applications. Correspondingly, the potential topics includes, but not limited to, the following:

(3) Suggested themes and article types for submissions.

Multisource remote sensing opening data collection (satellite, UAV, ground, etc) for coastal monitoring.

Multisource remote sensing image preprocessing (moasicing, denoising, dimension reduction, etc) for coastal mapping.

Multisource remote sensing image fusion (spatial-spectral fusion, spatio-temporal fusion, optical-SAR fusion, heterogeneous with satellite, UAV, and ground observations, etc) for coastal mapping.

Multisource remote sensing image classification for coastal mapping.

Multisource remote sensing image change detection for coastal zones.

Time-series image analysis for monitoring coastal zones.

Carbon sequestration estimation on multisource remote sensing for coastal zones.

Multisource remote sensing for coastal applications (coastal line, mangrove forest, etc)

Prof. Dr. Weiwei Sun
Prof. Dr. Xiangchao Meng
Prof. Dr. Jiangtao Peng
Prof. Dr. Xudong Zhu
Prof. Dr. Xiyong Hou
Prof. Dr. Gang Yang
Guest Editors

Manuscript Submission Information

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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

coastal mapping
coastal monitoring
multisource remote sensing
image preprocessing
image fusion
image classification
change detection
carbon sequestration estimation
coastal applications

Published Papers (6 papers)

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Research

19 pages, 7063 KiB

Open AccessArticle

Impact of Land Reclamation on Coastal Water in a Semi-Enclosed Bay

by Min-cheng Tu and Yu-chieh Huang

Remote Sens. 2023, 15(2), 510; https://doi.org/10.3390/rs15020510 - 14 Jan 2023

Cited by 1 | Viewed by 2456

Abstract

Land reclamation has a profound impact on coastal environments. On the Chinese coast, the new Xiang’an International Airport has been built on newly reclaimed land. The impact of the massive land reclamation project (finished in 2018) on water quality and coast conditions in a nearby semi-enclosed bay is investigated using remotely sensed data. Factors affecting surface water quality and coast conditions are further analyzed using multiple regression. All water quality and coast condition indices show no long-term trend from 2005 to 2021. The suspended solid concentration (with a maximum value of 96.11 mg/L) is much lower than the threshold of 188 mg/L. When considering variations in sediment concentration, the probability that the concentration reaches the threshold is less than

1 \times 10^{- 6}

%; therefore, suspended solids have little threat to the local oyster-growing industry. The trend of dissolved inorganic nitrogen concentration is steady, implying little alteration to nutrient circulation in the semi-enclosed bay. Within the observation timeframe of 2005–2021, a recent sedimentation trend (surrogated by the normalized difference water index) appears after 2018 but it needs to be confirmed by a longer observation. Statistical models based on multiple regression highlight the following links: (1) the sediment source is outside the bay, (2) the overland runoff from newly claimed land dilutes nutrient concentrations, and (3) the coast conditions are mainly affected by tides and rainfall. Neither actively reclaimed or cumulative reclaimed areas form a direct causal relationship to water quality or coast conditions in the semi-enclosed bay. Full article

(This article belongs to the Special Issue Multisource Remote Sensing for Coastal Mapping, Monitoring, and Applications)

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23 pages, 49978 KiB

Open AccessArticle

High-Resolution Mapping of Seaweed Aquaculture along the Jiangsu Coast of China Using Google Earth Engine (2016–2022)

by Jie Cheng, Nan Jia, Ruishan Chen, Xiaona Guo, Jianzhong Ge and Fucang Zhou

Remote Sens. 2022, 14(24), 6202; https://doi.org/10.3390/rs14246202 - 07 Dec 2022

Cited by 9 | Viewed by 3033

Abstract

Seaweed aquaculture produces enormous economic and ecological service benefits, making significant contributions to achieving global Sustainable Development Goals (SDGs). However, large-scale development of seaweed aquaculture and the unreasonable use of aquaculture rafts may trigger green tide, bringing negative ecological, social, and economic impacts. Therefore, it is vital to monitor the seaweed aquaculture industry accurately. Here, we mapped 10-m-resolution seaweed aquaculture along the Jiangsu coast of China based on active and passive remote sensing (Sentinel-1/2) and Random Forest using Google Earth Engine. The results demonstrate satisfactory model performance and data accuracy. The square seaweed aquaculture in the Lianyungang Offshore (Mode-I) has gradually expanded to the deep sea since 2016, with a maximum area of 194.06 km² in 2018. Between 2021 and 2022, the area of the strip-shaped seaweed aquaculture in Subei radiation shoals (Mode-II) was considerably reduced, with most of the reduced land lying on the east side of the Dafeng Elk National Nature Reserve. In general, the area of the seaweed aquaculture in the prohibited breeding area was reduced from 20.32 km² to 3.13 km², and the area of the seaweed aquaculture in the restricted breeding area was reduced from 149.71 km² to 33.15 km². Results show that under the policy restriction, the scale of unsustainable seaweed aquaculture along the Jiangsu coast has been greatly reduced within seven years. This study can provide an efficient approach for the medium-scale extraction of seaweed aquaculture and provide decision support for the sustainable development of marine aquaculture. Full article

(This article belongs to the Special Issue Multisource Remote Sensing for Coastal Mapping, Monitoring, and Applications)

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27 pages, 34983 KiB

Open AccessArticle

Multi-Resolution Collaborative Fusion of SAR, Multispectral and Hyperspectral Images for Coastal Wetlands Mapping

by Yi Yuan, Xiangchao Meng, Weiwei Sun, Gang Yang, Lihua Wang, Jiangtao Peng and Yumiao Wang

Remote Sens. 2022, 14(14), 3492; https://doi.org/10.3390/rs14143492 - 21 Jul 2022

Cited by 13 | Viewed by 3062

Abstract

The hyperspectral, multispectral, and synthetic aperture radar (SAR) remote sensing images provide complementary advantages in high spectral resolution, high spatial resolution, and geometric and polarimetric properties, generally. How to effectively integrate cross-modal information to obtain a high spatial resolution hyperspectral image with the characteristics of the SAR is promising. However, due to divergent imaging mechanisms of modalities, existing SAR and optical image fusion techniques generally remain limited due to the spectral or spatial distortions, especially for complex surface features such as coastal wetlands. This paper provides, for the first time, an efficient multi-resolution collaborative fusion method for multispectral, hyperspectral, and SAR images. We improve generic multi-resolution analysis with spectral-spatial weighted modulation and spectral compensation to achieve minimal spectral loss. The backscattering gradients of SAR are guided to fuse, which is calculated from saliency gradients with edge preserving. The experiments were performed on ZiYuan-1 02D (ZY-1 02D) and GaoFen-5B (AHSI) hyperspectral, Sentinel-2 and GaoFen-5B (VIMI) multispectral, and Sentinel-1 SAR images in the challenging coastal wetlands. Specifically, the fusion results were comprehensively tested and verified on the qualitative, quantitative, and classification metrics. The experimental results show the competitive performance of the proposed method. Full article

(This article belongs to the Special Issue Multisource Remote Sensing for Coastal Mapping, Monitoring, and Applications)

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23 pages, 10976 KiB

Open AccessArticle

Stepwise Fusion of Hyperspectral, Multispectral and Panchromatic Images with Spectral Grouping Strategy: A Comparative Study Using GF5 and GF1 Images

by Leping Huang, Zhongwen Hu, Xin Luo, Qian Zhang, Jingzhe Wang and Guofeng Wu

Remote Sens. 2022, 14(4), 1021; https://doi.org/10.3390/rs14041021 - 20 Feb 2022

Cited by 7 | Viewed by 2430

Abstract

Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images. Full article

(This article belongs to the Special Issue Multisource Remote Sensing for Coastal Mapping, Monitoring, and Applications)

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20 pages, 6132 KiB

Open AccessArticle

Multispectral and SAR Image Fusion Based on Laplacian Pyramid and Sparse Representation

by Hai Zhang, Huanfeng Shen, Qiangqiang Yuan and Xiaobin Guan

Remote Sens. 2022, 14(4), 870; https://doi.org/10.3390/rs14040870 - 11 Feb 2022

Cited by 13 | Viewed by 2895

Abstract

Complementary information from multi-sensors can be combined to improve the availability and reliability of stand-alone data. Typically, multispectral (MS) images contain plentiful spectral information of the Earth’s surface that is beneficial for identifying land cover types, while synthetic aperture radar (SAR) images can provide abundant information on the texture and structure of target objects. Therefore, this paper presents a fusion framework to integrate the information from MS and SAR images based on the Laplacian pyramid (LP) and sparse representation (SR) theory. LP is performed to decompose both the multispectral and SAR images into high-frequency components and low-frequency components, so that different processing strategies can be applied to multi-scale information. Low-frequency components are merged based on SR theory, whereas high-frequency components are combined based on a certain activity-level measurement, identifying salient features. Finally, LP reconstruction is performed to obtain the integrated image. We conduct experiments on several datasets to verify the effectiveness of the proposed method. Both visual interpretation and statistical analyses demonstrate that the proposed method strikes a satisfactory balance between spectral information preservation and the enhancement of spatial and textual characteristics. In addition, a further discussion regarding the adjustability property of the proposed method shows its flexibility for further application scenarios. Full article

(This article belongs to the Special Issue Multisource Remote Sensing for Coastal Mapping, Monitoring, and Applications)

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19 pages, 6072 KiB

Open AccessArticle

A New Adaptive Remote Sensing Extraction Algorithm for Complex Muddy Coast Waterline

by Ziheng Yang, Lihua Wang, Weiwei Sun, Weixin Xu, Bo Tian, Yunxuan Zhou, Gang Yang and Chao Chen

Remote Sens. 2022, 14(4), 861; https://doi.org/10.3390/rs14040861 - 11 Feb 2022

Cited by 10 | Viewed by 1854

Abstract

Coastline is an important geographical element of the boundary between ocean and land. Due to the impact of the ocean-land interactions at multiple temporal-spatial scales and the intensified human activities, the waterline of muddy coast is undergoing long-term and continuous dynamic changes. Using traditional remote sensing-based waterline extraction methods, it is difficult to achieve ideal results for muddy coast waterlines, which are faced with problems such as limited algorithm stability, weak algorithm migration, and discontinuous coastlines extraction results. In response to the above challenges, three different types of muddy coasts, Yancheng, Jiuduansha and Xiangshan were selected as the study areas. Based on the Sentinel-2 MSI images, we proposed an adaptive remote sensing extraction algorithm framework for the complex muddy coast waterline, named AEMCW (Adaptive Extraction for Muddy Coast Waterline), including main procedures of high-pass filtering, histogram statistics and adaptive threshold determination, which has the capability to obtain continuous and high-precision muddy coastal waterline. NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index) and ED (Edge Detection) methods were selected to compare the extraction effect of AEMCW method. The length and spatial accuracy of these four methods were evaluated with the same criteria. The accuracy evaluation presented that the length errors of ED method in all three study areas were minimum, but the waterline results were offset more to the land side, due to spectral similarity, turbid water and tidal flats having similar values of NDWI and MNDWI. Therefore, the length and spatial accuracies of NDWI and MNDWI methods were lower than AEMCW method. The length errors of the AEMCW algorithm in Yancheng, Jiuduansha, and Xiangshan were 14.4%, 18.0%, and 7.7%, respectively. The producer accuracies were 94.3%, 109.6%, and 94.2%, respectively. The user accuracies were 82.4%, 92.9%, and 87.5%, respectively. These results indicated that the proposed AEMCW algorithm can effectively restrain the influence of spectral noise from various land cover types and ensure the continuity of waterline extraction results. The adaptive threshold determination equation reduced the influence of human factors on threshold selection. The further application on ZY-1 02D hyperspectral images in the Yancheng area verified the proposed algorithm is transferable and has good stability. Full article

(This article belongs to the Special Issue Multisource Remote Sensing for Coastal Mapping, Monitoring, and Applications)

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