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Remote Sensing for Wetland Restoration

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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: 31 May 2024 | Viewed by 8787

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Special Issue Editors

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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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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Prof. Dr. Dehua Mao

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Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, Changchun 130102, China
Interests: wetland mapping; wetland ecological parameter inversion; remote sensing assessment of wetland ecosystem services
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Dr. Chao Chen

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Guest Editor

School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou, China
Interests: remote sensing of environment
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Dr. Dong Li

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Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
Interests: coastal wetland; sustainable development

Special Issue Information

Dear Colleagues,

Wetlands, known as the ‘kidney of the earth’, represent highly productive and critical habitats for a wide variety of plants and animals. However, due to human activities and climate change, the world’s wetlands have been continuously destroyed, resulting in a gradual degradation in ecological functions. Fortunately, more and more countries have joined the Ramsar Convention, and substantial effective work has been performed to protect and restore wetlands. Remote sensing techniques can provide a cost-effective means of selecting restoration sites and observing their progress over time. With the continuous progress of image processing algorithms and remote sensing images of different types and time sequences enable the detection of changes in wetland extent and quality, wetland function, wetland and water body buffers, land use and land cover in watersheds, extent of ditching, and water quality.

The fifth China Wetland Remote Sensing Conference will be held on 27–29 July 2023 in Yantai, China, on the theme ‘Remote Sensing for Wetland Restoration’. The participants will exchange their latest research on wetland remote sensing theory, method and technology application; discuss the utilization, protection and management of wetlands; and promote the protection and restoration of wetlands. We welcome articles from the global research community who are actively involved in this theme. As such, this Special Issue is open to anyone conducting research in the field of wetland remote sensing. Potential topics include, but are not limited to, the following areas:

Wetland classification and landscape pattern evolution;
Remote sensing monitoring for wetland ecological elements;
Remote sensing assessment of wetland ecosystem services;
Remote sensing for wetland protection;
Remote sensing for wetland restoration;
Remote sensing of coastal wetlands;
Remote sensing of inland wetland;
New technology and method of wetland remote sensing;
Wetland remote sensing big data;
Wetland restoration policies and measures.

Prof. Dr. Xiyong Hou
Prof. Dr. Weiwei Sun
Prof. Dr. Dehua Mao
Dr. Chao Chen
Dr. Dong Li
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

wetland
wetland functions
wetland ecosystem services
wetland degradation
wetland protection
wetland restoration
multisource remote sensing
quantitative remote sensing
machine learning
sustainable development

Related Special Issue

Remote Sensing of Wetlands and Biodiversity in Remote Sensing (19 articles)

Published Papers (7 papers)

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Research

21 pages, 9003 KiB

Open AccessArticle

Analysis of the Spatiotemporal Characteristics and Influencing Factors of the NDVI Based on the GEE Cloud Platform and Landsat Images

by Zhisong Liu, Yankun Chen and Chao Chen

Remote Sens. 2023, 15(20), 4980; https://doi.org/10.3390/rs15204980 - 16 Oct 2023

Cited by 2 | Viewed by 1165

Abstract

Vegetation is an important type of land cover. Long-term, large-scale, and high-precision vegetation monitoring is of great significance for ecological environment investigation and regional sustainable development in protected areas. This paper develops a long-term remote sensing monitoring method for vegetation by calculating the normalized difference vegetation index (NDVI) based on the Google Earth Engine (GEE) cloud platform and Landsat satellite remote sensing images. First, based on Landsat long-term satellite images and GEE, the spatiotemporal distribution map of the NDVI is accurately drawn. Subsequently, the NDVI is accurately classified, and the time trend analysis of the NDVI is conducted based on the NDVI mean trend graphs, transition matrices, etc. Then, combined with Moran’s I, high/low clusters, and other methods, the spatial pattern characteristics of the NDVI are analyzed. Finally, climate factors, terrain factors, and anthropologic factors are considered comprehensively. An analysis of the factors affecting the evolution of the NDVI is performed. Taking Zhoushan Island, China, as an example, an experiment is conducted, and the results reveal that (1) the average NDVI exhibits a decreasing trend from 1985 to 2022, decreasing from 0.53 in 1985 to 0.46 in 2022. (2) Regarding vegetation index transitions, the high NDVI areas (0.6–1) exhibit the most substantial shift toward moderately high NDVI values (0.4–0.6), covering an area of 83.10 km². (3) There is an obvious spatial agglomeration phenomenon in the NDVI on Zhoushan Island. The high-high NDVI clusters and the significant hot spots are predominantly concentrated in the island’s interior regions, while the low-low NDVI clusters and the significant cold spots are mainly situated along the coastal areas. (4) The DEM, slope, and temperature have a greater influence among the single factors on the spatial pattern distribution of the NDVI in 2015. There are significant differences in the spatial pattern distribution of the NDVI between the temperature and DEM, temperature and slope, DEM and precipitation, slope and precipitation, aspect and population, and aspect and gross domestic product (GDP). The DEM and slope, DEM and temperature, and DEM and population are three sets of factors with a strong influence on spatial pattern interaction. This study provides data support for the scientific management of vegetation resources on Zhoushan Island and is of great significance to the sustainable development of the island region. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Restoration)

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21 pages, 16496 KiB

Open AccessArticle

Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

by Hua Fang, Weidong Man, Mingyue Liu, Yongbin Zhang, Xingtong Chen, Xiang Li, Jiannan He and Di Tian

Remote Sens. 2023, 15(18), 4465; https://doi.org/10.3390/rs15184465 - 11 Sep 2023

Viewed by 1244

Abstract

The leaf area index (LAI) is an essential biophysical parameter for describing the vegetation canopy structure and predicting its growth and productivity. Using unmanned aerial vehicle (UAV) hyperspectral imagery to accurately estimate the LAI is of great significance for Spartina alterniflora (S. alterniflora) growth status monitoring. In this study, UAV hyperspectral imagery and the LAI of S. alterniflora during the flourishing growth period were acquired. The hyperspectral data were preprocessed with Savitzky–Golay (SG) smoothing, and the first derivative (FD) and the second derivative (SD) spectral transformations of the data were then carried out. Then, using the band combination index (BCI) method, the characteristic bands related to the LAI were extracted from the hyperspectral image data obtained with the UAV, and spectral indices (SIs) were constructed through the characteristic bands. Finally, three machine learning (ML) regression methods—optimized support vector regression (OSVR), optimized random forest regression (ORFR), and optimized extreme gradient boosting regression (OXGBoostR)—were used to establish LAI estimation models. The results showed the following: (1) the three ML methods accurately predicted the LAI, and the optimal model was provided by the ORFR method, with an R² of 0.85, an RMSE of 0.19, and an RPD of 4.33; (2) the combination of FD SIs improved the model accuracy, with the R² value improving by 41.7%; (3) the band combinations screened using the BCI method were mainly concentrated in the red and near-infrared bands; (4) the higher LAI was distributed on the seaward side of the study area, while the lower LAI was located at the junction between the S. alterniflora and the tidal flat. This study serves as both theoretical and technological support for research on the LAI of S. alterniflora and as a solid foundation for the use of UAV remote sensing technologies in the supervisory control of S. alterniflora. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Restoration)

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22 pages, 4637 KiB

Open AccessArticle

Spatiotemporal Variation in Driving Factors of Vegetation Dynamics in the Yellow River Delta Estuarine Wetlands from 2000 to 2020

by Zhongen Niu, Bingcheng Si, Dong Li, Ying Zhao, Xiyong Hou, Linlin Li, Bin Wang, Bing Song, Mengyu Zhang, Xiyu Li, Na Zeng, Xiaobo Zhu, Yan Lv and Ziqi Mai

Remote Sens. 2023, 15(17), 4332; https://doi.org/10.3390/rs15174332 - 02 Sep 2023

Viewed by 961

Abstract

Previous studies of vegetation dynamics in the Yellow River Delta (YRD) predominantly relied on sparse time series or coarse-resolution images, which not only overlooked the rapid and spatially heterogeneous changes, but also limited our understanding of driving mechanisms. Here, employing spatiotemporal data fusion methods, we constructed a novel fused enhanced vegetation index (EVI) dataset with a high spatiotemporal resolution (30-meter and 8-day resolution) for the YRD from 2000 to 2020, and we analyzed the vegetation variations and their driving factors within and outside the YRD Nation Natural Reserve (YRDNRR). The fused EVI effectively captured spatiotemporal vegetation dynamics. Notably, within the YRDNRR core area, the fused EVI showed no significant trend before 2010, while a significant increase emerged post-2010, with an annual growth of 7%, the invasion of Spartina alterniflora explained 78% of this EVI increment. In the YRDNRR experimental area, the fused EVI exhibited a distinct interannual trend, which was characterized by an initial increase (2000–2006, p < 0.01), followed by a subsequent decrease (2006–2011, p < 0.01) and, ultimately, a renewed increase (2011–2020, p > 0.05); the dynamics of the fused EVI were mainly affected by the spring runoff (R² = 0.71), while in years with lower runoff, it was also affected by the spring precipitation (R² = 0.70). Outside of the protected area, the fused EVI demonstrated a substantial increase from 2000 to 2010 due to agricultural land expansion and human management practices, followed by stabilization post-2010. These findings enhance our comprehension of intricate vegetation dynamics in the YRD, holding significant relevance in terms of wetland preservation and management. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Restoration)

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20 pages, 11771 KiB

Open AccessArticle

Estimation of Coastal Wetland Soil Organic Carbon Content in Western Bohai Bay Using Remote Sensing, Climate, and Topographic Data

by Yongbin Zhang, Caiyao Kou, Mingyue Liu, Weidong Man, Fuping Li, Chunyan Lu, Jingru Song, Tanglei Song, Qingwen Zhang, Xiang Li and Di Tian

Remote Sens. 2023, 15(17), 4241; https://doi.org/10.3390/rs15174241 - 29 Aug 2023

Cited by 3 | Viewed by 1354

Abstract

Coastal wetland soil organic carbon (CW-SOC) is crucial for wetland ecosystem conservation and carbon cycling. The accurate prediction of CW-SOC content is significant for soil carbon sequestration. This study, which employed three machine learning (ML) methods, including random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost), aimed to estimate CW-SOC content using 98 soil samples, SAR images, optical images, and climate and topographic data. Three statistical metrics and leave-one-out cross-validation were used to evaluate model performance. Optimal models using different ML methods were applied to predict the spatial distribution of CW-SOC content. The results showed the following: (1) The models built using optical images had higher predictive accuracy than models built using synthetic aperture radar (SAR) images. The model that combined SAR images, optical images, and climate data demonstrated the highest prediction accuracy. Compared to the model using only optical images and SAR images, the prediction accuracy was improved by 0.063 and 0.115, respectively. (2) Regardless of the combination of predictive variables, the XGBoost method achieved higher prediction accuracy than the RF and GBM methods. (3) Optical images were the main explanatory variables for predicting CW-SOC content, explaining more than 65% of the variability. (4) The CW-SOC content predicted by the three ML methods showed similar spatial distribution characteristics. The central part of the study area had higher CW-SOC content, while the southern and northern regions had lower levels. This study accurately predicted the spatial distribution of CW-SOC content, providing data support for ecological environmental protection and carbon neutrality of coastal wetlands. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Restoration)

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22 pages, 23332 KiB

Open AccessArticle

Satellite Data Reveal Concerns Regarding Mangrove Restoration Efforts in Southern China

by Chao Fan, Xiyong Hou, Yuxin Zhang and Dong Li

Remote Sens. 2023, 15(17), 4151; https://doi.org/10.3390/rs15174151 - 24 Aug 2023

Cited by 2 | Viewed by 964

Abstract

Mangrove restoration projects are often evaluated based on the increase in mangrove forest (MF) area, but the reliability of this indicator as a measure of successful restoration is questionable. Considering both numerical and quality dimensions, this study assessed mangrove restoration efforts in the Leizhou Peninsula and Beibu Gulf (LP-BG) in China. The hypothesis was that due to the limited social capital investment in mangrove restoration, there exist hierarchical differences in the urgency of mangrove restoration. Time-series Landsat imagery from the Google Earth Engine platform was used to analyze the MF distribution from 2000 to 2020. A resilience indicator, incorporating resistance and adaptive capacity, was constructed to assess MF quality within identified mangrove boundaries. The results revealed an increase in MF area from 6655.87 ha in 2000 to 14,607.93 ha in 2020. However, the majority (79.6%) of MF patches exhibited low resilience (values < 3), with only a minority (51 patches) demonstrating high resilience. Interestingly, MFs within the mangrove reserve displayed higher resilience, but these areas did not exhibit a significant spatial expansion of MFs. These findings highlight the limitation of relying solely on the net growth in the MF area as an indicator of successful restoration. Instead, an ecologically optimal solution is recommended, focusing on expanding conservation boundaries to include remnant MFs outside protected areas, rather than creating new planting areas. This study provides an assessment framework to evaluate the efficiency of mangrove restoration efforts and offers insights for local decision makers to guide future restoration endeavors. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Restoration)

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29 pages, 6215 KiB

Open AccessArticle

Effects of Multi-Growth Periods UAV Images on Classifying Karst Wetland Vegetation Communities Using Object-Based Optimization Stacking Algorithm

by Ya Zhang, Bolin Fu, Xidong Sun, Hang Yao, Shurong Zhang, Yan Wu, Hongyuan Kuang and Tengfang Deng

Remote Sens. 2023, 15(16), 4003; https://doi.org/10.3390/rs15164003 - 12 Aug 2023

Cited by 1 | Viewed by 945

Abstract

Combining machine learning algorithms with multi-temporal remote sensing data for fine classification of wetland vegetation has received wide attention from researchers. However, wetland vegetation has different physiological characteristics and phenological information in different growth periods, so it is worth exploring how to use different growth period characteristics to achieve fine classification of vegetation communities. To resolve these issues, we developed an ensemble learning model by stacking Random Forest (RF), CatBoost, and XGBoost algorithms for karst wetland vegetation community mapping and evaluated its classification performance using three growth periods of UAV images. We constructed six classification scenarios to quantitatively evaluate the effects of combining multi-growth periods UAV images on identifying vegetation communities in the Huixian Karst Wetland of International Importance. Finally, we clarified the influence and contribution of different feature bands on vegetation communities’ classification from local and global perspectives based on the SHAP (Shapley Additive explanations) method. The results indicated that (1) the overall accuracies of the four algorithms ranged from 82.03% to 93.37%, and the classification performance was Stacking > CatBoost > RF > XGBoost in order. (2) The Stacking algorithm significantly improved the classification results of vegetation communities, especially Huakolasa, Reed-Imperate, Linden-Camphora, and Cephalanthus tetrandrus-Paliurus ramosissimus. Stacking had better classification performance and generalization ability than the other three machine learning algorithms. (3) Our study confirmed that the combination of spring, summer, and autumn growth periods of UAV images produced the highest classification accuracy (OA, 93.37%). In three growth periods, summer-based UAVs achieved the highest classification accuracy (OA, 85.94%), followed by spring (OA, 85.32%) and autumn (OA, 84.47%) growth period images. (4) The interpretation of black-box stacking model outputs found that vegetation indexes and texture features provided more significant contributions to classifying karst wetland vegetation communities than the original spectral bands, geometry features, and position features. The vegetation indexes (COM and NGBDI) and texture features (Homogeneity and Standard Deviation) were very sensitive when distinguishing Bermudagrass, Bamboo, and Linden-Camphora. These research findings provide a scientific basis for the protection, restoration, and sustainable development of karst wetlands. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Restoration)

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18 pages, 15222 KiB

Open AccessArticle

Monitoring Spartina alterniflora Expansion Mode and Dieback Using Multisource High-Resolution Imagery in Yancheng Coastal Wetland, China

by Dandan Yan, Zhaoqing Luan, Jingtai Li, Siying Xie and Yu Wang

Remote Sens. 2023, 15(15), 3853; https://doi.org/10.3390/rs15153853 - 03 Aug 2023

Viewed by 908

Abstract

Spartina alterniflora (smooth cordgrass), China’s most common invasive species, has posed significant challenges to native plant communities and coastal environments. Monitoring the invasion and dieback process of S. alterniflora by multisource high-resolution imagery is necessary to manage the invasion of the species. Current spatial analyses, however, are insufficient. As a result, we first extracted S. alterniflora by integrating multisource high-resolution images through the multiscale object-oriented classification method, then identified the expansion patterns of S. alterniflora on the seaward side by the landscape expansion index, and conformed the main drivers of S. alterniflora dieback on the landward side in the Jiangsu Dafeng Milu National Nature Reserve. The findings revealed that the area of S. alterniflora decreased in size from 1511.26 ha in 2010 to 910.25 ha in 2020. S. alterniflora continues to grow to the sea and along the tidal creek on the seaward side, with a total increase of 159.13 ha. External isolation expansion patterns accounted for 65.16% of the total expansion patches, with marginal expansion patches accounting for 24.22% and tidal creek-leading expansion patches accounting for 10.62%. While the landward side showed a declining trend, the total area decreased by 852.36 ha, with an annual average change rate of 8.67%. S. alterniflora dieback was negatively related to the number of tidal creeks and positively related to the number of wild Elaphures davidianus and the length of artificial ditches. Our findings provide a scientific foundation for the ecological control of S. alterniflora. Its presence in coastal wetlands inspires evidence-based protection and management strategies to protect the coastal wetland ecosystem. Full article

(This article belongs to the Special Issue Remote Sensing for Wetland Restoration)

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