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Remote Sensing of Wetlands and Biodiversity
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Remote Sensing of Wetlands and Biodiversity

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 41554

Special Issue Editors

Prof. Dr. Dehua Mao

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mingming Jia

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, Changchun 130102, China
Interests: coastal wetland remote sensing; fine-scale wetland monitoring; mangrove mapping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zongming Wang

E-Mail Website
Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, Changchun 130102, China
Interests: remote sensing of wetland; ecosystem services; land cover changes
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhenguo Niu

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: wetland remote sensing; wetland biodiversity mapping; assessment of wetland protection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weiwei Sun

E-Mail Website
Guest Editor
Department of Geography and Spatial Information Techniques, Ningbo University Beijing, Ningbo 315211, China
Interests: remote sensing; hyperspectral image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yinghai Ke

E-Mail Website
Guest Editor
Beijing Laboratory of Water Security, Base of the State Key Laboratory of Urban Environment Process and Digital Modeling, Capital Normal University, Beijing 100089, China
Interests: remote sensing application in ecohydrology and hydrobiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wetland is recognized as one of the three dominant ecosystems on the earth, and is known as the "kidney of the earth". Wetlands provide habitats for 40% of the world's terrestrial organisms and 87% of water birds, and are an important foundation for maintaining biodiversity. However, wetland loss, degradation, and related biodiversity decline have become some of the most significant eco-environmental problems worldwide. Wetlands are also an important carbon sink, and have a significant impact on the balance of terrestrial carbon source and sink. Under the dual stress of climate change and human activities, the global wetland loss and degradation rate is much higher than other terrestrial ecosystems, and the decreasing trend still exists.

In recent years, the rapid development of remote sensing technology in quantitative, multi-temporal, multi-platform, massive information, intelligent identification, and other aspects has made it an indispensable technology in wetland scientific research and management decision-making. The application of geographic information technology with remote sensing as the core, forming the ability to conduct rapid dynamic monitoring and provide management decision support for China's wetlands, is an important foundation for guaranteeing national ecological security and supporting ecological civilization construction in China.

The fourth China Wetland Remote Sensing Conference will be held on 13 August 2022 in online format, meeting around the theme "remote sensing of wetlands and biodiversity". The participants will be able to exchange their latest research results on wetland remote sensing theory, method and technology application; discuss the utilization, protection and management of wetlands in the contexts of social and economic development and global change; and promote the protection and restoration of wetlands and the construction of ecological civilization in China. We welcome articles from the global research community actively involved in this theme. As such, this Special Issue is open to anyone conducting research in the field of wetland remote sensing. The potential topics include but are not limited to the following:

  • Wetland mapping or classification at a broad scale;
  • Remote sensing of global change and wetland ecosystem carbon pool;
  • Remote sensing of river and lake water environment;
  • Remote sensing of wetland biodiversity;
  • Remote sensing of estuary and coastal wetlands;
  • Remote sensing of alpine and high-latitude wetlands;
  • Urban wetlands and ecological remote sensing;
  • Fine-scale wetland monitoring and intelligent supervision.

Prof. Dr. Dehua Mao
Prof. Dr. Mingming Jia
Prof. Dr. Zongming Wang
Prof. Dr. Zhenguo Niu
Prof. Dr. Weiwei Sun
Dr. Yinghai Ke
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 mapping
  • biodiversity
  • multisource remote sensing
  • big earth data
  • machine learning
  • sustainable development goals (SDG)

Related Special Issue

Published Papers (19 papers)

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Research

17 pages, 6149 KiB  
Article
Sentinel-2 MSI Observations of Water Clarity in Inland Waters across Hainan Island and Implications for SDG 6.3.2 Evaluation
by Ruiting Qiu, Shenglei Wang, Jiankang Shi, Wei Shen, Wenzhi Zhang, Fangfang Zhang and Junsheng Li
Remote Sens. 2023, 15(6), 1600; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061600 - 15 Mar 2023
Cited by 4 | Viewed by 1248
Abstract
Freshwater on islands represents a precious resource and highly vulnerable ecosystem. For monitoring freshwater, satellite remote sensing is efficient and has large-scale application. This study proposed a modified model of the quasi-analytical algorithm (ZSD-QAAv6m) to retrieve the water clarity of [...] Read more.
Freshwater on islands represents a precious resource and highly vulnerable ecosystem. For monitoring freshwater, satellite remote sensing is efficient and has large-scale application. This study proposed a modified model of the quasi-analytical algorithm (ZSD-QAAv6m) to retrieve the water clarity of inland waters (>1 km2) across Hainan Island, China using Sentinel-2 multispectral instrument data. By adjusting the threshold of Rrs(665), the proposed model could accurately estimate water clarity with diverse optical properties on the island and avoid underestimation in moderately clear waters. Based upon this, the first spatiotemporal analysis of recent water clarity in Hainan Island was conducted. The results show that lake water clarity in the central region was generally higher (with average value of 1.4 m) than that of coastal regions (with average value of 1.2 m). Seasonally, the water clarity during the wet season was usually lower than that in the dry season, with average values of 1.1 m and 1.3 m across the island respectively. From 2017 to 2021, the proportion of water bodies with water clarity > 0.5 m increased from 60% to 100%. The overall spatial pattern of water clarity was correlated to the regional vegetation cover in Hainan Island, with higher clarity associated with higher vegetation cover in the central regions. The seasonal variation of water clarity may be attributed to heavy rainfall and runoff during the wet season; while the distinct annual variation may be benefited from the strengthened surface water protections in Hainan Province in recent years. This study provides a practical approach for evaluating the SDG 6.3.2 indicator in Hainan Island using remote sensed water clarity as a comprehensive water quality indicator and the findings could facilitate the island’s water resource management and conservation. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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19 pages, 5623 KiB  
Article
Contribution of Land Cover Classification Results Based on Sentinel-1 and 2 to the Accreditation of Wetland Cities
by Xiaoya Wang, Weiguo Jiang, Yawen Deng, Xiaogan Yin, Kaifeng Peng, Pinzeng Rao and Zhuo Li
Remote Sens. 2023, 15(5), 1275; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051275 - 25 Feb 2023
Cited by 4 | Viewed by 1442
Abstract
Wetland cities were proposed by the International Wetland Convention Organization for the protection of urban wetlands. Few studies have performed land cover classifications for internationally recognized wetland cities or explored what contribution the classification results can make to the establishment of additional wetland [...] Read more.
Wetland cities were proposed by the International Wetland Convention Organization for the protection of urban wetlands. Few studies have performed land cover classifications for internationally recognized wetland cities or explored what contribution the classification results can make to the establishment of additional wetland cities to date. Based on Sentinel-1 and 2 data, this study used a 10-fold random forest method to classify the land cover of the first six wetland cities recognized in China. A land cover dataset, which had a resolution of 10 m and included four wetland types, was obtained and the wetland area and protected wetland areas of the six cities were calculated. The results showed that (1) the classification accuracy of six cities was good, the overall accuracy was above 90%, and the Kappa coefficient was above 0.88. (2) Cropland or forested areas were the most common non-wetland land coverage type in wetland cities and accounted for more than 20% or 40% of the land coverages, while water was the most common wetland type and accounted for more than 2% of the land coverages. From 2015 to 2020, the built area in most cities increased, while cropland and forest decreased significantly. (3) The wetland rate was 6.68–37.56% and the wetland protection rate was 49.48–73.74% in the six wetland cities. From 2015 to 2020, the wetland rate of the six cities were relatively stable, and the wetland protection rate of inland cities (Yinchuan, Changde, Harbin and Changshu) increased significantly, while those of coastal cities (Haikou and Dongying) decreased, which might be related to the change in coastline. Therefore, we found that the wetlands in these cities were well protected. Land cover classification for wetland cities can provide a reference for using remote sensing techniques used to monitor internationally wetland cities while also supporting the creation of additional wetland cities. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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24 pages, 8115 KiB  
Article
An Object-Oriented Method for Extracting Single-Object Aquaculture Ponds from 10 m Resolution Sentinel-2 Images on Google Earth Engine
by Boyi Li, Adu Gong, Zikun Chen, Xiang Pan, Lingling Li, Jinglin Li and Wenxuan Bao
Remote Sens. 2023, 15(3), 856; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030856 - 03 Feb 2023
Cited by 8 | Viewed by 2509
Abstract
Aquaculture plays a key role in achieving Sustainable Development Goals (SDGs), while it is difficult to accurately extract single-object aquaculture ponds (SOAPs) from medium-resolution remote sensing images (Mr-RSIs). Due to the limited spatial resolutions of Mr-RSIs, most studies have aimed to obtain aquaculture [...] Read more.
Aquaculture plays a key role in achieving Sustainable Development Goals (SDGs), while it is difficult to accurately extract single-object aquaculture ponds (SOAPs) from medium-resolution remote sensing images (Mr-RSIs). Due to the limited spatial resolutions of Mr-RSIs, most studies have aimed to obtain aquaculture areas rather than SOAPs. This study proposed an object-oriented method for extracting SOAPs. We developed an iterative algorithm combining grayscale morphology and edge detection to segment water bodies and proposed a segmentation degree detection approach to select and edit potential SOAPs. Then a classification decision tree combining aquaculture knowledge about morphological, spectral, and spatial characteristics of SOAPs was constructed for object filter. We selected a 707.26 km2 study region in Sri Lanka and realized our method on Google Earth Engine (GEE). A 25.11 km2 plot was chosen for verification, where 433 SOAPs were manually labeled from 0.5 m high-resolution RSIs. The results showed that our method could extract SOAPs with high accuracy. The relative error of total areas between extracted result and the labeled dataset was 1.13%. The MIoU of the proposed method was 0.6965, representing an improvement of between 0.1925 and 0.3268 over the comparative segmentation algorithms provided by GEE. The proposed method provides an available solution for extracting SOAPs over a large region and shows high spatiotemporal transferability and potential for identifying other objects. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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19 pages, 4473 KiB  
Article
Inversion and Monitoring of the TP Concentration in Taihu Lake Using the Landsat-8 and Sentinel-2 Images
by Yongchun Liang, Fang Yin, Danni Xie, Lei Liu, Yang Zhang and Tariq Ashraf
Remote Sens. 2022, 14(24), 6284; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246284 - 12 Dec 2022
Cited by 7 | Viewed by 1814
Abstract
Eutrophication is a significant factor that damages the water ecosystem’s species balance. The total phosphorus (TP) concentration is a vital water quality indicator in assessing surface water eutrophication. This paper predicts the spatial distribution of TP concentration using remote sensing, measured data, and [...] Read more.
Eutrophication is a significant factor that damages the water ecosystem’s species balance. The total phosphorus (TP) concentration is a vital water quality indicator in assessing surface water eutrophication. This paper predicts the spatial distribution of TP concentration using remote sensing, measured data, and the partial least squares regression (PLSR) method. Based on the correlation analysis, the models were built and tested using the TP concentration and Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) image spectra. The results demonstrated that the best technique based on band combinations of the Sentinel-2 and Landsat-8 images achieved good precision. The coefficient of determination (R2), root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) were 0.771, 0.023 mg/L, and 2.086 for Sentinel-2 images and 0.630, 0.032 mg/L, and 1.644 for Landsat-8 images, respectively. The TP concentration maps were interpolated using the inverse distance weighting method, and the inversion results obtained from the images were in good agreement. The western and northwestern regions of Taihu Lake, where significant cyanobacterial blooms occurred, had TP concentrations greater than 0.20 mg/L; nevertheless, the central and eastern regions had amounts ranging from 0.05 to 0.20 mg/L. In order to prove the extensibility of the model, the optimal algorithm was applied to the Sentinel-2 and Landsat-8 images in 2017. The optimal algorithm based on Landsat-8 images has a better verification effect (RMSEP = 0.027 mg/L, and R = 0.879 for one Landsat-8 image), and the optimal algorithm based on Sentinel-2 images has moderate verification effect (RMSEP = 0.054 mg/L and 0.045 mg/L, and R = 0.771 and 0.787 for two Sentinel-2 images). The interpolation and inversion maps are in good agreement, indicating that the model is suitable for the Landsat-8 and Sentinel-2 images, which can be complementary for higher temporal resolutions. Monitoring water quality using multiple remote sensing images can provide the scientific basis for water quality dynamic monitoring and prevention in China. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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25 pages, 7358 KiB  
Article
Precise Wetland Mapping in Southeast Asia for the Ramsar Strategic Plan 2016–24
by Yang Liu, Huaiqing Zhang, Zeyu Cui, Yuanqing Zuo, Kexin Lei, Jing Zhang, Tingdong Yang and Ping Ji
Remote Sens. 2022, 14(22), 5730; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225730 - 13 Nov 2022
Viewed by 2096
Abstract
A precise distribution map of wetlands can provide basic data of wetland conservation and management for Ramsar parties in each region. In this study, based on the Google Earth Engine (GEE) platform and Sentinel-2 images, the integrated inundation dynamic, phenological, and geographical features [...] Read more.
A precise distribution map of wetlands can provide basic data of wetland conservation and management for Ramsar parties in each region. In this study, based on the Google Earth Engine (GEE) platform and Sentinel-2 images, the integrated inundation dynamic, phenological, and geographical features for a multi-class tropical wetland mapping method (IPG-MTWM) was used to generate the Southeast Asia wetland cover map (SEAWeC) in 2020, which has a 10 m spatial resolution with 11 wetland types. The overall accuracy (OA) of SEAWeC was 82.52%, which, in comparison with other mappings the SEAWeC, performs well. The results of SEAWeC show that (1) in 2020, the total wetland area in Southeast Asia was 123,268.61 km2, (2) for the category I, the coastal wetlands has the largest area, reaching 58,534.78 km2, accounting for 47.49%, (3) for the category II, the coastal swamp has the largest area, reaching 48,002.66 km2, accounting for 38.94% of the total wetland area in Southeast Asia, and (4) significant difference in wetland rate (WR) between countries in Southeast Asia, in which Singapore has a WR of 6.96%, ranking first in Southeast Asia. The SEAWeC can provide the detailed spatial and type distribution data as basic data for the Southeast Asia to support the Ramsar strategic plan 2016–24. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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36 pages, 69329 KiB  
Article
Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images
by Yuyang Li, Bolin Fu, Xidong Sun, Donglin Fan, Yeqiao Wang, Hongchang He, Ertao Gao, Wen He and Yuefeng Yao
Remote Sens. 2022, 14(21), 5533; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215533 - 02 Nov 2022
Cited by 10 | Viewed by 1943
Abstract
Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a [...] Read more.
Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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25 pages, 13257 KiB  
Article
Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm
by Hang Yao, Bolin Fu, Ya Zhang, Sunzhe Li, Shuyu Xie, Jiaoling Qin, Donglin Fan and Ertao Gao
Remote Sens. 2022, 14(21), 5478; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215478 - 31 Oct 2022
Cited by 9 | Viewed by 1969
Abstract
Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still [...] Read more.
Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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17 pages, 2767 KiB  
Article
Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images
by Shiqi Xu, Sijia Li, Zui Tao, Kaishan Song, Zhidan Wen, Yong Li and Fangfang Chen
Remote Sens. 2022, 14(20), 5136; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205136 - 14 Oct 2022
Cited by 13 | Viewed by 1801
Abstract
Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this [...] Read more.
Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 μg/L, MAE = 0.57 μg/L, slope = 0.94, and R2 = 0.98; Validation: N = 40, RMSE = 2.12 μg/L, MAE = 1.58 μg/L, slope = 0.91, R2 = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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13 pages, 2347 KiB  
Article
Analysis of Spatio-Temporal Dynamics of Chinese Inland Water Clarity at Multiple Spatial Scales between 1984 and 2018
by Hui Tao, Kaishan Song, Ge Liu, Qiang Wang, Zhidan Wen, Junbin Hou, Yingxin Shang and Sijia Li
Remote Sens. 2022, 14(20), 5091; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205091 - 12 Oct 2022
Viewed by 1287
Abstract
Water clarity (Secchi disk depth, SDD) provides a sensitive tool to examine the spatial pattern and historical trend in lakes’ trophic status. However, this metric has been insufficiently explored despite the availability of remotely-sensed data. Based on the published SDD datasets derived from [...] Read more.
Water clarity (Secchi disk depth, SDD) provides a sensitive tool to examine the spatial pattern and historical trend in lakes’ trophic status. However, this metric has been insufficiently explored despite the availability of remotely-sensed data. Based on the published SDD datasets derived from Landsat images, we analyzed the spatial and inter-annual variations in water clarity and examined the impact of natural and anthropogenic factors on these trends at multiple scales, i.e., five lake regions, provinces, and watersheds. Lake clarity was lowest in Northeast (0.60 ± 0.09 m) and East China (1.23 ± 0.17 m) and highest in the Tibet Plateau (3.32 ± 0.38 m). Over the past 35 years, we found a significant trend of increased SDD in 18 (out of 32) provinces (only Yunnan province exhibited a significant decreasing trend) and in 77 (out of 155) watersheds (only 5 watersheds showed a significant decreasing trend). Lakes in eastern-northeastern China exhibited a higher probability of decreasing trend, while the trend was inverse for lakes in the Tibet-Qinghai region. The results of water clarity interannual change trends showed they were closely related to the spatial scale of analysis. At the watershed level, these trends were mainly driven by anthropogenic factors, with night-time brightness (13.84%), agricultural fertilizer use (11.17%), and wastewater (9.64%) being the most important. Natural factors (temperature, wind, and NDVI) explained about 18.2% of the SDD variance. Our findings for the SDD spatio-temporal trend provide valuable information for guiding water protection management policy-making and reinforcement in China. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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20 pages, 3532 KiB  
Article
Hydrological Drivers for the Spatial Distribution of Wetland Herbaceous Communities in Poyang Lake
by Wenqin Huang, Tengfei Hu, Jingqiao Mao, Carsten Montzka, Roland Bol, Songxian Wan, Jianxin Li, Jin Yue and Huichao Dai
Remote Sens. 2022, 14(19), 4870; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194870 - 29 Sep 2022
Cited by 3 | Viewed by 1573
Abstract
Hydrological processes are known as major driving forces in structuring wetland plant communities, but the specific relationships are not always well understood. The recent dry conditions of Poyang Lake (i.e., the largest freshwater lake in China) are having a profound impact on its [...] Read more.
Hydrological processes are known as major driving forces in structuring wetland plant communities, but the specific relationships are not always well understood. The recent dry conditions of Poyang Lake (i.e., the largest freshwater lake in China) are having a profound impact on its wetland vegetation, leading to the degradation of the entire wetland ecosystem. We developed an integrated framework to quantitatively investigate the relationship between the spatial distribution of major wetland herbaceous communities and the hydrological regimes of Poyang Lake. First, the wetland herbaceous community classification was built using a support-vector machine and simultaneous parameter optimization, achieving an overall accuracy of over 98%. Secondly, based on the inundation conditions since 2000, four hydrological drivers of the spatial distribution of these communities were evaluated by canonical correspondence analysis. Finally, the hydrological niches of the communities were quantified by Gaussian regression and quantile methods. The results show that there were significant interspecific differences in terms of the hydrological niche. For example, Carex cinerascens Ass was the most adaptable to inundation, while Triarrhena lutarioriparia + Phragmites australis Ass was the least. Our integrated analytical framework can contribute to hydrological management to better maintain the wetland plant community structure in the Poyang Lake area. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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21 pages, 6815 KiB  
Article
Estimation of Soil Organic Carbon Content in Coastal Wetlands with Measured VIS-NIR Spectroscopy Using Optimized Support Vector Machines and Random Forests
by Jingru Song, Junhai Gao, Yongbin Zhang, Fuping Li, Weidong Man, Mingyue Liu, Jinhua Wang, Mengqian Li, Hao Zheng, Xiaowu Yang and Chunjing Li
Remote Sens. 2022, 14(17), 4372; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174372 - 02 Sep 2022
Cited by 19 | Viewed by 2330
Abstract
Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an [...] Read more.
Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R2 value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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21 pages, 5949 KiB  
Article
Dynamic Changes and Driving Forces of Alpine Wetlands on the Qinghai–Tibetan Plateau Based on Long-Term Time Series Satellite Data: A Case Study in the Gansu Maqu Wetlands
by Bo Zhang, Zhenguo Niu, Dongqi Zhang and Xuanlin Huo
Remote Sens. 2022, 14(17), 4147; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174147 - 24 Aug 2022
Cited by 12 | Viewed by 2110
Abstract
The Qinghai–Tibet Plateau (QTP), also known as the Third Pole of the Earth, is sensitive to climate change, and it has become a hotspot area for research. As a typical natural ecosystem on the QTP, alpine wetlands are particularly sensitive to climate change. [...] Read more.
The Qinghai–Tibet Plateau (QTP), also known as the Third Pole of the Earth, is sensitive to climate change, and it has become a hotspot area for research. As a typical natural ecosystem on the QTP, alpine wetlands are particularly sensitive to climate change. The identification of different types of alpine wetland and analysis of changes in their distributions and areas are the most direct indicators for characterizing the impact of climate change on wetlands. To understand the dynamic change process of the alpine wetlands in the QTP and their responses to climate change, the Maqu wetlands, located at the source of the Three Rivers in the eastern part of the QTP, was taken as an example; the Google Earth Engine (GEE) remote sensing cloud platform and long-term dense Landsat time series data from 1990 to 2020 were used to map the annual wetland classification and to analyze the evolution characteristics of the wetlands and their driving forces. The results revealed that (1) based on dense Landsat time series data, different alpine wetland types can be effectively distinguished, including swamp, swamp meadow, and wet meadow. (2) From 1990 to 2020, the area of the Maqu wetlands exhibited an overall fluctuating decrease, with the total area decreasing by about 23.35%, among which the swamp area decreased the most (by 27.15%). The overall type of change was from wet to dry. All of the types of wetlands were concentrated between 3400 and 3600 m above sea level, and the reduction in the wetland area was concentrated on slopes < 3°, with the greatest loss of wetland area occurring on shady slopes. (3) The driving forces of the changes in the wetlands were predominantly temperature and precipitation, and the greatest correlation was between the total wetland area and the growing season temperature. The results of this study provide valuable information for the conservation of alpine wetlands. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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16 pages, 4338 KiB  
Article
Remote Estimation of the Particulate Phosphorus Concentrations in Inland Water Bodies: A Case Study in Hongze Lake
by Chenggong Du, Kun Shi, Naisen Liu, Yunmei Li, Heng Lyu, Chen Yan and Jinheng Pan
Remote Sens. 2022, 14(16), 3863; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14163863 - 09 Aug 2022
Cited by 3 | Viewed by 1553
Abstract
Phosphorus is the most important nutrient associated with lake eutrophication and changes in cyanobacterial blooms, and particulate phosphorus (PP) is the main form of phosphorus found in highly turbid inland waters. Therefore, it is urgent to monitor PP concentrations in inland water bodies. [...] Read more.
Phosphorus is the most important nutrient associated with lake eutrophication and changes in cyanobacterial blooms, and particulate phosphorus (PP) is the main form of phosphorus found in highly turbid inland waters. Therefore, it is urgent to monitor PP concentrations in inland water bodies. In this study, we take Hongze Lake as the research area and establish a semianalytical model to estimate PP concentrations based on the total particle absorption coefficient (ap); the mean absolute percentage error (MAPE) and root-mean-square error (RMSE) values, which indicate the model accuracy, were 14.90% and 0.009 mg/L, respectively. In addition, the construction process and parameter selection criteria of the remote sensing-based PP concentration estimation model were derived using remote sensing data obtained at different spectral resolutions. Sentinel 3 Ocean and Land Color Instrument (OLCI) and Landsat 9 Operational Land Imager version 2 (OLI-2) data were selected as representatives to verify the accuracy of the model; compared to these two datasets, the MAPE values of the models were 16.32% and 26.84%, respectively, while the RMSE values were 0.010 mg/L and 0.014 mg/L, respectively. Finally, the models were applied to Sentinel 3 OLCI and Landsat 9 OLI-2 images obtained on 16 January 2022. The results show that the spatiotemporal distributions of PP concentrations in Hongze Lake estimated from these two images were relatively consistent, but the OLI data reflected overestimations and underestimations in some areas. These research results provide a new methodology for estimating PP concentrations through remote sensing methods and help to further improve the accuracy of remotely sensed PP concentration estimations in inland water bodies. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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17 pages, 7988 KiB  
Article
Annual Wetland Mapping in Metropolis by Temporal Sample Migration and Random Forest Classification with Time Series Landsat Data and Google Earth Engine
by Ming Wang, Dehua Mao, Yeqiao Wang, Kaishan Song, Hengqi Yan, Mingming Jia and Zongming Wang
Remote Sens. 2022, 14(13), 3191; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133191 - 02 Jul 2022
Cited by 11 | Viewed by 2444
Abstract
Wetlands provide various ecosystem services to urban areas, which are crucial for sustainable urban management. With intensified urbanization, there has been marked loss of urban natural wetland, degradation, and related urban disasters in the past several decades. Rapid and accurate mapping of urban [...] Read more.
Wetlands provide various ecosystem services to urban areas, which are crucial for sustainable urban management. With intensified urbanization, there has been marked loss of urban natural wetland, degradation, and related urban disasters in the past several decades. Rapid and accurate mapping of urban wetland extent and change is thus critical for improving urban planning toward sustainability. Here, we have developed a rapid method for continuous mapping of urban wetlands (MUW) by combining automatic sample migration and the random forest algorithm (SM&RF), the so-called MUW_SM&RF. Using time series Landsat images, annual training samples were generated through spectral angular distance (SAD) and time series analysis. Combined with the RF algorithm, annual wetland maps in urban areas were derived. Employing the Google Earth Engine platform (GEE), the MUW_SM&RF was evaluated in four metropolitan areas in different geographical and climatic regions of China from 1990 to 2020, including Tianjin, Hangzhou, Guangzhou, and Wuhan. In all four study areas, the generated annual wetland maps had an overall accuracy of over 87% and a Kappa coefficient above 0.815. Compared with previously published datasets, the urban wetland areas derived using the MUW_SM&RF approach achieved improved accuracy and thus demonstrated its robustness for rapid mapping of urban wetlands. Urban wetlands in all four cities had variable distribution patterns and showed significantly decreased trends in the past three decades. The annual urban wetland data product generated by the MUW_SM&RF can provide invaluable information for sustainable urban planning and management, so as for assessment related to the United Nation’s sustainable development goals. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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20 pages, 4817 KiB  
Article
Remote Estimation of Water Clarity and Suspended Particulate Matter in Qinghai Lake from 2001 to 2020 Using MODIS Images
by Zhenyu Tan, Zhigang Cao, Ming Shen, Jun Chen, Qingjun Song and Hongtao Duan
Remote Sens. 2022, 14(13), 3094; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133094 - 27 Jun 2022
Cited by 7 | Viewed by 1980
Abstract
Climate change and human activities have been heavily affecting oceanic and inland waters, and it is critical to have a comprehensive understanding of the aquatic optical properties of lakes. Since many key watercolor parameters of Qinghai Lake are not yet available, this paper [...] Read more.
Climate change and human activities have been heavily affecting oceanic and inland waters, and it is critical to have a comprehensive understanding of the aquatic optical properties of lakes. Since many key watercolor parameters of Qinghai Lake are not yet available, this paper aims to study the spatial and temporal variations of the water clarity (i.e., Secchi-disk depth, ZSD) and suspended particulate matter concentration (CSPM) in Qinghai Lake from 2001 to 2020 using MODIS images. First, the four atmospheric correction models, including the NIR–SWIR, MUMM, POLYMER, and C2RCC were tested. The NIR–SWIR with decent accuracy in all bands was chosen for the experiment. Then, four existing models for ZSD and six models for CSPM were evaluated. Two semi-analytical models proposed by Lee (2015) and Jiang (2021) were selected for ZSD (R2 = 0.74) and CSPM (R2 = 0.73), respectively. Finally, the distribution and variation of the ZSD and CSPM were derived over the past 20 years. Overall, the water of Qinghai Lake is quite clear: the monthly mean ZSD is 5.34 ± 1.33 m, and CSPM is 2.05 ± 1.22 mg/L. Further analytical results reveal that the ZSD and CSPM are highly correlated, and the relationship can be formulated with ZSD=8.072e−0.212CSPM (R2 = 0.65). Moreover, turbid water mainly exists along the edge of Qinghai Lake, especially on the northwestern and northeastern shores. The variation in the lakeshore exhibits some irregularity, while the main area of the lake experiences mild water quality deterioration. Statistically, 81.67% of the total area is dominated by constantly increased CSPM, and the area with decreased CSPM occupies 4.56%. There has been distinct seasonal water quality deterioration in the non-frozen period (from May to October). The water quality broadly deteriorated from 2001 to 2008. The year 2008 witnessed a sudden distinct improvement, and after that, the water quality experienced an extremely inconspicuous degradation. This study can fill the gap regarding the long-time monitoring of water clarity and total suspended matter in Qinghai Lake and is expected to provide a scientific reference for the protection and management of the lake. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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19 pages, 10356 KiB  
Article
A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data
by Liwei Xing, Zhenguo Niu, Cuicui Jiao, Jing Zhang, Shuqing Han, Guodong Cheng and Jianzhai Wu
Remote Sens. 2022, 14(4), 1037; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14041037 - 21 Feb 2022
Cited by 5 | Viewed by 2386
Abstract
Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland [...] Read more.
Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland maps with a spatial resolution of 20 m using a seasonal-rule-based method in the Zhalong and Momoge National Nature Reserves. This study used Sentinel-1 and Sentinel-2 data, along with a bi-weekly composition method to generate a 15-day image time series. The random forest algorithm was used to classify the images into vegetation, waterbodies, bare land, and wet bare land during each time period. Several rules were incorporated based on the intra-annual changes in the seasonal wetlands and annual wetland maps of the study regions were generated. Validation processes showed that the overall accuracy and kappa coefficient were above 89.8% and 0.87, respectively. The seasonal-rule-based method was able to identify seasonal marshes, flooded wetlands, and artificial wetlands (e.g., paddy fields). Zonal analysis indicated that seasonal wetland types, including flooded wetlands and seasonal marshes, accounted for over 50% of the total wetland area in both Zhalong and Momoge National Nature Reserves; and permanent wetlands, including permanent water and permanent marsh, only accounted for 11% and 12% in the two reserves, respectively. This study proposes a new method to generate reliable annual wetland maps that include seasonal wetlands, providing an accurate dataset for interannual change analyses and wetland protection decision-making. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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19 pages, 6287 KiB  
Article
Assessing Surface Water Losses and Gains under Rapid Urbanization for SDG 6.6.1 Using Long-Term Landsat Imagery in the Guangdong-Hong Kong-Macao Greater Bay Area, China
by Yawen Deng, Weiguo Jiang, Zhifeng Wu, Ziyan Ling, Kaifeng Peng and Yue Deng
Remote Sens. 2022, 14(4), 881; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040881 - 12 Feb 2022
Cited by 11 | Viewed by 2733
Abstract
As one of the most open and dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been urbanizing rapidly in recent decades. The surface water in the GBA also has been suffering from urbanization and intensified human activities. The study [...] Read more.
As one of the most open and dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been urbanizing rapidly in recent decades. The surface water in the GBA also has been suffering from urbanization and intensified human activities. The study aimed to characterize the spatiotemporal patterns and assess the losses and gains of surface water caused by urbanization in the GBA via long time-series remote sensing data, which could support the progress towards sustainable development goals (SDGs) set by the United Nations, especially for measuring SDG 6.6.1 indicator. Firstly, utilizing 4750 continuous Landsat TM/ETM+/OLI images during 1986–2020 and the Google Earth Engine cloud platform, the multiple index water detection rule (MIWDR) was performed to extract surface water extent in the GBA. Secondly, we achieved surface water dynamic type classification based on annual water inundation frequency time-series in the GBA. Finally, the spatial distribution and temporal variation of urbanization-induced water losses and gains were analyzed through a land cover transfer matrix. Results showed that (1) the average minimal and maximal surface water extents of the GBA during 1986–2020 were 2017.62 km2 and 6129.55 km2, respectively. The maximal surface water extent fell rapidly from 7897.96 km2 in 2001 to 5087.46 km2 in 2020, with a loss speed of 155.41 km2 per year (R2 = 0.86). (2) The surface water areas of permanent and dynamic types were 1529.02 km2 and 2064.99 km2 during 2000–2020, accounting for 42.54% and 57.46% of all water-related areas, respectively. (3) The surface water extent occupied by impervious land surfaces showed a significant linear downward trend (R2 = 0.98, slope = 36.41 km2 per year), while the surface water restored from impervious land surfaces denoted a slight growing trend (R2 = 0.86, slope = 0.99 km2 per year). Our study monitored the long-term changes in the surface water of the GBA, which can provide valuable information for the sustainable development of the GBA urban agglomeration. In addition, the proposed framework can easily be implemented in other similar regions worldwide. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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22 pages, 13925 KiB  
Article
UAV Mapping of the Chlorophyll Content in a Tidal Flat Wetland Using a Combination of Spectral and Frequency Indices
by Wei Zhuo, Nan Wu, Runhe Shi and Zuo Wang
Remote Sens. 2022, 14(4), 827; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040827 - 10 Feb 2022
Cited by 11 | Viewed by 2262
Abstract
The chlorophyll content of leaves is an important indicator of plant environmental stress, photosynthetic capacity, and is widely used to diagnose the growth and health status of vegetation. Traditional chlorophyll content inversion is based on the vegetation index under pure species, which rarely [...] Read more.
The chlorophyll content of leaves is an important indicator of plant environmental stress, photosynthetic capacity, and is widely used to diagnose the growth and health status of vegetation. Traditional chlorophyll content inversion is based on the vegetation index under pure species, which rarely considers the impact of interspecific competition and species mixture on the inversion accuracy. To solve these limitations, the harmonic analysis (HA) and the Hilbert–Huang transform (HHT) were introduced to obtain the frequency index, which were combined with spectral index as the input parameters to estimate chlorophyll content based on the unmanned aerial vehicle (UAV) image. The research results indicated that: (1) Based on a comparison of the model accuracy for three different types of indices in the same period, the estimation accuracy of the pure spectral index was the lowest, followed by that of the frequency index, whereas the mixed index estimation effect was the best. (2) The estimation accuracy in November was lower than that in other months; the pure spectral index coefficient of determination (R2) was only 0.5208, and the root–mean–square error (RMSE) was 4.2144. The estimation effect in September was the best. The model R2 under the mixed index reached 0.8283, and the RMSE was 2.0907. (3) The canopy chlorophyll content (CCC) estimation under the frequency domain index was generally better than that of the pure spectral index, indicating that the frequency information was more sensitive to subtle differences in the spectrum of mixed vegetation. These research results show that the combination of spectral and frequency information can effectively improve the mapping accuracy of the chlorophyll content, and provid a theoretical basis and technology for monitoring the chlorophyll content of mixed vegetation in wetlands. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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17 pages, 29190 KiB  
Article
Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images
by Yuxin Zhao, Dehua Mao, Dongyou Zhang, Zongming Wang, Baojia Du, Hengqi Yan, Zhiqiang Qiu, Kaidong Feng, Jingfa Wang and Mingming Jia
Remote Sens. 2022, 14(3), 694; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030694 - 01 Feb 2022
Cited by 12 | Viewed by 2340
Abstract
Phragmites australis (P. australis) is one of the most important plant species found in wetland ecosystems, and its aboveground biomass (AGB) is a key indicator for assessing the quality or health of a wetland site. In this study, we combined [...] Read more.
Phragmites australis (P. australis) is one of the most important plant species found in wetland ecosystems, and its aboveground biomass (AGB) is a key indicator for assessing the quality or health of a wetland site. In this study, we combined Sentinel-1/2 images and field observation data collected in 2020, to delineate the distribution of P. australis in the Momoge Ramsar Wetland site by using a random forest method, and further, to estimate AGB by comparing multiple linear regression models. The results showed that the overall classification accuracy of P. australis using the random forest method was 89.13% and the P. australis area in the site was 135.74 km2 in 2020. Among various remote sensing variables, the largest correlation coefficient was observed between dry weight of AGB of P. australis and Sentinel-2 red edge B7, and between fresh weight of P. australis AGB and red edge B5. The optimal models for estimating dry and fresh weight of P. australis AGB were multiple linear regression models, with an accuracy of 75.4% and 69.2%, respectively. In 2020, it was estimated that the total fresh weight of P. australis AGB in this Ramsar site was 21.2 × 107 kg and the total dry weight was 7.2 × 107 kg. The larger weight of P. australis AGB was identified mainly at central and western sites. The application of Sentinel-2 red-edge band for AGB estimation can significantly improve the model estimation accuracy. The findings of this study will provide a scientific basis for the management and protection of wetland ecosystems and sustainable utilization of P. australis resources. Full article
(This article belongs to the Special Issue Remote Sensing of Wetlands and Biodiversity)
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