Remote sensing of coastal wetlands and estuaries

Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m−2, which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration.

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WetlandNet: Semantic Segmentation for Remote Sensing Images of Coastal Wetlands via Improved UNet with Deconvolution

Advances in Intelligent Systems and Computing - Genetic and Evolutionary Computing ◽

10.1007/978-981-15-3308-2_32 ◽

2020 ◽

pp. 281-292

Author(s):

Binge Cui ◽

Yonghui Zhang ◽

Xinhui Li ◽

Jing Wu ◽

Yan Lu

Keyword(s):

Remote Sensing ◽

Coastal Wetlands ◽

Semantic Segmentation ◽

Remote Sensing Images

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Mapping Coastal Wetlands of the Bohai Rim at a Spatial Resolution of 10 m Using Multiple Open-Access Satellite Data and Terrain Indices

Remote Sensing ◽

10.3390/rs12244114 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4114

Author(s):

Shaobo Sun ◽

Yonggen Zhang ◽

Zhaoliang Song ◽

Baozhang Chen ◽

Yangjian Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Open Access ◽

Spatial Resolution ◽

Coastal Wetlands ◽

Coastal Wetland ◽

Regional Scale ◽

Google Earth ◽

Human Welfare ◽

Bohai Rim ◽

Terrain Indices

Coastal wetlands provide essential ecosystem services and are closely related to human welfare. However, they can experience substantial degradation, especially in regions in which there is intense human activity. To control these increasingly severe problems and to develop corresponding management policies in coastal wetlands, it is critical to accurately map coastal wetlands. Although remote sensing is the most efficient way to monitor coastal wetlands at a regional scale, it traditionally involves a large amount of work, high cost, and low spatial resolution when mapping coastal wetlands at a large scale. In this study, we developed a workflow for rapidly mapping coastal wetlands at a 10 m spatial resolution, based on the recently emergent Google Earth Engine platform, using a machine learning algorithm, open-access Synthetic Aperture Radar (SAR) and optical images from the Sentinel satellites, and two terrain indices. We then generated a coastal wetland map of the Bohai Rim (BRCW10) based on the workflow. It has a producer accuracy of 82.7%, according to validation using 150 wetland samples. The BRCW10 data reflected finer information when compared to wetland maps derived from two sets of global high-spatial-resolution land cover data, due to the fusion of multiple data sources. The study highlights the benefits of simultaneously merging SAR and optical remote sensing images when mapping coastal wetlands.

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Satellite Remote Sensing for the Conservation of East Asia’s Coastal Wetlands

Satellite Remote Sensing for Conservation Action ◽

10.1017/9781108631129.004 ◽

2018 ◽

pp. 54-81 ◽

Cited By ~ 1

Author(s):

Nicholas J. Murray

Keyword(s):

Remote Sensing ◽

Coastal Wetlands ◽

Satellite Remote Sensing

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Assessing the Resilience of Coastal Wetlands to Extreme Hydrologic Events Using Vegetation Indices: A Review

Remote Sensing ◽

10.3390/rs10091390 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1390 ◽

Cited By ~ 3

Author(s):

Subrina Tahsin ◽

Stephen C. Medeiros ◽

Arvind Singh

Keyword(s):

Remote Sensing ◽

Coastal Wetlands ◽

Coastal Wetland ◽

Assessment Tool ◽

Anthropogenic Impacts ◽

Vegetation Indices ◽

Heavy Precipitation ◽

Life Forms ◽

Climatic Fluctuations ◽

Wetland Ecosystems

Coastal wetlands (CWs) offer numerous imperative functions that support a diverse array of life forms that are poorly adapted for other environments and provide an economic base for human communities. Unfortunately, CWs have been experiencing significant threats due to meteorological and climatic fluctuations as well as anthropogenic impacts. The wetlands and marshes in Apalachicola Bay, Florida have endured the impacts of several extreme hydrologic events (EHEs) over the past few decades. These extreme hydrologic events include drought, hurricane, heavy precipitation and fluvial flooding. Remote sensing has been used and continues to demonstrate promise for acquiring spatial and temporal information about CWs thereby making it easier to track and quantify long term changes driven by EHEs. These wetland ecosystems are also adversely impacted by increased human activities such as wetland conversion to agricultural, aquaculture, industrial or residential use; construction of dikes along the shoreline; and sprawl of built areas. In this paper, we review previous works on coastal wetland resilience to EHEs. We synthesize these concepts in the context of remote sensing as the primary assessment tool with focus on derived vegetation indices to monitor CWs at regional and global scales.

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Coastal wetlands facing climate change and anthropogenic activities: A remote sensing analysis and modelling application

Ocean & Coastal Management ◽

10.1016/j.ocecoaman.2017.01.005 ◽

2017 ◽

Vol 138 ◽

pp. 1-10 ◽

Cited By ~ 18

Author(s):

Wen-ting Wu ◽

Yun-xuan Zhou ◽

Bo Tian

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Coastal Wetlands ◽

Anthropogenic Activities ◽

Modelling Application

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THỰC TRẠNG VÀ NHỮNG THÁCH THỨC TRONG QUẢN LÝ ĐẤT NGẬP NƯỚC TẠI VÙNG CỬA SÔNG Ô LÂU, TỈNH THỪA THIÊN HUẾ

Tạp chí Khoa học và công nghệ nông nghiệp, Trường Đại học Nông Lâm Huế ◽

10.46826/huaf-jasat.v4n2y2020.438 ◽

2020 ◽

Vol 4 (2) ◽

pp. 1861-1870

Author(s):

Dương Quốc Nõn ◽

Nguyễn Hữu Ngữ ◽

Trương Đỗ Minh Phượng ◽

Lê Hữu Ngọc Thanh ◽

Nguyễn Thị Nhật Linh ◽

...

Keyword(s):

Remote Sensing ◽

Natural Resources ◽

Coastal Wetlands ◽

Wetland Management ◽

Viet Nam ◽

Agricultural Crops ◽

Inland Wetlands ◽

Strategic Plans ◽

Management And Conservation

Nghiên cứu này nhằm mục đích làm rõ những đặc điểm và những thách thức trong quản lý, bảo tồn đất ngập nước (ĐNN) tại vùng cửa sông Ô Lâu (CSÔL), tỉnh Thừa Thiên Huế. Kết hợp phương pháp phỏng vấn nông hộ, phỏng vấn cán bộ với phương pháp bản đồ, GIS, viễn thám đã cho thấy, vùng CSÔL có diện tích khoảng 11.000 ha, trong đó, vùng lõi có diện tích là khoảng 433 ha. Theo tiêu chuẩn phân loại ĐNN của Việt Nam, khu vực này có 3 nhóm chính là i) nhóm ĐNN biển và ven biển; ii) nhóm ĐNN nội địa; và iii) nhóm ĐNN nhân tạo. Hiện nay, người dân vẫn đang khai thác các nguồn tài nguyên của vùng CSÔL cho các hoạt động sinh kế. Khoảng 99,6 ha cây bụi tại các bãi bồi đã bị thay thế bởi các loại cây nông nghiệp. Tài nguyên, cảnh quan ĐNN tại CSÔL đang bị biến đổi mạnh mẽ và chức năng sinh thái của khu vực này cũng đang bị suy giảm mạnh. Để phục hồi các chức năng của vùng CSÔL, cần nhiều giải pháp từ cả chính quyền địa phương, người dân và các nhà khoa học. Trong đó, quan trọng nhất là nhận thức của người dân và ý chí của các cấp quản lý trong quá trình hoạch định chiến lược phát triển của vùng. ABSTRACT This study aimed at determining the O Lau river’s wetlands (OLRW) characteristics and identifying challenges in wetland management and conservation. By using various methods such as households and local government’s staff interview, mapping, geographic information system (GIS), remote sensing, the research results showed that the OLRW was about 11.000 hectares in which its core zone was about 433 hectares. Following Vietnam’s classification of wetlands, OLRW has three main categories, namely: i) marine and coastal wetlands; ii) inland wetlands; and iii) man-made wetlands. Currently, inhabitants are exploiting OLRW’s natural resources for their livelihood activities. Approximately 99,6 hectares of shrub-dominated wetlands were replaced by agricultural crops. OLRW’s natural resources and landscape have been destroying by human’s activities. In addition, its ecological function has also been reducing. For OLRW’s ecological functional resilience, it is necessary for the local government, inhabitants and sicientists to take countermeasures. The most important keys are inhabitants’ perception and local government’s mind in deciding to make of the development of the strategic plans.

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