scholarly journals Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

2019 ◽  
Vol 11 (8) ◽  
pp. 952 ◽  
Author(s):  
Aizhu Zhang ◽  
Genyun Sun ◽  
Ping Ma ◽  
Xiuping Jia ◽  
Jinchang Ren ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Eastern China ◽  
Jiaozhou Bay ◽  
Superior Performance ◽  
Wetland Mapping ◽  
Mlp Classifier ◽  
Morphological Attribute ◽  
Sentinel 2

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.

scholarly journals Spatio-Temporal Change of Land Use in a Coastal Reclamation Area: A Complex Network Approach

2021 ◽  
Vol 13 (16) ◽  
pp. 8690
Author(s):  
Caiyao Xu ◽  
Lijie Pu ◽  
Fanbin Kong ◽  
Bowei Li
Keyword(s):  
Land Use ◽  
Complex Network ◽  
Coastal Wetland ◽  
Eastern China ◽  
Wetland Ecosystems ◽  
Ecological Protection ◽  
Land Use Types ◽  
Coastal Reclamation ◽  
Reclamation Area ◽  
Spatio Temporal

Coastal ecological protection and restoration projects aimed to restore and recover the ecological environment of coastal wetland with high-intensity human reclamation activity, while the integrity of the coastal wetland system with human reclamation activity and the ability of individual land use types to control the overall system were not fully considered. In this study, a six-stage land use conversion network was constructed by using a complex network model to analyze coastal land use dynamic changes in the coastal reclamation area located in eastern China from 1977 to 2016. The results showed that land use types had gradually transformed from being dominated by natural types to artificial types, and the speed of transformation was accelerating. The proportion of un-reclaimed area decreased from 93% in 1977 to 46% in 2007, and finally fell to 8% in 2014 and 2016. Tidal flat and halophytic vegetation were the main output land use types, while cropland, woodland and aquaculture pond were the main input land use types. Cropland had the highest value of betweenness centrality, which played a key role in land use change from 1992 to 2014. The land use system of the coastal reclamation area was the most stable in 2002–2007, followed by 1984–1992, and the most unstable in 2007–2014. The Chinese and local government should carry out some measures to improve the land use in coastal wetland ecosystems, including the allocation and integration of land use for production space, living space, and ecological space, and develop multi-functionality of land use to realize the coastal high-quality development and coastal ecological protection and restoration.

scholarly journals Coastal wetlands in Lianyungang, Jiangsu Province, China: probably the most important site globally for the Asian Dowitcher (Limnodromus semipalmatus)

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ziyou Yang ◽  
Jing Li ◽  
Yongxiang Han ◽  
Chris J. Hassell ◽  
Kar-Sin Katherine Leung ◽  
...  
Keyword(s):  
Coastal Wetlands ◽  
Coastal Wetland ◽  
Migratory Bird ◽  
Jiangsu Province ◽  
Bohai Bay ◽  
High Concentration ◽  
Human Disturbances ◽  
Stopover Site ◽  
Stochastic Events ◽  
Global Population

Abstract Background Despite an increasing number of surveys and a growing interest in birdwatching, the population and distribution of Asian Dowitcher (Limnodromus semipalmatus), a species endemic to the East Asian–Australasian and Central Asian Flyways, remains poorly understood, and published information about the species is largely outdated. In boreal spring 2019, over 22,432 Asian Dowitchers were recorded in a coastal wetland at Lianyungang, Jiangsu Province, China, constituting 97.5% of its estimated global population. Methods In 2019 and 2020, we conducted field surveys at Lianyungang to determine the numbers of Asian Dowitchers using the area during both southward and northward migrations. We also assessed the distribution and abundance of Asian Dowitchers elsewhere along the China coast by searching literature and consulting expert opinion. Results The coastal wetlands of Lianyungang are the most important stopover site for Asian Dowitchers during both northward and southward migrations; they supported over 90% of the estimated global population during northward migration in two consecutive years (May 2019 and 2020). This area also supported at least 15.83% and 28.42% (or 30.74% and 53.51% using modelled estimates) of the global population during southward migration in 2019 and 2020 respectively. Coastal wetlands in the west and north of Bohai Bay also have been important stopover sites for the species since the 1990s. Although comprehensive, long-term monitoring data are lacking, available evidence suggests that the population of the species may have declined. Conclusions The high concentration of Asian Dowitchers at Lianyungang during migration means the species is highly susceptible to human disturbances and natural stochastic events. The coastal wetlands of Lianyungang should be protected and potentially qualify for inclusion in China’s forthcoming nomination for World Heritage listing of Migratory Bird Sanctuaries along the Coast of Yellow Sea-Bohai Gulf of China (Phase II) in 2023. Additional research is needed to understand Asian Dowitchers’ distribution and ecology, as well as why such a high proportion of their population rely on the Lianyungang coast.

Wetland Mapping of Northern Provinces of Iran Using Sentinel-1 and Sentinel-2 in Google Earth Engine

2021 ◽  
Author(s):  
Mohammad Ali Hemati ◽  
Mahdi Hasanlau ◽  
Masaud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Google Earth ◽  
Wetland Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

scholarly journals Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

2021 ◽  
Vol 13 (20) ◽  
pp. 4106
Author(s):  
Shuai Wang ◽  
Mingyi Zhou ◽  
Qianlai Zhuang ◽  
Liping Guo
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Coastal Wetlands ◽  
Environmental Variables ◽  
Northeast China ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Wetland Ecosystems ◽  
Flat Terrain ◽  
Soc Density

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.

scholarly journals Comparison of Qinzhou bay wetland landscape information extraction by three methods

2014 ◽  
Vol XL-4 ◽  
pp. 21-28 ◽  
Author(s):  
X. Chang ◽  
Q. Zhang ◽  
M. Luo ◽  
C. Dong
Keyword(s):  
Coastal Wetlands ◽  
Supervised Classification ◽  
Coastal Wetland ◽  
Object Oriented ◽  
Visual Interpretation ◽  
Wetland Area ◽  
The Third ◽  
Tasseled Cap Transformation ◽  
Qinzhou Bay ◽  
Landscape Information

Wetland ecosystem plays an important role on the environment and sustainable socio-economic development. Based on the TM images in 2010 with a pretreament of Tasseled Cap transformation, three different methods are used to extract the Qinzhou Bay coastal wetlands using Supervised Classification (SC), Decision Trees (DT) and Object -oriented (OO) methods. Firstly coastal wetlands are picked out by artificial visual interpretation as discriminant standard. The result shows that when the same evaluation template used, the accuracy and Kappa coefficient of SC, DT and OO are 92.00 %, 0.8952; 89.00 %, 0.8582; 91.00 %, 0.8848 respectively. The total area of coastal wetland is 218.3 km<sup>2</sup> by artificial visual interpretation, and the extracted wetland area of SC, DT and OO is 219 km<sup>2</sup>, 193.70 km<sup>2</sup>, 217.40 km<sup>2</sup> respectively. The result indicates that SC is in the f irst place, followed by OO approach, and the third DT method when used to extract Qingzhou Bay coastal wetland.

scholarly journals Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

2020 ◽  
Vol 12 (19) ◽  
pp. 3270
Author(s):  
Kinh Bac Dang ◽  
Manh Ha Nguyen ◽  
Duc Anh Nguyen ◽  
Thi Thanh Hai Phan ◽  
Tuan Linh Giang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coastal Wetland ◽  
Coastal Hazards ◽  
Support Vector ◽  
Wetland Classification ◽  
Natural Wetland ◽  
Convolutional Networks ◽  
Transition Areas ◽  
Wetland Types ◽  
Sentinel 2

The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time.

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

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.

scholarly journals The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

2018 ◽  
Vol 11 (1) ◽  
pp. 43 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Bahram Salehi ◽  
Fariba Mohammadimanesh ◽  
Saeid Homayouni ◽  
Eric Gill
Keyword(s):  
Large Scale ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wetland Mapping ◽  
Object Based ◽  
Wetland Inventory ◽  
Inventory Map ◽  
Google Earth Engine ◽  
Provincial Scale ◽  
Sentinel 2

Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.

Occurrence, spatiotemporal variation, and ecological risk of antibiotics in the water of the semi-enclosed urbanized Jiaozhou Bay in eastern China

2020 ◽  
Vol 184 ◽  
pp. 116187 ◽  
Author(s):  
Shuang Lu ◽  
Chunye Lin ◽  
Kai Lei ◽  
Baodong Wang ◽  
Ming Xin ◽  
...  
Keyword(s):  
Ecological Risk ◽  
Eastern China ◽  
Jiaozhou Bay ◽  
Spatiotemporal Variation
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