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Xia Wang
Research Center for Environmental Ecology and Engineering, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, China

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Journal article
Published: 07 February 2019 in Remote Sensing
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Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

ACS Style

Xia Wang; Feng Ling; Huaiying Yao; Yaolin Liu; Shuna Xu. Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image. Remote Sensing 2019, 11, 327 .

AMA Style

Xia Wang, Feng Ling, Huaiying Yao, Yaolin Liu, Shuna Xu. Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image. Remote Sensing. 2019; 11 (3):327.

Chicago/Turabian Style

Xia Wang; Feng Ling; Huaiying Yao; Yaolin Liu; Shuna Xu. 2019. "Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image." Remote Sensing 11, no. 3: 327.

Journal article
Published: 25 October 2017 in Water
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Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive à trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

ACS Style

Yaolin Liu; Xia Wang; Feng Ling; Shuna Xu; Chengcheng Wang. Analysis of Coastline Extraction from Landsat-8 OLI Imagery. Water 2017, 9, 816 .

AMA Style

Yaolin Liu, Xia Wang, Feng Ling, Shuna Xu, Chengcheng Wang. Analysis of Coastline Extraction from Landsat-8 OLI Imagery. Water. 2017; 9 (11):816.

Chicago/Turabian Style

Yaolin Liu; Xia Wang; Feng Ling; Shuna Xu; Chengcheng Wang. 2017. "Analysis of Coastline Extraction from Landsat-8 OLI Imagery." Water 9, no. 11: 816.

Journal article
Published: 02 March 2017 in ISPRS International Journal of Geo-Information
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Ningbo City in Zhejiang Province is one of the largest port cities in China and has achieved high economic development during the past decades. The port construction, land reclamation, urban development and silt deposition in the Ningbo coastal zone have resulted in extensive coastline change. In this study, the spatio-temporal change of the Ningbo coastlines during 1976–2015 was detected and analysed using Landsat time-series images from different sensors, including Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI). Fourteen individual scenes (covering seven phases) of cloud-free Landsat images within the required tidal range of ±63 cm were collected. The ZiYuan-3 (ZY-3) image of 2015 was used to extract the reference coastline for the accuracy assessment. The normalised difference water index (NDWI) and the modified normalized difference water index (MNDWI) were applied to discriminate surface water and land features, respectively. The on-screen digitising approach was then used to further refine the extracted time-series coastlines in the period from 1976 to 2015. Six relevant indices, length, length change, annual length change, fractal dimension (FD), average net shoreline movement (NSM) and average annual NSM, were calculated to analyse and explore the spatio-temporal change features of Ningbo coastlines. Results show that the length of the Ningbo coastlines increased from 910 km to 986 km, and the value of FD increased from 1.09 to 1.12, and the coastline morphology changed from sinuous to straight. The average NSM increased from 187 m to 298 m and the average annual NSM reached 85 m/year, indicating the advance of coastlines towards the sea at a high level. The spatio-temporal change patterns also varied in different areas. In Hangzhou Bay, significant advancement along the coastlines was experienced since 2001 mainly because of urban construction and land reclamation. In Xiangshan Bay, the forces of nature played a major role in coastline dynamics before 2008, whilst port construction, urban construction and island link projections moved the coastlines towards the sea. The coastline changes of Sanmen Bay were affected by the interaction of nature and human activities. All these observations indicate that forces of nature and human activities were the two important influential factors for the observed coastline change. In this case, the coastline complexity variation was considered responsible for various coastline patterns change of the Ningbo coast. In addition, erosion and accretion occurred in turn because of forces of nature and human activities, such as urban development and agricultural exploitation.

ACS Style

Xia Wang; Yaolin Liu; Feng Ling; Yanfang Liu; Feiguo Fang. Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015. ISPRS International Journal of Geo-Information 2017, 6, 68 .

AMA Style

Xia Wang, Yaolin Liu, Feng Ling, Yanfang Liu, Feiguo Fang. Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015. ISPRS International Journal of Geo-Information. 2017; 6 (3):68.

Chicago/Turabian Style

Xia Wang; Yaolin Liu; Feng Ling; Yanfang Liu; Feiguo Fang. 2017. "Spatio-Temporal Change Detection of Ningbo Coastline Using Landsat Time-Series Images during 1976–2015." ISPRS International Journal of Geo-Information 6, no. 3: 68.