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Chang Huang     University Educator/Researcher 
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Chang Huang published an article in August 2018.
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Wolfgang Wagner

256 shared publications

Department of Geodesy and Geoinformation, TU Wien, Gußhausstraße 27-29, 1040 Vienna, Austria

Shiqiang Zhang

38 shared publications

Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity; Northwest University; Xi'an China

Linyi Li

20 shared publications

Wuhan University, School of Remote Sensing and Information Engineering, Wuhan

Yun Chen

15 shared publications

CSIRO Land and Water, Canberra 2601, Australia

Tingbao Xu

8 shared publications

Fenner School of Environment and Society, The Australian National University, Canberra, ACT 2601, Australia

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Article 2 Reads 0 Citations Seeing Surface Water From Space Chang Huang Published: 01 August 2018
Eos, doi: 10.1029/2018eo103123
DOI See at publisher website ABS Show/hide abstract
Satellite-based optical sensors can detect, measure and monitor changes in lakes, reservoirs, rivers and wetlands, providing useful data with multiple applications for science and society.
Article 0 Reads 5 Citations Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review Chang Huang, Yun Chen, Shiqiang Zhang, Jianping Wu Published: 06 June 2018
Reviews of Geophysics, doi: 10.1029/2018rg000598
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Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite‐based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio‐temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio‐temporal fusion have been developed to alleviate this contradiction. Spatio‐temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with Synthetic Aperture Radar (SAR) data. Digital Elevation Model (DEM) data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques make the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multi‐source data is the future direction for improved global and regional water monitoring.
Article 0 Reads 0 Citations GPM卫星降水数据在黑河流域的适用性评价 思梦 王, Wang Si-Meng, 大钊 王, 昌 黄, Wang Da-Zhao, Huang Chang Published: 01 January 2018
JOURNAL OF NATURAL RESOURCES, doi: 10.31497/zrzyxb.20171180
DOI See at publisher website
Article 1 Read 2 Citations Spatial Downscaling of Suomi NPP–VIIRS Image for Lake Mapping Chang Huang, Yun Chen, Shiqiang Zhang, Linyi Li, Kaifang Shi... Published: 30 October 2017
Water, doi: 10.3390/w9110834
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Capturing the dynamics of a lake-water area using remotely sensed images has always been an essential task. Most of the fine spatial resolution data are unsuitable for this purpose because of their low temporal resolution and limited scene coverage. A Visible Infrared Imaging Radiometer Suite on board the Suomi National Polar-orbiting Partnership (Suomi NPP–VIIRS) is a newly-available and appropriate sensor for monitoring large lakes due to its frequent revisits and wide swath (more than 3000 km). However, it provides visible and infrared images at relatively coarse spatial resolutions, which would sometimes hamper the accurate mapping of lake shorelines. This study, therefore, proposes a two-step downscaling method that combines spectral unmixing and subpixel mapping to produce a finer resolution lake map from NPP–VIIRS imagery, which is then applied to delineate the shorelines of five plateau lakes in Yunnan Province, as well as the shoreline dynamics of Poyang Lake at three separate times. A newly published global water dynamic dataset is employed in this study to improve the downscaling method. Results suggest that the proposed method can generate a finer resolution lake map that exhibits more details of the shoreline than hard classification. The downscaling results of the Suomi NPP–VIIRS generally achieve higher than 75% accuracy, while the downscaling results of a Landsat-simulated fraction map could have accuracy higher than 85%. This reveals that errors and uncertainties exist in both procedures, but mainly come from the spectral unmixing procedure which retrieves water fractions from NPP–VIIRS data.
Article 4 Reads 3 Citations A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data Chang Huang, Ba Duy Nguyen, Shiqiang Zhang, Senmao Cao, Wolf... Published: 30 April 2017
ISPRS International Journal of Geo-Information, doi: 10.3390/ijgi6050140
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The Sentinel-1 mission provides frequent coverage of global land areas and is hence able to monitor surface water dynamics at a fine spatial resolution better than any other Synthetic Aperture Radar (SAR) mission before. However, SAR data acquired by Sentinel-1 also suffer from terrain effects when being used for mapping surface water, just as other SAR data do. Terrain indices derived from Digital Elevation Models (DEMs) are easy but effective approaches to reduce this kind of interference, considering the close relationship between surface water movement and topography. This study compares two popular terrain indices, namely the Multi-resolution Valley Bottom Flatness (MrVBF) and the Height Above Nearest Drainage (HAND), toward their performance on assisting surface water mapping using Sentinel-1 SAR data. Four study sites with different terrain characteristics were selected to cover a very wide range of topographic conditions. For two of these sites that are floodplain dominated, both normal and flooded scenarios were examined. MrVBF and HAND values for the whole study areas, as well as statistics of these values within water areas were compared. The sensitivity of applying different thresholds for MrVBF and HAND to mask out terrain effect was investigated by adopting quantity disagreement and allocation disagreement as the accuracy indicators. It was found that both indices help improve water mapping, reducing the total disagreement by as much as 1.6%. The HAND index performs slightly better in most of the study cases, with less sensitivity to thresholding. MrVBF classifies low-lying areas with more details, which sometimes makes it more effective in eliminating false water bodies in rugged terrain. It is therefore recommended to use HAND for large scale or global scale water mapping. However, for water detection in complex terrain areas, MrVBF also performs very well.
CONFERENCE-ARTICLE 5 Reads 0 Citations Mapping Lake-water area at sub-pixel scale using Suomi NPP-VIIRS imagery Chang Huang, Yun Chen, Shiqiang Zhang Published: 22 November 2016
Proceedings of The 1st International Electronic Conference on Water Sciences, doi: 10.3390/ecws-1-f001
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Capturing the variation of lake-water area using remotely sensed imagery is an essential topic in many related fields. There are a variety of remote sensing data that can serve this purpose. Generally speaking, higher spatial resolution data are able to derive better results. However, most high spatial resolution data are sometimes defective because of their low temporal resolution and limited scene coverage. Visible Infrared Imaging Radiometer Suite onboard Suomi National Polar-orbiting Partnership (Suomi NPP-VIIRS) provides a newly-available and appropriate manner for monitoring large lakes because of its frequent revisit and wide breadth. But its spatial resolution is relatively low, from 375m to 750m. This study introduces a two-step method that integrates spectral unmixing and sub-pixel mapping to map lake-water area at sub-pixel scale from NPP-VIIRS imagery. Accuracy was assessed by employing corresponding Landsat images as the reference. Five plateau lakes in Yunnan province, China, were selected as the case study areas. Results suggest that the proposed method is able to derive finer resolution lake maps that show more details of the shoreline. The accuracy was significantly improved comparing to traditional classification method. Analysis also reveals that errors and uncertainties also exist in this method. Most of them come from the spectral unmixing procedure that retrieve water fraction from NPP-VIIRS data. Therefore, in order to achieve better lake mapping result, future work should concentrate more on improving this part to produce a better water fraction map first.

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