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Chang Huang     University Educator/Researcher 
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Chang Huang published an article in January 2019.
Top co-authors See all
Wolfgang Wagner

257 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

7 shared publications

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

Publication Record
Distribution of Articles published per year 
(2012 - 2019)
Total number of journals
published in
Publications See all
Article 0 Reads 0 Citations Enhanced Super-Resolution Mapping of Urban Floods Based on the Fusion of Support Vector Machine and General Regression N... Linyi Li, Yun Chen, Tingbao Xu, Kaifang Shi, Chang Huang, Ru... Published: 01 January 2019
IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/lgrs.2019.2894350
DOI See at publisher website
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 6 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 5 Reads 6 Citations Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road Kaifang Shi, Bailang Yu, Chang Huang, Jianping Wu, Xiufeng S... Published: 01 May 2018
Energy, doi: 10.1016/
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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
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Article 2 Reads 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
DOI See at publisher website ABS Show/hide abstract
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.