Please login first
Shiqiang Zhang      
Timeline See timeline
Shiqiang Zhang published an article in June 2018.
Top co-authors
Chang Huang

29 shared publications

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

Yun Chen

8 shared publications

CSIRO Land and Water; Canberra ACT 2601 Australia

4
Publications
4
Reads
0
Downloads
5
Citations
Publication Record
Distribution of Articles published per year 
(2016 - 2018)
Total number of journals
published in
 
4
 
Publications
Article 0 Reads 3 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
DOI See at publisher website ABS Show/hide abstract
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 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.
CONFERENCE-ARTICLE 4 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
The 1st International Electronic Conference on Water Sciences, doi: 10.3390/ecws-1-f001
DOI See at publisher website ABS Show/hide abstract

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.

Conference 0 Reads 1 Citation Surface water change detection using change vector analysis Chang Huang, Xiaoyu Zan, Xuewen Yang, Shiqiang Zhang Published: 01 July 2016
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), doi: 10.1109/igarss.2016.7729732
DOI See at publisher website
Top