Remote Sensing of Environment and Resources

       We aim to improve the capabilities to monitor the quality and sustainability of the eco-environments, and to explore minerals and other resources of earth and deep-space, studying inversion mechanism of key parameters of environment and resources by hyperspectral remote sensing (HRS) data, developing HRS-based models and algorithms for feature selection, detailed classification, spectral unmixing, parameter inversion, addressing the key scientific issues and technologies for HRS applications in environment and resources fields, providing the key algorithm framework for information products such as land cover change detection, environment pollution monitoring, mineral mapping, fragile ecosystem evaluation.. 


Mars Hydrous Minerals Abundance retrieval by hyperspectral CRISM image in Gale Crater


           Xia ZHANG, Shudong WANG,Jia LIU, Nan WANG, Yanli SUN, Honglei LIN, Weichao SUN, Huihui ZHOU, Wenjing WANG, Jingguo TIAN, Xiaoyuan ZHANG.


Research Projects:

1、 National Natural Science Fund of China for Young Scholars (Grant No. 41501391): Semi-supervised unmixing based on nonnegative matrix factorization for hyperspectral remote sensing images 2016-2018.

2、 Research grant funded by China Postdoctoral Science Foundation: Hyperspectral unmixing with global distribution and spatial heterogeneity of ground objects, 2015/10-2017/10

3、 National Natural Science Fund of China (Grant No. 41371359): Study on hyperspectral intelligent observation modes for various typical under underlying surfaces. 2014-2017

4、 National high technology research and development special found, China (Grant No:2012AA12A309)。

5、 Project of China Geological Survey: Spatial- information- technology- based methods for assessing Resources and Environments carrying capability

6、 Task of “135 Plan” of RADI: Quantitative retrieval of Mars hydrous minerals, 2015-2018.

7、 Task of “135 Plan” of RADI: Developing algorithms of atmospheric correction, precise classification, image fusion for hyperspectral remote sensing data, 2015-2018.

8、 Common key technology research of High resolution remote sensing(D76): Hyper- and multi- spectral Image fusion and urban ecological environment evaluation for GF-5 data, 2015-2016.

9、 Geographical conditions survey projects: Desertification monitoring for Northwest land of Liaoning based remote sensing technology, 2014/10-2015/11.

10、Major Special Project-the China High-Resolution Earth Observation System-Development of the ecological products of the Loess Plateau based on remote sensing technology 2015-2016.

11、Open Fund of State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS201508): Study on landscape pattern of source or sink of N-point source pollution source in compound agriculture and forestry grass area using remote sensing analysis method. 2015-2016.

12、Satellite Environment Center Project of Environmental Protection Department: High resolution remote sensing ecosystem product integration and its analysis, 2015-2016.

13、“Research on Construction and Application of Spatial Information System for Resources on “the Belt and Road” region: Research and development on Spatial Information System for Monitoring Major Oil and Gas Infrastructure, 2015/9-2017/12.

14、Task of National 863 plan on moon exploration: Main mineral information extraction by Chang’E-1 IIM data and pixel unmixing algorithm, 2010-2012.

15、National Natural Science Fund of China (Grant No. 40971205): Karst rocky desertification information extraction based on spectral feature analysis, 2009-2012.

16、Civil research project of the Technology and Industry for National Defense: Key technical specifics demonstration and soil heavy metal monitoring application model research for next generation environmental monitoring satellite, 2008-2012.

17、National Science and technology support program:Research on multi-source spatial data processing for Beijing area, 2008 - 2012.

18、National Natural Science Foundation of China(Grant No. 40601069):Cropping pattern information extraction based on vegetation index time series, 2006-2009.





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