My research is on the use of remote sensing and geographic information systems (GIS) technology to monitor and map natural resources and human settlements. Computer based pattern recognition, statistical analysis, mathematical modeling are studied for the extraction of information from imagery data acquired by sensors in the field or on board of aircraft and satellites. My goal is to develop effective and efficient methods that provide spatial information or spatial evidence to scientists, policy makers and decision makers. Such information can be used to better understand environmental changes, to make better policies for environmental protection, and to form wiser decisions on the use of our resourcesfor sustainable development of human society.
My specific interests include computer recognition of forest species based on both morphological and spectral information in remotely sensed data; estimating biophysical and biochemical parameters from forest lands through processing and analysis of hyperspectral and high spatial resolution data; land-cover and land-use classification and change detection at various spatial scales; forest fire mapping with AVHRR data and fire emission estimation; lidar data analysis for natural resources measurement; environmental measurements and infectious disease (schistosomiasis and malaria) transmission modeling with GIS and remote sensing.
We propose a research field called, Photo-Ecometrics, the science and technology using digital image analysis and spectral analysis for precise ecological measurements. We focus on forest inventory, crown reconstruction, biophysical and biochemical data and species recognition. Projects are funded by NASA, USDA and the NSF of China.
Use of GIS and remote sensing for schistosomiasis control in China with support from NIH, Ministry of Science and Technology and NSF of China.
Invasive weed mapping in Utah and Southern California with support from NSF and USGS.
Ecosystem modeling for carbon balance estimation over large areas using hyperspectral and lidar remote sensing. Global change monitoring.
Wetland species mapping, growth modeling, grazing interference modeling and associated snail density estimation using field survey and remotely sensed data.
Students and postdocs in my group can choose to develop their own projects with my assistance or participate in many aspects of remote sensing and GIS ranging from instrument design and test to purely theoretical analysis of spatial data for resource management and global change studies.