Envision My Future Research

During the future 5 years, I will further provide my expertise in soil moisture mapping and ecological monitoring and extend my knowledge of geostatistics, machine learning, spatial analysis, and artificial intelligence into the area of global and regional change, environmental health, and human health. A major challenge in the applications when it comes to earth science is how to do research across scales. Traditional research based on field investigate is very labor-intensive, and cost a lot. Although the data acquired are precise, they only cover a limited area on a very local scale. However, to tell the full story, one needs to get a whole picture on a broad scale. For example, in biodiversity investigation, scientists were forced to analyze small plots of trees and extrapolate their measurements for the entire forest — a problematic approach due to inaccuracies in the data. Under some circumstances, it is not possible to do field investigations due to unlimited accessibility. To overcome these limitations, my future research will focus on integrating multi-scale remote sensing platforms to facilitate field investigation. These platforms include multi-source remote sensing satellites on global and regional scales; close-range UAV and that carries instruments on an intermediate scale; terrestrial-based LiDAR on an in-situ scale.

With the combination of artificial intelligence, statistical approaches, and the above multi-scale remote sensing platforms, this novel approach will contribute to the three big areas that I am interested in: climate change scenario analysis; spatial ecological/biological mapping; human health. The link between climate change and ecological/biological mapping are environmental/anthropogenic stressors; and the link between biological mapping and human health are human-environmental interactions, as well as the stressors within human micro-environment. My specific research interests are:

(1) UAV-based multispectral/hyperspectral image application in precision agriculture; (2) remote and proximal sensing for agroclimatic study from a human-environmental interaction perspective; (3) drought and food-energy-water nexus associated with climate change at global and regional scales using multi-level modeling and machine learning; (4) spatio-temporal data analysis with big data analytics and their application in global change and regional scale efforts.