Hamid Dashti
Postdoctoral Research Associate at UW-Madison Remote Sensing | Geospatial Data Science | Climate Change |
Postdoctoral Research Associate at UW-Madison Remote Sensing | Geospatial Data Science | Climate Change |
I am a geospatial scientist with expertise in remote sensing and geospatial data science. I am currently a postdoctoral research associate at the University of Wisconsin-Madison, and I have previously held postdoctoral positions at the University of Arizona and Boise State University. My research focuses on using remote sensing and geospatial data science to study climate change and its impacts on nature and society.
I use machine learning to interpret and predict from big geospatial datasets. I have used ML to investigate land use and land cover change, assess the impacts of disturbances, and predict land surface variables from remote sensing data. I primarily use Python for data handling and model fitting, but I am also proficient in R.
One of my key areas of expertise is running large and complex process-based models. During my career, I have worked with various climate and Earth system models, including CLM, ED-V2, WRF, DART (data assimilation), and GCAM. I have experience processing the large outputs of models such as the CMIP project. My interests lie in integrating these models with other data sources, such as remote sensing observations, eddy covariance tower observations, field data, and ecological theory, to study the carbon cycle, land surface radiation budget, disturbances, land use/land cover change, and other Earth system processes.
I use 1-D and 3-D radiative transfer modeling to simulate light interaction with land surface materials. I then use numerical inversion techniques to estimate land surface materials from these simulations. I have extensive experience with optical (hyperspectral/multispectral), lidar (point clouds and full-waveform), and solar induced fluorescence data from a variety of platforms, including ground-based spectrometers, terrestrial laser scanners, drones, airplanes, and satellites. In addition to developing and applying new remote sensing algorithms, I also integrate remote sensing data with Earth system models, field data, and theory to gain insights into real-world problems.