Hernandez-Serna, Andres

Bio

I'm a Principal Faculty Specialist at the Global Land Analysis and Discovery (GLAD) laboratory in the Department of Geographical Sciences at the University of Maryland. My research interests include using Deep learning and Lidar data for ecological, soundscape, biodiversity and land cover/land use change monitoring applications. I am also interested in the use of remote sensing techniques to track and analyze landscape transformations and patterns. 

Right now I'm working on:

• Developing time-series built-up area maps for our new Bezos Fund project

• Leading Deep learning initiates on agriculture and urbanization; implement different architectures of Deep learning and features using GPUs.

• Conducting research for GLAD on land-cover change with a special focus on forest cover and agricultural land cover at a global scale utilizing remote sensing.

• Comparing the performance of Landsat combined with GEDI for tree height estimation between 2019-present.

• Testing performance of Landsat and G-LIHT  data for measuring continuous changes in tree height and tree cover, using regression tree models to estimate tree height and tree cover from 1985 to 2019.

• Collecting Lidar-RIEGL data by flying an M600  drone in various agricultural and primary forest locations (e.g. Senegal, Congo, US, Paraguay) to quantify the area respective biomass and tree height

• Assisting capacity building projects in Latin America to help derive national scale forest loss estimates.

• Participating in field work to examine expansion of soybean cultivated area in Brazil, Argentina, Bolivia, Paraguay, Uruguay and US.