Potapov, P., Tyukavina, A., Turubanova, S., Talero, Y., Hernandez-Serna, A., Hansen, M.C., Saah, D., Tenneson, K., Poortinga A., Aekakkararungroj, A., Chishtie, F., Towashiraporn P., Bhandari, B., Aung, K.S., Nguyen, Q.H. (2019) Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000‐2017 Landsat time-series. Remote Sensing of Environment 232, 111278
Andrés Hernández Serna
BS Biology Universidad de Antioquia, Colombia
MS Ecology and Evolution University of Puerto Rico, Puerto Rico
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.
More details at https://sites.google.com/view/andreshernandezserna
Right now I'm working on:
- 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.
- Developing new analytical approaches for extracting information and new applications for existing systems.
- Leading Deep learning initiates on agriculture and urbanization; implement different architectures of Deep learning and features using GPUs.
- 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. Congo, US) 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.