GEDI L3 and L4 Tutorials

An algorithm that predicts yearly Aboveground Biomass for Finnish forests using satellite imagery. [NeurIPS 2023 Datasets & Benchmarks Track]

This project uses satellite images on Google Earth Engine to predict canopy height and estimate carbon content in the University of Malaya forest area.

Modeling SAR backscatter and forest AGB relationships.

Python package for analyzing NASA GEDI data. GEDI is a LiDAR dataset that is acquired using the International Space Station.

IBM Environmental Intelligence

Data central to the analysis reported by Walker et al. (2020) on the carbon dynamics of Amazon protected lands.

Remote sensing of aboveground carbon in thicket using multi-spectral images

Code for analyzing the effect of recent land use on forest structure and aboveground biomass (AGB) in Tropical Montane Cloud Forest

Aboveground Biomass Density Estimation Using Deep Learning: Insight from NEON Ground-Truth Data and Simulated GEDI Waveform