Rob Tapella

Circumpolar boreal aboveground biomass density (AGBD) and vegetation height (v2) is being transferred to the Oak Ridge National Laboratory DAAC.

The map is built on MAAP with machine-learning models combining the 2020 archive of ICESat-2 height estimates with predictors from topography and Harmonized Landsat/Sentinel-2 surface reflectance.

The v2 dataset is available on the MAAP STAC. It features:

• better representation of 0 heights (v6 ATL08); 
• both Landsat and Sentinel-2 HLS inputs; 
• neighborhood tile model enhancements; 
• updated tile-level training data sampling; 
• fixed topographic coviates data calculations;
• post-processing masking bug-fix
• no moss/lichen mask
• refactored code base to improve run-time efficiency

This release completes the boreal component of a global carbon stock assessment derived from NASA space borne data.

Authors:
Paul Montesano | NASA Goddard Space Flight Center | ADNET Systems, Inc
Laura Duncanson | Dept. Geographical Science, U. Maryland College Park

The Fire Events Data Suite (FEDS) uses remotely-sensed fire hotspots to generate near-real-time estimates of fire behavior. FEDS provides fire perimeters, estimates of the active fire line, and mean radiative power for the fire. FEDS data helps scientists understand how fires evolve and spread through time. FEDS tracks fire in the continental United States and Canada. FEDS has been used to support NASA Disaster’s response to the Palisades fire, and featured in stories on the Park fire in CA, and the 2023 Canadian wildfire season. 

You can see FEDS fire perimeters on NASA’s Fire Information Resource Management System, or access the data through an API.

FEDS was developed by collaborators at University of California, Irvine, and the citation describing the algorithm is: 

Chen, Y., Hantson, S., Andela, N., Coffield, S. R., Graff, C. A., Morton, D. C., Ott, L. E., Foufoula-Georgiou, E., Smyth, P., Goulden, M. L., & Randerson, J. T. (2022). California wildfire spread derived using VIIRS satellite observations and an object-based tracking system. Scientific Data, 9(1), Article 1. 

https://doi.org/10.1038/s41597-022-01343-0

Space-based laser altimetry has revolutionized our capacity to characterize terrestrial ecosystems through the direct observation of vegetation structure and the terrain beneath it. Data from NASA’s ICESat-2 mission provide the first comprehensive look at canopy structure for boreal forests from space-based lidar. The objective of this research was to create ICESat-2 aboveground biomass density (AGBD) models for the global entirety of boreal forests at a 30 m spatial resolution and apply those models to ICESat-2 data from the 2019–2021 period. Although limited in dense canopy, ICESat-2 is the only space-based laser altimeter capable of mapping vegetation in northern latitudes. Along each ICESat-2 orbit track, ground and vegetation height is captured with additional modeling required to characterize biomass. By implementing a similar methodology of estimating AGBD as GEDI, ICESat-2 AGBD estimates can complement GEDI’s estimates for a full global accounting of aboveground carbon. Using a suite of field measurements with contemporaneous airborne lidar data over boreal forests, ICESat-2 photons were simulated over many field sites and the impact of two methods of computing relative height (RH) metrics on AGBD at a 30 m along-track spatial resolution were tested; with and without ground photons. AGBD models were developed specifically for ICESat-2 segments having land cover as either Evergreen Needleleaf or Deciduous Broadleaf Trees, whereas a generalized boreal-wide AGBD model was developed for ICESat-2 segments whose land cover was neither. Applying our AGBD models to a set of over 19 million ICESat-2 observations yielded a 30 m along-track AGBD product for the pan-boreal. The ability demonstrated herein to calculate ICESat-2 biomass estimates at a 30 m spatial resolution provides the scientific underpinning for a full, spatially explicit, global accounting of aboveground biomass.

Reference: https://www.sciencedirect.com/science/article/pii/S2666017224000348