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October: LANDFIRE in the (NPR) news! (INFORMAL) Open Office Hours this Thursday (disturbance w/ Inga); LANDFIRE and machine learning to predict burn severity (pub)
LANDFIRE postcard
Grizzly Bear at Mussel Inlet of the Great Bear Rainforest in Canada. The 21-million-acre Great Bear Rainforest is the largest coastal temperate rainforest on Earth.
Photo Credit: © Jon McCormack/TNC

LANDFIRE & Forest Inventory and Analysis (FIA) 15 Years of Growing Research and Partnerships 

See the storymap here
United States fire regime map; text: Building a Beautiful and Clear map from Massive, Complex Data; REad the NPR story by Daniel Wood

Early this summer NPR interviewed LANDFIRE's Randy Swaty as part of a visual narrative about wildfire risk in the U.S. (published in August, 2021). This October, NPR reporter, Daniel Wood retraced his (data visualization and processing) steps and takes readers along for the ride.

READ

(These quick emails will occur whenever LANDFIRE releases new product updates, product fixes, etc.)
One example ⬇️
Data Alert: LF 2016 Remap Annual Disturbance data was updated with new attribute tables for 2015 (Dist15) and 2016 (Dist16).
Updated product(s) or information: LF 2016 Remap (LF 2.0.0) Annual Disturbance 2015 and 2016 product attribute tables (CSV and DBF) were updated on 09/03/2021. The impact is limited to rows within the CSV or DBF relevant to LF 2016 Remap Alaska (AK).
Problem or issue description: LF 2016 Remap Annual Disturbance 2015 and 2016 products were updated to incorporate corrected attribute tables. READ MORE
Let's talk Media
(INFORMAL) Open Office Hours - this Thursday

Text: Open Office Hours Image: United States, LANDFIRE logo
Grab that third cup of coffee and join LANDFIRE for our (INFORMAL) Open Office Hours chat. 
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October 28, 1 pm (ET) | Exploring LANDFIRE Disturbance Data
REGISTER* | Inga La Puma, KBR, Contractors to the U.S. Geological Survey (USGS)

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*this link will not change each month*
December 2: 1 pm (ET) (Note: Special date): Topic TBD
This series is managed by TNC's LANDFIRE Team and will include participation from a variety of federal and non-federal partners and guests
good reading; image: book
Hamilton DA, Brothers KL, Jones SD, Colwell J, Winters J. Wildland Fire Tree Mortality Mapping from Hyperspatial Imagery Using Machine Learning. Remote Sensing. 2021; 13(2):290. https://doi.org/10.3390/rs13020290
 
(Figure caption below: (a) Hyperspatial data after a burn. (b) Types of fire: Red = Active, Yellow = Passive, Black = inconclusive crown fire activity)two side by side images. left image: aerial view of burned forestland; right: pixelated raster data showing fire activity
The authors use machine learning and the use of unmanned aircraft systems (UAS) to make predictions about burn severity as a measure of canopy reduction and wildland fire impacts. By comparing pre-fire LANDFIRE Canopy Cover product with post-fire hyperspatial derived canopy cover, users can effectively measure canopy fire reduction as a measure of tree mortality from wildland fire. READ
LANDFIRE program management
Henry Bastian
DOI Business Lead
Frank Fay
USFS Business Lead
Tim Hatten
USGS Project Manager
Jim Smith
TNC-LF Project Lead
Inga La Puma
LF Technical Lead

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