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June 2020: MoD-FIS Release; Remap Webinar (TOMORROW)
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double rainbow at Crater Lake National Park
On July 20, 2014, a lightning storm rolled through Crater Lake National Park in Southern Oregon resulting in multiple fires and a terribly smokey Oregon summer. However, on the day of the storm, an incredible double rainbow appeared during the last light of the evening. A truly mystical and breathtaking scene resulted in disaster and chaos. © Jasman Mander /TNC Photo Contest 2019.
Spring MoD-FIS has been released! 
(What is MoD-FIS?)
The Spring MoD-FIS (Modeling Dynamic Fuels with an Index System) has been released! This release covers the Northern Great Basin and Southwestern region of the United States. In addition, and in response to user feedback, it also includes a change in extent to include areas of California coastal zones and some additional areas in the eastern zones. MoD-FIS captures the seasonal nature of fuels in these regions and incorporates seasonal variability of herbaceous cover (i.e. cheatgrass), to capture changes to fire behavior fuel models based on the current fire season herbaceous production. Using current and historic Normalized Difference Vegetation Index data from the Landsat archive and LF’s new base map suite of products (LF 2016 Remap), LF maps current year herbaceous cover compared to historic averages. The result is updated Existing Vegetation Cover, Existing Vegetation Height, and Fire Behavior Fuel Model layers that reflect the current fire season herbaceous cover and resultant fuel availability.
What is an Exposure Analysis?
Interagency Fuel Treatment Decision Support System (IFTDSS) Exposure Analysis (EA) is the identification where there is a higher probability that wildfires could threaten people, structures, or other high-value assets. This exposure information is critical to direct and prioritize actions to meet land management goals and to mitigate the effects of fire on communities and wildland firefighters. The IFTDSS EA is based on an assessment of wildfire hazard—likelihood and intensity—that quantifies the Landscape Burn Probability (LBP) model outputs where they overlap with Highly Valued Resources or Assets (HVRAs). The resulting outputs aid in HVRA evaluation by enabling the comparison of burn probability, conditional flame length, and hazard across an analysis area to help improve understanding of landscape conditions and identify areas where possible actions could be implemented.  

An EA report "...summarizes the burnable pixels within the HVRA Set (using LANDFIRE data), encompassed by the non-buffered landscape, or an area interest (AOI) within that non-buffered landscape, if an AOI is specified when requesting the report." Find out more about EA Reports within the IFTDSS help center here.
Webinars
TOMORROW: LANDFIRE Remap in the Southeastern U.S.
June 17, 2020, 1 pm ET | Register
Speaker: Jim Smith, TNC LANDFIRE Program Lead

LANDFIRE Remap is a multi-year effort to improve the product suite by recreating base LANDFIRE products using new plot information, new imagery and production techniques. Presenter Jim Smith, TNC LANDFIRE Program Lead, will review LANDFIRE product offerings, describe what has changed from previous versions, what has not changed, and provide information on future program plans, all from the perspective of a user in the Southeast U.S. Following the presentation there will be time for audience Q/A with the speaker.


New to our Webinar Library 
Recorded May 21, 2020
Remap in the North Central U.S
Recorded May 27, 2020
Data, Science, and Methods behind the Wildfire Risk to Communities Website
Recorded May 28, 2020
Good Reading...LF at work
New Publication: Locating Forest Management Units Using Remote Sensing and Geostatistical Tools in North-Central Washington, USA; Palaiologos Palaiologou, Maureen Essen, John Hogland, and Kostas Kalabokidis ; Sensors 2020, 20(9), 2454; https://doi.org/10.3390/s20092454; April 2020 

This study shares an approach to locate and map forest management units with high accuracy and relatively rapid turnaround. LF Existing Vegetation Type and Disturbance were used to quantify forest management practices for four counties in North-Central Washington (Kittitas, Okanogan, Chelan, and Douglas). These and other variables were used as predictors in Random Forest machine learning classification models. Known locations of forest treatment units were used to create samples to train the Random Forest models to estimate where changes in forest structure occurred. Visually inspected derived polygons aided in manually assigning one treatment class (clearcut or thinning) and prescribed fire units were derived. After the bulk of the analyses were completed, the results were combined with existing LF vegetation disturbance and forest treatment data to create a 21-year dataset (1999–2019) for the study area.
LANDFIRE Business Leads
Henry Bastian
DOI Business Lead
Frank Fay
USFS Business Lead
Tim Hatten
USGS Project Manager
Jim Smith
TNC-LF Project Lead
Birgit Peterson
USGS-LF Technical Lead

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The LANDFIRE Program is a cooperative agreement between the USDA Forest Service, agencies of the Department of the Interior, and The Nature Conservancy. In accordance with Federal law and U.S. Department of Agriculture policy, the Program is prohibited from discriminating on the basis of race, color, national origin, sex, age, or disability.