There is a bit of jargon and unfamiliar terms on the poster. Please describe specifically some of the ecological patterns in forest structure that saved money. Is there really bad data? How do you counter my argument that data is either useful or not useful?

The question surrounding the soundness of data is applied to whether or not the data is being utilized at the proper scale. After my thesis was completed, I was able to explain to the tribe the connection between my work using FCCS and the LANDFIRE data products. We discovered the national LANDFIRE datasets were being used within the HFPAS system to determine local/regional funding allocations. This is how the thesis helped save money for the tribe. An unintended benefit of my thesis demonstrated the amount of updates necessary, in order to reflect local conditions for the tribe thus, correcting the LANDFIRE data. Based upon the amount of data that would need to be corrected, the tribe argued in Washington DC that the LANDFIRE and HFPAS systems require updates in order to adequately reflect local or regional fire serverity and until those updates were completed, their funding levels should not be determined using the HFPAS system. They were successful in getting money restored to their budget, essentially moving away from the funding allocation plan that was originally established. That was the story for the Confederated Salish and Kootenai Tribe. I am not aware of my work impacting other tribal budgets however, my work did provide the other tribes a methodology, that I shared with tribes via the Intertribal Timber Council, to update their LANDFIRE data for their reservations.

So in this case the data was not useful because it was utilized at the wrong scale.

Your video and poster required some insider knowledge that I unfortunately do not have so my question is a bit general. Broadly, how does climate change impact land use policy on a local scale; i.e., are many other “corrections” going to be required to achieve an equitable and sustainable policy?

If I follow the story correctly, the “eureka” effect of this research was to show, at a relatively large/regional scale, that a lot more fuel is lying on the ground than previously believed? This in turn had the effect of restoring fire management program funding for the Tribes. If so, were the model predicted data ground truth-ed? On a related note, will it be possible for other Tribes and/or BIA to generate defensible CLUSTER-type models/inventories if they do not have the benefit of the Continuous Forest Inventory data that CSKT had? This inventory seemed like the real keystone to the project.

The impact of this project was certainly financially significant to the local tribe, has served as a “starting point procedure” for other tribes, but I am most humbled by the message that you have sent to your national IGERT colleagues that they can impact environmental and economic decision-making and action. Your reply to Dr. Yavitt answered my other question- thanks!

Dear Laurel,

Having worked quite a bit with LANDFIRE data myself, I certainly share your scepticism about it. I wasn’t aware though how much the LANDFIRE data affected funding allocations.

The differences between the two approach of mapping fuelbeds is very striking, and generally, I tend to believe that more local data is better. But how could one quantify which of the two fuelbeds maps is ‘right’, or how accurate eiter is?

Best,
Volker

Thank you for your comments. I appreciate your knowledge of LANDFIRE.

Yes, the differences between the two approaches are very striking. Within the past year, I’ve come to understand that the BIA was also looking at other methodologies to address this issue for tribes? I don’t know if my methodology will be considered the ‘right’ one? Mainly, because whether or not refinements to data/mapping models is completed, it does not change the fact that the funding allocation plan (HFPAS) is using data at the wrong scale. It is the economic impacts of that allocation plan that has caused the greatest impacts to tribes. Unless the entire country is updated in LANDFIRE, the problem remains. The BIA is only one agency within the Department of Interior (DOI) and this funding mechanism impacted all of DOI agencies.

To have an equitable platform, the scales need to be adjusted. Either local data is rolled into LANDFIRE across the nation then, used in HFPAS? Or, HFPAS is rolled back to remedy the need for local data? That is how I see it after hearing from CSKT and other tribes that have been impacted by this funding policy.

The impact of your work at the landscape scale (on the CSKT reservation) and national scale (through the congressionally-mandated Indian Forest Management Assessment Team) is outstanding. Congratulations, Laurel!

Well done, Laurel. Kudos for matching the scale of the analysis to the scale of the question and for integrating relevant data from multiple sources. Very glad to hear your analysis so strongly positively influenced the outcome on the ground.

Without Laurel’s critical and detailed comparison of the differences in the estimated fuel loading of various forest types on the lands of the Salish-Kootenai Tribes, proposed funds allocated by the Federal Government to support fuel management on tribal lands would have fallen far short of those actually justified by her more detailed analysis. Because all fire and fuels funding allocations were to be based upon the stand type/fuel loading mapping system that emerges when the USDA Forest Service’s LANDFIRE system is applied to land, Laurel’s work was able to demonstrate how the inclusion of local data and knowledge is an important step in correcting these model outputs. Instead of the original proposed allocation of $69,000, the Tribes actually received $900,000. $831,000 goes a long ways to improving forest lands and their ability to provide goods and services to the Tribes as well as jobs for Tribal members. Great job, Laurel.

Thank you, Professor Hinckley. That was precisely the situation. My work provided the necessary detailed information to demonstrate the need for incorporation of local data, in order to correct the national model. In addition, it helped demonstrate a a pathway that other tribes (that were also facing this situation) could use to update their fuelbed definitions. That was an added benefit to my work and it was extremely rewarding!

This is a great example of scientific data supporting the needs of the Tribal Forestry department. Congratulations on keeping funding where it is necessary to support the ecosystem and forestry industry of the Confederated Salish and Kootenai Tribes of Montana.

Yes. Helping gain funding for the tribe was extremely rewarding. We all hope our research makes a difference. For my research to have such a large impact, immediately was tremendous. Very gratifying! Thank you, LaVerne Bitsie-Baldwin.

Good job Laurel. Using the best data to set policy is a priority. I’m happy to see your efforts have such a positive impact. Keep it up.

This is terrific, Laurel. Clear, strong, compelling. Keep up the good work!

Thank you, Professor Perez-Garcia. I appreciate your review of my work!

Thank you, Professor Hart. I appreciate the review and I’m encouraged by the positive feedback from everyone on this site and via Facebook!

Great Job!

Thank you, Victoria!

It’s wonderful that the results of your research were able to have tangible benefits. Congratulations!

Do you know if the “BIA and other Tribes who are updating their LANDFIRE data” are, or will be, using similar methods to you and the CSK?

Thanks for sharing the work!

Thank you, Lloyd for reviewing this work. I don’t know if other tribes or the BIA in general is using my methodology? I know of another individual that worked on a ‘fix’ to the problem but his approach was using GIS and not incorporating stand data (CFI). That individual is now a part of my IPhD committee and he works in that field, for the BIA. He has not indicated what route is being taken thus, I’m not sure if one general ‘fix’ has been selected. There are a lot of tribes to consider.

Nice work Laurel, thanks for sharing and keep up the great work.