VPS Script
Idaho Disasters III
BLM_ISU

>>> I. (Opening)

music throughout - And then the Mountains Moved by James Joshua Otto licensed under creative commons.

>>> II. (Synopsis) 

Across the Western United States contemporary wildfire trends have increased in size and frequency. This is partly due to the proliferation of the non-native annual grass species, Bromus tectorum more commonly known as cheatgrass. The abundance of cheatgrass has created a positive feedback cycle of wildfire disturbance and subsequent cheatgrass invasion.  Cheatgrass changes the fire regime in a landscape because it dries up before native plants have a chance to take hold. and is a fine fuel. 

>>> (Society Concerns) 

It is becoming more difficult for our end users at Bureau of Land Management and the Idaho Department of Lands To mitigate fire risk as the WUI grows communities spread further into the wildlands. The potential for fire risk increases as people build homes in less urbanized areas. As we saw with the Charllot fire in 2012, these fires can spread rapidly and although the c fire was just over 1000 acers the cost of suppression and lost property was huge it will go down in Idaho as one of the more catastrophic fires. 

>>> III. (Community Concerns) 

Wildfire is one of the primary drivers of ecosystem change in semi-arid savanna ecosystems. The Landscape that you see behind me has undergone nine such wildfire events since the 1930s and has transformed this landscape from a shrub dominated one into an annual grassland.

>>> IV. (Community Concerns cont.) 

The United States spends more than 3 billion dollars a year in direct wildfire suppression costs. Cheatgrass has altered many landscapes due to its ability to grow quickly in the late fall and early spring, reproduce and rapidly senesce. The phenology of this invasive species has disrupted many plant communities and has become the driving force of perpetual occurrence of wildfire in of the Intermountain West.

>>> V. (Objectives)  

The main objectives for Idaho Disasters III team Jeff May, Zachary Simpson, Jenna Williams, are to produce a vegetation distribution model which includes locations with a higher presence of cheatgrass and subsequently create a fire susceptibility model that will aid end-users in identifying areas more  vulnerable to wildfire

(Shelli interview.) 

>>> VI. (Study Area) 

The study area consisted of the Southeastern portion of the Snake River Plain, specifically, Landsat WRS-2 Path 39 R ow 30.

>>> VII. (Data/Methods) 

Level 1T Landsat 8 Operational Land imager (OLI) Imagery was acquired from the USGS Earth Explorer web application for 2013, 2014, and 2015 from late March through September, comprising the complete growing season for this area.   Pheno- was used to analyze Historical meteorological data for the study period to identify images that were phonologically similar for each year  and conducive to active cheatgrass growth while the growth cycle of native vegetation had yet to begin. 

>>> VIII. (CTA/Methods) 

Classification Tree Analysis was used to distinguish different types of vegetation. mSAVI2 , Tasseled-Cap-Transformation and the Normalized Differenced Bare Soil Index (NDBSI) was used to discriminate between the various vegetation classes. 397 classification sites were gathered for training and validation, of which 40% were used for independent validation

>>> IX. (CTA/results)

The fuel model produced illuminated sagebrush/herbaceous, cheatgrass, bare soil and montane forest classes. 
75% overall accuracy was achieved from independent validation with a kappa coefficient of 0.67. 

>>>X. (Fire Susceptibility methods/results)

The classified fuel model was combined with topographic variables to produce two fire susceptibility maps that identify which areas are most combustible.  One shows fire susceptibility on a per pixel basis while the other indicates fire susceptibility of level 6 hydrological unit code boundaries. 

>>> XI. (Conclusions)  
This study demonstrates the ability of Landsat 8 Earth observations to identify specific species contributing to increased wildfire occurrence and how these results can be subsequently used to produce fire susceptibility maps that lends decision support to fire managers.  

>>> XII Credits, acknowledgments

>>> XIII DEVELOP Closing