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Katherine Mistick
Recent & Ongoing Research
2025
As a Research Associate for the Utah Remote Sensing Applications Lab my research focuses on lidar remote sensing and geospatial modeling related to wildland firefighter safety. As a skilled geospatial analyst with expertise in R and Python, I excel at organizing, analyzing, and visualizing spatial data using R (terra, sf, leaflet, shiny) and python (arcpy, pandas). I am adept at collecting and processing data from diverse sources (e.g. imaging spectrometers, handheld lidar, USGS 3DEP lidar, NAIP, landfire) and at applying advanced spatial and statistical analytics techniques to uncover meaningful patterns.
Recent projects include supporting real-time spectroscopy product dissemination, modeling fire temperature from imaging spectrometer data, modeling visibility at landscape scales, developing an ArcGIS Toolbox with geoprocessing tools for improving wildland firefighter safety, and estimating biomass using airborne and handheld lidar in piñyon-juniper woodlands.
Active Fire
Currently I am working predominantly with imaging spectrometer data collected over active fires. We are working towards separating flaming combustion from smoldering combustion, and determining both the temperature and fraction of fire on a per-pixel basis. Read about some of the past work here.
I also worked on the FireSense Spring 2025 Campaign in the southeastern United States relaying real-time spectroscopy products (RTSPs) from the AVIRIS-3 imaging spectrometer to fire managers on the ground. This work involved downloading, visualizing, and disseminating data in real time to a diverse audience. Check out a summary of this work here.
Modeling Visibility
Based on machine learning techniques and airborne lidar, we introduced an R package to model visibility across diverse landscapes in the contiguous United States. This study introduced a novel method for spatially-exhaustive visibility mapping using airborne lidar and random forests that only requires a sparse sample of computationally expensive viewsheds.
The paper, published in the International Journal of Geographical Information Science, can be found here. The R package developed to support this work is available on my github page.
Firefighter Safety Toolbox
The Safe Separation Distance Evaluator (SSDE) (Campbell et al. 2022) was developed in Google Earth Engine to map safe separation distance in an online, easily accessible format. I supported the continuous development of this initiative by converting the existing tool from JavaScript to python and then into a suite of tools in an ArcGIS Toolbox that can be deployed by any ArcGIS user.
The toolbox and associated code is available on my github page.
Airborne and handheld lidar biomass mapping
This project explored the use of lidar remote sensing for mapping aboveground biomass in piñon-juniper woodlands. The study compared the performance of airborne laser scanning (ALS) and mobile laser scanning (MLS) platforms. I contributed primarily to the field work supporting this study, gaining skills with ecological surveys, ArcGIS Field Maps, Survey 123, and mobile laser scanning.
Figure 1 from Campbell, Eastburn, Mistick, Smith & Stovall (2023) depicting pinyon-juniper woodlands and their Great Basin spatial extent.
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