Visibility-informed mapping of potential firefighter lookout locations using maximum entropy modelling
Katherine A. Mistick
A * , Michael J. Campbell A and Philip E. Dennison A
A
Abstract
Situational awareness is an essential component of wildland firefighter safety. In the US, crew lookouts provide situational awareness by proxy from ground-level locations with visibility of both fire and crew members.
To use machine learning to predict potential lookout locations based on incident data, mapped visibility, topography, vegetation, and roads.
Lidar-derived topographic and fuel structural variables were used to generate maps of visibility across 30 study areas that possessed lookout location data. Visibility at multiple viewing distances, distance to roads, topographic position index, canopy height, and canopy cover served as predictors in presence-only maximum entropy modelling to predict lookout suitability based on 66 known lookout locations from recent fires.
The model yielded a receiver-operating characteristic area under the curve of 0.929 with 67% of lookouts correctly identified by the model using a 0.5 probability threshold. Spatially explicit model prediction resulted in a map of the probability a location would be suitable for a lookout; when combined with a map of dominant view direction these tools could provide meaningful support to fire crews.
This approach could be applied to produce maps summarising potential lookout suitability and dominant view direction across wildland environments for use in pre-fire planning.
Keywords: firefighter safety, lidar, lookout, machine learning, maxent, situational awareness, spatial modelling, visibility.
References
Arnold JD, Brewer SC, Dennison PE (2014) Modeling climate-fire connections within the Great Basin and Upper Colorado River Basin, western United States. Fire Ecology 10, 64-75.
| Crossref | Google Scholar |
Baek H, Lim J (2018) Design of future UAV-relay tactical data link for reliable UAV control and situational awareness. IEEE Communications Magazine 56, 144-150.
| Crossref | Google Scholar |
Bailon-Ruiz R, Bit-Monnot A, Lacroix S (2022) Real-time wildfire monitoring with a fleet of UAVs. Robotics and Autonomous Systems 152, 104071.
| Crossref | Google Scholar |
Buettner WC, Beeton TA, Schultz CA, Caggiano MD, Greiner MS (2023) Using PODs to integrate fire and fuels planning. International Journal of Wildland Fire 32, 1704-1710.
| Crossref | Google Scholar |
Campbell MJ, Dennison PE, Butler BW (2017a) A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping. International Journal of Wildland Fire 26, 884-895.
| Crossref | Google Scholar |
Campbell MJ, Dennison PE, Butler BW (2017b) Safe separation distance score: a new metric for evaluating wildland firefighter safety zones using lidar. International Journal of Geographical Information Science 31, 1448-1466.
| Crossref | Google Scholar |
Campbell MJ, Page WG, Dennison PE, Butler BW (2019) Escape route index: a spatially-explicit measure of wildland firefighter egress capacity. Fire 2, 40.
| Crossref | Google Scholar |
Campbell MJ, Dennison PE, Thompson MP, Butler BW (2022) Assessing potential safety zone suitability using a new online mapping tool. Fire 5, 5.
| Crossref | Google Scholar |
Chao F, Chongjun Y, Zhuo C, Xiaojing Y, Hantao G (2011) Parallel algorithm for viewshed analysis on a modern GPU. International Journal of Digital Earth 4, 471-486.
| Crossref | Google Scholar |
Chen F, Du Y, Niu S, Zhao J (2015) Modeling Forest Lightning Fire Occurrence in the Daxinganling Mountains of Northeastern China with MAXENT. Forests 6, 1422-1438.
| Crossref | Google Scholar |
Cosgun U, Coşkun M, Toprak F, Yıldız D, Coşkun S, Taşoğlu E, Öztürk A (2023) Visibility evaluation and suitability analysis of fire lookout towers in Mediterranean Region, southwest Anatolia/Türkiye. Fire 6, 305.
| Crossref | Google Scholar |
De Angelis A, Ricotta C, Conedera M, Pezzatti GB (2015) Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions. PLoS One 10, e0116875.
| Crossref | Google Scholar | PubMed |
Dennison PE, Fryer GK, Cova TJ (2014) Identification of firefighter safety zones using lidar. Environmental Modelling & Software 59, 91-97.
| Crossref | Google Scholar |
Fillmore SD, Paveglio TB (2023) Use of the Wildland Fire Decision Support System (WFDSS) for full suppression and managed fires within the Southwestern Region of the US Forest Service. International Journal of Wildland Fire 32, 622-635.
| Crossref | Google Scholar |
Gleason P (1991) Lookouts, Communication, Escape Routes and Safety Zones, ‘LCES’. Available at https://www.nwcg.gov/sites/default/files/wfldp/docs/lces-gleason.pdf
Hijmans R, Phillips S, Leathwick J, Elith J (2023) dismo: Species Distribution Modeling. Available at https://CRAN.R-project.org/package=dismo
InciWeb (2022) Hermits Peak Fire. Available at https://inciweb.nwcg.gov/incident-information/nmsnf-hermits-peak-fire
Inglis NC, Vukomanovic J, Costanza J, Singh KK (2022) From viewsheds to viewscapes: trends in landscape visibility and visual quality research. Landscape and Urban Planning 224, 104424.
| Crossref | Google Scholar |
Kucuk O, Topaloglu O, Altunel AO, Cetin M (2017) Visibility analysis of fire lookout towers in the Boyabat State Forest Enterprise in Turkey. Environmental Monitoring and Assessment 189, 329.
| Crossref | Google Scholar | PubMed |
Llobera M (2003) Extending GIS-based visual analysis: the concept of visualscapes. International Journal of Geographical Information Science 17, 25-48.
| Crossref | Google Scholar |
Martín Y, Zúñiga-Antón M, Rodrigues Mimbrero M (2019) Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain. Geomatics, Natural Hazards and Risk 10, 385-411.
| Crossref | Google Scholar |
Mistick K, Campbell M (2024) VisiMod: Map Modeled Visibility Index Across Wildland Landscapes. Available at https://github.com/kamistick/VisiMod
Mistick KA, Dennison PE, Campbell MJ, Thompson MP (2022) Using geographic information to analyze wildland firefighter situational awareness: impacts of spatial resolution on visibility assessment. Fire 5, 151.
| Crossref | Google Scholar |
Mistick KA, Campbell MJ, Thompson MP, Dennison PE (2023) Using airborne lidar and machine learning to predict visibility across diverse vegetation and terrain conditions. International Journal of Geographical Information Science 37, 1728-1764.
| Crossref | Google Scholar |
Moreno R, Zamora R, Molina JR, Vasquez A, Herrera MÁ (2011) Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Ecological Informatics 6, 364-370.
| Crossref | Google Scholar |
National Agriculture Imagery Program (NAIP) (2016) USGS EROS Archive - Aerial Photography - National Agriculture Imagery Program (NAIP). 10.5066/F7QN651G
National Wildfire Coordinating Group (2006) Geographic Information System Standard Operating Procedures on Incidents. Available at http://npshistory.com/publications/fire/gstop-2006.pdf
National Wildfire Coordinating Group (2022) NWCG Incident Response Pocket Guide (IRPG), PMS 461. Available at https://fs-prod-nwcg.s3.us-gov-west-1.amazonaws.com/s3fs-public/publication/pms461.pdf?VersionId=lXUgkLMK9mRTMyssaamowdM3y6u7CKpl
National Wildfire Coordinating Group (2020) S-131 Unit 3: Lookouts, Communications, Escape Routes, and Safety Zones (LCES). In ‘NWCG Instructor Guide’. pp. 1–25. Available at https://training.nwcg.gov/dl/s131/s-131-ig03.pdf
Nazeri M, Jusoff K, Madani N, Mahmud AR, Bahman AR, Kumar L (2012) Predictive modeling and mapping of Malayan Sun Bear (Helarctos malayanus) distribution using maximum entropy. PLoS One 7, e48104.
| Crossref | Google Scholar | PubMed |
Open Street Map Contributors (2023) Open Street Map. Available at https://www.openstreetmap.org
O’Connor C, Thompson M, Rodríguez Y Silva F (2016) Getting ahead of the wildfire problem: quantifying and mapping management challenges and opportunities. Geosciences 6, 35.
| Crossref | Google Scholar |
O’Connor CD, Calkin DE, Thompson MP (2017) An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management. International Journal of Wildland Fire 26, 587-597.
| Crossref | Google Scholar |
Phillips SJ (2017) ‘A Brief Tutorial on Maxent.’ (AT&T Research) Available at http://biodiversityinformatics.amnh.org/open_source/maxent/
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231-259.
| Crossref | Google Scholar |
R Core Team (2023) R: A Language and Environment for Statistical Computing. Available at https://www.R-project.org/
Rodríguez y Silva F, O’Connor CD, Thompson MP, Molina Martínez JR, Calkin DE (2020) Modelling suppression difficulty: current and future applications. International Journal of Wildland Fire 29, 739-751.
| Crossref | Google Scholar |
Roussel J-R, Auty D (2024) Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. Available at https://cran.r-project.org/package=lidR
Roussel J-R, Auty D, Coops NC, Tompalski P, Goodbody TRH, Meador AS, Bourdon J-F, De Boissieu F, Achim A (2020) lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sensing of Environment 251, 112061.
| Crossref | Google Scholar |
Sahraoui Y, Vuidel G, Joly D, Foltête J-C (2018) Integrated GIS software for computing landscape visibility metrics. Transactions in GIS 22, 1310-1323.
| Crossref | Google Scholar |
Sanchez-Fernandez AJ, Romero LF, Bandera G, Tabik S (2021) A data relocation approach for terrain surface analysis on multi-GPU systems: a case study on the total viewshed problem. International Journal of Geographical Information Science 35, 1500-1520.
| Crossref | Google Scholar |
Seraj E, Silva A, Gombolay M (2022) Multi-UAV planning for cooperative wildfire coverage and tracking with quality-of-service guarantees. Autonomous Agents and Multi-Agent Systems 36, 39.
| Crossref | Google Scholar |
Stojanovic N, Stojanovic D (2013) Performance improvement of viewshed analysis using GPU. In ‘2013 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS)’, Nis, Serbia. pp. 397–400. (IEEE: Nis, Serbia) 10.1109/TELSKS.2013.6704407
Sullivan PR, Campbell MJ, Dennison PE, Brewer SC, Butler BW (2020) Modeling wildland firefighter travel rates by terrain slope: results from GPS-tracking of Type 1 crew movement. Fire 3, 52.
| Crossref | Google Scholar |
Tabik S, Zapata EL, Romero LF (2013) Simultaneous computation of total viewshed on large high resolution grids. International Journal of Geographical Information Science 27, 804-814.
| Crossref | Google Scholar |
Thompson MP, Gannon BM, Caggiano MD (2021) Forest roads and operational wildfire response planning. Forests 12, 110.
| Crossref | Google Scholar |
Thompson MP, O’Connor CD, Gannon BM, Caggiano MD, Dunn CJ, Schultz CA, Calkin DE, Pietruszka B, Greiner SM, Stratton R, Morisette JT (2022) Potential operational delineations: new horizons for proactive, risk-informed strategic land and fire management. Fire Ecology 18, 17.
| Crossref | Google Scholar |
U.S. Geological Survey National Geospatial Technical Operations Center (2023) USGS National Transportation Dataset (NTD) Downloadable Data Collection: U.S. Geological Survey. https://www.usgs.gov/the-national-map-data-delivery
Valavi R, Guillera‐Arroita G, Lahoz‐Monfort JJ, Elith J (2022) Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code. Ecological Monographs 92, e01486.
| Crossref | Google Scholar |
Wiley EO, McNyset KM, Peterson AT, Robins CR, Stewart AM (2003) Niche modeling and geographic range predictions in the marine environment using a machine-learning algorithm. Oceanography 16(3), 120-127.
| Crossref | Google Scholar |
Yan H, Feng L, Zhao Y, Feng L, Zhu C, Qu Y, Wang H (2020) Predicting the potential distribution of an invasive species, Erigeron canadensis L., in China with a maximum entropy model. Global Ecology and Conservation 21, e00822.
| Crossref | Google Scholar |
