Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA)
Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were in...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/13/8/1200 |
| _version_ | 1797410209028636672 |
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| author | Adrián Jiménez-Ruano William M. Jolly Patrick H. Freeborn Daniel José Vega-Nieva Norma Angélica Monjarás-Vega Carlos Iván Briones-Herrera Marcos Rodrigues |
| author_facet | Adrián Jiménez-Ruano William M. Jolly Patrick H. Freeborn Daniel José Vega-Nieva Norma Angélica Monjarás-Vega Carlos Iván Briones-Herrera Marcos Rodrigues |
| author_sort | Adrián Jiménez-Ruano |
| collection | DOAJ |
| description | Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were intersected with static, 30 m resolution spatial data that captured topography, fuel availability, and human transport infrastructure. Once combined, the data were used to train several simple and multiple logistic generalized linear models (GLMs) and generalized additive models (GAMs) to predict the spatial likelihood of natural and human-caused ignitions. Increasingly more complex models that included spatial smoothing terms were better at distinguishing locations with and without natural and human-caused ignitions, achieving area under the receiver operating characteristic curves (AUCs) of 0.84 and 0.89, respectively. Whilst both ignition types were more likely to occur at intermediate fuel loads, as characterized by the local maximum Normalized Difference Vegetation Index (NDVI), naturally-ignited wildfires were more locally influenced by slope, while human-caused wildfires were more locally influenced by distance to roads. Static maps of ignition likelihood were verified by demonstrating that mean annual ignition densities (# yr<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> km<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>) were higher within areas of higher predicted probabilities. Although the spatial models developed herein only address the static component of wildfire hazard, they provide a foundation upon which dynamic data can be superimposed to forecast and map wildfire ignition probabilities statewide on a timely basis. |
| first_indexed | 2024-03-09T04:26:34Z |
| format | Article |
| id | doaj.art-2ebf06a000f641369eacac0d2d9730e3 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T04:26:34Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-2ebf06a000f641369eacac0d2d9730e32023-12-03T13:39:45ZengMDPI AGForests1999-49072022-07-01138120010.3390/f13081200Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA)Adrián Jiménez-Ruano0William M. Jolly1Patrick H. Freeborn2Daniel José Vega-Nieva3Norma Angélica Monjarás-Vega4Carlos Iván Briones-Herrera5Marcos Rodrigues6Missoula Fire Sciences Laboratory, Rocky Mountain Research Station, USDA Forest Service, 5775 Hwy 10 W, Missoula, MT 59808, USAMissoula Fire Sciences Laboratory, Rocky Mountain Research Station, USDA Forest Service, 5775 Hwy 10 W, Missoula, MT 59808, USAMissoula Fire Sciences Laboratory, Rocky Mountain Research Station, USDA Forest Service, 5775 Hwy 10 W, Missoula, MT 59808, USAFacultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Durango 34000, MexicoFacultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Durango 34000, MexicoFacultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Durango 34000, MexicoDepartment of Agriculture and Forest Engineering, University of Lleida, Alcalde Rovira Roure 191, 25198 Lleida, SpainSpatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were intersected with static, 30 m resolution spatial data that captured topography, fuel availability, and human transport infrastructure. Once combined, the data were used to train several simple and multiple logistic generalized linear models (GLMs) and generalized additive models (GAMs) to predict the spatial likelihood of natural and human-caused ignitions. Increasingly more complex models that included spatial smoothing terms were better at distinguishing locations with and without natural and human-caused ignitions, achieving area under the receiver operating characteristic curves (AUCs) of 0.84 and 0.89, respectively. Whilst both ignition types were more likely to occur at intermediate fuel loads, as characterized by the local maximum Normalized Difference Vegetation Index (NDVI), naturally-ignited wildfires were more locally influenced by slope, while human-caused wildfires were more locally influenced by distance to roads. Static maps of ignition likelihood were verified by demonstrating that mean annual ignition densities (# yr<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula> km<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>) were higher within areas of higher predicted probabilities. Although the spatial models developed herein only address the static component of wildfire hazard, they provide a foundation upon which dynamic data can be superimposed to forecast and map wildfire ignition probabilities statewide on a timely basis.https://www.mdpi.com/1999-4907/13/8/1200wildfire occurrenceignition locationGLMGAMslopeNDVI |
| spellingShingle | Adrián Jiménez-Ruano William M. Jolly Patrick H. Freeborn Daniel José Vega-Nieva Norma Angélica Monjarás-Vega Carlos Iván Briones-Herrera Marcos Rodrigues Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA) Forests wildfire occurrence ignition location GLM GAM slope NDVI |
| title | Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA) |
| title_full | Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA) |
| title_fullStr | Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA) |
| title_full_unstemmed | Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA) |
| title_short | Spatial Predictions of Human and Natural-Caused Wildfire Likelihood across Montana (USA) |
| title_sort | spatial predictions of human and natural caused wildfire likelihood across montana usa |
| topic | wildfire occurrence ignition location GLM GAM slope NDVI |
| url | https://www.mdpi.com/1999-4907/13/8/1200 |
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