Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico
| Autor: | Ernesto Alvarado-Celestino, María Isabel Cruz-López, H. K. Preisler, Ana Daría Ruiz-González, Pablito M. López-Serrano, José Javier Corral-Rivas, Erik Calleros-Flores, M. Cuahutle, Juan Gabriel Álvarez-González, R. E. Burgan, Jaime Briseño-Reyes, Eusebio Montiel-Antuna, Armando González-Cabán, Daniel José Vega-Nieva, Enrique Jiménez, María G. Nava-Miranda, Reiner Ressl |
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| Přispěvatelé: | Universidade de Santiago de Compostela. Departamento de Enxeñaría Agroforestal |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
fire occurrence risk
010504 meteorology & atmospheric sciences Climate change Remote sensors fire danger systems 01 natural sciences Hotspot (geology) Vegetation type Vegetation stress medicine MODIS fire hotspots Fire occurrence risk Fire danger systems 0105 earth and related environmental sciences 040101 forestry Forestry 04 agricultural and veterinary sciences lcsh:QK900-989 Quantile regression Spectroradiometer Fire hotspots Climatology lcsh:Plant ecology 0401 agriculture forestry and fisheries Environmental science Dryness medicine.symptom |
| Zdroj: | Forests, Vol 9, Iss 4, p 190 (2018) Forests; Volume 9; Issue 4; Pages: 190 Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela instname |
| Popis: | Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change. This study addresses the development of models to predict the number of 10-day observed Moderate-Resolution Imaging Spectroradiometer (MODIS) active fire hotspots—expressed as a Fire Hotspot Density index (FHD)—from an Accumulated Fuel Dryness Index (AcFDI), for 17 main vegetation types and regions in Mexico, for the period 2011–2015. The AcFDI was calculated by applying vegetation-specific thresholds for fire occurrence to a satellite-based fuel dryness index (FDI), which was developed after the structure of the Fire Potential Index (FPI). Linear and non-linear models were tested for the prediction of FHD from FDI and AcFDI. Non-linear quantile regression models gave the best results for predicting FHD using AcFDI, together with auto-regression from previously observed hotspot density values. The predictions of 10-day observed FHD values were reasonably good with R2 values of 0.5 to 0.7 suggesting the potential to be used as an operational tool for predicting the expected number of fire hotspots by vegetation type and region in Mexico. The presented modeling strategy could be replicated for any fire danger index in any region, based on information from MODIS or other remote sensors. Funding for this work was provided by CONAFOR/CONACYT Project C0-3-2014 “Development of a Forest Fire Danger Prediction System for Mexico”. SI |
| Databáze: | OpenAIRE |
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