A forest fire risk assessment using NOAA AVHRR images in the Valencia area, eastern Spain

González-Alonso, F.; Cuevas, J. M.; Casanova, J. L.; Calle, A.; Illera, P.

doi:10.1080/014311697217837

Cited by 33 publications

(7 citation statements)

References 4 publications

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“…Fire occurrence is influenced by biophysical as well as human factors. How the frequency and distribution of fires, as well as their ignition agents, vary with climatic and meteorological conditions (amount and seasonal distribution of precipitation or relative humidity), vegetation type and water status or land use has been documented in the literature (Andreae, 1992; Cahoon et al ., 1992; Larsen, 1996; Gonzalez‐Alonso et al ., 1997; Kitzberger & Veblen, 1997; Dwyer et al ., 2000; França & Setzer, 2001). Regarding human influences, palaeo‐records demonstrate that for more than 1 million years people have been altering the natural patterns of fire occurrence intensity and frequency (Levine, 1991; Andreae, 1992; Kitzberger & Veblen, 1997).…”

Section: Introductionmentioning

confidence: 99%

Continental fire density patterns in South America

Bella

Jobbágy

Paruelo

et al. 2006

Global Ecology and Biogeography

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Aims Quantification of the effects and interactions of natural and anthropogenic factors, including climate, canopy structure, land use and management conditions, on vegetation burning. The study of these relationships is fundamental to predict regional fire patterns and develop sound management and regulation policies for biomass burning at national and global levels.Location Southern South America, including Argentina, Brazil, Paraguay, Uruguay, Bolivia and Chile.Methods Based on National Oceanic and Atmosphere Administration-Advance Very High Resolution Radiometer (NOAA-AVHRR) satellite images, we identified fires in southern South America with a daily frequency for two periods (1999/2000 and 2000/01) using a contextual fire detection algorithm and integrating the density of these fires at a monthly scale into a 0.5 × 0.5 ° grid. We combined vegetation and climate global databases and land use information from national census data to explore the relationship of these factors with fires across the region. ResultsThe whole study region had a mean fire density of 0.10 and 0.05 fires km − 2 year − 1 in 1999/2000 and 2000/01, respectively, with extreme values as high as 1.37 in fires km − 2 year − 1 in Para State, Brazil. Water deficit estimates, derived from a climatic water balance, showed the better correlation with fire density ( r = 0.28; P < 0.001; n = 4467), interacting strongly with land use. In areas with low agricultural use fire density increased with water deficit, whereas in highly agricultural areas this relationship was not observed. Agriculture significantly reduced fire density in prairies and savannas but increased its frequency in rain forests. Main conclusionsThese results suggest that agriculture prevents biomass burning in semiarid areas but enhances it in humid environments, where biomass accumulates at faster rates.

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Section: Introductionmentioning

confidence: 99%

Continental fire density patterns in South America

Bella

Jobbágy

Paruelo

et al. 2006

Global Ecology and Biogeography

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“…Attempts to assess wildfire risk in other parts of the world have typically been implemented as Geographic Information System-based overlays (Burgan and Hartford 1988, Chuvieco and Congalton 1989, Julio 1990, Chou et al 1993, Castro 1994, Salas and Chuvieco 1994, Carapella 1996, Gonzalez-Alonso et al 1997). However, we chose not to duplicate these methods for the Upper Midwest, as such studies usually are performed at only a single scale using subjective assessment of the influence of local fire factors.…”

Section: Introductionmentioning

confidence: 99%

Environmental and Social Factors Influencing Wildfires in the Upper Midwest, United States

Cardille

Ventura

Turner

2001

Ecological Applications

259

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Although the vast majority of contemporary wildfires in the Upper Midwest of the United States have a human origin, there has been no comprehensive analysis of the roles played by abiotic, biotic, and human factors in determining the spatial patterns of their origins across the region. The Upper Midwest, a 2.8 × 105 km2 area in the northern, largely forested parts of the states of Minnesota, Wisconsin, and Michigan, contains regions of varied land cover, soil type, human settlement densities, and land management strategies that may influence differences in the observed spatial distribution of wildfires. Using a wide array of satellite‐ and ground‐based data for this region, we investigated the relationship between wildfire activity and environmental and social factors for >18 000 reported fires of all sizes between 1985 and 1995. We worked at two spatial scales to address the following questions: (1) Which abiotic, biotic, and human variables best explained decade‐scale regional fire activity during the study period? (2) Did the set of factors related to large fires differ from the set influencing all fires? (3) Did varying the spatial scale of analysis dramatically change the influence of predictive variables? (4) Did the set of factors influencing the number of fires in an area differ from the set of factors influencing the probability of the occurrence of even a single fire? These data suggest that there is no simple “Lake States fire regime” for the Upper Midwest. Instead, interpretation of modern fire patterns depends on both the fire size considered and the measurement of fire activity. Spatial distributions of wildfires using two size thresholds and viewed at two spatial scales are clearly related to a combination of abiotic, biotic, and human factors: no single factor or factor type dominates. However, the significant factors for each question were readily interpretable and consistent with other analyses of natural and human influences on fire patterns in the region. Factors seen as significant at one scale were frequently also significant at the other, indicating the robustness of the analysis across the two spatial resolutions. The methods for conducting this spatially explicit analysis of modern fire patterns (generalized linear regression at multiple scales using long‐term wildfire data and a suite of environmental and social variables) should be widely applicable to other areas. Results of this study can serve as the basis for daily, seasonal, or interannual studies as well as the foundation for simulation models of future wildfire distribution.

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“…El sensor AVHRR (Avanced Very Hight Resolution Radiometer) a bordo del satélite NOAA (Nacional Oceanic and Atmospheric Administration) ha sido ampliamente utilizado en el estudio de los incendios forestales (Barbosa et al, 1999;Pereira et al, 1999;Fernández et al, 1997, González-Alonso et al 1996, González-Alonso et al, 1997, debido a su cobertura espacial, espectral y temporal, sobre todo cuando se trabaja con grandes extensiones (Illera et al, 1995). Actualmente nuevos sensores como el MODIS (Moderate Resolution Imaging Spectroradiometer) están abriendo una nueva era en la estimación de superficies quemadas debido a las mejoras espaciales, espectrales y temporales que presentan dichos sensores , Merino-deMiguel et al, 2005.…”

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Canary Islands burned land mapping using MODIS and HOTSPOT data

et al. 2008

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This study presents a rapid, accurate and economic methodology for forest fire burned areas estimation using remote sensing techniques. The algorithm used is based on thresholds identifications from infrared spectral band, using MODIS images and hotspots time series delivered from NASA. The threshold is defined by the maximum correlation between the infrared band and hotspot influence area. The results show the usefulness of the presented methodology in forest fire burned area estimation and its relevance for prioritizing forest management activities and for planning future reforestations work in the affected area.

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A forest fire risk assessment using NOAA AVHRR images in the Valencia area, eastern Spain

Cited by 33 publications

References 4 publications

Continental fire density patterns in South America

Continental fire density patterns in South America

Environmental and Social Factors Influencing Wildfires in the Upper Midwest, United States

Canary Islands burned land mapping using MODIS and HOTSPOT data

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