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Development of a synthetic record of fire probability and proportion of late fires from simulated growth of ground stratum and annual rainfall in the ... from: Environmental Modelling and Software]
Book Details
PublisherElsevier
ISBN / ASINB000RR95A2
ISBN-13978B000RR95A0
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
In this study, we sought to address the issue of how to derive an extended synthetic record of fire incidence and timing at regional scale that would be representative of a short remotely-sensed calibration record. We used annual rainfall and simulated annual ground stratum growth to develop multiple regression relationships for prediction of annual fire probability and proportion of late (August-November) fires from AVHRR NDVI fire footprint data across Australian tropical savannas. Relationships were examined using spatial averaging in moving windows varying from 3x3 to 61x61 pixels in size. Model fits as measured by R^2 improved as window size increased, but output layers became smoother and less representative of natural heterogeneity. A 25x25 pixel window was selected as the best compromise between model fit and smoothing. A 113-year synthetic record of annual fire probability and proportion of late fires was generated using the spatially explicit layers of model coefficients. The statistical properties of the synthetic fire probabilities were compared with those derived from the available fire footprint record, using a simple vegetation classification based on ground stratum type for spatial stratification. The two data sets showed a strong correspondence for both burned area and fire probability; spatial variation in mean and coefficient of variation of fire probability was representative of that observed in the historical record. There was significant temporal variation in the synthetic annual fire probability for different vegetation zones across the tropical savanna region for the full 113-year length of record. This simple approach could readily be applied to other areas of the world provided rainfall data are available and annual ground stratum growth can be simulated with a suitable model or estimated with remote sensing.
Description:
In this study, we sought to address the issue of how to derive an extended synthetic record of fire incidence and timing at regional scale that would be representative of a short remotely-sensed calibration record. We used annual rainfall and simulated annual ground stratum growth to develop multiple regression relationships for prediction of annual fire probability and proportion of late (August-November) fires from AVHRR NDVI fire footprint data across Australian tropical savannas. Relationships were examined using spatial averaging in moving windows varying from 3x3 to 61x61 pixels in size. Model fits as measured by R^2 improved as window size increased, but output layers became smoother and less representative of natural heterogeneity. A 25x25 pixel window was selected as the best compromise between model fit and smoothing. A 113-year synthetic record of annual fire probability and proportion of late fires was generated using the spatially explicit layers of model coefficients. The statistical properties of the synthetic fire probabilities were compared with those derived from the available fire footprint record, using a simple vegetation classification based on ground stratum type for spatial stratification. The two data sets showed a strong correspondence for both burned area and fire probability; spatial variation in mean and coefficient of variation of fire probability was representative of that observed in the historical record. There was significant temporal variation in the synthetic annual fire probability for different vegetation zones across the tropical savanna region for the full 113-year length of record. This simple approach could readily be applied to other areas of the world provided rainfall data are available and annual ground stratum growth can be simulated with a suitable model or estimated with remote sensing.
