Geostatistical modeling of forest fire severity

Document Type

Conference Paper/Presentation

Publication Date



Connecting remotely sensed measures of burn severity (i.e. Differenced Normalized Burn Ratio [DNBR]) with fuel properties during a burn is important for biomass consumption estimation. Results from a step-wise geostatistical analysis designed to measure the relative influence of physiographic and climatic factors affecting forest fire burn severity are presented. Universal and co-kriging inverse methods were used to assess spatial covariance and generate DNBR predictions and error assessments. Inputs to the model include topography, annual direct incident radiation, fire weather (i.e. temperature, relative humidity), and fuel loading. Annual direct incident radiation and fire weather exhibited correlations with burn severity implying a link with fuel moisture. Inclusion of mechanistic fuel moisture models is suggested to supplement the proximate measures used.

Publisher's Statement

© 2007 American Geophysical Union.

Publication Title

AGU Fall Meeting 2007