Space and airborne sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Confusion about fire intensity, fire severity, burn severity, and related terms can result in the potential misuse of the inferred information by land managers and remote sensing practitioners who require unambiguous remote sensing products for fire management. The objective of the present paper is to provide a comprehensive review of current and potential remote sensing methods used to assess fire behavior and effects and ecological responses to fire. We clarify the terminology to facilitate development and interpretation of comprehensible and defensible remote sensing products, present the potential and limitations of a variety of approaches for remotely measuring active fires and their post-fire ecological effects, and discuss challenges and future directions of fire-related remote sensing research.

Wildland fire management has reached a crossroads. Current perspectives are not capable of answering interdisciplinary adaptation and mitigation challenges posed by increases in wildfire risk to human populations and the need to reintegrate fire as a vital landscape process. Fire science has been, and continues to be, performed in isolated “silos,” including institutions (e.g., agencies versus universities), organizational structures (e.g., federal agency mandates versus local and state procedures for responding to fire), and research foci (e.g., physical science, natural science, and social science). These silos tend to promote research, management, and policy that focus only on targeted aspects of the “wicked” wildfire problem. In this article, we provide guiding principles to bridge diverse fire science efforts to advance an integrated agenda of wildfire research that can help overcome disciplinary silos and provide insight on how to build fire-resilient communities.

We describe and evaluate a new analysis technique, spatial wavelet analysis (SWA), to automatically estimate the location, height, and crown diameter of individual trees within mixed conifer open canopy stands from light detection and ranging (lidar) data. Two-dimensional Mexican hat wavelets, over a range of likely tree crown diameters, were convolved with lidar canopy height models. Identification of local maxima within the resultant wavelet transformation image then allowed determination of the location, height, and crown diameters of individual trees. In this analysis, which focused solely on individual trees within open canopy forests, 30 trees incorporating seven dominant North American tree species were assessed. Two-dimensional (2D) wavelet-derived estimates were well correlated with field measures of tree height (r = 0.97) and crown diameter (r = 0.86). The 2D wavelet-derived estimates compared favorably with estimates derived using an established method that uses variable window filters (VWF) to estimate the same variables but relies on a priori knowledge of the tree height - crown diameter relationship. The 2D spatial wavelet analysis presented herein could potentially allow automated, large-scale, remote estimation of timber board feet, foliar biomass, canopy volume, and aboveground carbon, although further research testing the limitations of the method in a variety of forest types with increasing canopy closures is warranted.