Differing Sensitivities to Fire Disturbance Result in Large Differences Among Remotely Sensed Products of Vegetation Disturbance

Abstract

Recent advances in high-performance computing (HPC) have promoted the creation of standardized remotely sensed products that map annual vegetation disturbance through two primary methods:(1) conventional approaches that integrate remote sensing-derived vegetation indices with field data and other data on disturbance events reported by public agencies on a year-to-year basis, and (2) ''big'' data approaches using HPC to automate algorithms and workflows across an entire time series. Given the recent proliferation of these annual products and their potential utility for understanding vegetation dynamics, it is important for product end users (that is, practitioners and researchers in domains other than remote sensing) to understand the differences in their representa-ference in reported disturbance between LAND-FIRE and GFC/NAFD was greater for fire disturbance than for non-fire disturbance; LANDFIRE reported more than double the total amounts of fire disturbance of GFC and NAFD in the study period. Based on our results, we encourage end users to choose the appropriate disturbance product based not only on spatial extent and habitat but also on the disturbance type of interest (that is, fire and non-fire). Overall, rather than focusing on accuracy, our study quantifies the extent to which the products exhibited differences in the amounts and locations of reported disturbance to provide insight into these products' representations of disturbance and help end users evaluate and choose the most appropriate product for their needs.

US National Maps Attributing Forest Change: 1986–2010

“…The best methods for comparing and harmonizing multiple maps describing area and location of forest change events, in lieu of historical reference data, are under research [58,142]. Quantifying map uncertainty and its spatial characteristics would also aid user's ability to choose and/or combine maps that support their specific needs [135,143]. It is important to note that only operational national monitoring programs (* in Table 1) can provide data legacy, a key element for many users in natural resource management and decision support frameworks.…”

“…The minimum Landsat-based canopy cover quantity, for reliable detection, may be between 10% and 20% canopy cover depending on environmental context, processing methods used and the definition of tree crown cover used in the training data [133,134]. The sensitivity of Landsat-based change detection algorithms to low magnitude or low intensity canopy cover loss events (percentages in either absolute or relative terms) is even more poorly characterized [54,58,135]. Low magnitude changes may have a lower accuracy than land clearing events.…”

“…magnitude or low intensity canopy cover loss events (percentages in either absolute or relative terms) is even more poorly characterized [54,58,135]. Low magnitude changes may have a lower accuracy than land clearing events.…”