http://cfs.nrcan.gc.ca/authors/read/22652 Publications by G.G. Moisen en-ca 2012-02-15 12:16:35 MST 2012-02-15 12:16:35 MST webmaster@nofc.cfs.nrcan.gc.ca http://cfs.nrcan.gc.ca/publications?id=33281 In Canadian boreal forests, wildfire is the predominant agent of natural disturbance often with millions of hectares burning annually. In addition to fire, nearly one quarter of Canada's boreal forest is also managed for industrial wood production. Post-fire logging (or salvage harvesting) is increasingly used to minimize economic losses from fire, notwithstanding that the ecological impacts of successive disturbance events remain poorly understood. Improved monitoring and management of post-fire environments will require new information regarding the location and timing of past operations. In this paper we present and evaluate a data integration approach for detecting spatial and temporal trends in post-fire logging. Here we utilize a series of maps relating timing and extent of burned area (from the Canadian Large Fire Database) and year of harvest (from Landsat change detection) to identify occurrences of post-fire logging between 1987 and 2008 for a portion of boreal forest located in central Saskatchewan, Canada. Using a design-based, stratified random sampling framework we found that 68% (95% confidence interval (CI) [53 to 84%]) of the detected post-fire logging was correctly classified, such that both fire and clearcutting disturbances were positively verified by the reference data. The majority of map error resulted from spectral confusion between harvested areas and rock outcroppings exposed by fire and from mislabeling harvested unburned islands as post-fire logging. To add further confidence to our classification accuracy, we also found that mapped post-fire logging displayed similar temporal trends over a ten year period as salvage volume reported for a forest management area partially contained within the study area. Based upon the significant relationship between these estimates (r = 0.97, p < 0.001) and the good degree of observed map accuracy, we believe that the presented approach offers a viable and flexible option for characterizing the spatial and temporal dynamics of post-fire logging in boreal forests. Maps which reliably identify areas of post-fire logging stand to improve our capacity to manage and model the ecological impacts associated with multiple disturbance events. Wed, 15 Feb 2012 http://cfs.nrcan.gc.ca/publications?id=33281 http://cfs.nrcan.gc.ca/publications?id=32283 Information regarding the extent, timing and magnitude of forest disturbance are key inputs required for accurate estimation of the terrestrial carbon balance. Equally important for studying carbon dynamics is the ability to distinguish the cause or type of forest disturbance occurring on the landscape. Wildfire and timber harvesting are common disturbances occurring in boreal forests, with each having differing carbon consequences (i.e., biomass removed, recovery rates). Development of methods to not only map, but distinguish these types of disturbance with satellite data will depend upon an improved understanding of their distinctive spectral properties. In this study, we mapped wildfires and clearcut harvests occurring in a Landsat time series (LTS) acquired in the boreal plains of Saskatchewan, Canada. This highly accurate reference map (kappa = 0.91) depicting the year and cause of historical disturbances was used to determine the spectral and temporal properties needed to accurately classify fire and clearcut disturbances. The results showed that spectral data from the short-wave infrared (SWIR; e.g., Landsat band 5) portion of the electromagnetic spectrum was most effective at separating fires and clearcut harvests possibly due to differences in structure, shadowing, and amounts of exposed soil left behind by the two disturbance types. Although SWIR data acquired 1 year after disturbance enabled the most accurate discrimination of fires and clearcut harvests, good separation (e.g., kappa ≥ 0.80) could still be achieved with Landsat band 5 and other SWIR-based indices 3 to 4 years after disturbance. Conversely, minimal disturbance responses in near infrared-based indices associated with green leaf area (e.g., NDVI) led to unreliably low classification accuracies regardless of time since disturbance. In addition to exploring the spectral and temporal manifestation of forest disturbance types, we also demonstrate how Landsat change maps which attribute cause of disturbance can be used to help elucidate the social, ecological and carbon consequences associated with wildfire and clearcut harvesting in Canadian boreal forests. Mon, 04 Apr 2011 http://cfs.nrcan.gc.ca/publications?id=32283 http://cfs.nrcan.gc.ca/publications?id=28142 Fri, 14 Mar 2008 http://cfs.nrcan.gc.ca/publications?id=28142