No abstract

We present a classification of duff, litter, fine woody debris, and logs that can be used to stratify a project area into sites with fuel loading that yield significantly different emissions and maximum soil surface temperature. Total particulate matter smaller than 2.5 μm in diameter and maximum soil surface temperature were simulated using the First Order Fire Effects Model. Simulation results were clustered into 10 Effects Groups using an agglomerative routine where each Effects Group defined a unique range of soil temperature and emissions. Classification tree analysis was used to estimate the critical duff, litter, fine woody debris, and log loadings associated with the soil temperature and emissions of each Effects Group. The resulting 21 fuel classes are called Fuel Loading Models and classified the study dataset with an ~34% misclassification rate. The classification can be used to describe fuel loadings for a plot or stand, or as map units for mapping fuel loadings across large regions. The classification process can be used to develop finer-scale fuel classifications for specific regions or ecosystems.

A new monitoring tool called FFI (FEAT/FIREMON Integrated) has been developed to assist managers with collection, storage and analysis of ecological information. The tool was developed through the complementary integration of two fire effects monitoring systems commonly used in the United States: FIREMON and the Fire Ecology Assessment Tool. FFI provides software components for: data entry, data storage, Geographic Information System, summary reports, analysis tools and Personal Digital Assistant use. In addition to a large set of standard FFI protocols, the Protocol Manager lets users define their own sampling protocol when custom data entry forms are needed. The standard FFI protocols and Protocol Manager allow FFI to be used for monitoring in a broad range of ecosystems. FFI is designed to help managers fulfil monitoring mandates set forth in land management policy. It supports scalable (project- to landscape-scale) monitoring at the field and research level, and encourages cooperative, interagency data management and information sharing. Though developed for application in the USA, FFI can potentially be used to meet monitoring needs internationally.

Watershed classification using multivariate techniques requires the incorporation of continuous datasets representing controlling environmental variables. Often, out of convenience and availability rather than importance to the structure of the system being modeled, the environmental data used originate from a variety of sources and scales. To demonstrate the importance of appropriate environmental data selection, classifications of six‐digit hydrologic units (1:24,000) across selected geographic areas within the Interior Columbia River Basin were produced. Canonical correspondence analysis was used to select and test environmental variables important in predicting Rosgen stream types and valley bottom classes. Then, hierarchical agglomerative clustering was used to group (classify) watersheds based on these variables. Statistically significant results were derived from the use of organized classification data with presumed predictive relationships to watershed properties, and a random distribution of environmental variables from the same datasets provided similar results. The results contained herein demonstrate that these analysis techniques do not necessarily select meaningful variables from a broad spectrum of data and that significant results are easily generated from randomly associated data. It is suggested that classifications produced using these multivariate techniques, especially when using multi‐scale data or data of unknown significance, are subject to invalid inferences and should be used with caution.