Influence of fire regimes on lodgepole pine stand age and density across the Yellowstone National Park (USA) landscape

A probabilistic spatial model was created based on empirical data to examine the influence of different fire regimes on stand structure of lodgepole pine (Pinus contorta var. latifolia) forests across a >500,000-ha landscape in Yellowstone National Park, Wyoming, USA. We asked how variation in the frequency of large fire events affects (1) the mean and annual variability of age and tree density (defined by postfire sapling density and subsequent stand density) of lodgepole pine stands and (2) the spatial pattern of stand age and density across the landscape. The model incorporates spatial and temporal variation in fire and serotiny in predicting postfire sapling densities of lodgepole pine. Empirical self-thinning and in-filling curves alter initital postfire sapling densities over decades to centuries. In response to a six-fold increase in the probability of large fires (0.003 to 0.018 year⁻¹), mean stand age declined from 291 to 121 years. Mean stand density did not increase appreciably at high elevations (1,029 to 1,249 stems ha⁻¹) where serotiny was low and postfire sapling density was relatively low (1,252 to 2,203 stems ha⁻¹). At low elevations, where prefire serotiny and postfire lodgepole pine density are high, mean stand densities increased from 2,807 to 7,664 stems ha⁻¹. Spatially, the patterns of stand age became more simplified across the landscape, yet patterns of stand density became more complex. In response to more frequent stand replacing fires, very high annual variability in postfire sapling density is expected, with higher means and greater variation in stand density across lodgepole pine landscapes, especially in the few decades following large fires.     

An object-oriented approach to assessing changes in tree cover in the Colorado Front Range 1938–1999

In the wake of numerous catastrophic wildfires, forest management policies have been implemented in recent years in the United States with the goals of reducing fire risk, including the National Fire Plan and the Healthy Forest Restoration Act. A key premise underlying these policies is that fire suppression has resulted in denser forests than were present historically that now have higher fire risk. To evaluate this premise for the northern Front Range, Colorado, we used object-oriented image analysis to compare change in tree cover delineated from historical and modern imagery. Historical photographs from 1938 and 1940 were scanned, orthorectified, and overlaid on Digital Orthoimagry Quarter Quadrangles (DOQQs) from 1999. Using an object-oriented image analysis technique, the photos were then finely segmented and classified into two classes: tree and non-tree. Trees are heterogeneous in appearance in black and white aerial photography, so we employed separate membership functions to identify four visually distinct types: ‘interior forest’, ‘isolated trees’, ‘dark forest’, and ‘edge forest’. Our classification strategy employed spatial relationships between objects in addition to spectral information, so that our classification is fairly robust to variations in illumination. Based on the classification of fine objects, we then calculated the percent tree cover within a larger set of objects for the two time periods. We estimate that average tree density across the study area increased minimally (4%) during the 60-year period, with considerable spatial variation across the landscape. The results of the analysis illustrate that, consistent with independent tree-ring evidence, the highest increase in tree density is in areas characterized by low initial density, south-facing slopes, low elevations, and ponderosa pine dominance. In contrast, the highest elevation areas dominated by mixed conifer and lodgepole pine forests revealed no significant change in tree cover. Furthermore, there is no significant difference between objects dominated by low, medium, and high departure from historical conditions, as classified in the Landfire Fire Regime Condition Class (FRCC) data product. The results of the study can help managers prioritize forest treatments aimed at restoring pre-suppression forest structure.