Sample-Based Forest Landscape Diversity Indices.

Studies of spatial patterns of landscapes are useful to quantify human impact, predict wildlife effects, or describe various landscape features. A robust landscape index should quantify two distinct components of landscape diversity: composition and configuration. One category of landscape index is the contagion index. A generalized measure of contagion is defined as a function of concentration. From this definition two contagion indices, \Gamma\sb1 (a new index) and \Gamma\sb2 (an entropy formulation), are derived from expected values of geometric random variables. A widely used relative contagion index, RC\sb2, is shown to be a scaled version of \Gamma\sb2.. Distributional properties of \\Gamma\sb1,\ \\Gamma\sb2, and \\Gamma\sb{2({\rm scaled})} (i.e., R\ C\sb2) are derived. They are shown to be asymptotically unbiased, consistent, and asymptotically normally distributed. Variance formulas for \\Gamma\sb1,\ \\Gamma\sb2, and \\Gamma\sb{2\rm(scaled)} are derived using the delta method A Monte Carlo study using subseries analysis and replicate histograms, for variance and distribution assessment, was done as a validity check. Behavior of \Gamma\sb1,\ \Gamma\sb2, and RC\sb2 were investigated with simulated random, uniform, and aggregated landscapes. Both \Gamma\sb1 and \Gamma\sb2 provide acceptable measures of contagion. The index RC\sb2 is shown to be an index of evenness, and not of contagion. It is demonstrated that relativized contagion indices are mathematically untenable. As an application, the pattern and changes in forest cover types over the last two decades were analyzed on three landscape level physiographic provinces of the state of Alabama: (i) The Great Appalachian Valley Province, (ii) The Blue Ridge-Talladega Mountain Province, and (iii) The Piedmont Province. The USDA Forest Service conducts periodic surveys of forest resources nationwide from plots distributed on a 3-mile by 3-mile (4.8-km by 4.8-km) grid randomly established within each county. Using forest inventory and analysis survey data on forest cover types, stratified by physiographic province, the \\Gamma\sb1 and \\Gamma\sb2 contagion values and their variances were calculated for each province for the survey years 1972, 1982, and 1990. One-way analysis of variance was used for hypothesis testing of contagion values across time and between provinces. Contagion values were very similar indicating similar processes operating across the physiographic provinces over the last two decades. In comparing \\Gamma\sb1 and \\Gamma\sb2, use of \\Gamma\sb1 in analysis of variance gave a more conservative test of contagion

Models to Implement a Sustainable Forest Management - An Overview of the ModisPinaster Model

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Creating a fuels baseline and establishing fire frequency relationships to develop a landscape management strategy at the Savannah River Site.

USDA Forest Service Proceedings RMRS-P-41. pp 351-366. Abstract—The Savannah River Site is a Department of Energy Nuclear Defense Facility and a National Environmental Research Park located in the upper coastal plain of South Carolina. Prescribed burning is conducted on 15,000 to 20,000 ac annually. We modifi ed standard forest inventory methods to incorporate a complete assessment of fuel components on 622 plots, assessing coarse woody debris, ladder fuels, and the litter and duff layers. Because of deficiencies in south-wide data on litter-duff bulk densities, which are the fuels most often consumed in prescribed fires, we developed new bulk density relationships. Total surface fuel loading across the landscape ranged from 0.8 to 48.7 tons/ac. The variables basal area, stand age, and site index were important in accounting for variability in ladder fuel, coarse woody debris, and litter-duff for pine types. For a given pine stand condition, litter-duff loading decreased in direct proportion to the number of burns in the preceding thirty years. Ladder fuels for loblolly and longleaf increased in direct proportion to the years since the last prescribed burn. The pattern of fuel loading on the SRS reflects stand dynamics, stand management and fire management. It is suggested that the Forest Inventory and Analysis Program can easily modify sampling protocols to incorporate collection of fuels data

A stochastic height-diameter model for maritime pine ecoregions in Galicia (northwestern Spain)

[EN] A stochastic height-diameter model was developed for maritime pine (Pinus pinaster Ait.) in Galicia (northwestern Spain). Four well-known growth functions were initially considered in this work, however, only Schnute's function performed adequately. A set of 20 695 pairs of height-diameter measures, collected in thinned and unthinned pure and even-aged stands, were used to fit the model. These stands were located throughout Galicia Autonomous Region covering a wide range of forest stands and site conditions. Since unequal error variance occurs, the generalized non-linear least squares method was used to take into account the error structure. Different weighting factors were employed to remove the heterogeneous variance of the errors. Because the local model (including only tree dimensions as explanatory variables) did not provide adequate results, stand variables were tested and incorporated into the height-diameter model. Ecoregion differences in the height-diameter relationship were analysed using the non-linear extra sum of squares method and the Lakkis-Jones test. Both tests showed that model parameters were significantly different between the two ecoregions normally defined for this species: coast and interior. The effect of thinning was examined; however, no benefits were obtained by introducing an additional thinning response variable in the prediction model. Finally, since trees with the same diameter usually do not have the same height, even within the same stand, a stochastic component was added to the deterministic height function. This approach mimics the natural variability of heights and therefore provides more realistic height predictionsSIThis study was financed by the Comisión Interministerial de Ciencia y Tecnología (CICYT), project No AGL2001-3871-C02-0

Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition

Light detection and ranging (LiDAR) technology has the potential to radically alter theway researchers and managers collect data onwildlife–habitat relationships. To date, the technology has fostered several novel approaches to characterizing avian habitat, but has been limited by the lack of detailed LiDAR-habitat attributes relevant to species across a continuum of spatial grain sizes and habitat requirements. We demonstrate a novel three-step approach for using LiDAR data to evaluate habitat based on multiple habitat attributes and accounting for their influence at multiple grain sizes using federally endangered red-cockaded woodpecker (RCW; Picoides borealis) foraging habitat data fromthe Savannah River Site (SRS) in South Carolina, USA. First,we used high density LiDAR data (10 returns/m2) to predict detailed forest attributes at 20-mresolution across the entire SRS using a complementary application of nonlinear seemingly unrelated regression andmultiple linear regressionmodels. Next,we expanded on previous applications of LiDAR by constructing 95% joint prediction confidence intervals to quantify prediction error at various spatial aggregations and habitat thresholds to determine a biologically and statistically meaningful grain size. Finally,we used aggregations of 20-m cells and associated confidence interval boundaries to demonstrate a newapproach to produce maps of RCWforaging habitat conditions based on the guidelines described in the species\u27 recovery plan. Predictive power (R2) of regression models developed to populate raster layers ranged from 0.34 to 0.81, and prediction error decreased as aggregate size increased, but minimal reductions in prediction error were observed beyond 0.64-ha (4 × 4 20-m cells) aggregates. Mapping habitat quality while accounting for prediction error provided a robust method to determine the potential range of habitat conditions and specific attributes that were limiting in terms of the amount of suitable habitat. The sequential steps of our analytical approach provide a useful framework to extract detailed and reliable habitat attributes for a forest-dwelling habitat specialist, broadening the potential to apply LiDAR in conservation and management of wildlife populations. A zipped folder of Google maps is attached below as a related file

Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition

Light detection and ranging (LiDAR) technology has the potential to radically alter theway researchers and managers collect data onwildlife–habitat relationships. To date, the technology has fostered several novel approaches to characterizing avian habitat, but has been limited by the lack of detailed LiDAR-habitat attributes relevant to species across a continuum of spatial grain sizes and habitat requirements. We demonstrate a novel three-step approach for using LiDAR data to evaluate habitat based on multiple habitat attributes and accounting for their influence at multiple grain sizes using federally endangered red-cockaded woodpecker (RCW; Picoides borealis) foraging habitat data fromthe Savannah River Site (SRS) in South Carolina, USA. First,we used high density LiDAR data (10 returns/m2) to predict detailed forest attributes at 20-mresolution across the entire SRS using a complementary application of nonlinear seemingly unrelated regression andmultiple linear regressionmodels. Next,we expanded on previous applications of LiDAR by constructing 95% joint prediction confidence intervals to quantify prediction error at various spatial aggregations and habitat thresholds to determine a biologically and statistically meaningful grain size. Finally,we used aggregations of 20-m cells and associated confidence interval boundaries to demonstrate a newapproach to produce maps of RCWforaging habitat conditions based on the guidelines described in the species\u27 recovery plan. Predictive power (R2) of regression models developed to populate raster layers ranged from 0.34 to 0.81, and prediction error decreased as aggregate size increased, but minimal reductions in prediction error were observed beyond 0.64-ha (4 × 4 20-m cells) aggregates. Mapping habitat quality while accounting for prediction error provided a robust method to determine the potential range of habitat conditions and specific attributes that were limiting in terms of the amount of suitable habitat. The sequential steps of our analytical approach provide a useful framework to extract detailed and reliable habitat attributes for a forest-dwelling habitat specialist, broadening the potential to apply LiDAR in conservation and management of wildlife populations. A zipped folder of Google maps is attached below as a related file

Report on Analysis of Forest Floor Bulk Density and Depth at the Savannah River Site.

The forest floor data from the Savannah River Site consists of two layers, the litter layer and the duff layer. The purpose for the study was to determine bulk density conversion factors to convert litter and duff depth values in inches to forest floor fuel values in tons per acre. The primary objective was to collect litter and duff samples to adequately characterize forest floor depth and bulk density for combinations of 4 common forest types (loblolly/slash pine, longleaf pine, pine and hardwood mix, upland hardwood), 3 age classes (5-20, 20-40, 40+ years old) and 3 categories of burning history (0-3, 3-10, 10+ years since last burn)