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

Biomass conversion and expansion factors are afected by thinning

Aim of the study: The objective of this paper is to investigate the use of Biomass Conversion and Expansion Factors (BCEFs) in maritime pine (Pinus pinaster Ait.) stands subjected to thinning. Area of the study: The study area refers to different ecosystems of maritime pine stands in Northern Portugal. Material and methods: The study is supported by time data series and cross sectional data collected in permanent plots established in the North of Portugal. An assessment of BCEF values for the aboveground compartments and for total was completed for each studied stand. Identification of key variables affecting the value of the BCEFs in time and with thinning was conducted using correlation analysis. Predictive models for estimation of the BCEFs values in time and after thinning were developed using nonlinear regression analysis. Research highlights: For periods of undisturbed growth, the results show an allometric relationship between the BCEFs, the dominant height and the mean diameter. Management practices such as thinning also influence the factors. Estimates of the ratio change before and after thinning depend on thinning severity and thinning type. The developed models allow estimating the biomass of the stands, for the aboveground compartments and for total, based on information of stand characteristics and of thinning descriptors. These estimates can be used to assess the forest dry wood stocks to be used for pulp, bioenergy or other purposes, as well as the biomass quantification to support the evaluation of the net primary productivityinfo:eu-repo/semantics/publishedVersio

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

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

Assessment of Biomass Energy Potential and Forest Carbon Stocks in Biscay (Spain)

The aim of this research is to identify, quantify and characterize the potential available forest biomass of Pinus radiata D. Don and Eucalyptus globulus Labill. across Biscay province in northern Spain. In order to do this, we have used information from the National Inventories of Spain to quantify the amount of carbon dioxide accumulated in the forests of Biscay by means of stratum-species-based forestry statistics. The total biomass and biomass fractions have been estimated using two different methods: allometric biomass equations (ABE) and biomass expansion factors (BEF). The second objective is to develop a methodology to quantify and produce a cartography of the prospective energy production of residual biomass from the most representative forest species of Biscay. For this purpose, we have used a Geographic Information System (GIS) computer tool. We have found that the stock of carbon accumulated in the main forest species in Biscay in 2014 amounts to 8.2 Tg (ABE) and 6.63 Tg (BEF) equivalent to 30 and 24.3 Tg of CO2, respectively. The quantity of forestry biomass residue (FBR) obtained has been estimated as 52,214 Mg.year(-1) dry matter. This amount means a prospective energy supply of 947,000 GJ.year(-1).This work has been supported by the Office of Research of the University of the Basque Country grant by Project NUPV08/22, by Project SAI10/147-SPE10UN90and by Project NUPV10/10. The authors acknowledge gratefully the technical and personalsupport provided by Jose Miguel Edeso, Aitor Bastarrica and Leyre Torre for drawing the figures

Uncertainty of Forest Biomass Estimates in North Temperate Forests Due to Allometry: Implications for Remote Sensing

Estimates of above ground biomass density in forests are crucial for refining global climate models and understanding climate change. Although data from field studies can be aggregated to estimate carbon stocks on global scales, the sparsity of such field data, temporal heterogeneity and methodological variations introduce large errors. Remote sensing measurements from spaceborne sensors are a realistic alternative for global carbon accounting; however, the uncertainty of such measurements is not well known and remains an active area of research. This article describes an effort to collect field data at the Harvard and Howland Forest sites, set in the temperate forests of the Northeastern United States in an attempt to establish ground truth forest biomass for calibration of remote sensing measurements. We present an assessment of the quality of ground truth biomass estimates derived from three different sets of diameter-based allometric equations over the Harvard and Howland Forests to establish the contribution of errors in ground truth data to the error in biomass estimates from remote sensing measurements