Calculation and comparison of different permanent sample plot types

Summary

Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data

Abstract

HispaVeg: a new online vegetation plot database for Spain

We describe a new online database, named HispaVeg, which currently holds data from 2663 vegetation plots of Spanish woodlands, scrublands and grasslands. Unlike other similar databases, a detailed description of the structure is stored with the floristic data of each plot (i.e., number and physiognomy of the vertical layers, cover values for each layer).Most of the vegetation plots are large rectangles (400 to 2000 square meters) with an average of 34 species per plot. The survey dates range from 1956 to present, with most of the records between 1964 and 1994. The elevation of the plots ranges from 0 to 2880, with most of the plots between 300 and 1500 m. HispaVeg is freely available to the scientific community. Users can query the online database, view printable reports for each plot and download spreadsheet-like raw data for subsets of vegetation plots.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Insignificant impact of prescribed fire on surface soil in a Pinus pinaster plantation, northern Portugal

Following a prescribed fire in a Pinus pinaster forest site located in the north-west Portugal, monitoring of any changes in selected soil characteristics and soil hydrology was undertaken to assess the effects of burning on the following: pH, electrical conductivity, water content, organic carbon and porosity. Thirty plots were established on a regular grid. At each sample plot before and after the fire, samples were collected (disturbed samples from depths of 0-1cm and 1-5cm; undisturbed core samples from 0-5cm). The results indicate that there was no measurable impact on the properties of the soil following this carefully conducted prescribed fire. The fire only affected the litter layer, as intended. Confirmation of this minimal impact on the soil was provided by regrowth of grasses and herbs already occurring two months after the fire. The implication is, therefore, that provided this wildfire-risk reduction strategy is carried out under existing strict guidelines, any impact on soil quality will be minimal.

Effects of Harvesting on Herbaceous Layer Diversity of a Central Appalachian Hardwood Forest

Clearcutting is a common harvesting practice in many eastern hardwood forests. Among the vegetation strata of these forests, the herbaceous layer is potentially the most sensitive in its response to harvest-mediated disturbances and has the highest species diversity. Thus, it is important to understand the response of herbaceous layer diversity to forest harvesting. Previous work on clearcut and mature stands at the Fernow Experimental Forest (FEF), West Virginia, has shown that, although, harvesting did not alter appreciably herbaceous layer cover, it influenced the relationship of cover to biotic and abiotic factors, such as tree density and soil nutrients, respectively. The purpose of this study was to examine the response of species diversity of the herbaceous layer to harvesting at FEF. Fifteen circular, 0.04 ha sample plots were established in each of four watersheds (60 plots in total) representing two stand age categories: two watersheds with 20 years even-age stands following clearcutting and two watersheds with mature second growth stands. All woody stems ≥2.5 cm diameter at breast height were identified, tallied, and measured for diameter. The herbaceous layer was sampled by identifying all vascular plants ≤1 m in height and estimating cover for each species in each of 10 (1 m2) circular sub-plots per sample plot (600 sub-plots total). Species diversity for each plot was calculated from herbaceous layer data using the ln-based Shannon Index (H′) equation. Ten stand and soil variables also were measured on each plot. Mean herbaceous layer cover for clearcut versus mature stands was 27.2±14.3% versus 20.2±8.1% (P>0.05), respectively and mean H′ was 1.67±0.42 versus 1.55±0.48 (P>0.05), respectively. Herbaceous layer diversity was negatively correlated with cation exchange capacity and extractable Ca and Mg in the mineral soil in clearcut stands. In contrast, herbaceous layer diversity was positively correlated with soil organic matter and clay content. Although, 20 years of recovery after clearcutting did not have significant effects on the species diversity of the herbaceous layer when examining stand age means alone, harvesting did appear to influence the spatial relationships between herbaceous layer diversity and biotic factors (e.g. tree density) and abiotic factors (e.g. soil nutrients).

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.

The determinants of improvements in health outcomes and of cost reduction in hospital inpatient care

This study aims to address two research questions. First, ‘Can we identify factors that are determinants both of improved health outcomes and of reduced costs for hospitalized patients with one of six common diagnoses?’ Second, ‘Can we identify other factors that are determinants of improved health outcomes for such hospitalized patients but which are not associated with costs?’ The Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database from 2003 to 2006 was employed in this study. The total study sample consisted of hospitals which had at least 30 patients each year for the given diagnosis: 954 hospitals for acute myocardial infarction (AMI), 1552 hospitals for congestive heart failure (CHF), 1120 hospitals for stroke (STR), 1283 hospitals for gastrointestinal hemorrhage (GIH), 979 hospitals for hip fracture (HIP), and 1716 hospitals for pneumonia (PNE). This study used simultaneous equations models to investigate the determinants of improvement in health outcomes and of cost reduction in hospital inpatient care for these six common diagnoses. In addition, the study used instrumental variables and two-stage least squares random effect model for unbalanced panel data estimation. The study concluded that a few factors were determinants of high quality and low cost. Specifically, high specialty was the determinant of high quality and low costs for CHF patients; small hospital size was the determinant of high quality and low costs for AMI patients. Furthermore, CHF patients who were treated in Midwest, South, and West region hospitals had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. Gastrointestinal hemorrhage and pneumonia patients who were treated in South region hospitals also had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. This study found that six non-cost factors were related to health outcomes for a few diagnoses: hospital volume, percentage emergency room admissions for a given diagnosis, hospital competition, specialty, bed size, and hospital region.^

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Permanent.Plot.ch - a database for Swiss permanent vegetation plots

The aim of the Permanent.Plot.ch project is the conservation of historical data about permanent plots in Switzerland and the monitoring of vegetation in a context of environmental changes (mainly climate and land use). Permanent plots are currently being recognized as valuable tools to monitor long-term effects of environmental changes on vegetation. Often used in short studies (3 to 5 years), they are generally abandoned at the end of projects. However, their full potential might only be revealed after 10 or more years, once the location is lost. For instance, some of the oldest permanent plots in Switzerland (first half of the 20th century) were nearly lost, although they are now very valuable data. The Permanent.Plot.ch national database (GIVD ID EU-CH-001), by storing historical and recent data, will allow to ensuring future access to data from permanent vegetation plots. As the database contains some private data, it is not directly available on internet but an overview of the data can be downloaded from internet (http://www.unil.ch/ppch) and precise data are available on request.

Development of a cross-European virtual sample repository and HIV resistance database

BACKGROUND: Several European HIV observational data bases have, over the last decade, accumulated a substantial number of resistance test results and developed large sample repositories, There is a need to link these efforts together, We here describe the development of such a novel tool that allows to bind these data bases together in a distributed fashion for which the control and data remains with the cohorts rather than classic data mergers.METHODS: As proof-of-concept we entered two basic queries into the tool: available resistance tests and available samples. We asked for patients still alive after 1998-01-01, and between 180 and 195 cm of height, and how many samples or resistance tests there would be available for these patients, The queries were uploaded with the tool to a central web server from which each participating cohort downloaded the queries with the tool and ran them against their database, The numbers gathered were then submitted back to the server and we could accumulate the number of available samples and resistance tests.RESULTS: We obtained the following results from the cohorts on available samples/resistance test: EuResist: not availableI11,194; EuroSIDA: 20,71611,992; ICONA: 3,751/500; Rega: 302/302; SHCS: 53,78311,485, In total, 78,552 samples and 15,473 resistance tests were available amongst these five cohorts. Once these data items have been identified, it is trivial to generate lists of relevant samples that would be usefuI for ultra deep sequencing in addition to the already available resistance tests, Saon the tool will include small analysis packages that allow each cohort to pull a report on their cohort profile and also survey emerging resistance trends in their own cohort,CONCLUSIONS: We plan on providing this tool to all cohorts within the Collaborative HIV and Anti-HIV Drug Resistance Network (CHAIN) and will provide the tool free of charge to others for any non-commercial use, The potential of this tool is to ease collaborations, that is, in projects requiring data to speed up identification of novel resistance mutations by increasing the number of observations across multiple cohorts instead of awaiting single cohorts or studies to reach the critical number needed to address such issues.

Distinguishing post-AGB impostors in a sample of pre-main sequence stars

Context. A sample of 27 sources, cataloged as pre-main sequence stars by the Pico dos Dias Survey (PDS), is analyzed to investigate a possible contamination by post-AGB stars. The far-infrared excess due to dust present in the circumstellar envelope is typical of both categories: young stars and objects that have already left the main sequence and are suffering severe mass loss. Aims. The two known post-AGB stars in our sample inspired us to seek for other very likely or possible post-AGB objects among PDS sources previously suggested to be Herbig Ae/Be stars, by revisiting the observational database of this sample. Methods. In a comparative study with well known post-AGBs, several characteristics were evaluated: (i) parameters related to the circumstellar emission; (ii) spatial distribution to verify the background contribution from dark clouds; (iii) spectral features; and (iv) optical and infrared colors. Results. These characteristics suggest that seven objects of the studied sample are very likely post-AGBs, five are possible post-AGBs, eight are unlikely post-AGBs, and the nature of seven objects remains unclear.

Detailed study of the K(L)->pi(0)pi(0)pi(0) Dalitz plot

Using a sample of 68.3x10(6) K(L)->pi(0)pi(0)pi(0) decays collected in 1996-1999 by the KTeV (E832) experiment at Fermilab, we present a detailed study of the K(L)->pi(0)pi(0)pi(0) Dalitz plot density. We report the first observation of interference from K(L)->pi(+)pi(-)pi(0) decays in which pi(+)pi(-) rescatters to pi(0)pi(0) in a final-state interaction. This rescattering effect is described by the Cabibbo-Isidori model, and it depends on the difference in pion scattering lengths between the isospin I=0 and I=2 states, a(0)-a(2). Using the Cabibbo-Isidori model, and fixing (a(0)-a(2))m(pi)(+)=0.268 +/- 0.017 as measured by the CERN-NA48 collaboration, we present the first measurement of the K(L)->pi(0)pi(0)pi(0) quadratic slope parameter that accounts for the rescattering effect: h(000)=(+0.59 +/- 0.20(stat)+/- 0.48(syst)+/- 1.06(ext))x10(-3), where the uncertainties are from data statistics, KTeV systematic errors, and external systematic errors. Fitting for both h(000) and a(0)-a(2), we find h(000)=(-2.09 +/- 0.62(stat)+/- 0.72(syst)+/- 0.28(ext))x10(-3), and m(pi)(+)(a(0)-a(2))=0.215 +/- 0.014(stat)+/- 0.025(syst)+/- 0.006(ext); our value for a(0)-a(2) is consistent with that from NA48.

Brazilian flavonoid database: Application of quality evaluation system

Much information on flavonoid content of Brazilian foods has already been obtained; however, this information is spread in scientific publications and non-published data. The objectives of this work were to compile and evaluate the quality of national flavonoid data according to the United States Department of Agriculture`s Data Quality Evaluation System (USDA-DQES) with few modifications, for future dissemination in the TBCA-USP (Brazilian Food Composition Database). For the compilation, the most abundant compounds in the flavonoid subclasses were considered (flavonols, flavones, isoflavones, flavanones, flavan-3-ols, and anthocyanidins) and the analysis of the compounds by HPLC was adopted as criteria for data inclusion. The evaluation system considers five categories, and the maximum score assigned to each category is 20. For each data, a confidence code (CC) was attributed (A, B, C and D), indicating the quality and reliability of the information. Flavonoid data (773) present in 197 Brazilian foods were evaluated. The CC ""C"" (as average) was attributed to 99% of the data and ""B"" (above average) to 1%. The main categories assigned low average scores were: number of samples; sampling plan and analytical quality control (average scores 2, 5 and 4, respectively). The analytical method category received an average score of 9. The category assigned the highest score was the sample handling (20 average). These results show that researchers need to be conscious about the importance of the number and plan of evaluated samples and the complete description and documentation of all the processes of methodology execution and analytical quality control. (C) 2010 Elsevier Inc. All rights reserved.