bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forest Growth and Yield Modeling
Authors: Vanclay Jerome K. 
Published: September 2014

Abstract
Forest growth models attempt to quantify the growth of a forest, and are commonly used for two principal purposes: to predict the future status of a forest and the nature of any harvests from that forest; and to help consider alternative cultivation practices. Models may also find other uses, such as in education, communicating information, etc. Depending on the purpose of the model, modelers may choose to emphasize physiological detail or statistical efficiency, but generally seek both biological and statistical accuracy.
Forest Inventory
Authors: Scott Charles T. , Gove Jeffrey H. 
Published: September 2014

Abstract
Forest inventory is an accounting of trees and their related characteristics of interest over a well‐defined land area. It may be compared to census methods for human populations. For example, one of the goals of the periodic decennial census of the US is to enumerate its human population and to retrieve demographic variables such as age, sex and race. This is accomplished by a comprehensive survey of all households in the country. Similarly, forest inventories seek to enumerate the population of trees within a forest and ascertain other information, such as their volume, value, growth and species composition. For all but the smallest tracts of land, complete enumeration of individuals is usually infeasible and survey sampling techniques are required. Unlike human or animal populations, trees are sessile organisms and there is no immigration or emigration to consider. However, tree populations vary widely in their species composition, age, size, site requirements, potential value, longevity and growth – all factors that may influence the design of a forest inventory. In addition, the tree population of interest may exist over a wide variety of topography, making access potentially costly and sometimes even dangerous. Such variety often dictates sampling protocols that may be optimal only for some portion of the population in question. Alternatively, it may call for varying sampling intensity or strategy (the combination of the sampling design and associated estimators) between different regions within the population.
Geostatistics, Model‐Based
Authors: Zhu Jun , Thurman Andrew L. 
Published: September 2014

AbstractClassical geostatistics involves statistical inference for random fields with a continuous spatial index based on geostatistical data, but the methods tend to be ad hoc. Alternatively, likelihood‐based and Bayesian methods provide more efficient estimation of model parameters and more rigorous quantification of uncertainty in prediction of responses at unsampled locations.
Habitat Survey and Sampling Designs
Authors: Gould William R. 
Published: September 2014

AbstractHabitat measurements will always be an important component of ecological studies. Traditionally, researchers have used methods applied to relatively small areas using either plot‐based or plotless methods. Variables such as percent cover, density, and biomass are commonly used together as a means of quantifying habitat in a particular area. The efficacy of habitat measurements depends on observer effects, vegetation form, and spatial pattern, and the scale at which measurements are made. In all cases, choice of variables and measurement techniques directly impact accuracy of results and must be carefully considered prior to data collection. Regardless of technique, random sampling strategies such as simple or stratified designs are essential for valid statistical inference. Recent advances in satellite and radar technology have resulted in a myriad of additional methods for collecting and analyzing habitat data. Such technology has also altered the scale and types of questions being asked. However, these technological advances are not without error and still require careful use and in many cases, ground‐truthing.
Informatics, Environmental
Authors: Tochtermann Klaus 
Published: September 2014

AbstractEnvironmental informatics is the science that deals with the development and application of methods and tools for environmental information processing. The main objectives of environmental informatics are to make the best possible use of information technologies: (a) to contribute to an effective and efficient protection of the environment; (b) to provide the public with information on the state of the environment and, thus, to increase environmental awareness; (c) to support environmental planning; and (d) to forecast future states of the environment.
Intercropping, Statistical Issues In
Authors: Federer Walter T. 
Published: September 2014

Abstract

Intercropping is defined as the simultaneous or sequential growing of more than one crop on the same area of land. It may involve intermixing the plants of the crops, growing them in alternate rows, or growing them sequentially through time. When dealing with a cropping system, it is necessary to study the response for each component of the mixture (when available) as well as for the combined data for the mixture. This will involve statistical procedures not found in standard statistics textbooks. A popular statistical procedure for analyzing results from experiments involving several intercropping treatments is a relative yield ratio or land equivalent ratio (LER). The yields of the individual crops in a mixture need to be available in order to compute a LER. An LER is defined as the sum of the ratios of the yield of the
i
th crop in a mixture to its yield as a sole crop.
Law and Environmental Statistics
Authors: Esterby Sylvia R. 
Published: September 2014

Abstract
Many environmental regulations and standards are based on statistical data and inferences drawn from them. Often the statistical analysis used by the US Environmental Protection Agency (EPA) or other governmental body, in developing an exposure or effluent limit or a monitoring program, is challenged by the industry or public interest group that is most affected by its decision. Then, the courts need to examine the record on which the EPA or other agency relied in setting the standard. Because there are several statutes regulating the environment in the United States as well as in Europe, in assessing the adequacy of the underlying scientific support of a regulation or protocol, one needs to be aware of the relevant statute. This article discusses several important statistical issues that have arisen in actual cases and briefly describe some other topics that arise in this area.
Modeling, Environmental
Authors: Solow Andrew R. 
Published: September 2014

AbstractIn discussing environmental models, it is useful to distinguish between process‐based or mechanistic models and phenomenological or empirical models. Although the distinction is not always clear, mechanistic models are based on a scientific theory about the underlying environmental processes, whereas empirical models are based on observation alone. This article focuses on statistical analysis under mechanistic environmental models. This is not to imply that empirical environmental models are less interesting or less useful. Rather, it is because empirical environmental models are, from a statistical perspective, no different from empirical models in other fields. While the methods of statistical analysis under mechanistic environmental models are certainly not unique, such models tend to share certain characteristics that determine in part the kind of statistical issues that arise, and even the kind of statistical methods used to address these issues.
Multivariate Kriging
Authors: Wackernagel Hans 
Published: September 2014

Abstract
Kriging is a special type of optimal linear prediction applied to random functions, with the particular requirement that the covariance structure of the random function should be known. Kriging may be extended to several variables by specifying a corresponding system of direct and cross covariance functions to be used in cokriging. Alternatively, secondary variables known at all sample and prediction locations can be included into (co‐)kriging as external drift.
Nearest Neighbor Methods: Overview with Examples
Authors: Dixon Philip M. 
Published: September 2014

AbstractThis article discusses nearest neighbor (NN) methods for the analysis of spatial point patterns and the analysis of field experiments. The distance from a point to its NN, or quantities derived from that distance, can be used to test whether the observed locations are a realization of a specific spatial point process. Edge effects, caused by recording data only within a bounded study area, can be important. When locations are marked (e.g.,by the species of plant), NN methods provide information on the relationship between the marks. NN methods for field experiments can be used to model local spatial variability.
Ornithological Data
Authors: T. Morgan Byron J. , Viallefont Anne 
Published: September 2014

AbstractThis article introduces the different types of ornithological data, obtained either at the population level (count data, occupancy data) or at the individual level (capture–mark–recapture data, radiotracking data, and geolocation data), as well as references to the modeling/analysis techniques appropriate for each type of data. It also gives a list of organizations and web sites through which such data can be made available, as well as an extensive list of references to up‐to‐date articles and books.
Plant Epidemics, Models and Analysis
Authors: Madden Laurence V. , Hughes G. 
Published: September 2014

Abstract
Plants are susceptible to many diseases; fungal, bacterial, viral, and other forms of epidemics occur commonly in agricultural systems, resulting in substantial crop yield reductions. There are two primary ways of measuring plant disease, incidence and severity. Plant disease incidence can be defined as the number of diseased plants, or plant parts expressed as a proportion of the total number of plants, or plant parts, assessed. Plant disease incidence is a binary variable, because individuals are either diseased or not. Plant disease severity, on the other hand, can be defined as the area or volume of plant tissue that is diseased, usually relative to the total plant tissue. It is a continuous variable, typically bounded by 0 and 1. In this article, emphasis is placed on incidence. Statistical issues regarding plant diseases in crops or natural systems concern the characterization of spatial patterns of disease, estimation of rates of disease increase over time, and rates of spatial spread. There is a variety of statistical tools available to quantify spatial pattern of disease incidence, which can be categorized based on the general type of analysis – point pattern or geostatistical analysis – or on whether the data consist of mapped or unmapped observations.
Point Processes, Spatial
Authors: Guttorp Peter M. , Brillinger David R. , Schoenberg Frederic Paik 
Published: September 2014

Abstract
The general theory of point processes has usually been focused on temporal developments. The special case of events that occur in space (or, more precisely, on a plane) has developed some particular approaches, usually assuming spatial stationarity and isotropy (i.e. the distribution of the process is invariant under translations and rotations, respectively). This article focuses on this type of process. Points, or events, take place at spatial locations, and each event is identified with its location.
Remote Sensing
Authors: Shao Guofan 
Published: September 2014

Abstract
Remote sensing is both a science and an art. Remote sensing has been involved from airborne to spaceborne applications since the 1960s. A variety of sensors are used to record both the visible and the invisible, such as infrared (IR), parts of the spectrum in the field of remote sensing. Depending on the number of spectral bands used, remote sensing includes panchromatic (one band), multispectral (several bands), and hyperspectral (dozens to hundreds of bands) remote sensing; depending on the source of energy/radiation, remote sensing can be passive or active. The ultimate goal of remote sensing is to extract information from remotely sensed data about the material properties of the earth's surface and of the atmosphere, together with their geographical relationships. Numerous approaches are available to extract information from remotely sensed data/imagery. Thematic maps derived with remote sensing methods contain errors, which can be assessed with reference data. It is important to use thematic maps with relatively high classification accuracy to avoid wrong conclusions or decision making based on the maps.
R
ipley's
K
Function
Authors: Dixon Philip M. 
Published: September 2014

Abstract

Ripley's K function summarizes spatial point process data. It can be used to describe a set of locations, test hypotheses about patterns, and estimate parameters in a spatial point process model. For a stationary point process,
K
(
t
) is the expected number of additional points within distance
t
of a focal point divided by the intensity of the process. A univariate version is used for one set of locations and a multivariate version is used when points can be labeled by a small number of groups. This article reviews the properties of Ripley's K function and two related functions, then illustrates the computation and interpretation using data on the locations of trees in a swamp hardwood forest.
Risk Assessment, Seismological
Authors: Özel Gamze 
Published: September 2014

AbstractSeismic risk is usually defined as the probability of exceeding a specified level of loss due to earthquakes for a set of goods within a reference period of time. Usually it is measured by considering the convolution of three main factors: hazard (probability of earthquakes), vulnerability (probability of damage, given the earthquake), and exposure (probability of loss given the damage). Thus, the seismic risk in a geographical region depends on many uncertain variables pertaining to the three main factors listed above. Some of the uncertainties are reducible through deeper investigation, while others reflect intrinsic randomness and are therefore unavoidable. Seismic risk assessment (SRA) is very important for public safety and hazards mitigation. It is also important for the correct determination of earthquake insurance premiums, and for understanding the social and psychological effects of earthquakes. This study represents an extensive review and evaluation of SRA methodology. SRA strategy, source parameters, attenuation, and local site effects are explained in detail. Global and national seismic hazard maps relating to seismic risk are presented. A variety of references concerned with the problem SRA are given.
Space‐Time Covariance Models
Authors: Gneiting Tilmann , Schlather Martin 
Published: September 2014

Abstract
Geostatistical approaches to modeling spatiotemporal data rely on flexible space–time covariance models. The area has been in vigorous development recently, and we review constructions of nonseparable spatiotemporal covariance functions that are based on geometric anisotropy, Fourier inversion, completely monotone functions, and dynamic physical models.
Spatial Optimization
Authors: Hof John 
Published: September 2014

Abstract
This article describes an emerging methodology that captures spatial relationships between different land areas in the process of maximizing an objective function subject to resource constraints. It discusses adjacency constraints and direct approaches. Adjacency constraints are utilized when scheduling timber harvests with legal or regulatory restrictions on the effective size of harvest area. Adjacency relationships are important because, for example, if two seemingly legal‐size clearcuts occur within a short time of each other in adjacent areas, then the effective size of the clearcut may not be legal. Direct approaches include the actual ecological spatial relationships of concern. An example is provided that accounts for wildlife habitat fragmentation in optimally placing timber harvests. The land area is divided into cells and choice variables are defined to represent complete scheduled management prescriptions for each cell. Wildlife population levels are then determined by a combination of potential growth, dispersal, and spatially located carrying capacities determined by the management prescription allocations. Other examples of this type of direct spatial optimization are also cited.
Species Distribution, Monitoring Changes in
Authors: Yoccoz Nigel G. 
Published: September 2014

Abstract

Changes in species distributions constitute strong evidence that global change is affecting ecosystems and biodiversity. Expansion of invasive species and retraction of endangered species are examples of environmental issues that need to be understood and addressed. Monitoring range shifts require that we can accurately estimate the state(s) of the system – that is, the present and past distributions – its rate of change, and assess the underlying causes. Existing monitoring programs differ in terms of data quality and coverage of the environmental space, and very few have considered different sources of errors as well as alternative explanations of observed changes. Modeling species distributions is needed to derive alternative predictions of future changes. The design of monitoring programs should assess the assumptions
and
the predictions of these models.
Temporal Change
Authors: Esterby Sylvia 
Published: September 2014

Abstract
The study of temporal change in environmental quality indicators is motivated by the need to know if the state of the environment has deteriorated or, in some cases, if it has improved as a result of the removal or lessening of stress on the system. The data available for study of temporal change are observational, and usually it is not even possible to relate there to observational data on the level of the stressor. Thus, statements about the change in the quality indicator are made on the basis of measurements and analysis, where other effects have been removed as much as possible from the term corresponding to change over time.
Tessellations
Authors: Schoenberg Frederic Paik 
Published: September 2014

Abstract

A
tessellation
may be defined as a division of a space into convex polygonal regions; divisions of the
plane
(

R
2

) are most often discussed. While geometrists centuries ago reserved the term tessellation merely for divisions of the plane into
regular
polygons of equal size, most authors now define tessellations very broadly so as to include any arrangements of nonoverlapping shapes covering

R
d

, as well as partitions of other metric spaces.
Thresholds, Ecological
Authors: Toms Judith D. 
Published: September 2014

Abstract
Ecological thresholds occur when the relationship between the response and explanatory variables is nonlinear, changing abruptly at one or more values. Such thresholds may be continuous or discontinuous, and different methods for estimating the location and number of thresholds have been developed for each type. For continuous thresholds, methods based on piecewise regression are preferred. Discontinuous thresholds can be transformed using the cumulative sum, resulting in transformed data with continuous thresholds, and piecewise regression techniques are then used for inference. Alternatively, Bayesian change‐point analysis can be used for inference. Special methods have been developed for detecting thresholds in spatial data.
Time Series, Ecological
Authors: Solow Andrew R. 
Published: September 2014

Abstract

The most common situation in which time series data are the central object of ecological analysis is in population dynamics/population modeling. Let
X

t

be the size or density of a population in period
t
. A general deterministic model of the dynamics of this population is the first‐order nonlinear difference equation:
X

t

=
X

t
−1

F
(
X

t
−1

) where
F
is a nonnegative function.
Time Series, Periodic
Authors: Lund Robert B. 
Published: September 2014

Abstract
This entry overviews time series with periodic autocovariance structures. Such series often arise in the environmental sciences. Here, extensions to the class of covariance stationary time series are introduced and used to model and describe the autocovariance periodicities. Detection of autocovariance periodicities is considered. A series of monthly averaged temperatures from Griffin, Georgia (United States) is used to illustrate aspects of the discourse.
Trace Level Detection
Authors: Little Tom Tadfor , Miller Guthrie , Martz Harry F. , Inkret William C.T. , Schillaci Mario E. 
Published: September 2014

Abstract
An important component of protecting individuals from the hazards of radiation exposure is to be able to determine whether radionuclides have been taken into the body, and in what quantities. For example, a urine sample from an individual who works with plutonium can be analyzed by α spectroscopy or mass spectroscopy to determine if plutonium is present in the sample, and hence to make an inference about possible plutonium intakes in the individual's history. Often, the quantities of plutonium that one seeks to detect are comparable to the measurement uncertainties. Hence, statistical considerations are a very important aspect of the detection methodology.
Trawl Surveys
Authors: Nordahl Victor , Clark Steve 
Published: September 2014

Abstract
Trawl surveys for fishery resources are conducted throughout the world by government and non‐government agencies. Modern fishery management requires standard time series of data on management units or ‘stocks’ and their response to exploitation: trends in abundance and biomass; spawning success; biological parameters; distribution; and environmental factors that can be provided only through standardized sampling programs. Trawl surveys provide the opportunity for controlled sampling in space and time.
Water Quality Modeling
Authors: Whitehead Paul G. 
Published: September 2014

Abstract
In addition to being major sources of water, river systems are used as the principal disposal pathways for industrial, agricultural, and domestic effluent. As demand for water increases and water quality deteriorates, there is a requirement for effective decision making techniques which can be applied to solve water quality management problems. Over the past century, increasing effort has been devoted to the development of suitable techniques, largely by engineers, system analysts, and scientists. As long ago as 1912 the Royal Commission on Environmental Pollution in the UK proposed a simple 1 to 8 ratio for effluent discharge to river flow in an attempt to clean up UK rivers. In the USA major developments by Streeter and Phelps led to the dissolved oxygen (DO) and biochemical oxygen demand (BOD) equations that enable steady‐state and dynamic modeling of oxygen balances in rivers. Major studies in the area of water quality modeling were undertaken in the US (e.g. Great Lakes, Delaware Study), the UK (e.g. Trent Study and Bedford Case Study), and in Australia (Peel–Harvey Inlet), and these all evolved new techniques to address major water quality problems.