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Written by renowned experts in the field, Sampling Strategies for
Natural Resources and the Environment covers the sampling
techniques used in ecology, forestry, environmental science, and
natural resources. The book presents methods to estimate aggregate
characteristics on a per unit area basis as well as on an elemental
basis. In addition to common sampling designs such as simple random
sampling and list sampling, the authors explore more specialized
designs for sampling vegetation, including randomized branch
sampling and 3P sampling. One of the book's unique features is the
emphasis on areal sampling designs, including plot/quadrat
sampling, Bitterlich sampling, line intersect sampling, and several
lesser known designs. The book also provides comprehensive
solutions to the problem of edge effect. Another distinguishing
aspect is the inclusion of sampling designs for continuums,
focusing on the methods of Monte Carlo integration. By presenting a
conceptual understanding of each sampling design and estimation
procedure as well as mathematical derivations and proofs in the
chapter appendices, this text promotes a deep understanding of the
underpinnings of sampling theory, estimation, and inference.
Moreover, it will help you reliably sample natural populations and
continuums.
Written by renowned experts in the field, Sampling Strategies for
Natural Resources and the Environment covers the sampling
techniques used in ecology, forestry, environmental science, and
natural resources. The book presents methods to estimate aggregate
characteristics on a per unit area basis as well as on an elemental
basis. In addition to common sampling designs such as simple random
sampling and list sampling, the authors explore more specialized
designs for sampling vegetation, including randomized branch
sampling and 3P sampling. One of the book's unique features is the
emphasis on areal sampling designs, including plot/quadrat
sampling, Bitterlich sampling, line intersect sampling, and several
lesser known designs. The book also provides comprehensive
solutions to the problem of edge effect. Another distinguishing
aspect is the inclusion of sampling designs for continuums,
focusing on the methods of Monte Carlo integration. By presenting a
conceptual understanding of each sampling design and estimation
procedure as well as mathematical derivations and proofs in the
chapter appendices, this text promotes a deep understanding of the
underpinnings of sampling theory, estimation, and inference.
Moreover, it will help you reliably sample natural populations and
continuums.
Correlated data arise in numerous contexts across a wide spectrum
of subject-matter disciplines. Modeling such data present special
challenges and opportunities that have received increasing scrutiny
by the statistical community in recent years. In October 1996 a
group of 210 statisticians and other scientists assembled on the
small island of Nantucket, U. S. A. , to present and discuss new
developments relating to Modelling Longitudinal and Spatially
Correlated Data: Methods, Applications, and Future Direc tions. Its
purpose was to provide a cross-disciplinary forum to explore the
commonalities and meaningful differences in the source and
treatment of such data. This volume is a compilation of some of the
important invited and volunteered presentations made during that
conference. The three days and evenings of oral and displayed
presentations were arranged into six broad thematic areas. The
session themes, the invited speakers and the topics they addressed
were as follows: * Generalized Linear Models: Peter
McCullagh-"Residual Likelihood in Linear and Generalized Linear
Models" * Longitudinal Data Analysis: Nan Laird-"Using the General
Linear Mixed Model to Analyze Unbalanced Repeated Measures and
Longi tudinal Data" * Spatio---Temporal Processes: David R.
Brillinger-"Statistical Analy sis of the Tracks of Moving
Particles" * Spatial Data Analysis: Noel A. Cressie-"Statistical
Models for Lat tice Data" * Modelling Messy Data: Raymond J.
Carroll-"Some Results on Gen eralized Linear Mixed Models with
Measurement Error in Covariates" * Future Directions: Peter J.
Deforestation and forest degradation have been identified as a key
source of global GHG emissions and reducing emissions from
deforestation and degradation (REDD) has been identified as an
important and low cost method for climate change mitigation. The
aim of the present study was to assess REDD as a greenhouse gas
reduction option and to assess the potential of REDD in India. SWOT
Analysis technique was used for the assessment of REDD while an
in-depth literature review, together with quantitative trend
analysis for forest degradation using Mann Kendall Test formed the
basis for assessment of the Indian situation vis-a-vis REDD.
Success of REDD Plus in India lies primarily in the 'plus'
component i.e. projects based on conservation and enhancement of
carbon stocks and reducing emissions from degradation in specific
regions.
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