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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.
The book is designed to be a textbook for university students
(MSc-PhD level) and a reference for researchers and practitioners.
It is an introduction to dynamic modelling of forest growth based
on ecological theory but aiming for practical applications for
forest management under environmental change. It is largely based
on the work and research findings of the authors, but it also
covers a wide range of literature relevant to process-based forest
modelling in general. The models presented in the book also serve
as tools for research and can be elaborated further as new research
findings emerge. The material in the book is arranged such that the
student starts from basic concepts and formulations, then moves
towards more advanced theories and methods, finally learning about
parameter estimation, model testing, and practical application.
Exercises with solutions and hands-on R-code are provided to help
the student digest the concepts and become proficient with the
methods. The book should be useful for both forest ecologists who
want to become modellers, and for applied mathematicians who want
to learn about forest ecology. The basic concepts and theory are
formulated in the first four chapters, including a review of
traditional descriptive forest models, basic concepts of carbon
balance modelling applied to trees, and theories and models of tree
and forest structure. Chapter 5 provides a synthesis in the form of
a core model which is further elaborated and applied in the
subsequent chapters. The more advanced theories and methods in
Chapters 6 and 7 comprise aspects of competition through tree
interactions, and eco-evolutionary modelling, including
optimisation and game theory, a topical and fast developing area of
ecological modelling under climate change. Chapters 8 and 9 are
devoted to parameter estimation and model calibration, showing how
empirical and process-based methods and related data sources can be
bridged to provide reliable predictions. Chapter 10 demonstrates
some practical applications and possible future development paths
of the approach. The approach in this book is unique in that the
models presented are based on ecological theory and research
findings, yet sufficiently simple in structure to lend themselves
readily to practical application, such as regional estimates of
harvest potential, or satellite-based monitoring of growth. The
applicability is also related to the objective of bridging
empirical and process-based approaches through data assimilation
methods that combine research-based ecological measurements with
standard forestry data. Importantly, the ecological basis means
that it is possible to build on the existing models to advance the
approach as new research findings become available.
The book is designed to be a textbook for university students
(MSc-PhD level) and a reference for researchers and practitioners.
It is an introduction to dynamic modelling of forest growth based
on ecological theory but aiming for practical applications for
forest management under environmental change. It is largely based
on the work and research findings of the authors, but it also
covers a wide range of literature relevant to process-based forest
modelling in general. The models presented in the book also serve
as tools for research and can be elaborated further as new research
findings emerge. The material in the book is arranged such that the
student starts from basic concepts and formulations, then moves
towards more advanced theories and methods, finally learning about
parameter estimation, model testing, and practical application.
Exercises with solutions and hands-on R-code are provided to help
the student digest the concepts and become proficient with the
methods. The book should be useful for both forest ecologists who
want to become modellers, and for applied mathematicians who want
to learn about forest ecology. The basic concepts and theory are
formulated in the first four chapters, including a review of
traditional descriptive forest models, basic concepts of carbon
balance modelling applied to trees, and theories and models of tree
and forest structure. Chapter 5 provides a synthesis in the form of
a core model which is further elaborated and applied in the
subsequent chapters. The more advanced theories and methods in
Chapters 6 and 7 comprise aspects of competition through tree
interactions, and eco-evolutionary modelling, including
optimisation and game theory, a topical and fast developing area of
ecological modelling under climate change. Chapters 8 and 9 are
devoted to parameter estimation and model calibration, showing how
empirical and process-based methods and related data sources can be
bridged to provide reliable predictions. Chapter 10 demonstrates
some practical applications and possible future development paths
of the approach. The approach in this book is unique in that the
models presented are based on ecological theory and research
findings, yet sufficiently simple in structure to lend themselves
readily to practical application, such as regional estimates of
harvest potential, or satellite-based monitoring of growth. The
applicability is also related to the objective of bridging
empirical and process-based approaches through data assimilation
methods that combine research-based ecological measurements with
standard forestry data. Importantly, the ecological basis means
that it is possible to build on the existing models to advance the
approach as new research findings become available.
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