GIS, Environmental Modeling and Engineering

Second Edition
Allan Brimicombe

CRC Press
© 2010 by Taylor and Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, an Informa business

International Standard Book Number: 978-1-4398-0870-2 (Hardback)

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Integrating models and geographical information systems

Roger Bivand and Anne Lucas

Since geographic information systems (GIS) currently dominate our perception
of how computing and geography should interface, and since GeoComputation
(GC) is providing analysts of spatial phenomena with ever more powerful
computing tools, it may be helpful to examine the experience that has accrued
concerning links between them. Our examination is both empirical and
normative, and the reader may find it useful to repeat at least some of our
literature surveys, since new papers and articles are accumulating rapidly.
Searching on the key words ‘GIS’ and ‘model*’ or ‘integral*’, where ‘*’ is the
wild card, led to a wide range of hits both in ISI Science and Social Science
Citations Indices, and in OCLC-FirstSearch. These sources primarily contain
journal articles, while conference proceedings may be searched at the Ohio State
University GIS Master Bibliography Project, and more recently through the
web-sites of conference organizers, such as NCGIA and GISDATA in Europe.
Adding these resources to what we already knew about the issues involved, we
were able to scan the field for interesting regularities, trends, and citation
clustering. read more

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Models and queries in a spatiotemporal GIS

Baher El-Geresy and Christopher Jones

Much interest has been evidenced lately in the combined handling of spatial and temporal
information in large spatial databases. In GIS, as well as in other fields (Silva et al.,
1997), research has been accumulating on different aspects of spatio-temporal
representation and reasoning (Stock, 1997). The combined handling of spatio-temporal
information allows for more sophisticated application and utilisation of these systems.
Developing a Temporal GIS (TGIS) leads to a system which is capable of tracing and
analysing the changing states of study areas, storing historic geographic states and
anticipating future states. A TGIS can ultimately be used to understand the processes
causing geographic change and relate different processes to derive patterns in the data. read more

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Essential Image Processing and GIS for Remote Sensing

Jian Guo Liu
Philippa J. Mason

Imperial College London, UK

This edition first published 2009, # 2009 by John Wiley & Sons Ltd.

Overview of the Book

From an applied viewpoint, and mainly for Earth observation, remote sensing is a tool for collecting raster data or images. Remotely sensed images represent an objective record of the spectrum relating to the physical properties and chemical composition of the Earth surface materials. Extracting information from images is, on the other hand, a subjective process. People with differing application foci will derive very different thematic information from the same source image. Image processing thus becomes a vital tool for the extraction of thematic and/or quantitative information from raw image data. For more comprehensive analysis, the images need to be analysed in conjunction with other complementary data, such as existing thematic maps of topography, geomorphology, geology and land use, or with geochemical and geophysical survey data, or ‘ground truth’ data, logistical and infrastructure information, which is where the geographical information system (GIS) comes into play. GIS contains highly sophisticated tools for the management, display and analysis of all kinds of spatially referenced information.

Remote sensing, image processing and GIS are all extremely broad subjects in their own right and are far too broad to be covered in one book. As illustrated in Figure 1, this book aims to pinpoint the overlap between the three subjects, providing an overview of essential techniques and a selection of case studies in a variety of application areas. The application cases are biased towards the earth sciences but the image processing and GIS techniques are generic and therefore transferable skills suited to all applications.
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Distributed Hydrologic Modeling Using GIS

Second Edition
School of Civil Engineering and Environmental Science,
University of Oklahoma,
Norman, U.S.A.

©2005 Springer Science + Business Media, Inc

Distributed modeling is becoming a more commonplace approach to
hydrology. During ten years serving with the USDA Soil Conservation
Service (SCS), now known as the Natural Resources Conservation Service
(NRCS), I became interested in how millions of dollars in construction
contract monies were spent based on simplistic hydrologic models. As a
project engineer in western Kansas, I was responsible for building flood
control dams (authorized under Public Law 566) in the Wet Walnut River
watershed. This watershed is within the Arkansas-Red River basin, as is the
Illinois River basin referred to extensively in this book. After building nearly
18 of these structures, I became Assistant State Engineer in Michigan and,
for a short time, State Engineer for NRCS. Again, we based our entire design
and construction program on simplified relationships variously referred to as
the SCS method. I recall announcing that I was going to pursue a doctoral
degree and develop a new hydrologic model. One of my agency’s chief
engineers remarked, “Oh no, not another model!” Since then, I hope that I
have not built just another model but have significantly advanced the state of
hydrologic modeling.
This book sets out principles for modeling hydrologic processes
distributed in space and time using the geographic information system (GIS),
a spatial data management tool. Any hydrologic model is an abstract
representation of a component of a natural process. The science and
engineering aspects of hydrology have been long clouded by gross
simplifications. Representation by lumping of parameters at the river basin
scale such that a single value of slope or hydraulic roughness controls the
basin response may have served well when computer resources were limited
and spatial datasets of soils, topography, landuse, and precipitation did not
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