Landscape Pattern Analysis for Assessing Ecosystem Condition / Edition 1

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As we begin the 21st century, one of our greatest challenges is the preservation and remediation of ecosystem integrity. This requires monitoring and assessment over large geographic areas, repeatedly over time, and therefore cannot be practically fulfilled by field measurements alone. Remotely sensed imagery therefore plays a crucial role by its ability to monitor large spatially continuous areas. This technology increasingly provides extensive spatial-temporal data; however, the challenge is to extract meaningful environmental information from such extensive data.

Landscape Pattern Analysis for Assessing Ecosystem Condition presents a new method for assessing spatial pattern in raster land cover maps based on satellite imagery in a way that incorporates multiple pixel resolutions. This is combined with more conventional single-resolution measurements of spatial pattern and simple non-spatial land cover proportions to assess predictability of both surface water quality and ecological integrity within watersheds of the state of Pennsylvania (USA). The efficiency of remote sensing for rapidly assessing large areas is realized through the ability to explain much of the variability of field observations that took several years and many people to obtain.

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Product Details

  • ISBN-13: 9780387376844
  • Publisher: Springer US
  • Publication date: 10/28/2006
  • Series: Environmental and Ecological Statistics Series , #1
  • Edition description: 2007
  • Edition number: 1
  • Pages: 130
  • Product dimensions: 6.20 (w) x 9.30 (h) x 0.50 (d)

Meet the Author

Glen Johnson is a Research Scientist with the New York State Department of Health and an Assistant Professor in the University at Albany, State University of New York, School of Public Health in Albany, New York. He holds a PhD in Quantitative Ecology, a Masters in Statistics and a Masters in Ecology.

With a cross-disciplinary background, Glen has been involved with environmental issues ranging from toxicology to landscape ecology. He has since ventured into public health and currently specializes in observational epidemiological studies using large databases and spatial analysis of environmental and public health data. As part of this activity, he teaches "GIS and Public Health" each year and serves on several interagency GIS workgroups within New York State.

G.P. Patil is know to everyone as "GP". He is Distinguished Professor of Mathematical and Environmental Statistics in the Department of Statistics at the Pennsylvania State University, and is a former Visiting Professor of Biostatistics at Harvard University in the Harvard School of Public Health.

He has a Ph.D. in Mathematics, D.Sc. in Statistics, one Honorary Degree in Biological Sciences, and another in Letters. GP is a Fellow of American Statistical Association, Fellow of American Association of Advancement of Science, Fellow of Institute of Mathematical Statistics, Elected Member of the International Statistical Institute, Founder Fellow of the National Institute of Ecology and the Society for Medical Statistics in India.

GP has been a founder of Statistical Ecology Section of International Association for Ecology and Ecological Society of America, a founder of Statistics and Environment Section of American Statistical Association, and a founder of the International Society for Risk Analysis. He is founding editor-in-chief of the international journal, Environmental and Ecological Statistics and founding director of the Penn State Center for Statistical Ecology and Environmental Statistics. He has published thirty volumes and three hundred research papers. GP has received several distinguished awards which include: Distinguished Statistical Ecologist Award of the International Association for Ecology, Distinguished Achievement Medal for Statistics and the Environment of the American Statistical Association, Distinguished Twentieth Century Service Award for Statistical Ecology and Environmental Statistics of the Ninth Lukacs Symposium, Best Paper Award of the American Fisheries Society, and lately, the Best Paper Award of the American Water Resources Association, among others.

Currently, GP is principal investigator of a multi-year NSF grant for surveillance geoinformatics for hotspot detection and prioritization across geographic regions and networks for digital government in the 21st Century. The project has a dual disciplinary and cross-disciplinary thrust. You are invited to do a live case study important for your in-house work.

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Table of Contents

List of Figures     ix
List of Tables     xiii
Preface     xv
Acknowledgments     xvii
Introduction     1
Loss of Biodiversity through Excessive Habitat Fragmentation     2
Land Use Pattern and Surface Water Quality     5
Quantifying Landscape Pattern     5
Application to Watershed-Delineated Landscapes in Pennsylvania     7
Land Cover Grids     8
Watersheds based on the State Water Management Plan     9
Methods for Quantitative Characterization of Landscape Pattern     13
Single-resolution Measurements     13
The Conditional Entropy Profile     16
Computing Conditional Entropy of Expected Frequencies based on Single -Resolution Maps     18
Illustrations     23
Example 1: Checkerboard Map     23
Example 2: Irregular Black and White Map     26
Example 3: Illustration with Actual Landscapes     36
Classifying Pennsylvania Watersheds on the Basis of Landscape Characteristics     41
Introduction     41
Clustering Watersheds into Common Groups     43
Comparison to Conditional Entropy Profiles     53
Predictability of Surface Water Pollution in PennsylvaniaUsing Watershed-Based Landscape Measurements     57
Introduction     57
Surface Water Pollution Assessment     57
Nitrogen Loading     57
Pollution Potential Index     60
Selecting an Initial Set of Landscape Pattern Variables     63
Linear Models for Relating Water Pollution Loading to Landscape Variables     66
Predicting In-Stream Nitrogen Loading     68
Predicting a Pollution Potential Index     70
Interpretation     72
Predictability of Bird Community-Based Ecological Integrity Using Landscape Variables     79
Introduction     79
Assessing Ecological Integrity using Songbird Community Composition     80
Background     80
Application to Breeding Bird Data     85
Block-Level BCI Values     85
Watershed-level Ecological Integrity     86
Relating Landscape Attributes to the Songbird-based Assessments of Watershed-wide Ecological Integrity     91
Linear Regression Modeling     91
Clustering     95
Conditional Entropy Profiles     97
Ecological Integrity based on All Species in the Breeding Bird Atlas     100
Relation to Landscape Attributes     101
Comparison of the Different Methods for Computing a Bird Community Index     104
Summary     106
Summary and Future Directions     109
References     115
Index     129
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