Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-05T19:55:37.533Z Has data issue: false hasContentIssue false

2 - Adequate data of known accuracy are critical to advancing the field of landscape ecology

Published online by Cambridge University Press:  12 January 2010

By
Louis R. Iverson
Edited by
Jianguo Wu  and
Richard J. Hobbs
Louis R. Iverson
Affiliation:
USDA Forest Service, 359 Main Road, Delaware, OH 43015, USA
Jianguo Wu
Affiliation:
Arizona State University
Richard J. Hobbs
Affiliation:
Murdoch University, Western Australia
Get access

Summary

Introduction

The science of landscape ecology is especially dependent on high-quality data because often it focuses on broad-scale patterns and processes and deals in the long term. Likewise, high quality data are necessary as the basis for building policy. When issues, such as climate change, can induce international political and economic consequences, it becomes clear that providing high-quality, long-term data is paramount. It is not an accident that this chapter is positioned near the front of this book. Of the priority research topics presented in this book, this is the most pervasive across other topics because the availability of high-quality data limits progress in other realms. Be it historic land-use data needed to understand the dynamics of land-use change, the independent data of varying scales needed to assess scaling phenomena or test new metrics, the socioeconomic/cultural data needed to integrate humans into landscape ecology, or the biological and population data needed to evaluate ecological flows, the quality of raw data, metadata, and derived data products is critical to the core of landscape ecology. For each of these key topics and perspectives, the availability and quality of data will affect research results and practical recommendations.

Data advances in past two decades

It has been two decades since the 1983 workshop that many say established the landscape ecology field in North America (Risser et al. 1984).

Type
Chapter
Information
Key Topics in Landscape Ecology , pp. 11 - 38
Publisher: Cambridge University Press
Print publication year: 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. R., Hardy, E. E., and Roach, J. T.. 1976. A land-use classification system for use with remote-sensor data. Circular 671. Reston, VA: United States Geological Survey.Google Scholar
Anderson, R. C. 1970. Prairies in the prairie state. Transactions of the Illinois State Academy of Science 63, 214–21.Google Scholar
Arbia, G., Griffith, D., and Haining, R.. 1998. Error propagation modelling in raster GIS: overlay operations. International Journal of Geographical Information Science 12, 145–67.CrossRefGoogle Scholar
August, P., L. R. Iverson, and J. Nugranad. 2002. Human conversion of terrestrial landscapes. Pages 198–224 in Gutzwiller, K., (ed.). Applying Landscape Ecology in Biological Conservation. New York: Springer-Verlag.CrossRefGoogle Scholar
Austin, M. P. 1998. An ecological perspective on biodiversity investigations: examples from Australian eucalypt forests. Annals of the Missouri Botanical Garden 85, 2–17.CrossRefGoogle Scholar
Beard, K. 2001. Roles of meta-information in uncertainty management. Pages 363–78 in Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., and Case, T. J. (eds.). Spatial Uncertainty in Ecology. New York: Springer-Verlag.CrossRefGoogle Scholar
Bernert, J. A., Eilers, J. M., Sullivan, T. J., Freemark, K. E., and Ribic, C.. 1997. A quantitative method for delineating regions: an example for the western Corn Belt plains ecoregion of the USA. Environmental Management 21, 405–20.CrossRefGoogle ScholarPubMed
Boerner, R. E. J., Morris, S. J., Sutherland, E. K., and Hutchinson, T. F.. 2000. Spatial variability in soil nitrogen dynamics after prescribed burning in Ohio mixed-oak forests. Landscape Ecology 15, 425–39.CrossRefGoogle Scholar
Breiman, L. 1996. Bagging predictors. Machine Learning 24, 123–40.CrossRefGoogle Scholar
Breiman, L. 2001. Random forests. Machine Learning 45, 5–32.CrossRefGoogle Scholar
Breiman, L., Freidman, J., Olshen, R., and Stone, C.. 1984. Classification and regression trees. Belmont, CA: Wadsworth.Google Scholar
Burrough, P. A. 1987. Spatial aspects of ecological data. Pages 213–57 in Jongman, R. H. G., Braak, C. J. F., and Jongeren, D. F. R. (eds.). Data Analysis in Community and Landscape Ecology. Wageningen: Pudoc.Google Scholar
Burrough, P. A. and McDonnell, R. A.. 1998. Principles of Geographical Information Systems. Oxford: Oxford University Press.Google Scholar
Burrows, S. N., Gower, S. T., Clayton, M. K., et al. 2002. Application of geostatistics to characterize leaf area index (LAI) from flux tower to landscape scales using a cyclic sampling design. Ecosystems 5, 667–79.Google Scholar
Buttenfield, B. P. 2001. Mapping ecological uncertainty. Pages 115–32 in Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., and Case, T. J. (eds.). Spatial Uncertainty in Ecology. New York: Springer-Verlag.CrossRefGoogle Scholar
Cairns, D. M. 2001. A comparison of methods for predicting vegetation type. Plant Ecology 156, 3–18.CrossRefGoogle Scholar
Cawsey, E. M., Austin, M. P., and Baker, B. L.. 2002. Regional vegetation mapping in Australia: a case study in the practical use of statistical modelling. Biodiversity and Conservation 11, 2239–74.CrossRefGoogle Scholar
Cliff, A. D. and Ord, J. K.. 1981. Spatial Processes: Models and Applications. London: Pion.Google Scholar
Cook, E. A., Iverson, L. R., and Graham, R. L. (eds.). 1987. The Relationship of Forest Productivity to Landsat Thematic Mapper Data and Supplemental Terrain Information. Sioux Falls, SD: American Society for Photogrammetry and Remote Sensing.Google Scholar
Cook, E. A., Iverson, L. R., and Graham, R. L.. 1989. Estimating forest productivity with thematic mapper and biogeographical data. Remote Sensing of Environment 28, 131–41.CrossRefGoogle Scholar
Cornelis, B. and S. Brunet. 2002. A policy-maker point of view on uncertainties in spatial decisions. Pages 168–85 in Shi, W., Fisher, P. F., and Goodchild, M. F. (eds.). Spatial Data Quality. New York: Taylor & Francis.CrossRefGoogle Scholar
Costanza, R., Sklar, F. H., and White, M. L.. 1990. Modeling coastal landscape dynamics. BioScience 40, 91–107.CrossRefGoogle Scholar
Cressie, N. 1991. Statistics for Spatial Data. New York: John Wiley & Sons Inc.Google Scholar
Cushman, S. A. and McGarigal, K.. 2002. Hierarchical, multi-scale decomposition of species-environment relationships. Landscape Ecology 17, 637–46.CrossRefGoogle Scholar
DeVeaux, R. D., Psichogios, D. C., and Ungar, L. H.. 1993. A comparison of two nonparametric estimation schemes: MARS and neural networks. Computations in Chemical Engineering 8, 819–37.CrossRefGoogle Scholar
Diamond, J. 1999. Guns, Germs, and Steel: The Fates of Human Societies. New York: W. W. Norton & Co.Google Scholar
Donovan, M. L., Rabe, D. L., and Olson, C. E.. 1987. Use of geographic information systems to develop habitat suitability models. Wildlife Society Bulletin 15, 574–9.Google Scholar
Drecki, I. 2002. Visualisation of uncertainty in geographical data. Pages 140–59 in Shi, W., Fisher, P. F., and Goodchild, M. F. (eds.). Spatial Data Quality. New York: Taylor & Francis.CrossRefGoogle Scholar
Duckham, M. and J. E. McCreadie. 2002. Error-aware GIS development. Pages 62–75 in Shi, W., Fisher, P. F., and Goodchild, M. F. (eds.). Spatial Data Quality. New York: Taylor & Francis.CrossRefGoogle Scholar
Eastman, R. 2001. Uncertainty management in GIS: decision support tools for effective use of spatial data resources. Pages 379–90 in Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., and Case, T. J. (eds.). Spatial Uncertainty in Ecology. New York: Springer-Verlag.CrossRefGoogle Scholar
Ehleringer, J. R. and Field, C. B.. 1993. Scaling Processes between Leaf and Landscape Levels. San Diego: Academic Press.Google Scholar
ESRI (Environmental Systems Research Institute). 1993. Arc/Info GRID Command Reference. Redlands, CA: Environmental Systems Research Institute.
Ferrier, S., Drielsma, M., Manion, G., and Watson, G.. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biodiversity and Conservation 11, 2309–38.CrossRefGoogle Scholar
Fortin, M.-J. 1994. Edge detection algorithms for two-dimensional ecological data. Ecology 75, 956–65.CrossRefGoogle Scholar
Fortin, M.-J. and Drapeau, P.. 1995. Delineation of ecological boundaries: comparison of approaches and significance tests. Oikos 72, 120–31.CrossRefGoogle Scholar
Fortin, M. -J., Olson, R. J., Ferson, S., et al. 2000. Issues related to the detection of boundaries. Landscape Ecology 15, 453–66.CrossRefGoogle Scholar
Foster, D. R., Knight, D. H., and Franklin, J. F.. 1998. Landscape patterns and legacies resulting from large, infrequent forest disturbances. Ecosystems 1, 497–510.CrossRefGoogle Scholar
Franklin, J. 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 19, 494–519.Google Scholar
Franklin, J. 1998. Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. Journal of Vegetation Science 9, 733–48.CrossRefGoogle Scholar
Freidman, J. H. 1991. Multivariate adaptive regression splines. Annals of Statistics 19, 1–141.CrossRefGoogle Scholar
Gan, E. and W. Shi. 2002. Error metadata management system. Pages 251–66 in Shi, W., Fisher, P. F., and Goodchild, M. F. (eds.). Spatial Data Quality. New York: Taylor & Francis.CrossRefGoogle Scholar
Gardner, R. H., Kemp, W. M., Kennedy, V. S., and Petersen, J. E.. 2001. Scaling Relations in Experimental Ecology. New York: Columbia University Press.CrossRefGoogle Scholar
Gardner, R. H., Milne, B. T., Turner, M. G., and O'Neill, R. V.. 1987. Neutral models for the analysis of broad-scale landscape pattern. Landscape Ecology 1, 19–28.CrossRefGoogle Scholar
Goodchild, M. F. 1998. Uncertainty: the Achilles heel of GIS?Geo Info Systems 1998 (November), 50–2.Google Scholar
Goodchild, M. F. 2002. Measurement-based GIS. Pages 5–17 in Shi, W., Fisher, P. F., and Goodchild, M. F. (eds.). Spatial Data Quality. New York: Taylor & Francis.CrossRefGoogle Scholar
Goodchild, M. F. and Gopal, S.. 1989. Accuracy of Spatial Databases. Bristol, PA: Taylor & Francis.Google Scholar
Guisan, A. and N. E. Zimmermann, . 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135, 147–86.CrossRefGoogle Scholar
Hargis, C. D., Bissonette, J. A., and David, J. L.. 1998. The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landscape Ecology 13, 167–86.CrossRefGoogle Scholar
Heinsch, F. A., Reeves, M., Bowker, C. F., et al. 2003. User's Guide, GPP and NPP (MOD17A2/A3) Products, NASA MODIS Land Algorithm. Bozeman, MT: Montana State University.Google Scholar
Heinz Center for Science, Economics and the Environment. 2002. The State of the Nation's Ecosystems. Cambridge, UK: Cambridge University Press.
Hershey, R. R. 1996. Understanding the spatial distribution of tree species in Pennsylvania. Pages 73–82 in Mowrer, H. T., Czaplewski, R. L., and Hamre, R. H. (eds.). Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. General Technical Report RM-GTR-277, Fort Collins, CO: Rocky Mountain Forest and Range Experiment Station, USDA Forest Service.Google Scholar
Hofierka, J., Parajka, J., Mitasova, M., and Mitas, L.. 2002. Multivariate interpolation of precipitation using regularized spline with tension. Transactions in GIS 6, 135–50.CrossRefGoogle Scholar
Holsinger, K. E. and IBRCS Working Group. 2003. IBRCS White Paper. Rationale, Blueprint, and Expectations for the National Ecological Observatory Network. Washington, DC: American Institute of Biological Sciences.Google Scholar
Hopkins, L. D. 1977. Methods for generating land suitability maps: a comparative evaluation. AIP Journal 43, 386–400.Google Scholar
Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., and Case, T. J.. 2001. Spatial Uncertainty in Ecology. New York: Springer-Verlag.CrossRefGoogle Scholar
Hutchinson, M. F. 1995. Interpolating mean rainfall using thin plate smoothing splines. International Journal of Geographical Information Systems 9, 385–403.CrossRefGoogle Scholar
Illinois Department of Energy and Natural Resources. 1994. The Changing Illinois Environment: Critical Resources. Volume 3. Technical Report. ILENR/RE-EA-94/05. Springfield, IL: Illinois Department of Energy and Natural Resources.
Iverson, L. R. 1988. Land-use changes in Illinois, USA: the influence of landscape attributes on current and historic use. Landscape Ecology 2, 45–61.CrossRefGoogle Scholar
Iverson, L. R., Cook, E. A., and Graham, R. L.. 1989a. A technique for extrapolating and validating forest cover data across large regions: calibrating AVHRR data with TM. International Journal of Remote Sensing 10, 1805–12.CrossRefGoogle Scholar
Iverson, L. R., Cook, E. A., and Graham, R. L.. 1994. Regional forest cover estimation via remote sensing: the calibration center concept. Landscape Ecology 9, 159–74.CrossRefGoogle Scholar
Iverson, L. R., Dale, M. E., Scott, C. T., and Prasad, A.. 1997a. A GIS-derived integrated moisture index to predict forest composition and productivity in Ohio forests. Landscape Ecology 12, 331–48.CrossRefGoogle Scholar
Iverson, L. R. and Hutchinson, T. F.. 2002. Soil temperature and moisture fluctuations during and after prescribed fire in mixed-oak forests, USA. Natural Areas Journal 22, 296–304.Google Scholar
Iverson, L. R., Oliver, R. L., Tucker, D. P., et al. 1989b. Forest Resources of Illinois: An Atlas and Analysis of Spatial and Temporal Trends. Champaign, IL: Illinois Natural History Survey Special Publication 11.Google Scholar
Iverson, L. R. and Perry, L. G.. 1985. Integration of biological pieces in the siting puzzle. Pages 99–131 in The Siting Puzzle: Piecing Together Economic Development and Environmental Quality. Proceedings of the 13th Annual ENR Conference. Springfield, IL: Illinois Department of Energy and Natural Resources.Google Scholar
Iverson, L. R. and Prasad, A.. 1998a. Estimating regional plant biodiversity with GIS modeling. Diversity and Distributions 4, 49–61.CrossRefGoogle Scholar
Iverson, L. R. and Prasad, A. M.. 1998b. Predicting abundance of 80 tree species following climate change in the eastern United States. Ecological Monographs 68, 465–85.CrossRefGoogle Scholar
Iverson, L. R. and A. M. Prasad. 1999. The Illinois Plant Information Network (database). www.fs.fed.us/ne/delaware/ilpin/ilpin.html.
Iverson, L. R., Prasad, A., and Ketzner, D. M.. 1997b. A summary of the Illinois flora based on the Illinois Plant Information Network. Transactions of the Illinois State Academy of Science 90, 41–64.Google Scholar
Iverson, L. R., A. M. Prasad, and A. Liaw. 2004a. New machine learning tools for predictive vegetation mapping after climate change: bagging and random forest perform better than regression tree analysis. Pages 317–20 in Smithers, R. (ed.). Proceedings, Cirencester, UK: UK-International Association for Landscape Ecology.Google Scholar
Iverson, L. R. and Risser, P. G.. 1987. Analyzing long-term changes in vegetation with geographic information system and remotely sensed data. Advances in Space Research 7, 183–94.CrossRefGoogle Scholar
Iverson, L. R., Yaussy, D. A., Rebbeck, J., et al. 2004b. A data recording method to monitor the spatial and temporal distribution of fire behavior from prescribed fires. International Journal of Wildland Fire 13, 1–12.CrossRefGoogle Scholar
James, F. C. and McCulloch, C. E.. 1990. Multivariate analysis in ecology and systematics: panacea or Pandora's box?Annual Review of Ecology and Systematics 21, 129–66.CrossRefGoogle Scholar
James, F. C., McCulloch, C. E., and Wiedenfield, D. A.. 1996. New approaches to the analysis of population trends in land birds. Ecology 77, 13–27.CrossRefGoogle Scholar
Klopatek, J. M., Olson, R. J., Emerson, C. J., and Jones, J. L.. 1979. Land-use conflicts with natural vegetation in the United States. Environmental Conservation 6, 191–200.CrossRefGoogle Scholar
Krummel, J. R., Gardner, R. H., Sugihara, G., O'Neill, R. V., and Coleman, P. R.. 1987. Landscape patterns in a disturbed environment. Oikos 48, 321–4.CrossRefGoogle Scholar
Lam, N. S. 1983. Spatial interpolation methods: a review. The American Cartographer 10, 129–49.CrossRefGoogle Scholar
Legendre, P. and Fortin, M.-J.. 1989. Spatial pattern and ecological analysis. Vegetatio 80, 107–38.CrossRefGoogle Scholar
Lehmann, A., Overton, J. M., and Austin, M. P.. 2002. Regression models for spatial prediction: their role for biodiversity and conservation. Biodiversity and Conservation 11, 2085–92.CrossRefGoogle Scholar
Leibhold, A. M. 2002. Integrating the statistical analysis of spatial data in ecology. Ecography 25, 553–7.CrossRefGoogle Scholar
Leibhold, A. M., Rossi, R. E., and Kemp, W. P.. 1993. Integrating the statistical analysis of spatial data in ecology. Annual Review of Entomology 38, 303–27.Google Scholar
Lewis, A. and M. F. Hutchinson. 2000. From data accuracy to data quality: using spatial statistics to predict the implications of spatial error in point data. Pages 17–35 in Mowrer, H. T. and Congalton, R. G. (eds.). Quantifying Spatial Uncertainty in Natural Resources. Chelsea, MI: Ann Arbor Press.Google Scholar
Li, H. and J. Wu. 2006. Uncertainty analysis in ecological studies: an overview. Pages 45–66 in Wu, J., Jones, B., Li, B., and Loucks, O. L. (eds.). Scaling and Uncertainty Analysis in Ecology: Methods and Applications. Dordrecht, the Netherlands: Springer.CrossRef
Lopez-Blanco, J., and Villers-Ruiz, L.. 1995. Delineating boundaries of environmental units for land management using a geomorphological approach and GIS: a study in Baja California, Mexico. Remote Sensing and Environment 53, 109–17.CrossRefGoogle Scholar
Lund, H. G. and Thomas, C. E.. 1995. A Primer on Evaluation and Use of Natural Resource Information for Corporate Data Bases. US Department of Agriculture Forest Service, General Technical Report WO-62. Washington, DC: Washington Office.CrossRefGoogle Scholar
Mac, M. J., Opler, P. A., Haecker, C. E. Puckett, and Doran, P. D.. 1998. Status and Trends of the Nation's Biological Resources. Reston, VA: US Geological Survey.Google Scholar
Matthews, S. N., O'Connor, R. J., Iverson, L. R., and Prasad, A. M.. 2004. Atlas of Current and Climate Change Distributions of Common Birds of the Eastern United States. General Technical Report NE-318. Newtown Square, PA: USDA Forest Service, Northeastern Research Station.Google Scholar
Mitasova, H. and Hofierka, J.. 1993. Interpolation by regularized spline with tension: II. Application to terrain modeling and surface geometry analysis. Mathematical Geology 25, 657–69.CrossRefGoogle Scholar
Mitasova, H., Hofierka, J., Zlocha, M., and Iverson, L. R.. 1996. Modeling topographic potential for erosion and deposition using GIS. International Journal of Geographical Information Systems 10, 629–41.CrossRefGoogle Scholar
Moeur, M. and Stage, A. R.. 1995. Most similar neighbor: an improved sampling inference procedure for natural resource planning. Forest Science 41, 337–59.Google Scholar
Moisen, G. G. and Frescino, T.. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling 157, 209–25.CrossRefGoogle Scholar
Moore, G. E. 1965. Cramming more components into integrated circuits. Electronics 38, 1–4.Google Scholar
Mowrer, H. T. and Congalton, R. G.. 2000. Quantifying Spatial Uncertainty in Natural Resources: Theory and Applications for GIS and Remote Sensing. Chelsea, MI: Ann Arbor Press.Google Scholar
Mowrer, H. T., Czaplewski, R. L., Hamre, R. H., and coords, Tech. 1996. Spatial Accuracy Assessment in Natural Resources and Environmental Sciences: Second International Symposium. General Technical Report RM-GTR-277. Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station.CrossRefGoogle Scholar
O'Malley, R., Cavender-Bares, K., and Clark, W. C.. 2003. Providing “better” data: not as simple as it might seem. Environmental Conservation 45, 8–18.Google Scholar
O'Neill, R. V., Krummel, J. R., Gardner, R. H., et al. 1988. Indices of landscape pattern. Landscape Ecology 1, 153–62.CrossRefGoogle Scholar
Ohmann, J. L. and Gregory, M. J.. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest-neighbor imputation in coastal Oregon, USA. Canadian Journal of Forest Research 32, 725–41.CrossRefGoogle Scholar
Olson, R. J., Emerson, C. J., and Nungesser, M. K.. 1980. Geoecology: A County-Level Environmental Data Base for the Conterminous United States. Publication No. 1537. Oak Ridge, TN: Oak Ridge National Laboratory Environmental Sciences Division.CrossRefGoogle Scholar
Palmer, M. W., Earls, P. G., Hoagland, B. W., White, P. S., and Wohlgemuth, T.. 2002. Quantitative tools for perfecting species lists. Environmetrics 13, 121–37.CrossRefGoogle Scholar
Phillips, D. L. and Marks, D. G.. 1996. Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models. Ecological Modeling 9, 213–30.CrossRefGoogle Scholar
Prasad, A. M. and L. R. Iverson. 2000. Predictive vegetation mapping using a custom built model-chooser: comparison of regression tree analysis and multivariate adaptive regression splines. In Proceedings CD-ROM. 4th International Conference on Integrating GIS and Environmental Modeling: Problems, Prospects and Research Needs. Banff, Alberta, Canada (http://www.colorado.edu/research/cires/banff/upload/159/index.html).
Price, D. T., McKenney, D. W., Nalder, I. A., Hutchinson, M. F., and Kesteven, J. L.. 2000. A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agricultural and Forest Meteorology 101, 81–94.CrossRefGoogle Scholar
Rastetter, E. B., King, A. W., Cosby, B. J., et al. 1992. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecological Applications 2, 55–70.CrossRefGoogle ScholarPubMed
Riemann-Hershey, R. and Reese, G.. 1999. Creating a “First-cut” Species Distribution Map for Large Areas from Forest Inventory Data. General Technical Report NE-256. Radnor, PA: Northeastern Research Station, USDA Forest Service.Google Scholar
Riitters, K., J. Wickham, R. O'Neill, B. Jones, and E. Smith. 2000. Global scale patterns of forest fragmentation. Conservation Ecology4, 3 [online] URL: http://www.consecol.org/Journal/vol4/iss2/art3.
Riitters, K. H. and Wickham, J. D.. 2003. How far to the nearest road. Frontiers in Ecology and the Environment 1, 125–9.CrossRefGoogle Scholar
Riitters, K. H., Wickham, J. D., O'Neill, R. V., et al. 2002. Fragmentation of continental United States forests. Ecosystems 5, 815–22.CrossRefGoogle Scholar
Ripley, B. D. 1994. Neural networks and related methods for classification. Journal of the Royal Statistical Society B 56, 409–56.Google Scholar
Risser, P. G. and L. R. Iverson. 1988. Geographic information systems and natural resource issues at the state level. Pages 231–239 in Botkin, D. B., Caswell, M. E., Estes, J. E., and Orio, A. A. (eds.). Our Role in Changing the Global Environment: What We Can Do About Large Scale Environmental Issues. New York: Academic Press.Google Scholar
Risser, P. G., Karr, J. R., and Forman, R. T. T.. 1984. Landscape Ecology: Directions and Approaches. Champaign, IL: Illinois Natural History Survey Special Publication Number 2.Google Scholar
Rodriguez, J. P. 2002. Range contraction in declining North American bird populations. Ecological Applications 1, 238–48.CrossRefGoogle Scholar
Rossi, R. E., Dungan, J. L., and Beck, L. R.. 1994. Kriging in the shadows: geostatistical interpolation for remote sensing. Remote Sensing of the Environment 49, 32–40.CrossRefGoogle Scholar
Rossi, R. E., Mulla, D. J., Journel, A. G., and Franz, E. H.. 1992. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecological Monographs 62, 719–35.CrossRefGoogle Scholar
Running, S. W. 2002. New satellite technologies enhance study of terrestrial biosphere. EOS, Transactions, American Geophysical Union 83, 458–60.CrossRefGoogle Scholar
Sauer, J. R., J. E. Hines, and J. Fallon. 2001. The North American breeding bird survey, results and analysis 1966–2000. Version 2001.2. Laurel, MD: USGS Patuxent Wildlife Research Center. (http://www.mbr.nbs.gov/bbs/htm96/trn626/all.html).
Schneider, D. C. 2001. The rise of the concept of scale in ecology. Bioscience 51, 545–54.CrossRefGoogle Scholar
Shi, W., Fisher, P. F., and Goodchild, M. F.. 2002a. Spatial Data Quality. New York: Taylor & Francis.CrossRefGoogle Scholar
Shi, W., M. F. Goodchild, and P. F. Fisher. 2002b. Epilog: a prospective on spatial data quality. Pages 304–9 in Shi, W., Fisher, P. F., and Goodchild, M. F. (eds.). Spatial Data Quality. New York: Taylor & Francis.CrossRefGoogle Scholar
Shifley, S. R. and Sullivan, N. H.. 2002. The Status of Timber Resources in the North Central United States. General Technical Report NC-228. St. Paul, MN: North Central Research Station, USDA Forest Service.CrossRefGoogle Scholar
Sklar, F. H. and C. T. Hunsaker. 2001. The use and uncertainties of spatial data for landscape models: an overview with examples from the Florida Everglades. Pages 15–46 in Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., and Case, T. J. (eds.). Spatial Uncertainty in Ecology. New York: Springer-Verlag.CrossRefGoogle Scholar
Spanner, M. A., Strahler, A. M., and Estes, J. E.. 1983. Soil loss prediction in a geographic information system format. Pages 89–102 in Seventeenth International Symposium on Remote Sensing of Environment. Ann Arbor, MI: Environmental Research Institute of Michigan.Google Scholar
Spear, M., J. Hall, and R. Wadsworth. 1996. Communication of uncertainty in spatial data to policy makers. Pages 199–207 in Mowrer, H. T., Czaplewski, R. L., and Hamre, R. H. (eds.). Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. General Technical Report RM-GTR-277, Fort Collins, CO: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station.Google Scholar
Stine, P. A. and C. T. Hunsaker. 2001. An introduction to uncertainty issues for spatial data used in ecological applications. Pages 91–107 in Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., and Case, T. J. (eds.). Spatial Uncertainty in Ecology. New York: Springer-Verlag.CrossRefGoogle Scholar
Turner, M. G. 1988. A spatial simulation model of land use changes in a piedmont county in Georgia. Applied Mathematics and Computation 27, 39–51.CrossRefGoogle Scholar
Turner, M. G., Costanza, R., and Sklar, F. H.. 1989. Methods to evaluate the performance of spatial simulation models. Ecological Modelling 48, 1–18.CrossRefGoogle Scholar
Turner, M. G., Romme, W. H., and Tinker, D. B.. 2003. Surprises and lessons from the 1988 Yellowstone fires. Frontiers in Ecology and the Environment 1, 351–8.CrossRefGoogle Scholar
Wallin, D. O., Swanson, F. J., and Marks, B.. 1994. Landscape pattern response to changes in pattern-generation rules: land-use legacies in forestry. Ecological Applications 4, 569–80.CrossRefGoogle Scholar
Walsh, S. J., Lightfoot, D. R., and Butler, D. R.. 1987. Recognition and assessment of error in geographic information systems. Photogrammetric Engineering and Remote Sensing 53, 1423–30.Google Scholar
Wang, F. and Hall, G. B.. 1996. Fuzzy representation of geographical boundaries in GIS. International Journal of Geographical Information Systems 10, 573–90.CrossRefGoogle Scholar
Wickham, J. D., O'Neill, R. V., Riitters, K. H., Wade, T. G., and Jones, K. B.. 1997. Sensitivity of selected landscape pattern metrics to misclassification and differences in land cover composition. Photogrammetric Engineering and Remote Sensing 63, 397–402.Google Scholar
Wickham, J. D. and Riitters, K. H.. 1995. Sensitivity of landscape metrics to pixel size. International Journal of Remote Sensing 16, 3585–94.CrossRefGoogle Scholar
Wiens, J. A. 1989. Spatial scaling in ecology. Functional Ecology 3, 385–97.CrossRefGoogle Scholar
Wu, J. and Hobbs, R.. 2002. Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landscape Ecology 17, 355–65.CrossRefGoogle Scholar
Zhang, X., Friedl, M. A., Schaaf, C. B., et al. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of the Environment 84, 471–5.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×