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  • 1 School of Forest Resources and Penn State Institutes of Environment, The Pennsylvania State Unversity University Park, PA 16802, USA
  • | 2 Center for Statistical Ecology and Environmental Statistics, Department of Statistics, The Pennsylvania State Unversity University Park, PA 16802, USA
  • | 3 Center for Statistical Ecology and Environmental Statistics, Department of Statistics, The Pennsylvania State Unversity University Park, PA 16802, USA
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Multi-band remotely sensed image data contain information on landscape pattern and temporal changes that are greatly underutilized in this technological era when monitoring of disturbance and ecological dynamics is increasingly important to address questions regarding sustainability of ecosystem health and climate change. Among the reasons for this loss of analytical opportunity are the inadequacy of methods for systematic extraction of pattern elements, incongruity between information paradigms for remote sensing and geographic information systems (GIS), and the sheer volume of remotely sensed image data when acquired regularly over time. Long-term cooperative landscape ecological investigations concerning habitat and change detection in conjunction with remote sensing and GIS have yielded a pattern-based approach to progressively segmenting images (PSI) that culminates in a doubly segmented image representation by sets of approximating signal vectors that serve as parsimonious proxies for pixel vectors. The coarser level of segmentation is entirely congruent with raster map structures for GIS, and yet mimics the appearance of an image display by colorization using information on typical spectral properties of segments contained in attribute tables. The components of the coarser representation as spatial segments constitute explicit elements of pattern at several levels. The explicit nature of these pattern elements enables spatial pattern matching for change detection that resolves difficulties with phenological variability and continuity of sensor configurations over time. Conversion to segmented representation can be applied to multi-temporal change indices so as to elicit longer-term patterns of change from temporal sequences of images. The finer level of segmentation for spectral detail enables restoration of image bands in the manner of a low-pass filter for analysis according to the usual paradigms of remote sensing. Mapping of the residuals for the finer detail of image approximation provides further information on exceptional features of landscape ecological pattern.

  • Hartigan, J. A. 1975. Clustering Algorithms. John Wiley & Sons, New York.

    Clustering Algorithms. , ().

  • Hartigan, J. A. and M. A. Wong. 1979. A k-means clustering algorithm [Algorithm AS-136]. Applied Statistics 28: 100-108.

    'A k-means clustering algorithm [Algorithm AS-136] ' () 28 Applied Statistics : 100 -108.

  • Hastie, T., R. Tibshirani, and J. Friedman. 2001. The Elements of Statistical Learning. Springer, New York.

    The Elements of Statistical Learning. , ().

  • Howarth, P. and G. Wickware. 1981. Procedures for Change Detection using Landsat Digital data. International Journal of Remote Sensing 2: 277-291.

    'Procedures for Change Detection using Landsat Digital data ' () 2 International Journal of Remote Sensing : 277 -291.

    • Search Google Scholar
  • Jain, A. K., M. N. Murty and P. J. Flynn. 1999. Data clustering: A review. ACM Computing Surveys, 31: 264-323.

    'Data clustering: A review ' () 31 ACM Computing Surveys : 264 -323.

  • James, M. 1985. Classification Algorithms. John Wiley & Sons, London, UK.

    Classification Algorithms. , ().

  • Wilson, J. and J. Gallant (eds.) 2000. Terrain Analysis: Principles and Applications. John Wiley and Sons, Inc., New York.

    Terrain Analysis: Principles and Applications. , ().

  • Johnson, R. and E. Kasischke. 1998. Change vector analysis: A technique for the multispectral monitoring of land cover and condition. International Journal of Remote Sensing 19: 411-26.

    'Change vector analysis: A technique for the multispectral monitoring of land cover and condition ' () 19 International Journal of Remote Sensing : 411 -26.

    • Search Google Scholar
  • Anderson, J., E. Hardy, J. Roach and R. Witmer. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. U. S. Geological Survey Professional Paper 964. Reston, VA: U. S. Geological Survey. 28 p.

    A Land Use and Land Cover Classification System for Use with Remote Sensor Data. U. S. Geological Survey Professional Paper 964 , () 28.

    • Search Google Scholar
  • Ball, D. J., and G. H. Hall. 1965. ISODATA, a novel technique for data analysis and pattern classification. Technical Report, Stanford Research Institute, Menlo Park, California.

    ISODATA, a novel technique for data analysis and pattern classification. Technical Report , ().

    • Search Google Scholar
  • Ball, D. J., and G. H. Hall. 1967. A clustering technique for summarizing multivariate data Behavior Science 12: 153-155.

    'A clustering technique for summarizing multivariate data ' () 12 Behavior Science : 153 -155.

    • Search Google Scholar
  • Baker, W. 1989. A review of models of landscape change. Landscape Ecology: 111-133.

    'A review of models of landscape change ' () Landscape Ecology : 111 -133.

  • Bruzzone, L. and D. Prieto. 2000. Automatic analysis of the difference image for unsupervised change detection. LEEE Transactions on Geoscience and Remote Sensing 38: 1171-1182.

    'Automatic analysis of the difference image for unsupervised change detection. LEEE Transactions on ' () 38 Geoscience and Remote Sensing : 1171 -1182.

    • Search Google Scholar
  • Chen, J., P. Gong, C. He, R. Pu and P. Shi. 2003. Land-use/land-cover change detection using improved change vector analysis. Photogrammetric Engineering and Remote Sensing 69: 369-379.

    'Land-use/land-cover change detection using improved change vector analysis ' () 69 Photogrammetric Engineering and Remote Sensing : 369 -379.

    • Search Google Scholar
  • Coppin, P. and M. Bauer. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews 13: 207-234.

    'Digital change detection in forest ecosystems with remote sensing imagery ' () 13 Remote Sensing Reviews : 207 -234.

    • Search Google Scholar
  • Congalton, R. and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publish-ers/CRC Press, Boca Raton, FL.

    Assessing the Accuracy of Remotely Sensed Data: Principles and Practices , ().

  • Dai, X. and S. Khorram. 1998. The effects of image misregistration on accuracy of remotely sensed change detection. LEEE Transactions on Geoscience and Remote Sensing 36: 1566-1577.

    'The effects of image misregistration on accuracy of remotely sensed change detection ' () 36 LEEE Transactions on Geoscience and Remote Sensing : 1566 -1577.

    • Search Google Scholar
  • Everitt, B. S., S. Landau and M. Leese. 2001. Cluster Analysis, Fourth edition. Arnold, London.

    Cluster Analysis , ().

  • Forman, R. T. T. 1995. Land Mosaics: The Ecology of Landscapes and Regions. Cambridge Univ. Press, Cambridge, U.K.

    Land Mosaics: The Ecology of Landscapes and Regions. , ().

  • Forman, R. T. T. and M. Godron. 1986. Landscape Ecology. John Wiley & Sons, New York.

    Landscape Ecology. , ().

  • Gibson, P. and C. Power. 2000. Introductory Remote Sensing: Digital Image Processing and Applications. Taylor and Francis, New York.

    Introductory Remote Sensing: Digital Image Processing and Applications. , ().

  • Kelly, P. and J. White. 1993. Preprocessing Remotely-Sensed Data for Efficient Analysis and Classification. Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry, Proceedings SPIE 1993. pp. 24-30.

    'Preprocessing Remotely-Sensed Data for Efficient Analysis and Classification. Applications of Artificial Intelligence 1993: Knowledge-Based Systems in Aerospace and Industry ' , , .

    • Search Google Scholar
  • Lambin, E. and A. Strahler. 1994a. Indicators of land-cover change for change vector analysis in multitemporal space at coarse spatial scales. International Journal of Remote Sensing 15: 2099-2119.

    'Indicators of land-cover change for change vector analysis in multitemporal space at coarse spatial scales ' () 15 International Journal of Remote Sensing : 2099 -2119.

    • Search Google Scholar
  • Lambin, E. F. and A. H. Strahler. 1994b. Change-vector analysis in multi-temporal space: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data Remote Sensing of Environment 48: 231-244.

    'Change-vector analysis in multi-temporal space: a tool to detect and categorize land-cover change processes using high temporal-resolution satellite data ' () 48 Remote Sensing of Environment : 231 -244.

    • Search Google Scholar
  • Li, J. and R. Gray. 2000. Image Segmentation and Compression Using Hidden Markov Models. Kluwer Academic Publishers, Nor-well, MA.

    Image Segmentation and Compression Using Hidden Markov Models. , ().

  • Lunetta, R. and C. Elvidge (eds.). 1998. Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Ann Arbor Press, Ann Arbor, MI.

    Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. , ().

    • Search Google Scholar
  • Lunetta, R., J. Lyon, B. Guindon and C. Elvidge. 1998. North American landscape characterization dataset development and data fusion issues. Photogrammetric Engineering and Remote Sensing 64: 821-829.

    'North American landscape characterization dataset development and data fusion issues ' () 64 Photogrammetric Engineering and Remote Sensing : 821 -829.

    • Search Google Scholar
  • Mas, J. 1999. Monitoring land-cover changes: A comparison of change detection techniques. International Journal of Remote Sensing 20: 139-152.

    'Monitoring land-cover changes: A comparison of change detection techniques ' () 20 International Journal of Remote Sensing : 139 -152.

    • Search Google Scholar
  • McGarigal, K. and B. J. Marks. 1995. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure. General Technical Report PNW 351, U. S. Forest Service, Pacific Northwest Research Station. 122 pp.

    FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure. General Technical Report PNW 351 , () 122.

    • Search Google Scholar
  • Michalek, J., T. Wagner, J. Luczkovich and R. Stoffle. 1993. Multispectral change vector analysis for monitoring coastal marine environments. Photogrammetric Engineering and Remote Sensing 59: 381-354.

    'Multispectral change vector analysis for monitoring coastal marine environments ' () 59 Photogrammetric Engineering and Remote Sensing : 381 -354.

    • Search Google Scholar
  • Miller, R., ed. 1994. Mapping the Diversity of Nature. Chapman & Hall, New York.

    Mapping the Diversity of Nature. , ().

  • Myers, W. 2003. Doubly segmented images for pattern-based approach to change detection. Final report on NASA Research Project NAG5-1054. Research Report PSIE 2003-6, Penn State Institutes of Environment, The Pennsylvania State University, Univ. Park, PA 16802 USA. 90 pp. + CD-ROM.

    Doubly segmented images for pattern-based approach to change detection. Final report on NASA Research Project NAG5-1054, Research Report PSIE 2003-6 , ().

    • Search Google Scholar
  • Myers, W., G. P. Patil and C. Taillie. 1999. Conceptualizing pattern analysis of spectral change relative to ecosystem status. Ecosystem Health 5: 285-293.

    'Conceptualizing pattern analysis of spectral change relative to ecosystem status ' () 5 Ecosystem Health : 285 -293.

    • Search Google Scholar
  • Patil, G. P., R. Brooks, W Myers, D. Rapport and C. Taillie. 2001. Ecosystem health and its measurement at landscape scale: Toward the next generation of quantitative assessments. Ecosystem Health 7: 307-316.

    'Ecosystem health and its measurement at landscape scale: Toward the next generation of quantitative assessments ' () 7 Ecosystem Health : 307 -316.

    • Search Google Scholar
  • Patil, G. P. and W Myers. 1999. Environmental and ecological health assessment of landscapes and watersheds with remote sensing data. Ecosystem Health 5:221-224.

    'Environmental and ecological health assessment of landscapes and watersheds with remote sensing data ' () 5 Ecosystem Health : 221 -224.

    • Search Google Scholar
  • Pratt, W. 1991. Digital Image Processing. John Wiley & Sons, New York.

    Digital Image Processing , ().

  • Rogan, J., J. Franklin and D. Roberts. 2003. A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery. Remote Sensing of Environment 80: 143-156.

    'A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery ' () 80 Remote Sensing of Environment : 143 -156.

    • Search Google Scholar
  • Rogan, J., J. Miller, D. Stow, J. Franklin, L. Levien and C. Fischer. 2003. Land-cover change monitoring with classification trees using landsat tm and ancillary data Photogrammetric Engineering and Remote Sensing 69: 793-804.

    'Land-cover change monitoring with classification trees using landsat tm and ancillary data ' () 69 Photogrammetric Engineering and Remote Sensing : 793 -804.

    • Search Google Scholar
  • Singh, A. 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 10: 989-1003.

    'Digital change detection techniques using remotely sensed data ' () 10 International Journal of Remote Sensing : 989 -1003.

    • Search Google Scholar
  • Sohl, T. and J. Dwyer. 1998. North American landscape characterization project: the production of a continental scale three-decade landsat data set. Geocarto International 13:43-51.

    'North American landscape characterization project: the production of a continental scale three-decade landsat data set ' () 13 Geocarto International : 43 -51.

    • Search Google Scholar
  • Song, C, C. Woodcock, K. Seto, M. Lenney and S. Macomber. 2001. Classification and change detection using landsat tm data: when and how to correct atmospheric effects. Remote Sensing of Environment 75: 230-244.

    'Classification and change detection using landsat tm data: when and how to correct atmospheric effects ' () 75 Remote Sensing of Environment : 230 -244.

    • Search Google Scholar
  • Tso, B. and P. Mather. 2001. Classification Methods for Remotely Sensed Data. Taylor and Francis, New York.

    Classification Methods for Remotely Sensed Data. , ().

  • Turner, M., R. Gardner and R. O'Neill. 2001. Landscape Ecology in Theory and Practice: Pattern and Process. Springer-Verlag, Inc., New York.

    Landscape Ecology in Theory and Practice: Pattern and Process , ().

  • Gong, P., E. LeDrew and J. Miller. 1992. Registration noise reduction in difference images of change detection. International Journal of Remote Sensing 13: 773-779.

    'Registration noise reduction in difference images of change detection ' () 13 International Journal of Remote Sensing : 773 -779.

    • Search Google Scholar
  • Gonzalez, R. C. and R. C. Woods. 1992. Digital Image Processing. Addison-Wesley Publishing Co., Reading, MA.

    Digital Image Processing. , ().

  • Gordon, A. D. 1999. Classification, 2nd edition. Chapman & Hall, Boca Raton, FL.

    Classification , ().

  • Groombridge, B. 1992. Global Biodiversity: Status of the Earth's Living Resources. Chapman & Hall, New York.

    Global Biodiversity: Status of the Earth's Living Resources , ().

  • Hall, F., D. Strebel, J. Nickeson and S. Goetz. 1991. Radiometric rectification: Toward a common radiometric response among multidate, multisensor images. Remote Sensing of Environment 35:11-27.

    'Radiometric rectification: Toward a common radiometric response among multidate, multisensor images ' () 35 Remote Sensing of Environment : 11 -27.

    • Search Google Scholar
  • Richards, J. A. and X. Jia. 1999. Remote Sensing Digital Image Analysis, 3rd edition. Springer-Verlag, Berlin.

    Remote Sensing Digital Image Analysis , ().

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Senior editors

Editor(s)-in-Chief: Podani, János

Editor(s)-in-Chief: Jordán, Ferenc

Honorary Editor(s): Orlóci, László

Editorial Board

  • Madhur Anand, CAN (forest ecology, computational ecology, and ecological complexity)
  • S. Bagella, ITA (temporal dynamics, including succession, community level patterns of species richness and diversity, experimental studies of plant, animal and microbial communities, plant communities of the Mediterranean)
  • P. Batáry, HUN (landscape ecology, agroecology, ecosystem services)
  • P. A. V. Borges, PRT (community level patterns of species richness and diversity, sampling in theory and practice)
  • A. Davis, GER (supervised learning, multitrophic interactions, food webs, multivariate analysis, ecological statistics, experimental design, fractals, parasitoids, species diversity, community assembly, ticks, biodiversity, climate change, biological networks, cranes, olfactometry, evolution)
  • Z. Elek, HUN (insect ecology, invertebrate conservation, population dynamics, especially of long-term field studies, insect sampling)
  • T. Kalapos, HUN (community level plant ecophysiology, grassland ecology, vegetation-soil relationship)
  • G. M. Kovács, HUN (microbial ecology, plant-fungus interactions, mycorrhizas)
  • W. C. Liu,TWN (community-based ecological theory and modelling issues, temporal dynamics, including succession, trophic interactions, competition, species response to the environment)
  • L. Mucina, AUS (vegetation survey, syntaxonomy, evolutionary community ecology, assembly rules, global vegetation patterns, mediterranean ecology)
  • P. Ódor, HUN (plant communities, bryophyte ecology, numerical methods)
  • F. Rigal, FRA (island biogeography, macroecology, functional diversity, arthropod ecology)
  • D. Rocchini, ITA (biodiversity, multiple scales, spatial scales, species distribution, spatial ecology, remote sensing, ecological informatics, computational ecology)
  • F. Samu, HUN (landscape ecology, biological control, generalist predators, spiders, arthropods, conservation biology, sampling methods)
  • U. Scharler, ZAF (ecological networks, food webs, estuaries, marine, mangroves, stoichiometry, temperate, subtropical)
  • D. Schmera, HUN (aquatic communities, functional diversity, ecological theory)
  • M. Scotti, GER (community-based ecological theory and modelling issues, trophic interactions, competition, species response to the environment, ecological networks)
  • B. Tóthmérész, HUN (biodiversity, soil zoology, spatial models, macroecology, ecological modeling)
  • S. Wollrab, GER (aquatic ecology, food web dynamics, plankton ecology, predator-prey interactions)

 

Advisory Board

  • S. Bartha, HUN
  • S.L. Collins, USA
  • T. Czárán, HUN
  • E. Feoli, ITA
  • N. Kenkel, CAN
  • J. Lepš, CZE
  • S. Mazzoleni, ITA
  • Cs. Moskát, HUN
  • B. Oborny, HUN
  • M.W. Palmer, USA
  • G.P. Patil, USA
  • V. de Patta Pillar, BRA
  • C. Ricotta, ITA
  • Á. Szentesi, HUN

PODANI, JÁNOS
E-mail: podani@ludens.elte.hu


JORDÁN, FERENC
E-mail: jordan.ferenc@gmail.com

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Community Ecology
Language English
Size A4
Year of
Foundation
2000
Volumes
per Year
1
Issues
per Year
2
Founder Akadémiai Kiadó
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Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245
Publisher Akadémiai Kiadó
Springer Nature Switzerland AG
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ISSN 1585-8553 (Print)
ISSN 1588-2756 (Online)