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  • 1
    Monograph available for loan
    Monograph available for loan
    Environmental remote sensing from regional to global scales (1994)
    Chichester [u.a.] : John Wiley & Sons
    Details Availability
    Call number: PIK N 075-92-0833
    Type of Medium: Monograph available for loan
    Pages: 224 pp.
    ISBN: 0471944343
    Location: A 18 - must be ordered
    Branch Library: PIK Library
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  • 2
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    A virtual species set for robust and reproducible Species Distribution Modelling tests (2015)
    PANGAEA
    In:  Supplement to: Garzon-Lopez, Carol X; Rocchini, Duccio; Bastin, Lucy; Foody, Giles M (2016): A virtual species set for Species Distribution Modelling robust and reproducible test. Data in Brief, 7, 476-479, https://doi.org/10.1016/j.dib.2016.02.058
    Details
    Publication Date: 2023-01-13
    Description: Predicting species potential and future distribution has become a relevant tool in biodiversity monitoring and conservation. In this data article we present the suitability map of a virtual species generated based on two bioclimatic variables, and a dataset containing more than 700.000 random observations at the extent of Europe. The dataset includes spatial attributes such as, distance to roads, protected areas, country codes, and the habitat suitability of two spatially clustered species (grassland and forest species) and a wide spread species.
    Keywords: File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
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    DATA PANGAEA
  • 3
    Electronic Resource
    Electronic Resource
    Mapping Land Cover from Remotely Sensed Data with a Softened Feedforward Neural Network Classification (2000)
    Springer
    Journal of intelligent and robotic systems 29 (2000), S. 433-449 
    Details
    ISSN: 1573-0409
    Keywords: neural network ; soft classification ; land cover ; remote sensing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Remote sensing has considerable potential as a source of data for land cover mapping. This potential remains to be fully realised due, in part, to the methods used to extract land cover information from the remotely sensed data. Widely used statistical classifiers provide a poor representation of land cover, make untenable assumptions about the data and convey no information on the quality of individual class allocations. This paper shows that a softened classification, providing information on the strength of membership to all classes for each image pixel, may be derived from a neural network. This information may be used to indicate classification quality on a per-pixel basis. Moreover, a soft or fuzzy classification may be derived to more appropriately represent land cover than the conventional hard classification.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008112125526
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  • 4
    Electronic Resource
    Electronic Resource
    Estimation of land coverage from a land cover classification derived from remotely sensed data (1995)
    Springer
    GeoJournal 36 (1995), S. 361-370 
    Details
    ISSN: 1572-9893
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geography
    Notes: Abstract Remotely sensed data are an attractive source of land cover information. In many applications the required information relates to the extent or coverage of land cover class(es) in a region, which is generally derived from a count of the pixels allocated to the class(es) of interest in a classification. A highly accurate classification is not required for the derivation of accurate estimates of class coverage, provided the classification is accompanied by appropriate information on its quality. For instance, the information on classification quality contained in the classification confusion matrix can be used to significantly increase the accuracy of the estimates of land coverage. This is illustrated with reference to a case study focused on the estimation of despoiled land coverage in administratively defined local district in industrial South Wales from Landsat TM data. The accuracy of the investigation was assessed relative to a map of despoiled land cover for this region produced by conventional methods. From an image classification of moderate accuracy, the classification accuracy ranged form 57–83% between the districts investigated, a pixel count provided estimated of despoiled land coverage that were only poorly correlated to the mapped coverage;r = 0.27. Using the information on the pattern of error in the class allocation contained in the classification confusion matrix the estimation accuracy was increased significantly, with a correlation ofr = 0.81 observed between the remote sensing based estimate and the mapped land coverage. Furthermore, the r.m.s. error in despoiled land coverage estimation was reduced by approximately half, to less than 1% district area, when the classification was used in conjunction with information on the pattern of classification error.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00807952
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  • 5
    Electronic Resource
    Electronic Resource
    Book reviews (1995)
    Springer
    GeoJournal 37 (1995), S. 573-584 
    Details
    ISSN: 1572-9893
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geography
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00806949
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  • 6
    Electronic Resource
    Electronic Resource
    Book reviews (1995)
    Springer
    GeoJournal 36 (1995), S. 86-86 
    Details
    ISSN: 1572-9893
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geography
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00812532
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  • 7
    Electronic Resource
    Electronic Resource
    Non-classificatory analysis and representation of heathland vegetation from remotely sensed imagery (1993)
    Springer
    GeoJournal 29 (1993), S. 343-350 
    Details
    ISSN: 1572-9893
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geography
    Notes: Abstract Remotely sensed imagery are an attractive source of data for vegetation mapping. Conventional image classification routines used to produce thematic maps from remotely sensed imagery rely on a one-pixel-one-class approach and generate discrete thematic units. Where the environmental phenomena to be mapped exhibit gradients such a representation is inappropriate. This paper discusses the representation of semi-natural vegetation, drawing on examples of heathland vegetation that lie along continua. Two alternative approaches to the representation of heathland vegetation are examined, both of which aim to model the continuous character of the vegetation with measures of the strength of membership to ‘discrete classes’, namely probabilities of class membership from a maximum likelihood classification and fuzzy membership functions from the fuzzy c-means algorithm. Since both approaches imply partial class membership they can be considered as non-classificatory, even though the measures of the strength of class membership may be derived from classification routines. The measures of class membership generated from both approaches were found to be significantly correlated to the variations in heathland composition along a transect which graded from dry heath to wet heath/bog. Furthermore for cases drawn from the class end-points both approaches were able to discriminate class membership accurately.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00807535
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  • 8
    Electronic Resource
    Electronic Resource
    Book reviews (1995)
    Springer
    GeoJournal 35 (1995), S. 503-504 
    Details
    ISSN: 1572-9893
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geography
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00824364
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  • 9
    Electronic Resource
    Electronic Resource
    Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery (1997)
    Springer
    Plant ecology 131 (1997), S. 143-154 
    Details
    ISSN: 1573-5052
    Keywords: Artificial neural networks ; Fraction images ; Remote sensing ; Tropical forests
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract At regional to global scales the only feasible approach to mapping and monitoring forests is through the use of coarse spatial resolution remotely sensed imagery. Significant errors in mapping may arise as such imagery may be dominated by pixels of mixed land cover composition which cannot be accommodated by conventional mapping approaches. This may lead to incorrect assessments of forest extent and thereby processes such as deforestation which may propagate into studies of environmental change. A method to unmix the class composition of image pixels is presented and used to map tropical forest cover in part of the Mato Grosso, Brazil. This method is based on an artificial neural network and has advantages over other techniques used in remote sensing. Fraction images depicting the proportional class coverage in each pixel were produced and shown to correspond closely to the actual land cover. The predicted and actual forest cover were, for instance, strongly correlated (up to r = 0.85, significant at the 99% level of confidence) and the predicted extent of forest over the test site much closer to the actual extent than that derived from a conventional approach to mapping from remotely sensed imagery.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009775619936
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  • 10
    Electronic Resource
    Electronic Resource
    Fuzzy mapping of tropical land cover along an environmental gradient from remotely sensed data with an artificial neural network (1999)
    Springer
    Journal of geographical systems 1 (1999), S. 23-35 
    Details
    ISSN: 1435-5949
    Keywords: Key words: Remote sensing ; fuzzi classification ; boundaries ; neural network ; JEL classification: C45 ; Q24 ; Q20
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geography
    Notes: Abstract. Remote sensing is the only feasible means of mapping and monitoring land cover at regional to global scales. Unfortunately the maps are generally derived through the use of a conventional 'hard' classification algorithm and depict classes separated by sharp boundaries. Such approaches and representations are often inappropriate particularly when the land cover being represented may be considered to be fuzzy. The definition of boundaries between classes can therefore be difficult from remotely sensed data, particularly for continuous land cover classes which are separated by a fuzzy boundary which may also vary spatially in time. In this paper a neural network was used to derive fuzzy classifications of land cover along a transect crossing the transition from moist semi-deciduous forest to savanna in West Africa in February and December 1990. The fuzzy classifications revealed both sharp and gradual boundaries between classes located along the transect. In particular, the fuzzy classifications enabled the definition of important boundary properties, such as width and temporal displacement.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s101090050003
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