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Geospatial Narratives and Their Spatio-Temporal Dynamics: Commonsense Reasoning for High-Level Analyses in Geographic Information Systems

ISPRS Int. J. Geo-Inf. 2014, 3(1), 206-208;

Introduction to the Special Issue: Geospatial Monitoring and Modeling of Environmental Change
Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010 S. Michele allAdige (TN), Italy
Received: 22 November 2013 / Accepted: 23 November 2013 / Published: 6 February 2014
Geospatial modeling is an approach to apply analysis to monitor environmental change over time considering different fields of re-search, including computer science, remote sensing, ecology, environmental science, life science, geography (see [1,2] for a critique).
The special issue was instigated to publish straightforward research on the matter in order to stimulate further discussion on the potential of geospatial modelling. Both theoretical and empirical papers are part of the issue with the support of the International Society for Photogrammetry and Remote Sensing, promoting an advanced forum for the science and technology of geographic information.
Due to the complexity of the theme being treated, the final issue composes seven heterogeneous and stimulating papers on geospatial monitoring and modeling of environmental change.
Table 1 attempts to summarize the focus of each of the articles published.
Table 1. Summary of the papers published in the special issue.
Table 1. Summary of the papers published in the special issue.
First AuthorMain FrameThemeRef.
Stuart Greenagriculturea novel 2D ranked pair plot of coordinates to show and analyze the geographic distribution of farms [3]
Ludovico Fratelandscape ecologynatural forest e-growth analyzed by midpoint displacement algorithms[4]
Pietro Zambellicomputer sciencePyGRASS library as an object-oriented Python Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS)[5]
Shivani Agarwalurban ecologyapplication of multi-spectral GeoEye imagery for mapping urban tree species [6]
Carlo Ricottalandscape ecologyapplication of the Rao quadratic diversity for multiscale analysis of land use changes[7]
Matteo Abratecomputer scienceweb based services to digitally preserve historical aerial photographs[8]
Mehul Bhattcomputer scienceconceptual models for representing geospatial events and their changes over time[9]
Multitemporal environmental change is analyzed in very different manners in these papers covering both computer-science [5,8,9] and ecological/environmental main fields of research [3,4,6,7].
The special issue included authors from 11 different institutions from the following countries: Germany, India, Ireland, Italy, Sweden, and USA. I am grateful to the whole Editorial office of the ISPRS International Journal of Geo-Information and to all the reviewers who supported the special issue with their skills, ensuring robust and challenging papers which will stimulate further discussion on geospatial monitoring and modelling of environmental change.

Conflict of Interest

The authors declare no conflict of interest.


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  3. Green, S.; O’Donoghue, C. Assessing the geographic representativity of farm accountancy data. ISPRS Int. J. Geo-Inf. 2013, 2, 50–66. [Google Scholar] [CrossRef][Green Version]
  4. Frate, L.; Carranza, M.L. Quantifying landscape-scale patterns of temperate forests over time by means of neutral simulation models. ISPRS Int. J. Geo-Inf. 2013, 2, 94–109. [Google Scholar] [CrossRef]
  5. Zambelli, P.; Gebbert, S.; Ciolli, M. Pygrass: An object oriented python application programming interface (API) for geographic resources analysis support system (GRASS) geographic information system (GIS). ISPRS Int. J. Geo-Inf. 2013, 2, 201–219. [Google Scholar] [CrossRef]
  6. Agarwal, S.; Vailshery, L.S.; Jaganmohan, M.; Nagendra, H. Mapping urban tree species using very high resolution satellite imagery: Comparing pixel-based and object-based approaches. ISPRS Int. J. Geo-Inf. 2013, 2, 220–236. [Google Scholar] [CrossRef]
  7. Ricotta, C.; Carranza, M.L. Measuring scale-dependent landscape structure with Raos quadratic diversity. ISPRS Int. J. Geo-Inf. 2013, 2, 405–412. [Google Scholar] [CrossRef]
  8. Abrate, M.; Bacciu, C.; Hast, A.; Marchetti, A.; Minutoli, S.; Tesconi, M. GeoMemories—A platform for visualizing historical, environmental and geospatial changes in the Italian landscape. ISPRS Int. J. Geo-Inf. 2013, 2, 432–455. [Google Scholar] [CrossRef]
  9. Bhatt, M.; Wallgrn, J.O. Geospatial narratives and their spatio-temporal dynamics: Commonsense reasoning for high-level analyses in geographic information systems. ISPRS Int. J. Geo-Inf. 2014, 1, 166–205. [Google Scholar] [CrossRef]
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