Special Issue "Geospatial Monitoring and Modelling of Environmental Change"
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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).
Deadline for manuscript submissions: closed (31 December 2012)
Special Issue Editor
Guest Editor
Dr. Duccio Rocchini
GIS and Remote Sensing Unit, Department of Biodiversity and Molecular Ecology, Research and Innovation Centre, Fondazione Edmund Mach, Via Mach 1, 38010 San Michele all'Adige (TN), Italy
Website: http://gis.fem-environment.eu/rocchini/
E-Mail: ducciorocchini@gmail.com
Phone: +39 0461615570
Fax: +39 3491425786
Interests: ecological Informatics; ecological heterogeneity and biodiversity estimate by satellite imagery; Free and Open Source Software for spatial ecology; statistical analysis of spatial and ecological data
Special Issue Information
Dear Colleagues,
Geospatial modelling came out with a throughput of analysis approaches to monitor environmental change over time considering different fields of research, including computer science, remote sensing, ecology, environmental science, life science, geography. The aim of this special issue is to publish straightforward research or review papers on the matter in order to stimulate further discussion on the potential of geospatial modelling. It is my pleasure to encourage both theoretical and empirical papers on the matter with the support of the International Society for Photogrammetry and Remote Sensing, promoting an advanced forum for the science and technology of geographic information.
Dr. Duccio Rocchini
Guest Editor
Submission
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed Open Access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts.
English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.
Keywords
- computer science
- ecology
- environmental science
- life science
- geography
- geospatial modelling
- monitoring
- remote sensing
Published Papers (6 papers)
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Received: 6 December 2012; in revised form: 25 January 2013 / Accepted: 28 January 2013 / Published: 6 February 2013
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Abstract: The environment affects agriculture, via soils, weather, etc. and agriculture affects the environment locally at farm level and via its impact on climate change. Locating agriculture within its spatial environment is thus important for farmers and policy makers. Within the EU countries collect detailed farm data to understand the technical and financial performance of farms; the Farm Accountancy Data Network. However, knowledge of the spatial-environmental context of these farms is reported at gross scale. In this paper, Irish farm accounting data is geo-referenced using address matching to a national address database. An analysis of the geographic distribution of the survey farms, illustrated through a novel 2D ranked pair plot of the coordinates, compared to the national distribution of farms shows a trend in the location of survey farms that leads to a statistical difference in the climatic variables associated with the farm. The farms in the survey have significantly higher accumulated solar radiation values than the national average. As a result, the survey may not be representative spatially of the pattern of environment x farm system. This could have important considerations when using FADN data in modelling climate change impacts on agri-economic performance.
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Received: 1 January 2013; in revised form: 19 February 2013 / Accepted: 21 February 2013 / Published: 1 March 2013
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Abstract: Several studies attempt to describe changes in the spatial patterns of forests over time, resorting to the comparison of landscape pattern indices (LPI), but new methods for quantifying landscape differences in a statistical context are necessary. In this paper, we quantified and assessed the statistical significance of the forests pattern changes, which have occurred since the end of WWII in Central Italy (Isernia). To do this; based on the proportion of forest cover (pi) and contagion (H) of three land cover maps (1954–1981–2006); we generated 100 forest maps with predictable results through the midpoint displacement algorithm. Then, for both observed and simulated maps, we computed a set of LPI (number of patches, cohesion, largest forest patch index and area weighted mean shape index) and we derived their empirical distributions; finally, we compared the empirical distributions using the non-parametric Kruskal-Wallis test. Our results show significant changes in the spatial pattern of forests and underline the process of natural forest re-growth, which, in the area, is constrained by “remnants” of traditional activities. The adopted approach could be extended to a large ensemble of landscapes and spatial scales and could become a standard procedure when comparing patterns in time.
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Received: 1 January 2013; in revised form: 21 January 2013 / Accepted: 21 February 2013 / Published: 11 March 2013
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Abstract: PyGRASS is an object-oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS), a powerful open source GIS widely used in academia, commercial settings and governmental agencies. We present the architecture of the PyGRASS library, covering interfaces to GRASS modules, vector and raster data, with a focus on the new capabilities that it provides to GRASS users and developers. Our design concept of the module interface allows the direct linking of inputs and outputs of GRASS modules to create process chains, including compatibility checks, process control and error handling. The module interface was designed to be easily extended to work with remote processing services (Web Processing Service (WPS), Web Service Definition Language (WSDL)/Simple Object Access Protocol (SOAP)). The new object-oriented Python programming API introduces an abstract layer that opens the possibility to use and access transparently the efficient raster and vector functions of GRASS that are implemented in C. The design goal was to provide an easy to use, but powerful, Python interface for users and developers who are not familiar with the programming language C and with the GRASS C-API. We demonstrate the capabilities, scalability and performance of PyGRASS with several dedicated tests and benchmarks. We compare and discuss the results of the benchmarks with dedicated C implementations.
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Received: 1 January 2013; in revised form: 25 February 2013 / Accepted: 26 February 2013 / Published: 13 March 2013
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Abstract: We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in the field. The six most common species, collectively representing 43% of the total trees sampled, were selected for mapping using pixel-based and object-based approaches. All pairs of species were separable based on spectral reflectance values in at least one band, with Peltophorum pterocarpum being most distinct from other species. Object-based approaches were consistently superior to pixel-based methods, which were particularly low in accuracy for tree species with small canopy sizes, such as Cocos nucifera and Roystonea regia. There was a strong and significant correlation between the number of trees determined on the ground and from object-based classification. Overall, object-based approaches appear capable of discriminating the six most common species in a challenging urban environment, with substantial heterogeneity of tree canopy sizes.
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Received: 5 March 2013; in revised form: 3 May 2013 / Accepted: 6 May 2013 / Published: 14 May 2013
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Abstract: In this paper, we apply a special application of the Rao quadratic diversity for multiscale analysis of land use changes in a mixed agricultural-forest landscape in Central Italy. The proposed approach is similar to a block-size analysis of compositional diversity for which a given landscape is overlaid with a series of square grids composed of increasingly larger boxes. The combination of land cover classes in each box is recorded, and the Rao quadratic diversity is computed for the frequency distribution of the land cover classes at each box-size. Plotting compositional diversity versus box-size provides information on the scale-dependent pattern of the landscape. Since the proposed methodology is not severely influenced by the co-registration accuracy of the underlying data sets, it may prove to be reasonably adequate for analyzing historical data sets of varying resolution and quality, like aerial photographs or categorical maps.
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Received: 20 March 2013; in revised form: 25 April 2013 / Accepted: 27 April 2013 / Published: 21 May 2013
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Abstract: The GeoMemories project aims at publishing on the Web and digitally preserving historical aerial photographs that are currently stored in physical form within the archives of the Aerofototeca Nazionale in Rome. We describe a system, available at http://www.geomemories.org, that lets users visualize the evolution of the Italian landscape throughout the last century. The Web portal allows comparison of recent satellite imagery with several layers of historical maps, obtained from the aerial photos through a complex workflow that merges them together. We present several case studies carried out in collaboration with geologists, historians and archaeologists, that illustrate the great potential of our system in different research fields. Experiments and advances in image processing technologies are envisaged as a key factor in solving the inherent issue of vast amounts of manual work, from georeferencing to mosaicking to analysis.
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Last update: 4 October 2012
