Reassignment of the farm structure statistical data using gis and spatialisation of the results based on remotely sensed data

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Abstract

From a rural land use perspective, an important development in Europe is that agricultural activities are being combined with other activities such as environmental care, maintaining the landscape, forestry, preserving recreational and tourist areas, etc. As a result, there is a strong need for statistical data on rural populations and particularly on landscapes and land use, which are by their nature spatial in form. The management, the processing and the display of such statistical data is therefore, to a large extend, a spatial process. In this respect, GIS is considered necessary in the production of census maps, for dealing with census logistics, for monitoring census activities, and for data dissemination [2]. With the advent of GIS, a wide range of spatial analysis methods has been developed for carrying out data transformations between different spatial structures. These methods help to present the data in a more meaningful and consistent manner and enable different data sets, based on different geographical units, to be brought together and overlaid. They also facilitate the spatial analysis of statistical data required in the development and/or calculation of more reliable indicators for the determination of the state and quality of the environment, and the ability to measure the effect of the agricultural economy, across regions and countries. Most policy makers concerned with agri-environmental issues at the national level are confronted with fragmented information and it is accordingly difficult to use the information in a way that effectively contributes to policy decision making. A necessary step in the assessment of agricultural policies and of their impact on the countryside and landscapes is the study of spatial units that constitute the underlying structure of these areas. Most statistical data in the European Union (EU), by means of the Farm Structure Survey (FSS) data, is organized and presented on the basis of NUTS (Nomenclature des Unites Territoriales Statistiques) system, to provide a single, uniform breakdown of a country. Nevertheless, these units are geographical areas that may vary substantially not only in their size and shape, but also over time. This work presents an interface between the statistical and geographical databases and provides a comparison between them by means of the FSS and CORINE Land Cover (CLC). The geographical database can be used as a means for the spatial disaggregation of FSS data into a more accurate geographical level and it is the first step towards a satisfactory spatial analysis. FSS and CLC commonly describe land cover and land use. Definition of an interface between their nomenclatures is a precondition for this spatial disaggregation. Notice that the comparison requires determining the aggregation level of the classes for which the correspondence has already been set, as well as, validation of the result by comparing the respective surface areas of the related classes. After the reclassification of the above data, common classes are created and presented on a map using an embedded GIS environment. FSS data also require a comparison with other sources of information, as for example topography, climatology of the different types of agricultural land, if someone wants, for example, to evaluate the risks of erosion or of pollution of watercourses by pesticides. Knowing agricultural areas by type of crop within survey districts is insufficient. It is necessary to localize this information more precisely. This will allow the reallocation of data into suitable areas, such as drainage basins, while limiting the loss of information. Notice that land use is difficult to define by photo-interpretation as well as from a large distance (i.e. >100m). However, this mode of observation is not preponderant concerning unused land, such as, shrub land, forest, bare land, permanent grassland and water/wetland. It concerns south Mediterranean areas where the land cover is very likely to be shrub land or bare lands. On the ground, the high rate of shrub land must reflect a difficulty for defining clearly the activity on such intermediate biotope. These areas can also be considered as unused because of the climate and the low density of population. As the forest unused areas, they might be used as rough grazing areas. From a general point of view, unused areas are much more located on homogenous land cover types (shrub land, land without tree, permanent grassland without tree, forest, etc.). Unused areas occupy a large part of Greece where the proportion rises to almost 40% of the country. To test the interface and provide the appropriate links between certain classes of the two databases the region of the island of Crete has been chosen. The statistical data used has been provided by the Basic FSS of 1999/2000 (Census of Agricultural for Livestock Breeding or simply Agricultural Census). However, to achieve compatibility between census and photo-interpretation data a recently developed, improved version of the CLC geographical database has been used. The new geo-statistical database, which takes into account the FSS nomenclature and definitions, provides a much better acquisition period (Landsat-TM 1998-1999) which is the same as the census reference period (1998-1999). The structure of the paper is as follows: The next section describes briefly the recently introduced geo-statistical database. It provides the main characteristics of the classification scheme used and it resolves the problems encountered when linking the data sources (i.e., the FSS and the CLC databases). Then, the section dealing with the software tool follows, which provides a sufficient description of the development. It should be noted that the developed tool is quite general; however, for validation purposes a case study has been conducted. This section is followed by the section of Data Analysis in which the results from the comparison of the related nomenclatures are presented. Finally, the last section presents the conclusions and discusses further developments of this work. © 2009 Springer Berlin Heidelberg.

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Sambrakos, M., & Tsiligiridis, T. (2009). Reassignment of the farm structure statistical data using gis and spatialisation of the results based on remotely sensed data. In Interfacing Geostatistics and GIS (pp. 223–234). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-33236-7_17

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