Spatial relations often are desired answers that a geographic information system (GIS) should generate in response to a user's query. Current GIS's provide only rudimentary support for processing and interpreting natural-language-like spatial relations, because their models and representations are primarily quantitative, while natural-language spatial relations are usually dominated by qualitative properties. Studies of the use of spatial relations in natural language showed that topology accounts for a significant portion of the geometric properties. This article develops a formal model that captures metric details for the description of natural-language spatial relations. The metric details are expressed as refinements of the categories identified by the 9-intersection, a model for topological spatial relations, and provide a more precise measure than does topology alone as to whether a geometric configuration matches with a spatial term or not. Similarly, these measures help in identifying the spatial term that describes a particular configuration. Two groups of metric details are derived: splitting ratios as the normalized values of lengths and areas of intersections; and closeness measures as the normalized distances between disjoint object parts. The resulting model of topological and metric properties was calibrated for 64 spatial terms in English, providing values for the best fit as well as value ranges for the significant parameters of each term. Three examples demonstrate how the framework and its calibrated values are used to determine the best spatial term for a relationship between two geometric objects.

Decisions on land use have become progressively more difficult in the last decade. The main reasons for this development lie in the increasing population combined with an increasing demand for new land and resources and in the growing consciousness for sustainable land and resource use. The objective of this paper is to incorporate the concept of multiple land use into geographic information systems (GIS)-based land suitability analysis using the Food and Agricultural Organization (FAO) approach. The effective environmental factors and vegetation parameters on apiculture were described as map layers within GIS so that each map layer represented one alternative. Three alternative land suitability patterns for beekeeping are presented. The study indicated that decreasing nectar or pollen species and shortening of the flowering period were the most limiting factors in land suitability for beekeeping. In contrast suitable distribution of water resources, the good climate condition and dominant unpalatable species by over grazing with extended flowering period increased the land suitability for beekeeping. Generally, 54% of the area had an acceptable score of excellent suitability for beekeeping. Therefore, apiculture may have an important role in increasing and promoting better land use.

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Rice is considered the main food source for over 40% of the world population and plays a crucial role in countries’ food security, food management, and economic aspects. The value of SAR remote sensing in agricultural studies has its source of illumination and not limited to cloud cover. This makes it highly preferable over optical sensors in cloud-shrouded countries. The objective of the study is to assess the capability of Sentinel-1 data for determining paddy planting methods, identifying unhealthy paddy and an attempt made to differentiate rice varieties through correlation of in situ measurements and temporal variation of SAR backscattered signals. Six Sentinel-1 images are stacked to cover the entire paddy lifecycle. The correlated field data and plant backscatter showed that transplanted paddy has backscatter higher than broadcasted paddy. Two drops of paddy backscatter coefficient occurred, the first one, at the reproductive stage when paddy was attacked by bacteria and the second drop was at the ripening stage due to the attack of pests. The five rice varieties planted in Seberang Perak, Malaysia had the backscatter with insignificant differences that cannot confirm the Sentinel-1 capability to differentiate planted rice varieties. According to the obtained results, the time series of Sentinel-1 data has the capability for paddy rice growth monitoring.

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