Quadtree storage of vector data†

Abstract

Vector data storage has various advantages in a cartographic or geographical information system (GIS) environment, but lacks internal spatial relationships between individual features. Quadtree structures have been extensively used to store and access raster data. This paper shows how quadtree methods may be adapted for use in spatially indexing vector data. It demonstrates that a vector quadtree stored in floating point representation overcomes the classical problem with raster quadtrees of data approximation. Examples of vector quadtrees applied to realistic size data sets are given     

The influence of bottom vegetation on remote sensing bathymetry: A case study using Landsat Thematic Mapper data in the Zanzibar Channel

By using echo-sounding data and considerable local knowledge of the environment the possibility to use data from Landsat TM for bathymetric mapping is analysed in the vicinity of Zanzibar. Different TM band combinations and basic image processing techniques are evaluated against the known water depths. The limitations of remote sensing techniques for bathymetric mapping are discussed, with emphasis on the problem of differences in reflective properties of the sea bottom. A recommendation for an optimal empirical method is given.     

Personalizing map content to improve task completion efficiency

Significant interaction challenges arise in both developing and using interactive map applications. Users encounter problems of information overload in using interactive maps to complete tasks. This is further exacerbated by device limitations and interaction constraints in increasingly popular mobile platforms. Application developers must then address restrictions related to screen size and limited bandwidth in order to effectively display maps on mobile devices. In order to address issues of user information overload and application efficiency in interactive map applications, we have developed a novel approach for delivering personalized vector maps. Ongoing task interactions between users and maps are monitored and captured implicitly in order to infer individual and group preferences related to specific map feature content. Personalized interactive maps that contain spatial feature content tailored specifically to users' individual preferences are then generated. Our approach addresses spatial information overload by providing only the map information necessary and sufficient to suit user interaction preferences, thus simplifying the completion of tasks performed with interactive maps. In turn, tailoring map content to specific user preferences considerably reduces the size of vector data sets necessary to transmit and render maps on mobile devices. We have developed a geographic information system prototype, MAPPER (MAP PERsonalization), that implements our approach. Experimental evaluations show that the use of personalized maps helps users complete their tasks more efficiently and can reduce information overload.     

Global geographical databases for modelling trace gas fluxes

Abstract

The design and development of global digital databases and their integrated use in studies of terrestrial sources and sinks of trace gases are discussed. Generic design characteristics that expand the utility of these databases are outlined including: hierarchical classification systems that provide for encoding and accessing data at several levels of detail, and fine spatial and thematic resolution which accommodate adaptations to a broad base of applications. The discussion follows the sequence of (1) developing primary databases on major surface characteristics, (2) selectively integrating combinations of data bases to produce secondary data sets of source categories, and (3) combining information on source categories with fluxes to produce tertiary data sets of emissions. These procedures are demonstrated with the example of estimating and evaluating alternative global methane emissions from natural wetlands.     

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

ABSTRACT

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.     

Spatialization of the SNOWPACK snow model for the Canadian Arctic to assess Peary caribou winter grazing conditions

Abstract

Peary caribou is the northernmost designatable unit for caribou species, and its population has declined by about 70% over the last three generations. The Committee on the Status of Endangered Wildlife in Canada identified difficult grazing conditions through the snow cover as being the most significant factor contributing to this decline. This study focuses on a spatially explicit assessment tool using snow model simulations (Swiss SNOWPACK model driven in an off-line mode by spatialized meteorological forcing data generated by the Canadian Regional Climate Model) to characterize snow conditions for Peary caribou grazing in the Canadian Arctic. The life cycle of Peary caribou has been subdivided into three critical periods: summer foraging and fall breeding (July–October), winter foraging (November–March), and spring calving (April–June). Winter snow conditions are analyzed and snow simulations compared to Peary caribou island counts to identify a snow parameter that could potentially act as a proxy for grazing conditions and explain fluctuations in Peary caribou numbers. This analysis concludes that caribou counts are affected by simulated snow density values >300 kg m^?3. A software tool mapping possibly favorable and unfavorable grazing conditions based on snow is proposed at a regional scale across the Canadian Arctic Archipelago. Specific output examples are given to show the utility of the tool, mapping pixels with cumulative snow thickness above densities of 300 kg m^?3, where cumulative seasonal thicknesses >7000 cm are considered unfavorable.     

The use of digital map data in the segmentation and classification of remotely-sensed images

Abstract

Remotely-sensed data constitute a major potential source of input to geographical information systems (GIS)However, these data often have a relatively poor classification accuracy compared with that of the cartographic data from maps with which they may be combined in the course of GIS analysis. The possibility exists of using data sets (in the form of digital maps) resident within a GIS in order to improve this accuracy, before the classified image is incorporated into the GIS. Results are discussed from a British Alvey Information Technology project to develop a system for the knowledge-based segmentation and classification of remotely-sensed terrain images, in which the knowledge contained in digital map     

A proposed base for a geographical information system for India

Abstract

Attempts have been made by different organizations in India to organize data in order to develop a suitable information system. These attempts were oriented towards specific uses, such as natural resources, data management, thematic mapping and the like. In some attempts software has been developed for some specific purpose. It is not an easy task to develop an information system for a country such as India. Its extent in latitude and longitude is well above average. There are federal and state agencies for the collection of data and the preparation of maps. The geographical information concerning India is available from the following sources: the administrative areas of states, union territories, districts, sub-districts and villages; survey sheets; remote sensing images; aerial photographs; and thematic base maps of the National Atlas and Thematic Mapping Organisation. There are other map series available which have been used for socio-economic mapping. By the end of 1987 the Indian Remote Sensing Satellite will be launched and then remotely-sensed data will be readily available. Considering the geographical bases available in the country, five levels can be identified for developing an information system for India. Some of the thematic maps at 1:1000 000 scale can be used for initiating such a geographical information system.     

Handling Geographic Information. Report of the Committee of Enquiry Chaired by Lord Chorley,Department of the Environment. (London: HMSO, 1987.) [Pp. 208. ] Price £l14-95. ISBN Oil 752015 2.

ABSTRACT

A wide range of environmental process simulations would benefit from the development of GIS able to cope with the additional dimensions of vertical space and time, and having extended spatial modelling facilities. The results of a project in the first of these areas, namely on the development of techniques for handling four-dimensional (4-D) data, are described. The key topics of data models, visualization and interpolation have been studied. Problems include the large size of some 4-D data sets, the sparseness of sampling in some dimensions compared with others, and the need to combine data sets from different sensors which may be of different dimensionalities and scales. The gridded volume data common to most environmental models are stored in a 4-D bintree form in which all dimensions are treated identically. Simple 4-D objects may also be stored, and links are provided between volume and object databases. The techniques have been implemented within a computational testbed allowing parallel processing to cope with large data sizes.     

Influences of different surveying and mapping methods on fractal characteristics of gully-head shoulder lines

Abstract

Accurate quantification of gully shoulder lines (gully borderlines) will help better understand gully formation and evolution. Surveying and mapping are the most important ways to obtain precise morphology. To evaluate the influences of different steps of surveying and of curve-fitting methods of mapping on the morphology of the shoulder line characterized by fractal dimensions, 13 shoulder lines at gully heads were surveyed using a total station and then mapped with different methods of curve fitting, with the fractal dimensions calculated by maps compared with those measured in the field. Fractal dimensions by field measurement ranged from 1.185 to 1.456. Compared with field measurements, the average absolute errors of polygonal line, quadratic B-spline, and arc-fitting methods are 0.045, 0.040, and 0.046, respectively; the average relative errors are 3.48, 3.13, and 3.59%. Therefore, the quadratic B-spline method has a higher accuracy. The standard error of the fractal dimension tends to be larger as average step length increases. The error is ~5% when the step length is 0.7 m, which is advisable for field surveying. This study will help promote the efficiency of field surveying and mapping, and thus promote the accuracy and credibility of gully morphology.     

Road network detection from SPOT imagery for updating geographical information systems in the rural–urban fringe

Abstract

Visual interpretation of high-resolution satellite data has been useful for mapping linear features, such as roads and updating land-use changes. However, it would be beneficial to map new road networks digitally from satellite data to update digital databases using semi-automated techniques. In this paper, an algorithm called Gradient Direction Profile Analysis (GDPA) is used to extract road networks digitally from SPOT High Resolution Visible (HRV) panchromatic data. The roads generated are compared with a visual interpretation of the SPOT HRV multispectral and panchromatic data. The technique is most effective in areas where road development is relatively recent. This is due to the spectral consistency of new road networks. As new road networks are those of most interest to the land manager, this is a useful technique for updating digital road network files within a geographical information system of urban areas.     

Uncertainty due to DEM error in landslide susceptibility mapping

Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km²) and the other for model extrapolation (approximately 4600 km²). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.     

Evaluation of a Model for Predicting Simultaneous Contrast on Color Maps

Perceptions of colors of individual map symbols are affected by simultaneous contrast, or induction, from surrounding colors. In an experiment with human subjects, reaction times were significantly slower, with significantly more errors, for test maps on which an induction model predicted that colors would be misinterpreted. Reaction times and error rates improved significantly with map color adjustments that the model predicted would remove induced confusions. These results represent progress toward establishing objective guidance for the design of effective color schemes in computer-assisted mapping environments common in cartography, geographic information systems, and scientific visualization.     

Simulating and mapping spatial complexity using multi-scale techniques

Abstract

A central problem in spatial analysis is the mapping of data for complex spatial fields using relatively simple data structures, such as those of a conventional GIS. This complexity can be measured using such indices as multi-scale variance, which reflects spatial autocorrelation, and multi-fractal dimension, which characterizes the values of fields. These indices are computed for three spatial processes: Gaussian noise, a simple mathematical function, and data for a random walk. Fractal analysis is then used to produce a vegetation map of the central region of California based on a satellite image. This analysis suggests that real world data lie on a continuum between the simple and the random, and that a major GIS challenge is the scientific representation and understanding of rapidly changing multi-scale fields.     

Intermediate disturbance and patterns of species richness

Abstract

The intermediate disturbance hypothesis (IDH) predicts highest species diversity in environments experiencing intermediate intensity disturbance, after an intermediate timespan. Because many landscapes comprise mosaics with complex disturbance histories, the theory implies that each patch in those mosaics should have a distinct level of diversity reflecting the magnitude of disturbance and the time since it occurred. We model changing patterns of species richness across a landscape experiencing varied scenarios of simulated disturbance in order to predict first the variation of richness through time in individual patches, based on their disturbance histories, and then the changing patterns of richness across the landscape through time, representing the cumulative impact of changing richness within the individual patches. Model outputs show that individual landscape patches have highly variable species richness through time, with the trajectory reflecting the timing, intensity and sequence of disturbances. When the results are mapped across the landscape, the resulting temporal and spatial complexity reveals a distribution of biodiversity that is strikingly contingent on the details of disturbance history. These results illustrate the danger of generalization (in either data interpretation or management decisions), as IDH actually imposes a highly variable pattern of diversity.     

Advanced spatial statistics for analysing and visualizing geo-referenced data

Abstract

Spatial statistics supplies advanced methods for analysing environmental data, and copes with observational interdependencies similar to the way principal components analysis treats multicollinearity. The U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program (EMAP) utilizes kriging from geostatistics for mapping and visualizing environmental data. A conceptual framework is articulated between the interpolation problem in kriging and the missing data problem in spatial statistics, with special reference to relations between the exponential semi-variogram and the conditional autoregressive models. Supercomputing experiments are summarized that simplify numerically the probability density function normalizing factor, which is of particular relevance to estimation tasks for the EMAP project.     

Spatial Sampling Strategies for Mapping a Volcanic Ground Water Plume in Sierra Valey,California

Accurately mapping a region’s ground water quality depends upon the spatial sampling strategies employed, including where and how often field data are collected. This study compares the relative values of three field sampling strategies for mapping a known migrating plume of volcanic ground water in Sierra Valley, California. The first strategy sampled wells once each year during 1957, 1972, and 1980 (n=63, 45, and 57, respectively) and portrayed spatial–temporal changes in ground water quality more clearly on maps than did two alternative sampling strategies. One of these alternatives, Strategy 2, sampled one well per township per year during 1957, 1972, and 1980 (n=11) and did not detect the migrating plume, despite being a recommended strategy. The other alternative, Strategy 3, frequently sampled in time a small, fixed group of indicator wells (n=13) every four years for the same period, again producing maps with little correlation to the original pattern detected by Strategy 1.