Characterization and Visualization of Spatial Patterns of Urbanisation and Sprawl through Metrics and Modeling

Characterisation of spatial patterns of urban dynamics of Coimbatore, India is done using temporal remote sensing data of 1989 to 2013 with spatial metrics. Urban morphology at local levels is assessed through density gradients and zonal approach show of higher spatial heterogeneity during late1980’s and early 90’s. Urban expansion picked up at city outskirts and buffer region dominated with large number of urban fragments indicating the sprawl. Urban space has increased from 1.87% (1989) to 21.26 % (2013) with the decline of other land uses particularly vegetation. Higher heterogeneous land use classes during 90’s, give way for a homogeneous landscape (with simple shapes and less edges) indicating the domination of urban category in 2013. Complex landscape with high number of patches and edges in the buffer region indicate of fragmentation due to urban sprawl in the region. Visualisation of urban growth through Fuzzy-AHP-CA model shows that built up area would increase to 32.64% by 2025. The trend points to lack of appropriate regional planning leading to intensification of spatial discontinuity with the unsustainable urban growth

Digital soil mapping, downscaling and updating conventional soil maps using GIS, RS, statistics and auxiliary data

Spatial distribution of soil types and soil properties in the landscape are important in many environmental researches. Conventional soil surveys are not designed to provide the high-resolution soil information required in environmental modelling and site-specific farm management. The objectives of this study were to investigate the relationship between soil development, soil evolution in the landscape, updating legacy soil maps and pedodiversity in an arid and semi-arid region. The application of Digital Soil Mapping (DSM) techniques was investigated with a particular focus to predict soil taxonomic classes and spatial distribution of soil types by soil observations and covariate sets representative of s,c,o,r,p,a,n factors. In the first study, focus is on establishing relationships between pedodiversity and landform evolution in a 86,000 ha region in Borujen, Chaharmahal-Va-Bakhtiari Province, Central Iran. From an overview study, we could conclude that landform evolution was mainly affected by topography and its components. A second study compares various DSM-methods and a conventional soil mapping approach for soil class maps in terms of accuracy, information value and cost in central Iran. Also, the effects of different sample sizes were investigated. Our results demonstrated that in most predicted maps, in DSM approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map. Furthermore, results showed that the conventional soil mapping approach was not as effective as DSM approach. In the third study, different models of the DSM approach were compared to predict the spatial distribution of some important soil properties such as clay content, soil organic carbon and calcium carbonate content. Among all studied models, the terrain attribute “elevation” is the most important variable to predict soil properties. Random forest had promising performance to predict soil organic carbon. But results revealed that all models could not predict the spatial distributions of clay content properly. The minimum area of land that can be legibly delineated in a traditional (printed) map is highly dependent upon mapping scale. For example, this area at a mapping scale of 1:24,000 is about 2.3 ha but at a mapping scale of 1:1,000,000 it is about 1000 ha. A mapping scale of 1:1,000,000 is just too coarse to show a fine-scale pattern or soil type with any degree of legibility, but finer-scale soil maps are more expensive and time-consuming to produce. Thus, spatial variation is often unavoidably obscured. The fourth study of this dissertation focuses on downscaling and updating soil map methods. Thus, the objectives were to apply supervised and unsupervised disaggregation approaches to disaggregate soil polygons of conventional soil map at a scale of 1: 1,000,000 in the selected area. Therefore, soil subgroups and great groups were selected because it is a basic taxonomic level in regional and national soil maps in Iran. In general, we conclude that DSM approach and also disaggregation approach are capable to predict soil types and properties, produce and update legacy soil maps. However, still a number of challenges need to be evaluated e.g. influence of expert knowledge on CSM approach, resolution of ancillary data, georeferenced legacy soil samples data to validate disaggregated soil maps

Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-hours warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context makes the development of real-time rainfall-runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3-hours. In this paper we develop a novel semi-distributed, data-driven, rainfall-runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network-based Fuzzy Inference System solutions is created using various combinations of auto-regressive, spatially-lumped radar and point-based rain gauge predictors. Different levels of spatially-aggregated radar-derived rainfall data are used to generate 4, 8 and 12 sub-catchment input drivers. In general, the semi-distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead-times greater than 3-hours. Performance is found to be optimal when spatial aggregation is restricted to 4 sub-catchments, with up to 30% improvements in the performance over lumped and point-based models being evident at 5-hour lead times. The potential benefits of applying semi-distributed, data-driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, is thus demonstrated

Urban footprint of Mumbai - the commercial capital of India

Urban footprint refers to the proportion of paved surface (built up, roads, etc.) with the reduction of other land use types in a region. Rapid increase in the urban areas is the major driver in landscape dynamics with the significant erosion in the quality and quantity of the natural ecosystems. The urban expansion process hence needs to be monitored, quantified and understood for effective planning and the sustainable management of natural resources. Cities and towns have been experiencing considerable growth in urban area, population size, social aspects, negative environmental and geographical influence, and complexity. Mumbai, the commercial capital of India, has experienced a spurt in infrastructural and industrial activities with globalization and opening up of Indian markets. Unplanned urbanization has resulted in dispersed growth inperi-urban pockets due to socio-economic aspects with the burgeoning population of the city. Consequent to this, there has been an uneven growth pattern apart from the increase in slums in and around the city. This has necessitated the understanding of the urbanization pattern and process focusing especially on the expanding geographical area, its geometry and the spatial pattern of its development. This communication discusses the urban footprint dynamics of Mumbai using multi-temporal remote sensing data with spatial metrics. Land use analysis indicated a decrease of vegetation by 20% with an increase in urban extent by 155% during the last three decades. Landscape metrics aided in assessing the spatial structure and composition of the urban footprints through the zonal analysis by dividing the region into four zones with concentric circles of 1 km incrementing radius from the city centre. The study reveals a significant variation in the composition of the urban patch dynamics with increasing complexity and aggregation of urban area at the centre and sprawl at the outskirts. Shannon's entropy further confirms of sprawl with time. Further zoning with the circular gradients aided in understanding the transition process of land use categories into urban patch

Spatializing the Soil-Ecological Factorial: Data Driven Integrated Land Management Tools

Soils form the dynamic interface of many processes key to the function of terrestrial ecosystems. Many soil properties both influence and are influenced by activity of flora and fauna. Interactions between soils, biota, and climate determine the potential ecosystem services that a given unique ecological site (ES) can support, and how resilient a site is to various pressures and disturbances. Soil data are needed to fully understand how these factors interact, but because this data is difficult to obtain, existing soil maps are sometimes not detailed enough to fully explore relationships. Environmental raster GIS data layers were used to increase the detail of maps by representing soil forming factors and associated ecological pedomemory legacies important to understanding ecological potential. This dissertation presents methods and tools to help create these new soil maps at appropriate resolution and theme for field scale assessment of ecological sites that enable land managers to plan and implement appropriate management decisions.;USDA-NRCS soil surveys were disaggregated to higher resolution maps using a semi-automated expert training routine to implement a random forest classification model. This transformed soil map polygons of variable thematic and spatial resolution (soil map unit concepts) to a consistent 30-meter raster grid of unified theme (soil taxa). Disaggregated maps (DM) showed highly variable accuracy (25--75% overall validation accuracy) that mirrored that of the original soil surveys evaluated in Arizona (AZ) and West Virginia (WV). However, disaggregated maps expressed the soil data at a much more detailed spatial scale with a more interpretable legend. The WV surveys exhibited much lower accuracy than the AZ survey evaluated. This lower accuracy in WV is likely due to the forested setting and highly dissected landscape, two factors that create more intrinsic soil variability that is harder to explain with spatial covariates.;Ecological site descriptions (ESD) document soil-ecosystem groups that produce unique amounts and types of biological constituents and respond similarly to disturbance and environmental variation. ESD are linked to soil map unit components in USDA-NRCS soil surveys and are used as the basis for land management planning on rangelands and forestlands. The component level connection makes DM a good way to spatialize ESD because both are spatially represented at the same thematic level, whereas conventional soil maps have polygons that often have multiple components linked to a delineation.;However, in the evaluation of mapping ESD via DM, the DM turned out not to document the key difference in spodic soil properties that distinguished the important ecotone between northern hardwood and alpine red spruce conifer ESDs in Pocahontas and Randolph counties, WV. So, to adjust, spodic soil properties were mapped directly using digital soil mapping approaches. A strong spatial model of spodic soil morphology presence was developed from a random forest probability model and showed correspondence to red spruce and hemlock occurrences in local historic land deed witness trees from records between 1752 and 1899. From this result, areas with spodic soil properties were assumed to be associated with historic red spruce communities, although 68% of those areas in the WV study area are currently under hardwood cover. This would seem to indicate that hardwoods have encroached on the historic extent of spruce, which is consistent with other recent studies. O-horizon thickness was also observed to be one cm thicker for every 10% greater importance value of red spruce or hemlock versus that of hardwood species at field sites. From these observations, it was calculated conservatively that at least 3.74-6.62 Tg of C have likely been lost from red spruce influenced ecological sites in WV due to historic disturbance related conversions of forest to hardwood composition. These results highlight the value of working within a soil-ecological factorial framework (e.g. an ESD) to contextualize land management options and potential derived services or negative consequences of each available action

Data integration for urban transport planning

Urban transport planning aims at balancing conflicting challenges by promoting more efficient transport systems while reducing negative impacts. The availability of better and more reliable data has not only stimulated new planning methodologies, but also created challenges for efficient data management and data integration. The major focus of this study is to improve methodologies for representing and integrating multi-source and multi-format urban transport data. This research approaches the issue of data integration based on the classification of urban transport data both from a functional and a representational perspective. The functional perspective considers characteristics of the urban transport system and planning requirements, and categorises data into supply, demand, performance and impact. The representational perspective considers transport data in terms of their spatial and non-spatial characteristics that are important for data representation. These two perspectives correspond to institutional and methodological data integration respectively, and are the foundation of transport data integration. This research is based on the city of Wuhan in China. The methodological issues of transport data integration are based on the representational perspective. A framework for data integration has been put forward, in which spatial data are classified as point, linear and areal types, and the non-spatial data are sorted out as values and temporal attributes. This research has respectively probed the integration of point, linear and areal transport data within a GIS environment. The locations of socio-economic activities are point-type data that need to be spatially referenced. A location referencing process requires a referencing base, source address units and referencing methods. The referencing base consists of such spatial features as streets, street addresses, points of interest and publicly known zones. These referencing bases have different levels of spatial preciseness and have to be kept in a hierarchy. Source addresses in Chinese cities are usually written as one sentence, which has to be divided into address units for automatic geo-coding. As it is difficult to separate from the sentences, the address units have to be clearly identified in survey forms. Depending on the types of address units, the referencing process makes use of either semantic name matching or address matching to link source addresses to features in the referencing base. The name-based and road-based referencing schemes constitute a comprehensive location referencing framework that is applicable to Chinese cities. The relationship between two sets of linear features can be identified with spatial overlay in the case of independent representation, or with internal linkage in a dependent representation. The bus line is such a feature that runs on the street network and can be dependently referenced by streets. In the heavily bus-oriented city of Wuhan, bus lines constitute a large public transit network that is important to transport planning and management. This research has extended conventional bus line representation to a more detailed level. Each bus line has been differentiated as two directional routes that are defined separately with reference to the street network. Accordingly, individual route stops are also represented in the database. These stop sites are spatial features with geometry that are linked to street segments and bus routes by linear location referencing methods. A data model linking base street network, bus lines and routes, line and route stops, and other bus operations data has been constructed. The benefits of the detailed model have been demonstrated in several transport applications. Zonal data transitions include three types of operations, i.e. aggregation, areal interpolation and disaggregation. This study focuses on disaggregating data from larger zones to smaller zones. In the context of Wuhan, zonal data disaggregation involves the allocation of statistical data from statistical units to smaller parcels. Given the availability of land use data, a weighted approach reflecting spatial variations has been applied in the disaggregation process. Two technical processes for disaggregation have been examined. Weighted area-weighting (WAW) is an adaptation of the classic area-weighting method, and Monte Carlo simulation (MC) is a stochastic process based on a raster data model. The MC outcome is more convenient for subsequent re-aggregation, and is also directly available for micro-simulation. An important contribution arising from this zonal integration study is that two standardised disaggregation tools have been developed within a GIS environment. The research has also explored the institutional aspect of data integration. The findings of this study show that there is generally a good institutional transport structure in the city of Wuhan and that there is also a growing awareness of using information technology. Professional cooperation exists among transport organisations, but not yet at a level for data sharing. An integrated data support framework requires data sharing. In such a framework, it should be possible to know where to get data for specific transport studies, or which kind of research an institution supports

The uncertainty enabled model web (UncertWeb)

UncertWeb is a European research project running from 2010-2013 that will realize the uncertainty enabled model web. The assumption is that data services, in order to be useful, need to provide information about the accuracy or uncertainty of the data in a machine-readable form. Models taking these data as imput should understand this and propagate errors through model computations, and quantify and communicate errors or uncertainties generated by the model approximations. The project will develop technology to realize this and provide demonstration case studies

Which spatial discretization for distributed hydrological models? Proposition of a methodology and illustration for medium to large-scale catchments

Distributed hydrological models are valuable tools to derive distributed estimation of water balance components or to study the impact of land-use or climate change on water resources and water quality. In these models, the choice of an appropriate spatial discretization is a crucial issue. It is obviously linked to the available data, their spatial resolution and the dominant hydrological processes. For a given catchment and a given data set, the "optimal" spatial discretization should be adapted to the modelling objectives, as the latter determine the dominant hydrological processes considered in the modelling. For small catchments, landscape heterogeneity can be represented explicitly, whereas for large catchments such fine representation is not feasible and simplification is needed. The question is thus: is it possible to design a flexible methodology to represent landscape heterogeneity efficiently, according to the problem to be solved? This methodology should allow a controlled and objective trade-off between available data, the scale of the dominant water cycle components and the modelling objectives. <br><br> In this paper, we propose a general methodology for such catchment discretization. It is based on the use of nested discretizations. The first level of discretization is composed of the sub-catchments, organised by the river network topology. The sub-catchment variability can be described using a second level of discretizations, which is called hydro-landscape units. This level of discretization is only performed if it is consistent with the modelling objectives, the active hydrological processes and data availability. The hydro-landscapes take into account different geophysical factors such as topography, land-use, pedology, but also suitable hydrological discontinuities such as ditches, hedges, dams, etc. For numerical reasons these hydro-landscapes can be further subdivided into smaller elements that will constitute the modelling units (third level of discretization). <br><br> The first part of the paper presents a review about catchment discretization in hydrological models from which we derived the principles of our general methodology. The second part of the paper focuses on the derivation of hydro-landscape units for medium to large scale catchments. For this sub-catchment discretization, we propose the use of principles borrowed from landscape classification. These principles are independent of the catchment size. They allow retaining suitable features required in the catchment description in order to fulfil a specific modelling objective. The method leads to unstructured and homogeneous areas within the sub-catchments, which can be used to derive modelling meshes. It avoids map smoothing by suppressing the smallest units, the role of which can be very important in hydrology, and provides a confidence map (the distance map) for the classification. The confidence map can be used for further uncertainty analysis of modelling results. The final discretization remains consistent with the resolution of input data and that of the source maps. The last part of the paper illustrates the method using available data for the upper Saône catchment in France. The interest of the method for an efficient representation of landscape heterogeneity is illustrated by a comparison with more traditional mapping approaches. Examples of possible models, which can be built on this spatial discretization, are finally given as perspectives for the work

Geographical information modelling for land resource survey

The increasing popularity of geographical information systems (GIS) has at least three major implications for land resources survey. Firstly, GIS allows alternative and richer representation of spatial phenomena than is possible with the traditional paper map. Secondly, digital technology has improved the accessibility of ancillary data, such as digital elevation models and remotely sensed imagery, and the possibilities of incorporating these into target database production. Thirdly, owing to the greater distance between data producers and consumers there is a greater need for uncertainty analysis. However, partly due to disciplinary gaps, the introduction of GIS has not resulted in a thorough adjustment of traditional survey methods. Against this background, the overall objective of this study was to explore and demonstrate the utility of new concepts and tools within the context of pedological and agronomical land surveys. To this end, research was conducted on the interface between five fields of study: geographic information theory, land resource survey, remote sensing, statistics and fuzzy set theory. A demonstration site was chosen around the village of Alora in southern Spain.Fuzzy set theory provides a formalism to deal with classes that are partly indistinct as a result of vague class intensions. Fuzzy sets are characterised by membership functions that assign real numbers from the interval [0, 1] to elements, thereby indicating the grade of membership in that set. When fuzzy membership functions are used to classify attribute data linked to geometrical elements, presence of spatial dependence among these elements ensures that they form spatially contiguous regions. These can be interpreted as objects with indeterminate boundaries or fuzzy objects. Fuzzy set theory thus adds to the conventional conceptual data models that assume either discrete spatial objects or continuous fields.This thesis includes two case studies that demonstrate the use of the fuzzy set theory in the acquisition and querying of geographical information. The first study explored the use of fuzzy c -means clustering of attribute data derived from a digital elevation model to represent transition zones in a soil-landscape model. Validity evaluation of the resulting terrain descriptions was based on the coefficient of determination of regressing topsoil clay data on membership grades. Vaguely bounded regions were more closely related to the observed variation of clay content () than crisply bounded units as used in a conventional soil survey ().The second case study involved the use of the fuzzy set theory in querying uncertain geographical data. It explains differences between fuzziness and stochastic uncertainty on the basis of an example query concerning loss of forest and ease of access. Relationships between probabilities and fuzzy set memberships were established using a linguistic probability qualifier (high probability) and the expectation of a membership function defined on a stochastic travel time. Fuzzy query processing was compared with crisp processing. The fuzzy query response contained more information because, unlike the crisp response, it indicated the degree to which individual locations matched the vague selection criteria.In a land resource survey, data acquisition typically involves collecting a small sample of precisely measured primary data as well as a larger or even exhaustive sample of related secondary data. Soil surveyors often rely on soil-landscape relationships and image interpretation to enable efficient mapping of soil properties. Yet, they generally fail to communicate about the knowledge and methods employed in deriving map units and statements about their content.In this thesis, a methodological framework is formulated and demonstrated that takes advantage of GIS to interactively formalise soil-landscape knowledge using stepwise image interpretation and inductive learning of soil-landscape relationships. It examines topology to record potential part of links between hierarchically nested terrain objects corresponding to distinct soil formation regimes. These relationships can be applied in similar areas to facilitate image interpretation by restricting possible lower level objects. GIS visualisation tools can be used to create images (e.g. perspective views) illustrating the landscape configuration of interpreted terrain objects. The framework is expected to support different methods for analysing and describing soil variation in relation to a terrain description, including those requiring alternative conceptual data models. In this thesis, though, it is only demonstrated with the discrete object model.Satellite remote sensing has become an important tool in land cover mapping, providing an attractive supplement to relatively inefficient ground surveys. A common approach to extract land cover data from remotely sensed imagery is by probabilistic classification of multispectral data. Additional information can be incorporated into such classification, for example by translating it into Bayesian prior probabilities for each land cover type. This is particularly advantageous in the case of spectral overlap among target classes, i.e. when unequivocal class assignment based on spectral data alone is impossible.This thesis demonstrates a procedure to iteratively estimate regional prior class probabilities pertaining to areas resulting from image stratification. This method thus allows the incorporation of additional information into the classification process without the requirement of known prior class probabilities. The demonstration project involved Landsat TM imagery from 1984 and 1995. Image stratification was based on a geological map of the study area. Overall classification accuracy improved from 76% to 90% (1984) and from 64% to 69% (1995) when employing iteratively estimated prior probabilities.The fact that any landscape description is a model based on a limited sample of measured target attribute data implies that it is never completely certain. The presence of error or inaccuracy in the data contributes significantly to such uncertainty. Usually, the accuracy of land survey datasets is indicated using global indices (e.g. see above). Error modelling, on the other hand, allows an indication of the spatial distribution of possible map inaccuracies to be given. This study explored two approaches to error modelling, which are demonstrated within the context of land cover analysis using remotely sensed imagery.The first approach involves the use of local class probabilities conditional to the pixels' spectral data. These probabilities are intermediate results of probabilistic image classification and indicate the magnitude and distribution of classification uncertainty. A case study demonstrated the implication of such uncertainty on change detection by comparing independently classified images. A major shortcoming of this approach is that it implicitly assumes data in neighbouring pixels to be independent. Moreover, it does not make full use of available reference data as it ignores their spatial component. It does not consider data locations nor does it use spatial dependence models that can be derived from the reference data.The assumption of independent pixels obviously impedes proper assessment of spatial uncertainty, such as joint uncertainty about the land cover class at several pixels taken together. Therefore, the second approach was based on geostatistical methods, which exploit spatial dependence rather than ignoring it. It is demonstrated how the above conditional probabilities can be updated by conditioning on sampled reference data at their locations. Stochastic simulation was used to generate a set of 500 equally probable maps, from which uncertainties regarding the spatial extent of contiguous olive orchards could be inferred.Future challenges include studies on other quality aspects of land survey datasets. The present research was limited to uncertainty analysis, so that, for example, data precision and fitness for use were not addressed. Other potential extensions to this work concern full inclusion of the third spatial dimension and modelling of temporal aspects.</p