Mapping three-dimensional geological features from remotely-sensed images and digital elevation models.

Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line thinning and extraction) and manual interpretation techniques are used to identify a set of 'geological primitives' (linear or arc features representing lithological boundaries) within these data. Inclusion of the DEM data provides the three-dimensional co-ordinates of these primitives enabling a least-squares fit to be employed to calculate dip and strike values, based, initially, on the assumption of a simple, linearly dipping structural model. A very large number of scene 'primitives' is identified using these procedures, only some of which have geological significance. Knowledge-based rules are therefore used to identify the relevant. For example, rules are developed to identify lake edges, forest boundaries, forest tracks, rock-vegetation boundaries, and areas of geomorphological interest. Confidence in the geological significance of some of the geological primitives is increased where they are found independently in both the DEM and remotely sensed data. The dip and strike values derived in this way are compared to information taken from the published geological map for this area, as well as measurements taken in the field. Many results are shown to correspond closely to those taken from the map and in the field, with an error of < 1°. These data and rules are incorporated into an expert system which, initially, produces a simple model of the geological structure. The system also provides a graphical user interface for manual control and interpretation, where necessary. Although the system currently only allows a relatively simple structural model (linearly dipping with faulting), in the future it will be possible to extend the system to model more complex features, such as anticlines, synclines, thrusts, nappes, and igneous intrusions

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

Understanding Heterogeneous EO Datasets: A Framework for Semantic Representations

Earth observation (EO) has become a valuable source of comprehensive, reliable, and persistent information for a wide number of applications. However, dealing with the complexity of land cover is sometimes difficult, as the variety of EO sensors reflects in the multitude of details recorded in several types of image data. Their properties dictate the category and nature of the perceptible land structures. The data heterogeneity hampers proper understanding, preventing the definition of universal procedures for content exploitation. The main shortcomings are due to the different human and sensor perception on objects, as well as to the lack of coincidence between visual elements and similarities obtained by computation. In order to bridge these sensory and semantic gaps, the paper presents a compound framework for EO image information extraction. The proposed approach acts like a common ground between the user's understanding, who is visually shortsighted to the visible domain, and the machines numerical interpretation of a much wider information. A hierarchical data representation is considered. At first, basic elements are automatically computed. Then, users can enforce their judgement on the data processing results until semantic structures are revealed. This procedure completes a user-machine knowledge transfer. The interaction is formalized as a dialogue, where communication is determined by a set of parameters guiding the computational process at each level of representation. The purpose is to maintain the data-driven observable connected to the level of semantics and to human awareness. The proposed concept offers flexibility and interoperability to users, allowing them to generate those results that best fit their application scenario. The experiments performed on different satellite images demonstrate the ability to increase the performances in case of semantic annotation by adjusting a set of parameters to the particularities of the analyzed data

Assessment and visualisation of uncertainty in remote sensing land cover classifications

The ability of space- and airborne instruments to measure the amount of electromagnetic radiation reflected and emitted by the Earths surface has proved to be valuable for the understanding of our environment, as it provides for an overwhelming flow of data on the appearance and condition of our planet. The data yielded by remote sensing can be subjected to various types of computer-assisted manipulation, to arrive at derived data sets tailored to different types of application. Computer-assisted classification of remotely sensed data into qualitative classes, for example, is useful for extracting information that can be exploited for cartographic purposes, such as in the generation of thematic maps of land cover types. For a proper cartographic application, the fitness for use of a set of remotely sensed data needs be assessed. The practicability of the data and their classification can be established by means of an accuracy assessment procedure. An error matrix is created for the classification by matching a random sample and its counterpart from a reference data set representing the actual environment. Accuracy assessment based on an error matrix, however, has several drawbacks. Among these is the non-spatial and general character of a global statement like 95% accuracy for an entire classification; moreover, accuracy assessment is a time-consuming and cost-intensive process. As a consequence, it is easily omitted which, of course, is undesirable and may lead to the use of data that are unfit for the application at hand. For assessing the fitness for use of a set of remotely sensed data, accuracy is not the only consideration. More generally, the phrase data quality is used to refer to the extent to which the characteristics of the data meet the requirements of the application aimed at by the user. A high quality indicates a relatively high information value for the considered application - a good fitness for use. Uncertainty is a key-issue in quality assessment and, therefore, in the assessment of fitness for use of a data set. During the life cycle of remotely sensed data uncertainties are introduced and propagated in an often unknown way. For investigating uncertainty, effective measures need to be designed. To this end, it is relevant to consider the purpose to which these measures are to be employed. Here, the focus is on an exploratory perspective. Exploratory analysis of a set of remotely sensed data aims at acquiring insight into the stability of various possible classifications of these data. For this purpose, knowledge about the uncertainties underlying these classifications is imperative. As in exploratory analysis, classification is an iterative process, needing not only measures for assessing the uncertainty in a classification but also effective ways to convey this information to the user. Visualisation is generally considered a useful means of communication of potentially relevant information. In this thesis a class of measures of uncertainty is presented, tailored to the purpose of exploratory analysis of remotely sensed data, together with various ways of cartographic visualisation of uncertainty. The uncertainty that is introduced during classification of a set of remotely sensed data is characterised by the probability vectors that are yielded as a by-product of most probabilistic classification procedures. Here, emphasis is laid on maximum a?x posteriori classifications where for every pixel in the data a vector of probabilities is calculated that specifies for each distinguished class its probability of being the true class. The probability vectors reflect the differences in uncertainty in the resulting classification and can be stored in a gis to serve as a basis for the derivation of weighted uncertainty measures such as entropy. Besides the assessment of uncertainty, efforts can be aimed at the reduction of the amount of uncertainty present in a remotely sensed data set. The maximum a posteriori classification rules being dealt with in this thesis allow for the introduction of a priori knowledge in the classification process, at different levels of sophistication -thereby exceeding the simple approaches embraced in existing image processing packages. Another strategy within the realm of dealing with spatial data uncertainty is based on the idea of decision analysis that allows for an optimal decision-making given uncertain information classes. Combining probability theory (defining the uncertainty related to the occurrence of a particular class) and utility theory (defining the desirability of the consequences resulting from the actions that are taken assuming that particular class) contributes to the selection of the best decision under the given conditions. This idea is particularly interesting when dealing with huge data sets under uncertain circumstances and with far-reaching consequences for wrong decisions (e.g. agricultural fraud detection by European Union). Both the probabilistic results from the classification procedure and other quality information are subjected to cartographic visualisation rules in order to develop a framework for the communication of this spatial metadata. Static as well as more dynamic approaches offer grips for the gis user who needs to consider simple but persuasive maps to assess the fitness for use of a classification. Commercial gis packages are still failing when the sound consideration of spatial data uncertainty is at stake, a fact that has incited the participants of the camotius project to look for the functionality of an uncertainty-sensitive information system. Such a system is valuable for the Dutch situation in which the extra value added by remotely sensed data is not always beyond all doubt; the explicit evaluation of these data as well as their inherent uncertainty reveals their true information value. Two case studies have stressed the role of remote sensing for planning purposes by demonstrating its ability to monitor changes in the extent of greenhouses over space and time, and making inventories of their area. The inclusion of uncertainty information allows for an exploratory approach in which an appeal can be made to several levels of knowledge in order to improve the processing results. It is stated that a user will be encouraged to use remotely sensed data if their extra value is clearly demonstrable. The components that have been scrutinised in the methodological part of this thesis are formalised in a demonstration programme that could serve as a blueprint for commercial gis packages. It can be downloaded from: http://cartography.geog.uu.nl/research/ph

Visualization techniques for data mining of Latur district satellite imagery

Abstract-This study presents a new visualization tool for classification of satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in and understanding of the results. In this study, we present a prototype visualization tool for visual data mining (VDM) of satellite imagery. The visualization tool is showcased in a classification study of highresolution imageries of Latur district in Maharashtra state of India

Modeling Boundaries of Influence among Positional Uncertainty Fields

Within a CIS environment, the proper use of information requires the identification of the uncertainty associated with it. As such, there has been a substantial amount of research dedicated to describing and quantifying spatial data uncertainty. Recent advances in sensor technology and image analysis techniques are making image-derived geospatial data increasingly popular. Along with development in sensor and image analysis technologies have come departures from conventional point-by-point measurements. Current advancements support the transition from traditional point measures to novel techniques that allow the extraction of complex objects as single entities (e.g., road outlines, buildings). As the methods of data extraction advance, so too must the methods of estimating the uncertainty associated with the data. Not only will object uncertainties be modeled, but the connections between these uncertainties will also be estimated. The current methods for determining spatial accuracy for lines and areas typically involve defining a zone of uncertainty around the measured line, within which the actual line exists with some probability. Yet within the research community, the proper shape of this \u27uncertainty band\u27 is a topic with much dissent. Less contemplated is the manner in which such areas of uncertainty interact and influence one another. The development of positional error models, from the epsilon band and error band to the rigorous G-band, has focused on statistical models for estimating independent line features. Yet these models are not suited to model the interactions between uncertainty fields of adjacent features. At some point, these distributed areas of uncertainty around the features will intersect and overlap one another. In such instances, a feature\u27s uncertainty zone is defined not only by its measurement, but also by the uncertainty associated with neighboring features. It is therefore useful to understand and model the interactions between adjacent uncertainty fields. This thesis presents an analysis of estimation and modeling techniques of spatial uncertainty, focusing on the interactions among fields of positional uncertainty for image-derived linear features. Such interactions are assumed to occur between linear features derived from varying methods and sources, allowing the application of an independent error model. A synthetic uncertainty map is derived for a set of linear and aerial features, containing distributed fields of uncertainty for individual features. These uncertainty fields are shown to be advantageous for communication and user understanding, as well as being conducive to a variety of image processing techniques. Such image techniques can combine overlapping uncertainty fields to model the interaction between them. Deformable contour models are used to extract sets of continuous uncertainty boundaries for linear features, and are subsequently applied to extract a boundary of influence shared by two uncertainty fields. These methods are then applied to a complex scene of uncertainties, modeling the interactions of multiple objects within the scene. The resulting boundary uncertainty representations are unique from the previous independent error models which do not take neighboring influences into account. By modeling the boundary of interaction among the uncertainties of neighboring features, a more integrated approach to error modeling and analysis can be developed for complex spatial scenes and datasets

Application of advanced techniques for the remote detection, modelling and spatial analysis of mesquite (prosopis spp.) invasion in Western Australia

Invasive plants pose serious threats to economic, social and environmental interests throughout the world. Developing strategies for their management requires a range of information that is often impractical to collect from ground based surveys. In other cases, such as retrospective analyses of historical invasion rates and patterns, data is rarely, if ever, available from such surveys. Instead, historical archives of remotely sensed imagery provides one of the only existing records, and are used in this research to determine invasion rates and reconstruct invasion patterns of a ca 70 year old exotic mesquite population (Leguminoseae: Prosopis spp.) in the Pilbara Region of Western Australia, thereby helping to identify ways to reduce spread and infill. A model was then developed using this, and other, information to predict which parts of the Pilbara are most a risk. This information can assist in identifying areas requiring the most vigilant intervention and pre-emptive measures. Precise information of the location and areal extent of an invasive species is also crucial for land managers and policy makers for crafting management strategies aimed at control, confinement or eradication of some or all of the population. Therefore, the third component of this research was to develop and test high spectral and spatial resolution airborne imagery as a potential monitoring tool for tracking changes at various intervals and quantifying the effectiveness of management strategies adopted. To this end, high spatial resolution digital multispectral imagery (4 channels, 1 m spatial resolution) and hyperspectral imagery (126 channels, 3 m spatial resolution) was acquired and compared for its potential for distinguishing mesquite from coexisting species and land covers.These three modules of research are summarised hereafter. To examine the rates and patterns of mesquite invasion through space and time, canopies were extracted from a temporal series of panchromatic aerial photography over an area of 450 ha using unsupervised classification. Non-mesquite trees and shrubs were not discernible from mesquite using this imagery (or technique) and so were masked out using an image acquired prior to invasion. The accuracy of the mesquite extractions were corroborated in the field and found to be high (R2 = 0.98, P36 m2 (66-94%) with both approaches and image types. However, both approaches used on the hyperspectral imagery were more reliable at capturing patches >36 m2 than the DMSI using either approach. The lowest omission and commission rates were obtained using pairwise separation on the hyperspectral imagery, which was significantly more accurate than DMSI using an overall separation approach (Z=2.78, P36 m2. However, hyperspectral imagery processed using pairwise separation appears to be superior, even though not statistically different to hyperspectral imagery processed using overall separation or DMSI processed using pairwise separation at the 95% confidence level. Mapping smaller patches may require the use of very high spatial resolution imagery, such as that achievable from unmanned airborne vehicles, coupled with a hyperspectral instrument. Alternatively, management may continue to rely on visual airborne surveys flown at low altitude and speed, which have proven to be capable at mapping small and isolated mesquite shrubs in the study area used in this research

Optical and radar remotely sensed data for large-area wildlife habitat mapping

Wildlife habitat mapping strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species-at-risk recovery and species inventory. Remote sensing has long been identified as a feasible and effective technology for large-area wildlife habitat mapping. However, existing and future uncertainties in remote sensing will definitely have a significant effect on relevant scientific research, such as the limitation of Landsat-series data; the negative impact of cloud and cloud shadows (CCS) in optical imagery; and landscape pattern analysis using remote sensing classification products. This thesis adopted a manuscript-style format; it addresses these challenges (or uncertainties) and opportunities through exploring the state-of-the-art optical and radar remotely sensed data for large-area wildlife habitat mapping, and investigating their feasibility and applicability primarily by comparison either on the level of direct remote sensing products (e.g. classification accuracy) or indirect ecological model (e.g. presence/absence and frequency of use model based on landscape pattern analysis). A framework designed to identify and investigate the potential remotely sensed data, including Disaster Monitoring Constellation (DMC), Landsat Thematic Mapper (TM), Indian Remote Sensing (IRS), and RADARSAT-2, has been developed. The chosen DMC and RADARSAT-2 imagery have acceptable capability of addressing the existing and potential challenges (or uncertainties) in remote sensing of large-area habitat mapping, in order to produce cloud-free thematic maps for the study of wildlife habitat. A quantitative comparison between Landsat-based and IRS-based analyses showed that the characteristics of remote sensing products play an important role in landscape pattern analysis to build grizzly bear presence/absence and frequency of use models

Earth Resources: A continuing bibliography with indexes, issue 36

This bibliography lists 576 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between October 1 and December 31, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis