Advances in data capture and computer technology have made possible the collection of three-dimensional, high-resolution, digital geological data from outcrop analogs. This paper presents new methodologies for the acquisition and utilization of three-dimensional information generated by groundbased laser scanning (lidar) of outcrops. A complete workfl ow is documented-from outcrop selection through data collection, processing and building of virtual outcropsto geological interpretation and the building of geocellular models using an industry-standard, reservoir-modeling software. Data sets from the Roda Sandstone in the Spanish Pyrenees and the Grabens region of Canyonlands National Park, Utah, USA, are used to illustrate the application of the workfl ow to sedimentary and structural problems at a reservoir scale.Subsurface reservoir models are limited by available geological data. Outcrop analogs from comparable systems, such as the Roda Sandstone and the Grabens, are commonly used to provide additional input to models of the subsurface. Outcrop geocellular models can be analyzed both statically and dynamically, wherein static examination involves visual inspection and the extraction of quan-titative data on body geometry, and dynamic investigation involves the simulation of fl uid fl ow through the analog model.The work presented in this study demonstrates the utility of lidar as a data collection technique for the building of more accurate outcrop-based geocellular models. The aim of this publication is to present the fi rst documentation of a complete workfl ow that extends from outcrop selection to model investigation through the presentation of two worked data sets.

Advances in data capture and computer technology have made possible the collection of three-dimensional, high-resolution, digital geological data from outcrop analogs. This paper presents new methodologies for the acquisition and utilization of three-dimensional information generated by groundbased laser scanning (lidar) of outcrops. A complete workfl ow is documented-from outcrop selection through data collection, processing and building of virtual outcropsto geological interpretation and the building of geocellular models using an industry-standard, reservoir-modeling software. Data sets from the Roda Sandstone in the Spanish Pyrenees and the Grabens region of Canyonlands National Park, Utah, USA, are used to illustrate the application of the workfl ow to sedimentary and structural problems at a reservoir scale. Subsurface reservoir models are limited by available geological data. Outcrop analogs from comparable systems, such as the Roda Sandstone and the Grabens, are commonly used to provide additional input to models of the subsurface. Outcrop geocellular models can be analyzed both statically and dynamically, wherein static examination involves visual inspection and the extraction of quantitative data on body geometry, and dynamic investigation involves the simulation of fl uid fl ow through the analog model. The work presented in this study demonstrates the utility of lidar as a data collection technique for the building of more accurate outcrop-based geocellular models. The aim of this publication is to present the fi rst documentation of a complete workfl ow that extends from outcrop selection to model investigation through the presentation of two worked data sets.

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In many close‐range applications it is essential to obtain information about the geometry of the target surface as well as its chemical composition. In this study, close‐range hyperspectral imaging was integrated with terrestrial laser scanning to provide mineral and chemical information for geological field studies. The spectral data was collected with the HySpex SWIR‐320m sensor, which operates in the infrared spectrum between the wavelengths of 1·3 and 2·5 μm. This sensor permits surfaces to be imaged with high spectral resolution, allowing detailed classification and analysis to be carried out. Photogrammetric processing of the hyperspectral imagery was achieved using an existing geometric model for rotating linear‐array‐based panoramic cameras. Bundle block adjustment of multiple images resulted in the registration of the spectral images in the lidar coordinate system, with a precision of around one image pixel. Although the image and control point network was not optimised for photogrammetric processing, it was possible to recover the exterior camera orientations, as well as additional camera calibration parameters. With the known image orientations, 3D lidar models could be textured with hyperspectral classifications, and the quality of the registration determined. The integration of the hyperspectral image products with the terrestrial lidar data enabled data interpretation and evaluation in a real‐world coordinate system, and provided a reliable means of linking material and geometric information.

Ground-based hyperspectral imaging combined with terrestrial lidar scanning is a novel technique for outcrop analysis, which has been applied to Early and Late Albian carbonates of the Pozalagua Quarry (Cantabrian Mountains, Spain). An image processing workflow has been developed for differentiating limestone from dolomite, providing additional sedimentary and diagenetic information, and the possibility to quantitatively delineate diagenetic phases in an accurate way. Spectral absorption signatures can be linked to specific sedimentary or diagenetic products, such as recent and palaeokarst, hydrothermal karst, (solution enlarged) fractures and different dolomite types. Some of the spectral signatures are related to iron, manganese, organic matter, clay and/or water content. Ground-truthing accessible parts of the quarry showed that the classification based on hyperspectral image interpretation was very accurate. This technique opens the possibility for quantitative data evaluation on sedimentary and diagenetic features in inaccessible outcrops. This study demonstrates the potential of ground-based imaging spectroscopy to provide information about the chemical-mineralogical distribution in outcrops, which could otherwise not be established using conventional field methods.

This paper reports research carried out to develop a novel method of monitoring coastal change, using an approach based on digital elevation models (DEMs). In recent years change monitoring has become an increasingly important issue, particularly for landforms and areas that are potentially hazardous to human life and assets. The coastal zone is currently a sensitive policy area for those involved with its management, as phenomena such as erosion and landslides affect the stability of both the natural and the built environment. With legal and financial implications of failing to predict and react to such geomorphological change, the provision of accurate and effective monitoring is essential. Long coastlines and dynamic processes make the application of traditional surveying difficult, but recent advances made in the geomatics discipline allow for more effective methodologies to be investigated.A solution is presented, based on two component technologies -the Global Positioning System (GPS) and digital small format aerial photogrammetry -using data fusion to eliminate the disadvantages associated with each technique individually. A sparse but highly accurate DEM, created using kinematic GPS, was used as control to orientate surfaces derived from the relative orientation stage of photogrammetric processing. A least squares surface matching algorithm was developed to perform the orientation, reducing the need for costly and inefficient ground control point survey. Change detection was then carried out between temporal data epochs for a rapidly eroding coastline (Filey Bay, North Yorkshire). The surface matching algorithm was employed to register the datasets and determine differences between the DEM series. Large areas of change were identified during the lifetime of the study. Results of this methodology were encouraging, the flexibility, redundancy and automation potential allowing an efficient approach to landform monitoring.

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