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Glacitectonic deformation in the Upper Weichselian led to the tectonic framework of large-scale folds and displaced thrust sheets of Maastrichtian (Upper Cretaceous) chalk and Pleistocene glacial deposits in the southwestern Baltic Sea region. They form surface expressions of sub-parallel ridges and elongated valleys in between and on the Jasmund Peninsula. Geomorphological mapping and detailed landform analyses give another insight into the arrangement and the formation history of these proglacial surface structures. Light detection and ranging (LiDAR) digital elevation models (DEM) analysis techniques were applied to a proglacial rather than a subglacial environment. Results suggest a division into a northern part with morphological ridges striking NW-SE and a southern part with SW-NE trending ridges. The observation of partly truncated northerly ridges and their superimposition by the southern sub-complex suggest that the northern part was generated earlier than the southern part. The applied spatial analyses tools were used to develop a new, self-consistent genetic model integrating all parts of the 100 km 2 large Jasmund Glacitectonic Complex. Results suggest a more consistent terminology for the tectonic setting and a revised genetic model for Jasmund, including three evolutional stages that are characterized by different ice flow patterns.

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Historically, when analyzing the effect of land-use on transportation demand, research has concentrated on a few key indicators, notably mode choice, VMT and number of trips. At the same time, this literature has primarily focused on the effects of individual land-use variables: e.g. what is the effect of land-use mixity or population density on mode choice. It is becoming increasingly clear however that the isolated impact of particular measures of land-use on individual and household transportation behavior is small, but that when dealt with using a clustered approach, their combined influence becomes both less ambiguous in direction and greater in magnitude. This paper contributes to the transportation and land-use literature by examining the effect of clusters of land-use indicators on activity spaces, an emerging but traditionally ignored, transportation behavior indicator. Regression analysis results point to a significant relationship between large and dispersed activity spaces, low levels of population and employment density, and low levels of public transit accessibility and land use mix.

The petroleum industry requires new technologies to improve the economics of exploration and production. Digital rock physics is a methodology that seeks to revolutionize reservoir characterization, an essential step in reservoir assessment, using computational methods. A combination of X-ray computed microtomography, digital pore network modeling, and 3D printing technology represents a novel workflow for transferring digital rock models into tangible samples that can be manufactured in a variety of materials and tested with standard laboratory equipment. Accurate replication of pore networks depends on the resolution of tomographic images, rock sample size, statistical algorithms for digital modeling, and the resolution of 3D printing. We performed this integrated approach on a sample of Idaho Gray Sandstone with an estimated porosity of 29% and permeability of 2200 mD. Tomographic images were collected at resolutions of 30 and 7 μm per voxel. This allowed the creation of digital porosity models segmented into grains and pores. Surfaces separating pores from grains were extracted from the digital rock volume and 3D printed in plastic as upscaled tangible models. Two model types, normal (with pores as voids) and inverse (with pores as solid), allowed visualization of the geometry of the grain matrix and topology of pores, while allowing characterization of pore connectivity. The current resolution of commodity 3D printers with a plastic filament (30 μm for pore space and 16 μm for grain matrix) is too low to precisely reproduce the Idaho Gray Sandstone at its original scale. However, the workflow described here also applies to advanced high-resolution 3D printers, which have been becoming more affordable with time. In summary, with its scale flexibility and fast manufacturing time, 3D printing has the potential to become a powerful tool for reservoir characterization. IntroductionThe petroleum industry has always been faced with a problem of correlation across multiple scales of investigation, for example, among seismic, well log, and core data. Although seismic profiles and wireline logs capture field-scale features, and while petrography and computed microtomography (CT) provide insight into pore-to bed-scale features of reservoir rocks, uncertainty in petrophysical properties due to differences in scale still persists. Moreover, calculations of petrophysical properties from microscopy images do not always match experimental data from cuttings and core plugs due to deficiencies in computational algorithms used for pore network modeling and fluid transport simulations. The physical pore network is an essential element of petroleum reservoir that is defined by the sizes, orientations, and connectivity of pores in a rock. Thus, accurate detection of pore space in reservoir rocks is crucial for a proper assessment of porosity-permeability relationships that ultimately affect prediction of hydrocarbon flow and ultimate recovery.

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Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.The pathogen starts infecting roots during early soybean growth stages [9,10] and causes root rot and poor root development [3]. Root infections are favored by cool, wet soil environments [11,12]. Foliar symptoms of interveinal chlorosis (yellowing between leaf veins) and necrosis (tissue browning following cell death) typically appear during reproductive stages [13] and cause premature defoliation and senescence under severe disease pressure [14]. The initial foliar symptoms show only as yellow traces on lower leaves, which makes the disease difficult to detect at early stages. Abundant soil moisture favors SDS foliar symptom expression [12,15], whereas infected plants may not develop foliar symptoms under dry field conditions. Disease distribution within a field is limited by the spatial distribution of the pathogen at the beginning of the growing season.Scouting for SDS foliar symptoms in the field is made difficult by the relatively late onset of visible foliar symptom expression, which often occurs after the soybean canopy has closed, and by the patchy distribution of SDS in soybean fields. Scouting for symptomatic plants is time-consuming, and confirmation of Fv infection requires destructive sampling [16]. Therefore, a more effective method for monitoring and quantifying the distribution of SDS in the field is needed.Early detection of plant diseases through remote sensing can be di...