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Soil erosion has been studied extensively in the last decades. Studies on soil particle movement through chemical tracers were mostly based on the use of radioactive elements. These however have a limited half-life, and are highly toxic and expensive to measure over large areas. This study examines the possibility to use Potassium (K), in the form of an agricultural fertilizer, as a soil particle tracer. Soluble Potassium is spectrally active and has an absorption feature near 2465 nm. This research hypothesises that flowing water moves surface soil particles with adsorbed K and can be traced by observing the change in absorption feature. A field-based water flow experiment was conducted on 3 plots in silty loam soils in the Netherlands. Two plots were treated with 0.6 and 2.48 mg/g of K2O fertilizer, and one plot was used as reference. Infrared reflectance spectra were collected to observe spatial variation in available K, before and after application of fertilizer, and after runoff simulation by water flow. Change in absorption feature depth near 2465nm was used to determine K abundance, and maps with K distribution were produced for each stage of the experiment. Laboratory analysis was done to validate and explain the field results. Statistical relationships between absorption feature parameters of K, water and clay were established to determine whether K was removed during the experiment. Based on coefficients of determination (R2), it can be concluded that an increase in moisture and clay content in a soil of over 18% poses a practical limitation in the use of K as a soil particle tracer with field infrared spectroscopy.

In infrastructural projects, communication between involved parties is difficult. This among other things, is caused by inferior quality and uncertain information concerning collected data and derived real world representations. Particularly in subsurface geotechnical representations uncertainties are high, since only sparse information is available for the interpretation. This leads to the introduction of "interpretational uncertainties" into the representation; that are, uncertainties introduced by the expert using own knowledge and experience for the data interpretation. This, in addition to the variety of data and information types, makes harmonization of geo-information extremely difficult. This paper summarizes available methods and software packages as used by different professionals in infrastructural development for the representation of real world and design objects as well as for the management of geo-information. Furthermore, it emphasizes existing problems and gaps towards harmonized handling of geo-information including uncertainty estimations; with focus on 'interpretational uncertainties' in subsurface representations.

Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with six laser rangefinders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph optimization. It performs loop-closure detection and correction to recognize previously visited places, and to correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multilayer LiDAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicate that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31-m-long acquisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multilayer LiDAR-based macrodrone, given the low deviation between the point clouds built by both drones. Approximately 85 % of the cloud-to-cloud distances were less than 10 cm.