Sustainable environmental and resource management futures for Sub-Saharan Africa

Reports

MEDACTA '93 Modern Educational Technologies     

Conflation of OpenStreetMap and Mobile Sports Tracking Data for Automatic Bicycle Routing

This article investigates how workout trajectories from a mobile sports tracking application can be used to provide automatic route suggestions for bicyclists. We apply a Hidden Markov Model (HMM)‐based method for matching cycling tracks to a “bicycle network” extracted from crowdsourced OpenStreetMap (OSM) data, and evaluate its effective differences in terms of optimal routing compared with a simple geometric point‐to‐curve method. OSM has quickly established itself as a popular resource for bicycle routing; however, its high‐level of detail presents challenges for its applicability to popularity‐based routing. We propose a solution where bikeways are prioritized in map‐matching, achieving good performance; the HMM‐based method matched correctly on average 94% of the route length. In addition, we show that the extremely biased nature of the trajectory dataset, which is typical of volunteered user‐generated data, can be of high importance in terms of popularity‐based routing. Most computed routes diverged depending on whether the number of users or number of tracks was used as an indicator of popularity, which may imply varying preferences among different types of cyclists. Revising the number of tracks by diversity of users to surmount local biases in the data had a more limited effect on routing.     

Integrating climate and local factors for geomorphological distribution models

Earth surface processes (ESPs) drive landscape development and ecosystem processes in high‐latitude regions by creating spatially heterogeneous abiotic and biotic conditions. Ongoing global change may potentially alter the activity of ESPs through feedback on ground conditions, vegetation and the carbon cycle. Consequently, accurate modeling of ESPs is important for improving understanding of the current and future distributions of these processes. The aims of this study were to: (1) integrate climate and multiple local predictors to develop realistic ensemble models for the four key ESPs occurring at high latitudes (slope processes, cryoturbation, nivation and palsa mires) based on the outputs of 10 modern statistical techniques; (2) test whether models of ESPs are improved by incorporating topography, soil and vegetation predictors to climate‐only models; (3) examine the relative importance of these variables in a multivariate setting. Overall, the models showed high transferability with the mean area under curve of a receiver operating characteristics (AUC) ranging from 0.83 to 0.96 and true skill statistics (TSS) from 0.52 to 0.87 for the most complex models. Even though the analyses highlighted the importance of the climate variables as the most influential predictors, three out of four models benefitted from the inclusion of local predictors. We conclude that disregarding local topography and soil conditions in spatial models of ESPs may cause a significant source of error in geomorphological distribution models. Copyright © 2014 John Wiley & Sons, Ltd.     

Radiometric stability assessment of an airborne photogrammetric sensor in a test field

Radiometric stability is a desired property of digital photogrammetric large-format sensors. This article presents a methodology for determining the radiometric stability of airborne imaging sensors in operational conditions in a test field and the results of stability evaluation of a large-format photogrammetric frame sensor DMC, from Intergraph. The imagery was collected in two days using nine different exposure settings, and images collected with variable exposure time and aperture were compared. The results showed promising stability in many cases, up to a level of 2% of the radiance, but less favorable results also appeared. Possible reasons for the unfavorable results could be the limitations of the experimental set-up or the instability of the sensor. DMC showed high radiometric performance potential, but high sensitivity to the exposure settings. Based on the results, recommendations for the future test field calibration and validation procedures were given. One limitation of the analysis was the insufficient information about the sensor stability potential; proposals were given to sensor manufacturers concerning the necessary information.     

Spatial interpolation of monthly climate data for Finland: comparing the performance of kriging and generalized additive models

The Finnish Meteorological Institute has calculated statistics for the new reference period of 1981–2010. During this project, the grid size has been reduced from 10 to 1 km, the evaluation of the interpolation has been improved, and comparisons between different methods has been performed. The climate variables of interest were monthly mean temperature and mean precipitation, for which the spatial variability was explained using auxiliary information: mean elevation, sea percentage, and lake percentage. We compared three methods for spatial prediction: kriging with external drift (KED), generalized additive models (GAM), and GAM combined with residual kriging (GK). Every interpolation file now has attached statistical key figures describing the bias and the normality of the prediction error. According to the cross-validation results, GAM was the best method for predicting mean temperatures, with only very small differences relative to the other methods. For mean precipitation, KED produced the most accurate predictions, followed by GK. In both cases, there was notable seasonal variation in the statistical skill scores. For the new reference period and future interpolations, KED was chosen as the primary method due to its robustness and accuracy.     

Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification

In this paper, a measurement system for the acquisition of a virtual hyperspectral LiDAR dataset is presented. As commercial hyperspectral LiDARs are not yet available, the system provides a novel type of data for the testing and developing of future hyperspectral LiDAR algorithms. The measurement system consists of two parts: first, backscattered reflectance spectra are collected using a spectrometer and a cutting-edge technology, white-light supercontinuum laser source; second, a commercial monochromatic LiDAR system is used for ranging. A virtual hyperspectral LiDAR dataset is produced by data fusion. Such a dataset was collected on a Norway spruce (Picea abies) sample. The performance of classification was tested using an experimental hyperspectral algorithm based on a novel combination of the Spectral Correlation Mapper and a region growing algorithm. The classifier was able to automatically distinguish between needles, branches and background, in other words, perform a difficult task using only traditional TLS data.     

The distribution of late-Quaternary woody taxa in northern Eurasia: evidence from a new macrofossil database

We present a database of late-Quaternary plant macrofossil records for northern Eurasia (from 23° to 180°E and 46° to 76°N) comprising 281 localities, over 2300 samples and over 13,000 individual records. Samples are individually radiocarbon dated or are assigned ages via age models fitted to sequences of calibrated radiocarbon dates within a section. Tree species characteristic of modern northern forests (e.g. Picea, Larix, tree-Betula) are recorded at least intermittently from prior to the last glacial maximum (LGM), through the LGM and Lateglacial, to the Holocene, and some records locate trees close to the limits of the Scandinavian ice sheet, supporting the hypothesis that some taxa persisted in northern refugia during the last glacial cycle. Northern trees show differing spatio-temporal patterns across Siberia: deciduous trees were widespread in the Lateglacial, with individuals occurring across much of their contemporary ranges, while evergreen conifers expanded northwards to their range limits in the Holocene.