Sampling efforts are constrained by limited availability of resources. Therefore, methods to reduce the number of samples, while still achieving reasonable accuracy are needed. Land-surface segmentation (LSS) has proven a powerful technique to partition digital elevation models (DEMs) and their derivatives into relatively homogeneous areas, which can be further employed as support in soil sampling. Though topography is one of the main soil forming factors, a robust assessment of the potential of this technique to digital soil mapping (DSM) is still missing. In this study, we aimed at evaluating the potential of LSS in stratifying a landscape into relatively homogeneous areas, which can be used as strata for guiding the selection of sampling points in DSM. The experiments were carried out in two study areas where soil samples were available. Land-surface derivatives were derived from DEMs and segmented with a tool based on the multiresolution segmentation algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment from the existing sample data, based on which predictions of soil classes/sub-orders and properties, i.e. soil texture and A-horizon thickness, were made. Results were compared with predictions based on simple random sampling (SRS) and conditioned Latin hypercube (cLHS). The segmentation-based sampling (SBS) scheme performed better than SRS and cLHS schemes in predicting the A-horizon thickness, soil texture fractions and soil classes, showing a high potential of LSS in stratifying a landscape for the purposes of DSM. The novelty of this study is in the way strata are constructed, rather than in the sampling design itself. Further research is needed to demonstrate the value of a SBS design for practical use. The analyses presented here further highlight the importance of considering locally adaptive techniques in optimization of sampling schemes and predictions of soil properties. 相似文献
We present a stepwise inversion procedure to assess the focal depth and model earthquake source complexity of seven moderate-sized earthquakes (6.2 > M w > 5.1) that occurred in the Afar depression and the surrounding region. The Afar depression is a region of highly extended and intruded lithosphere, and zones of incipient seafloor spreading. A time-domain inversion of full moment tensor was performed to model direct P and SH waves of teleseismic data. Waveform inversion of the selected events estimated focal depths in the range of 17–22 km, deeper than previously published results. This suggests that the brittle–ductile transition zone beneath parts of the Afar depression extends more than 22 km. The effect of near-source velocity structure on the moment tensor elements was also investigated and was found to respond little to the models considered. Synthetic tests indicate that the size of the estimated, non-physical, non-isotropic source component is rather sensitive to incorrect depth estimation. The dominant double couple part of the moment tensor solutions for most of the events indicates that their occurrence is mainly due to shearing. Parameters associated with source directivity (rupture velocity and azimuth) were also investigated. Re-evaluation of the analysed events shows predominantly normal faulting consistent with the relative plate motions in the region. 相似文献
The results of unsupervised pattern recognition methods are critically dependent on the measure ofsimilarity used for clustering objects. There is little a priori information available on the relative utilityof various similarity measures. We introduce here an alternative similarity measure based on the metrictensor measure (MTM). Two standard clustering strategies are tested with the proposed similaritymeasure: hierarchical clustering and the K-median method. As data we use the ARCH obsidian data,a data set on Hungarian coal, and trace element data on Hungarian paprika. Differences from theMahalanobis distance measure are described for intraclass relations. 相似文献