Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping

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.     

Interannual Variability in Pollen Dispersal and Deposition on the Tropical Quelccaya Ice Cap*

Pollen collected from snow samples on the Quelccaya Ice Cap in 2000 and 2001 reveals significant interannual variability in pollen assemblage, concentration, and provenance. Samples from 2000, a La Niña year, contain high pollen concentrations and resemble samples from the Andean forests (Yungas) to the east. Samples from 2001, an El Niño year, contain fewer pollen and resemble those from the Altiplano. We suggest that varying wind patterns under different El Niño/Southern Oscillation (ENSO) conditions may affect the processes of pollen transport over the Altiplano and on the ice cap, although confounding variables such as flowering phenology and sublimation should also be considered     

The 1786 earthquake-triggered landslide dam and subsequent dam-break flood on the Dadu River, southwestern China — Reply

A short reply to discussion by Chanson.