How to merge a DEM?

A DEM is one of the most useful information for spatial modelling and monitoring. Several DEMs have been published in the public domain like SRTM and ASTER GDEM with and without considering the horizontal and vertical misallocation of single input data. Results of that are for example the inherent errors in the ASTER GDEM V1 dataset as well as the known striping in the SRTM dataset. Therefore, this abstract aims to show a method for the horizontal and vertical alignment of different DEM tiles as well as merging to create a seamless DEM

Development of global soil information facilities

ISRIC - World Soil Information has a mandate to serve the international community as custodian of global soil information and to increase awareness and understanding of the role of soils in major global issues. To adapt to the current demand for soil information, ISRIC is updating its enterprise data management system, including procedures for registering acquired data, such as lineage, versioning, quality assessment, and control. Data can be submitted, queried, and analysed using a growing range of web-based services - ultimately aiming at full and open exchange of data, metadata, and products - through the ICSU-accredited World Data Centre for Soils

Outcome in patients perceived as receiving excessive care across different ethical climates: a prospective study in 68 intensive care units in Europe and the USA

Purpose: Whether the quality of the ethical climate in the intensive care unit (ICU) improves the identification of patients receiving excessive care and affects patient outcomes is unknown. Methods: In this prospective observational study, perceptions of excessive care (PECs) by clinicians working in 68 ICUs in Europe and the USA were collected daily during a 28-day period. The quality of the ethical climate in the ICUs was assessed via a validated questionnaire. We compared the combined endpoint (death, not at home or poor quality of life at 1 year) of patients with PECs and the time from PECs until written treatment-limitation decisions (TLDs) and death across the four climates defined via cluster analysis. Results: Of the 4747 eligible clinicians, 2992 (63%) evaluated the ethical climate in their ICU. Of the 321 and 623 patients not admitted for monitoring only in ICUs with a good (n = 12, 18%) and poor (n = 24, 35%) climate, 36 (11%) and 74 (12%), respectively were identified with PECs by at least two clinicians. Of the 35 and 71 identified patients with an available combined endpoint, 100% (95% CI 90.0–1.00) and 85.9% (75.4–92.0) (P = 0.02) attained that endpoint. The risk of death (HR 1.88, 95% CI 1.20–2.92) or receiving a written TLD (HR 2.32, CI 1.11–4.85) in patients with PECs by at least two clinicians was higher in ICUs with a good climate than in those with a poor one. The differences between ICUs with an average climate, with (n = 12, 18%) or without (n = 20, 29%) nursing involvement at the end of life, and ICUs with a poor climate were less obvious but still in favour of the former. Conclusion: Enhancing the quality of the ethical climate in the ICU may improve both the identification of patients receiving excessive care and the decision-making process at the end of life

Geomorphometry: Concepts, software, applications

Geomorphometry is the science of quantitative land-surface analysis. It draws upon mathematical, statistical, and image-processing techniques to quantify the shape of earth's topography at various spatial scales. The focus of geomorphometry is the calculation of surface-form measures (land-surface parameters) and features (objects), which may be used to improve the mapping and modelling of landforms to assist in the evaluation of soils, vegetation, land use, natural hazards, and other information. This book provides a practical guide to preparing Digital Elevation Models (DEM) for analysis and extracting land-surface parameters and objects from DEMs through a variety of software. It further offers detailed instructions on applying parameters and objects in soil, agricultural, environmental and earth sciences. This is a manual of state-of-the-art methods to serve the various researchers who use geomorphometry. Soil scientists will use this book to further learn the methods for classifying and measuring the chemical, biological, and fertility properties of soils and gain a further understaing of the role of soil as a natural resource. Geologists will find value in the instruction this book provides for measuring the physical features of the soil such as elevation, porosity, and structure which geologists use to predict natural disasters such as earthquakes, volcanoes, and flooding

Functional digital soil mapping for the prediction of available water capacity in Nigeria using legacy data

Soil information, particularly water storage capacity, is of utmost importance for assessing and managing land resources for sustainable land management. We investigated using digital soil mapping (DSM) and digital soil functional mapping (DSFM) procedures to predict available water capacity (AWC) of soils in Nigeria using three published Pedotransfer functions (PTFs). Soil information, particularly water storage capacity, is of utmost importance for assessing and managing land resources for sustainable land management. We used digital soil mapping (DSM) and digital soil functional mapping (DSFM) procedures to predict available water capacity (AWC) of soils in Nigeria based on three published Pedotransfer functions (PTFs). We followed the specifications of the GlobalSoilMap.net project to produce predictions at a grid resolution of 100 m using regression tree models applied to a compiled soil point database together with auxiliary environmental predictors. Mean AWC (cm cm-1) estimates for Nigeria using methods published by Hodnett and Tomasella (PTF-HT), Zacharias and Wessolek (PTF-ZW), and Minasny and Hartemink (PTF-MH) PTFs were 0.08, 0.21, and 0.12 cm cm-1 for the 0- to 5-cm depth interval and 0.16, 0.08, and 0.08 for the cumulative depth (0–200 cm). The AWC estimates from the PTFs and from the literature for a number of discrete points and locations generally compared well. Comparison of AWC estimated from predicted soil properties (AWPp) against those estimated directly from profile observations (AWPd) for a number of discrete point locations showed a significant relationship only for PTF-HT (R2 = 0.24, p <0.05, for the 0–5 cm depth interval) and PTF-ZW (R2 = 0.25, p <0.05, for the cumulative depth). Soil properties predictions using remote sensing environmental covariates alone yielded similar results compared to predictions using a more extensive environmental covariate datasets. Overall, the process adopted for estimating AWC in this study shows promising results, but field measurements are still needed for validation and fine tuning of the process

Heavy metals in European soils: A geostatistical analysis of the FOREGS geochemical database

This paper presents the results of modeling the distribution of eight critical heavy metals (arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc) in topsoils using 1588 georeferenced samples from the Forum of European Geological Surveys Geochemical database (26 European countries). The concentrations were mapped using regression-kriging (RK) and accuracy of predictions evaluated using the leave-one-out cross validation method. A large number of auxiliary raster maps (topographic indexes, land cover, geology, vegetation indexes, night lights images and earth quake magnitudes) were used to improve the predictions. These were first converted to 36 principal components and then used to explain spatial distribution of heavy metals. The study revealed that this database is suitable for geostatistical analyses: the predictors explained from 21% (Cr) to 35% (Pb) of variability; the residuals showed spatial autocorrelation. The Principal Component Analysis of the mapped heavy metals revealed that the administrative units (NUTS level3) with highest overall concentrations are: (1) Liege (Arrondissement) (BE), Attiki (GR), Darlington (UK), Coventry (UK), Sunderland (UK), Kozani (GR), Grevena (GR), Hartlepool & Stockton (UK), Huy (BE), Aachen (DE) (As, Cd, Hg and Pb) and (2) central Greece and Liguria region in Italy (Cr, Cu and Ni). The evaluation of the mapping accuracy showed that the RK models for As, Ni and Pb can be considered satisfactory (prediction accuracy 45-52% of total variance), marginally satisfactory for Cr, Cu, Hg and Zn (36-41%), while the model for Cd is unsatisfactorily accurate (30%). The critical elements limiting the mapping accuracy are: (a) the problem of sporadic high values (hot-spots); and (b) relatively coarse resolution of the input maps. Automation of the geostatistical mapping and use of auxiliary spatial layers opens a possibility to develop mapping systems that can automatically update outputs by including new field observations and higher quality auxiliary maps. This approach also demonstrates the benefits of organizing standardized joint European monitoring projects, in comparison to the merging of several national monitoring projects