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Conductive sapwood (xylem) area (Ax) of all trees in a given forested area is the main factor contributing to spatial tree transpiration. One hundred ninety-five trees of 9 species in the Kalahari region of Botswana were felled, stained, cut into discs, and measured to develop allometric equations predicting Ax from estimates of stem (As) and canopy (Ac) areas. Stem discs were also subjected to laboratory-based computed tomography, which well detected wood density contrasts but was not diagnostic with regard to delineation of Ax. The staining experiment, along with the help of visual and computed tomography analysis, allowed the definition of 4, tree-species categories of Ax, C1-C4. In C1 ( Acacia erioloba, Terminalia sericea, and Burke Africana), the staining and visual delineation of Ax matched the natural color difference between sapwood and heartwood; in C2 ( Dichrostachys cinerea and Ochna pulchra), sapwood was divided into external conductive and internal nonconductive annuli; in C3 ( Acacia fleckii and Acacia luederitzii), sapwood had sharp staining boundary between external highly conductive and internal low-conductive annuli; and in C4 ( Lonchocarpus nelsii and Boscia albitrunca), stems had no heartwood. Per-species 0-intercept linear regression models, Ax = slope .As (slope = 0.392 ÷ 0.794; R2 = 96.7 ÷ 99.8%) and Ax = slope .Ac (slope = 1.477 ÷ 17.044; R2 = 82.1 ÷ 92.2%) yielded excellent to good predictive allometric equations. The first equation is suitable for Ax scaling of small-size Kalahari areas, where the As of all trees can be estimated on the ground, whereas the second, as contribution to automated tree transpiration mapping of large-size Kalahari areas, where the Ac of trees can be derived through remote sensing interpretation of high-resolution images. [ABSTRACT FROM AUTHOR]

This research studied the changes in leaf reflectance spectra (350-2500 nm) due to metal phytoextraction into barley plants grown in metal-spiked soils (3 levels of Cd, Pb, As and their metal-mixture treatments). Growth of barley was adversely affected due to 100 mg As kg-1 and metal-mixture (10 Cd+150 Pb+100 As; mg kg-1) treatments. Metal phytoextraction were in order of: root >straw ≥leaves >grains. Results of reflectance spectra of leaves show the influence of As-treatment only, causing spectral changes in visible and infrared domains mostly, as apparent from the significant correlation between leaf-As and leaf-spectra. Chlorophyll and water stress indices and band depths analyses showed significant correlations to leaf-As, and can be used to distinguish metal-stressed plants. Finally, regression models demonstrate the potential use of hyperspectral reflectance data to monitor plant health during phytoremediation process and to estimate leaf-As in barley, particularly in this study. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study we develop an empirical method to estimate the volume threshold for predicting coseismic landslide dam formation using landscape parameters obtained from digital elevation models (DEMs). We hypothesize that the potential runout and volume of landslides, together with river features, determine the likelihood of the formation of a landslide dam. To develop this method, a database was created by randomly selecting 140 damming and 200 non-damming landslides from 501 landslide dams and > 60 000 landslides induced by the Mw 7.9 2008 Wenchuan earthquake in China. We used this database to parameterize empirical runout models by stepwise multivariate regression. We find that factors controlling landslide runout are landslide initiation volume, landslide type, internal relief ( H) and the H/ L ratio (between H and landslide horizontal distance to river, L). In order to obtain a first volume threshold for a landslide to reach a river, the runout regression equations were converted into inverse volume equations by taking the runout to be the distance to river. A second volume threshold above which a landslide is predicted to block a river was determined by the correlation between river width and landslide volume of the known damming landslides. The larger of these two thresholds was taken as the final damming threshold. This method was applied to several landslide types over a fine geographic grid of assumed initiation points in a selected catchment. The overall prediction accuracy was 97.4% and 86.0% for non-damming and damming landslides, respectively. The model was further tested by predicting the damming landslides over the whole region, with promising results. We conclude that our method is robust and reliable for the Wenchuan event. In combination with pre-event landslide susceptibility and frequency-size assessments, it can be used to predict likely damming locations of future coseismic landslides, thereby helping to plan emergency response. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Highlights: [•] Land surface diurnal temperature differences (DTD) were negatively correlated with soil organic matter (SOM) in low-relief plains. [•] The soil-adjusted vegetation index (SAVI) was positively correlated with SOM in these areas. [•] Regression kriging with a mixed model using DTD, SAVI and soil type predicted SOM. [•] The generic mixed model RK method must be calibrated for each area separately. [Copyright &y& Elsevier]

Abstract: Mapping regolith thickness to bedrock is important for environmental modeling in general and for seismic hazard assessment in particular. However, regolith thickness is often ignored in such studies because of its presumed difficulty of mapping in many terrains. To overcome this limitation we developed a generic remote sensing and geophysics based approach to model regolith thickness for areas with limited possibility of direct field observations. The approach was tested in a seismically-active and depositional landscape in northern Pakistan. Regolith thickness was sampled at exposed bedrock outcrops along the river bed and scarps. At unexposed sites the regolith-bedrock interface was identified through electrical resistivity survey, thus providing an indirect measurement of regolith thickness. A geomorphic classification of landforms and topographic attributes were derived from a remote sensing-based (ASTER) digital elevation model and SPOT-5 satellite imagery. Landform was the most important predictor (adjusted R2 =72.1%), showing the importance of geomorphic interpretation. A multivariate linear model based on landform, elevation and distance to stream was able to predict the regolith thickness (adjusted R2 =91.7), including field-observed abrupt changes at landform boundaries. [Copyright &y& Elsevier]

This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the interpolation method variously called “Universal Kriging” (UK) and “Kriging with External Drift” (KED), where auxiliary predictors are used directly to solve the kriging weights. The advantage of RK is the ability to extend the method to a broader range of regression techniques and to allow separate interpretation of the two interpolated components. Data processing and interpretation of results are illustrated with three case studies covering the national territory of Croatia. The case studies use land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, a physical model is used to estimate values of temperature at unvisited locations and RK is then used to calibrate the model with ground observations. The discussion addresses pragmatic issues: implementation of RK in existing software packages, comparison of RK with alternative interpolation techniques, and practical limitations to using RK. The most serious constraint to wider use of RK is that the analyst must carry out various steps in different software environments, both statistical and GIS. [Copyright &y& Elsevier]

The present regression models in digital soil mapping usually assume that relationships between soil properties and environmental variables are always fixed (as in MLR) or varying (as in GWR) in geographical space. In reality, some of the environmental variables may be fixed in affecting soil property variation and some are local varying. In this study, a mixed geographically weighted regression (MGWR) method which can deal with fixed and varying spatial relationships between a target variable and its environmental variables were proposed and used to predict topsoil soil organic matter (SOM) concentration in two study areas (Heshan, Heilongjiang province and Xuancheng, Anhui province, China) at two scales. Three groups of sample sets were created based on the total samples in the study areas to evaluate the robustness and stability of the model. Multiple linear regression (MLR), geographically weighted regression (GWR), GWR-kriging (GWRK), local regression-kriging (LRK), kriging with an external drift (KED), and ordinary kriging (OK) were used for comparison with MGWR. The validation results showed that the use of MGWR reduced the RMSE of GWR by 10.5% and 7.6% on average, reduced the RMSE of MLR by 12.8% and 9.9% on average for Heshan and Xuancheng study areas respectively. MGWR also showed a good competitiveness when compared with GWRK, LRK, KED and OK. In Heshan study area, the influence of flow length, relative position index, foot slope and distance to the nearest drainage were constant, whereas the elevation, topographic wetness index and valley index showed different influence in different regions. In Xuancheng study area, the fixed environmental variables were profile curvature, topographic wetness index and slope, whereas the varying environmental variables were precipitation, temperature, elevation, and limestone. The results indicate that the accuracy of predictions can be improved by adaptive coefficient according to the variation of environmental variables as implemented in MGWR compared with others considering only the local or global relationships. It was concluded that mixed geographically weighted regression model could be a potential method for digital soil mapping. [ABSTRACT FROM AUTHOR]