An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m^?2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.     

Reliable Determination of Ge in Solid Environmental Samples Using a Chemical Preparation Procedure Developed for Si Isotopes and ICP‐MS Analysis

Germanium (Ge) exists at trace levels in the Earth's crust and is a powerful geochemical tracer of the silicon (Si) cycle. This study proposes a simple and reliable method for determining Ge contents in environmental samples using ICP‐MS. As Si and Ge have very similar chemical properties, we investigated the applicability of the chemical preparation procedure developed for Si isotopes for the determination of Ge in environmental samples. Advantages of this procedure are as follows: (a) efficient removal of the matrix and main interferences affecting Ge determinations by ICP‐MS, (b) a low limit of detection (6 ng l^?1), (c) relative repeatability of approximately 3% obtained on ⁷⁴Ge and (d) robustness and accuracy based on agreement within errors with the published Ge values for rock reference materials (BHVO‐2, AGV‐2 and BCR‐2). This procedure allowed revision of the Ge values of three soil reference materials (1.67 ± 0.09 μg g^?1, 2.41 ± 0.18 μg g^?1, 1.89 ± 0.10 μg g^?1 for GBW 07401, GBW 07404 and GBW 07407, respectively) and proposal of a value for the plant reference material ERM‐CD281 (70 ± 3 μg g^?1). This method provides a convenient procedure for determining Ge mass fractions in environmental samples and opens the possibility of coupling two tracers of the Si biogeochemical cycle with a single measurement procedure.     

Trajectory models and reference frames for crustal motion geodesy

We sketch the evolution of station trajectory models used in crustal motion geodesy over the last several decades, and describe some recent generalizations of these models that allow geodesists and geophysicists to parameterize accelerating patterns of displacement in general, and postseismic transient deformation in particular. Modern trajectory models are composed of three sub-models that represent secular trends, annual oscillations, and instantaneous jumps in coordinate time series. Traditionally the trend model invoked constant station velocity. This can be generalized by assuming that position is a polynomial function of time. The trajectory model can also be augmented as needed, by including one or more logarithmic transients in order to account for typical multi-year patterns of postseismic transient motion. Many geodetic and geophysical research groups are using general classes of trajectory model to characterize their crustal displacement time series, but few if any of them are using these trajectory models to define and realize the terrestrial reference frames (RFs) in which their time series are expressed. We describe a global GPS reanalysis program in which we use two general classes of trajectory model, tuned on a station by station basis. We define the network trajectory model as the set of station trajectory models encompassing every station in the network. We use the network trajectory model from the each global analysis to assign prior position estimates for the next round of GPS data processing. We allow our daily orbital solutions to relax so as to maintain their consistency with the network polyhedron. After several iterations we produce GPS time series expressed in a RF similar to, but not identical with   ITRF2008. We find that each iteration produces an improvement in the daily repeatability of our global time series and in the predictive power of our trajectory models.