Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks. - Lavado Contador, J F; Maneta, M; Schnabel, S (Environmental monitoring and assessment, 2006)
The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.
Keywords: Biological; Dehesa; forecasting soil moisture; humidity; Models; Neural Networks (Computer); sampling; Soil; topographic variables

cork oak wood a hard material to sample and to measure - Leal, Sofia; González-Pérez, Aranzazu; Vasquez-Piqué, Javier; Spiecker, Heinrich; Pereira, Helena (Suberwood2005: New challenges for integration of cork oak forests and products, Scientific and Technical Conference, Huelva, Spain, 20, 21 and 22 October 2005., 2005)
Different studies within the Suberwood project required measurements of wood growth in cork oak trees (Quercus suber L.), including tree ring analysis, as a basis to analyse the influence on tree growth of stress effects, namely those deriving from climatic conditions (i.e. water deficit) or from the cork extraction. The research partners have experienced serious difficulties in these tasks and some examples are presented and discussed. The first difficulty regards sampling because cork oak harvesting is forbidden in Portugal and advantage has to be taken from legally authorised tree fellings. The available material therefore consists mainly in thinning material (young trees), dead or diseased trees, and trees with damaged stems due to improper cork harvesting, which are useless for such studies. In most cases the information concerning the stand management is lacking, namely regarding the time of cork extraction and of other silvicultural operations (i.e. pruning). Therefore the experimental design regarding treatments and repetitions often cannot follow the conventional statistical guidelines. The cork oak wood also has anatomical and structural features that bring considerable complexity for ring definition. The wood has a semi-ring porosity with poor growth ring definition and it is very difficult, and frequently impossible, to accurately identify growth rings and to link measurements to a certain year. Additionally cork extraction disturbs wood growth and anatomy. Different observation approaches were tested: overall microscopical observations, continuous radial measurement of vessel dimensions and density, continuous radial microdensitometric profiles, cambial wounding. The use of non-destructive samples (i.e. cores or small wood samples) adds to the uncertainty encountered. Stem discs offer a better observation but differences among different radii are often very large.
Keywords: Anatomy; growth rings; Quercus suber L.; sampling; wood growth