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Soil Information (soil + information)
Selected AbstractsMultivariate calibration of hyperspectral ,-ray energy spectra for proximal soil sensingEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 1 2007R. A. Viscarra Rossel Summary The development of proximal soil sensors to collect fine-scale soil information for environmental monitoring, modelling and precision agriculture is vital. Conventional soil sampling and laboratory analyses are time-consuming and expensive. In this paper we look at the possibility of calibrating hyperspectral ,-ray energy spectra to predict various surface and subsurface soil properties. The spectra were collected with a proximal, on-the-go ,-ray spectrometer. We surveyed two geographically and physiographically different fields in New South Wales, Australia, and collected hyperspectral ,-ray data consisting of 256 energy bands at more than 20 000 sites in each field. Bootstrap aggregation with partial least squares regression (or bagging-PLSR) was used to calibrate the ,-ray spectra of each field for predictions of selected soil properties. However, significant amounts of pre-processing were necessary to expose the correlations between the ,-ray spectra and the soil data. We first filtered the spectra spatially using local kriging, then further de-noised, normalized and detrended them. The resulting bagging-PLSR models of each field were tested using leave-one-out cross-validation. Bagging-PLSR provided robust predictions of clay, coarse sand and Fe contents in the 0,15 cm soil layer and pH and coarse sand contents in the 15,50 cm soil layer. Furthermore, bagging-PLSR provided us with a measure of the uncertainty of predictions. This study is apparently the first to use a multivariate calibration technique with on-the-go proximal ,-ray spectrometry. Proximally sensed ,-ray spectrometry proved to be a useful tool for predicting soil properties in different soil landscapes. [source] The apparent electrical conductivity as a surrogate variable for predicting earthworm abundances in tilled soilsJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 4 2010Monika Joschko Abstract Noninvasive geophysical methods have a great potential for improving soil-biological studies at field or regional scales: they enable the rapid acquisition of soil information which may help to identify potential habitats for soil biota. A precondition for this application is the existence of close relationships between geophysical measurements and the soil organism of interest. This study was conducted to determine whether field measurements of apparent electrical conductivity (ECa) are related to abundances of earthworms in tilled soils. Relationships between ECa and earthworm populations were investigated along transects at 42 plots under reduced and conventional tillage at a 74 ha field on sandy-loam soil in NE Germany. Relations were analyzed with linear-regression and spatial analysis. The apparent electrical conductivity (ECa) was quantitatively related to earthworm abundances sampled 5 months after the geophysical measurements. No relationship was found, however, in plots under conventional tillage when analyzed separately. If earthworm abundances were known at every other location along the transects and if the state-space approach was used for analysis, the analysis of ECa measurements and earthworm abundances indicated that 50% of the earthworm samples could have been substituted by ECa measurements. Further research is needed to fully evaluate the potential of ECa measurements for predicting earthworm habitats in tilled soil. [source] Digital soil mapping in Germany,a reviewJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 3 2006Thorsten Behrens Abstract Digital soil mapping as a tool to generate spatial soil information provides solutions for the growing demand for high-resolution soil maps worldwide. Even in highly developed countries like Germany, digital soil mapping becomes essential due to the decreasing, time-consuming, and expensive field surveys which are no longer affordable by the soil surveys of the individual federal states. This article summarizes the present state of soil survey in Germany in terms of digitally available soil data, applied digital soil mapping, and research in the broader field of pedometrics and discusses future perspectives. Based on the geomorphologic conditions in Germany, relief is a major driving force in soil genesis. This is expressed by the digital,soil mapping research which highlights the great importance of digital terrain attributes in combination with information on parent material in soil prediction. An example of digital soil mapping using classification trees in Thuringia is given as an introduction in digital soil-class mapping based on correlations to environmental covariates within the scope of the German classification system. [source] Digital soil mapping using artificial neural networksJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 1 2005Thorsten Behrens Abstract In the context of a growing demand of high-resolution spatial soil information for environmental planning and modeling, fast and accurate prediction methods are needed to provide high-quality digital soil maps. Thus, this study focuses on the development of a methodology based on artificial neural networks (ANN) that is able to spatially predict soil units. Within a test area in Rhineland-Palatinate (Germany), covering an area of about 600 km2, a digital soil map was predicted. Based on feed-forward ANN with the resilient backpropagation learning algorithm, the optimal network topology was determined with one hidden layer and 15 to 30 cells depending on the soil unit to be predicted. To describe the occurrence of a soil unit and to train the ANN, 69 different terrain attributes, 53 geologic-petrographic units, and 3 types of land use were extracted from existing maps and databases. 80% of the predicted soil units (n = 33) showed training errors (mean square error) of the ANN below 0.1, 43% were even below 0.05. Validation returned a mean accuracy of over 92% for the trained network outputs. Altogether, the presented methodology based on ANN and an extended digital terrain-analysis approach is time-saving and cost effective and provides remarkable results. Digitale Bodenkartierung mithilfe von Künstlichen Neuronalen Netzen Vor dem Hintergrund einer steigenden Nachfrage nach hoch auflösenden bodenkundlichen Flächeninformationen für die Umweltplanung und Modellierung werden schnelle und genaue Vorhersagemodelle benötigt, um hochqualitative Bodenprognosekarten zur Verfügung stellen zu können. Kernpunkt der hier vorgestellten Untersuchung ist daher die Entwicklung einer Methodik zur Erstellung von Bodenprognosekarten auf der Grundlage Künstlicher Neuronaler Netze (KNN). Als Untersuchungsgebiet diente eine Fläche von über 600 km2 im Pfälzer Wald. Vorwärts propagierende KNN auf Basis des "Resilent Backpropagation"-Algorithmus mit einer verdeckten Schicht aus 15 bis 30 Zellen erwiesen sich als optimal für die Prognose von Bodenformengesellschaften. Um das Auftreten einer Bodenformengesellschaft zu beschreiben und die KNN zu trainieren, wurden 69 Reliefparameter, 3 Nutzungsklassen sowie 53 geologisch-petrographische Einheiten verwendet. 80,% der vorhergesagten Bodenformengesellschaften (n = 33) zeigten Trainingsfehler (mittlerer quadratischer Fehler der KNN) von unter 0,1; 43,% sogar von unter 0,05. Die Validierung ergab Genauigkeiten in dem kartierten Gesamtraum von durchschnittlich über 92,% für die prognostizierten Bodenformengesellschaften. Zusammenfassend kann festgehalten werden, dass die vorgestellte Methodik auf der Basis von KNN und einer umfangreichen Digitalen Reliefanalyse einen zeit- und kosteneffektiven Ansatz zur Prognose von Bodenkarten darstellt, der hervorragende Ergebnisse liefern kann. [source] | ||||||||||