Computers & Geosciences, Volume 84, November 2015, Pages 1–9
Julia B. Curto, Tatiana Diniz, Roberta M. Vidotti, Richard J. Blakely, and Reinhardt A. Fuck
“We offer a methodology to analyze the spatial and statistical properties of the tilt derivative of magnetic anomalies, thus facilitating the mapping of the location and depth of concealed magnetic sources. This methodology uses commonly available graphical information system (GIS) software to estimate and interpolate horizontal distances between key attributes of the tilt derivative, which then are used to estimate depth and location of causative bodies.
Fig. 8. (a) Reduced-to-pole magnetic anomaly ﬁeld of the study area. (b) Main geological features of the northwest Paraná Basin. The Arenópolis magmatic arc and the Paraguay Belt represent the Neoproterozoic basement of the basin. Dashed black lines are magnetic lineaments (Curto et al., 2014): (A) Serra Negra, (B) Baliza, (C) General Carneiro.
“Application of the method to synthetic data illustrates its reliability to determine depths to magnetic contacts. We also achieved consistent depth results using real data from the northwest portion of the Paraná Basin, Brazil, where magnetic anomaly interpretations are complicated by low geomagnetic inclinations and rocks with remanent magnetization. The tilt-derivative method provides more continuous and higher resolution contact information than the 3D Euler deconvolution technique.”
Computers & Geosciences, Volume 84, November 2015, Pages 86–95
By Simon Nieland, Niklas Moran, Birgit Kleinschmit, and Michael Förster
“Semantic heterogeneity remains a barrier to data comparability and standardisation of results in different fields of spatial research. Because of its thematic complexity, differing acquisition methods and national nomenclatures, interoperability of biodiversity monitoring information is especially difficult. Since data collection methods and interpretation manuals broadly vary there is a need for automatised, objective methodologies for the generation of comparable data-sets. Ontology-based applications offer vast opportunities in data management and standardisation. This study examines two data-sets of protected heathlands in Germany and Belgium which are based on remote sensing image classification and semantically formalised in an OWL2 ontology.
Two step approach of the modiﬁed SPARK methodology. Part A shows the kernel reclassiﬁer
with an schematic 5 x 5 kernel. Input is a classiﬁcation result, which illustrates the classiﬁed
categories in different colours (see Part A and Part B(a)). The kernel includes three classes (sand
(S), forest (F), and heath (H)). The kernel reclassiﬁer performs a calculation of the class
frequency in a frequency table and compares the outcomes to the associated rule. If the kernel
corresponds to all formulated rules the centre pixel (grey) can be assigned. The interpolator
eliminates patches that have not been assigned (b) and patches that are under a certain MMU (c) and
interpolates gaps in a nearest neighbour interpolation procedure (d). (For interpretation of the
references to colour in this ﬁgure caption, the reader is referred to the web version of this
“The proposed methodology uses semantic relations of the two data-sets, which are (semi-)automatically derived from remote sensing imagery, to generate objective and comparable information about the status of protected areas by utilising kernel-based spatial reclassification. This automatised method suggests a generalisation approach, which is able to generate delineation of Special Areas of Conservation (SAC) of the European biodiversity Natura 2000 network. Furthermore, it is able to transfer generalisation rules between areas surveyed with varying acquisition methods in different countries by taking into account automated inference of the underlying semantics. The generalisation results were compared with the manual delineation of terrestrial monitoring. For the different habitats in the two sites an accuracy of above 70% was detected. However, it has to be highlighted that the delineation of the ground-truth data inherits a high degree of uncertainty, which is discussed in this study.”