Open AccessBook
Geostatistics for natural resources evaluation
Pierre Goovaerts
- 01 Jan 1997
4.2K
TL;DR: In this article, an advanced-level introduction to geostatistics and Geostatistical methodology is provided, including tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation.
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Abstract: This book provides an advanced-level introduction to geostatistics and geostatistical methodology. The discussion includes tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation. It also details the theoretical background underlying most GSLIB programs.
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Citations
Stochastic simulation of regionalized ground motions using wavelet packets and cokriging analysis
Duruo Huang,Gang Wang +1 more
TL;DR: In this paper, the spatial cross-correlations of wavelet packet parameters are determined through geostatistical analysis of regionalized ground-motion data from the Northridge and Chi-Chi earthquakes.
48
GIS-based spatial precipitation estimation using next generation radar and raingauge data
TL;DR: The NEXRAD-VC developed in this study can serve as an effective and efficient tool to batch process large amounts of NexRAD data for hydrologic and ecological modeling.
48
Variogram maps from LiDAR data as fingerprints of surface morphology on scree slopes
TL;DR: In this paper, an aerial LiDAR topographic dataset is analyzed and interpolated by means of geostatistical techniques in order to examine the morphology of a scree slope area in the Eastern Italian Alps.
GIS multi-criteria decision analysis for assessment and mapping of neotectonic landscape deformation: a case study from Crete
TL;DR: In this article, a study of drainage systems in a tectonically active region is based on the Geographical Information Systems (GIS) integration of data from an analytic hierarchy process (AHP) and a weighted linear combination (WLC) procedure with multiple criteria data.
48
Can topographical and yield data substantially improve total soil carbon mapping by regression kriging
TL;DR: In this paper, the authors assess improvement in mapping accuracy due to dense topographical and long-term yield monitoring information and show that under soil and topographical conditions similar to those of this study, dense topography data or dense long-time yield data might not lead to substantial improvement in C mapping accuracy.
48
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