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
Spatial distribution prediction of soil As in a large-scale arsenic slag contaminated site based on an integrated model and multi-source environmental data.
TL;DR: The RFOK model proposed in this study has excellent spatial distribution prediction ability for soil heavy metal pollution with large spatial variation characteristics, which can fully explain the nonlinear relationship between pollutant content and its environmental impact elements.
62
Deriving ground surface digital elevation models from LiDAR data with geostatistics
TL;DR: It is concluded that, for the case studies presented, OK offers greater accuracy of prediction than IDW while KT demonstrates benefits over OK, and to make the most of the high quality LiDAR data KT seems the most appropriate technique in this case.
62
Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula
TL;DR: In this paper, the authors compared the performance of the FAO-56 Penman-Monteith (FAO-PM) and Hargreaves and Samani (HS) methods for estimating reference crop evapotranspiration (ETO) in the Iberian Peninsula.
62
A super-resolution mapping method using local indicator variograms
TL;DR: A novel SRM method is developed based on a sequentially produced with local indicator variogram (SLIV) SRM model that offers comparable accuracy results to those using globally derived spatial structures, indicating the methodology to be a promising practice.
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