Results 51 to 60 of about 2,754 (181)

Thermal Buffering, Heat Advection and Crustal Thinning in the Ryoke Metamorphic Complex, Yanai, Southwest Japan

open access: yesJournal of Metamorphic Geology, EarlyView.
ABSTRACT The metamorphic zoning and geothermobarometry of the Ryoke metamorphic complex in the Yanai area, southwest Japan, show that its thermobaric structure was buffered by the dehydration melting of biotite. The temperatures over most of the area covered by the three high‐grade zones (8.5‐ to 19.0‐km depth) are consistent with those of the ...
Takeshi Ikeda   +3 more
wiley   +1 more source

Generalized product-sum variogram models of the data of the Center of Marine Research

open access: yesLietuvos Matematikos Rinkinys, 2004
Environmental data usually depends on both spatial and temporal components. Therefore, it is essential to have statistical models to describe how the data vary across space and time.
Ingrida Krūminienė   +1 more
doaj   +1 more source

Nonparametric Estimation of The Variogram an Application [PDF]

open access: yesKirkuk Journal of Science, 2017
This Research Deals with Non Parametric Estimation of Variogram Function . As it is known The Variogram Function is Considered As a very Important Parameter in Investigating The Spatial Dependence for The Spatial Stochastic Process .The Non ...
Taha Yaseen H, Mohammed N.I.Qassim
doaj   +1 more source

Validating habitat suitability assessments with post‐translocation animal movement data

open access: yesRestoration Ecology, EarlyView.
Wildlife translocations are essential for restoring species to their native habitats, and species distribution models (SDMs) have proven useful for identifying suitable habitats to guide this process. Validating model predictions with data from individual's post‐translocation, however, is rare due to the frequent lack of monitoring data from ...
Ramiro D. Crego   +7 more
wiley   +1 more source

Optimal designs for variogram estimation

open access: yesEnvironmetrics, 1999
The variogram plays a central role in the analysis of geostatistical data. A valid variogram model is selected and the parameters of that model are estimated before kriging (spatial prediction) is performed. These inference procedures are generally based upon examination of the empirical variogram, which consists of average squared differences of data ...
Müller, Werner, Zimmerman, Dale L.
openaire   +3 more sources

Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping

open access: yesRemote Sensing, 2019
Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging ...
Sachit Rajbhandari   +4 more
doaj   +1 more source

Generalized Additive Model With Dynamic Coefficients for Spatiotemporal Ozone Predictions

open access: yesEnvironmetrics, Volume 37, Issue 3, April 2026.
ABSTRACT Accurate prediction of surface‐level ozone concentrations is critical for air quality management and public health protection. This study develops a flexible spatiotemporal statistical modeling framework to predict daily mean O3 concentrations across Italy by integrating satellite‐derived ozone estimates with ground‐based observations and high‐
Abdollah Jalilian   +3 more
wiley   +1 more source

Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data

open access: yesAgriEngineering
Precision agriculture for coffee production requires spatial knowledge of crop yield. However, difficulties in implementation lie in low-sampled areas. In addition, the asynchronicity of this crop adds complexity to the modeling.
César de Oliveira Ferreira Silva   +3 more
doaj   +1 more source

GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications

open access: yesGeoscience Data Journal, Volume 13, Issue 2, April 2026.
Global surface climate datasets contain structural uncertainty that is difficult to attribute to individual processing steps. We present GloMarGridding, a Python package that isolates the spatial interpolation component using Gaussian Process Regression (or kriging) to generate spatially complete fields and uncertainty estimates. The techniques used in
Richard C. Cornes   +6 more
wiley   +1 more source

Modelling infiltration and geostatistical analysis of spatial variability of sorptivity and transmissivity in a flood spreading area

open access: yesSpanish Journal of Agricultural Research, 2014
Knowledge of infiltration characteristics is useful in hydrological studies of agricultural soils. Soil hydraulic parameters such as steady infiltration rate, sorptivity, and transmissivity can exhibit appreciable spatial variability. The main objectives
Fereshte Haghighi Fashi   +2 more
doaj   +1 more source

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