When modelling a large area, models that can take into a count the variation from the general mean in small sub-areas could perform better in prediction than a general model fitted to entire dataset. One method for adjusting the large-area models for such variation is kriging, in which the predictions are corrected with the aid of neighbouring observations. A variogram represents the spatial co...

Optical earth observation satellite sensors often provide a coarse spatial resolution (CR) multispectral (MS) image together with fine (FR) panchromatic (PAN) image. Pansharpening is technique applied to such sensor images generate an FR MS by injecting detail taken from the PAN while simultaneously preserving spectral information of methods are mostly on per-pixel basis and use extract detail....

We have developed a domain agnostic ordinary kriging algorithm accessible via a standardsbased service-oriented architecture for sensor networks. We exploit the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards. We need on-demand interpolation maps so runtime performance is a major priority. Our sensor data comes from wind in-situ observation stations in an area approximate...

Drought monitoring is a fundamental component of drought risk management. It is normally performed usingvarious drought indices that are effectively continuous functions of rainfall and other hydrometeorological variables.In many instances, drought indices are used for monitoring purposes. Geostatistical methods allow the interpolationof spatially referenced data and the prediction of values fo...

This paper applies a novel Kriging model to the interpolation of stochastic simulation with high computational expense. The novel Kriging model is developed by using Taylor expansion to construct a drift function for Kriging, thus named Taylor Kriging. The interpolation capability of Taylor Kriging for stochastic simulation is empirically compared with those of Simple Kriging and Ordinary Krigi...

Spatial disagregation is needed when environmental models of climate, air quality, hydrology, etc. require input data at a finer scale than available or when models produce outputs at a coarser scale than required by users. Area-to-Point Kriging is a common geostatistical framework to address the problem of spatial disaggregation from block to point support. Spatial disaggregation using Area-to...

The objective of this work is to define procedures to improve spatial resolution of SRTM data and to evaluate their applicability in the Serra Negra region, in the district of Patrocínio, state of Minas Gerais in Brazil. The region's structure is a result of past tectonic processes that have arched it into a dome shape. Besides the already existing agriculture exploitation, Serra Negra also has...

Sampling and estimation of geographical attributes that vary across space (e.g., area temperature, urban pollution level, provincial cultivated land, regional population mortality and state agricultural production) are common yet important constituents of many real-world applications. Spatial attribute estimation and the associated accuracy depend on the available sampling design and statistica...

Area-class maps are conventionally delineated by human hands based on limited observed (or high-quality) data and expert knowledge. It is recognized that areaclass maps contain spatial uncertainty because the classification of unobserved locations is not completely certain. Spatial uncertainty associated with an area-class map may be quantified by spatial statistical approaches through estimati...