Characteristic variogram for land use in Multispectral Images

Mera, Eduardo; Condal, A.; Ríos, C.; Silva, L. Da

doi:10.1088/1742-6596/720/1/012047

Cited by 1 publication

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“…If both neighboring values are above or below the average, the outcome becomes positive reflecting the presence of a similar pattern. Otherwise, the negative outcome of the two mean Theoretically, the expected random value of the Moran I is −1/(n − 1), where n equals the number of observations (Mera, Condal, Rios, & Silva, 2016). This means that the zero cutoff cannot be used as a reference point to distinguish positive and negative spatial autocorrelation (particular in the presence of a small dataset).…”

Section: Spatial Autocorrelationmentioning

confidence: 99%

Spatial Analysis with myGeoffice.org: The Pb Jura Lake, Switzerland, Contamination Case

Negreiros¹,

Neves²

2019

GEP

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Protecting groundwater from lead contamination is an important public-health concern and a major national environmental issue worldwide. This article addresses myGeoffice Web Internet service for geographers, in general, and geo-statistics researchers, in particular, with the famous water contamination case at Jura lake, Switzerland (a typical rural-urban region). Based on 189 samples of lead (Pb), five key investigation steps for a scientific perspective of any pollution incident are presented: Descriptive analysis (including nearest neighborhood, G(h) and Kernel techniques), spatial autocorrelation (Moran location scatterplot and Moran I) and Ordinary Kriging (OK) interpolation. The uncertainty and cost assessments issues are exceptionally tackled with Indicator Kriging (including the conditional cumulative distribution function, Shannon local entropy, probabilistic intervals and E-type estimation) and Gaussian geo-simulation. The total lead pollution exhibited patterns of high and low levels of concentrations along the all lake, leading to the conclusion that water is unsuitable for human consumption, in general, and unsuitable for any living organism, in particular sub-areas. It is also hoped that future GIS readers will follow this approach for their spatial cases with myGeoffice.

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Section: Spatial Autocorrelationmentioning

confidence: 99%