R and Geostatistics
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Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images

Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images | R and Geostatistics | Scoop.it

The benchmark problem proposed in this paper is to identify regions in aerial or satellite images with geometric patterns and describe the geometric properties of the constituent elements of the pattern and their spatial distribution. This is a relevant topic in image analysis processes where spatial regular patterns are studied. This paper first presents two approaches based on multi-directional semivariograms for reducing the processing time required to compute omnidirectional semivariograms. A set of parameters for describing the structure of a semivariogram, introduced by Balaguer et al. (2010), is extracted from an experimental semivariogram and analysed to quantify the heterogeneity of the distribution of elements (trees) with periodic patterns in images of orchards. An assessment is made using four image datasets. The first dataset is composed of synthetic images that simulate regularly spaced tree crops and real images, and is used to evaluate the influence that the orientation of elements (regularly spaced trees) in the objects (crop plots) have in the descriptive parameter values. This dataset is also used to compare different semivariogram computational approaches. The other three are also composed of synthetic images and are used to evaluate the semivariogram parameters under different spatial heterogeneity conditions, and are generated by varying patterns and tree characteristics, i.e. existence or absence of faults, regular/irregular distributions, and size of the elements. Finally, the proposed methodology is applied to real aerial orthoimages of orchard plots.

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California Soil Resource Lab :: Additive Time Series Decomposition in R: Soil Moisture and Temperature Data

California Soil Resource Lab :: Additive Time Series Decomposition in R: Soil Moisture and Temperature Data | R and Geostatistics | Scoop.it
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Simple demonstration of working with time-series data collected from Decagon Devices soil moisture and temperature sensors. These sensors were installed in a potted plant, that was semi-regularly watered, and data were collected for about 80 days on an hourly basis. Several basic operations in Rare demonstrated:

reading raw data in CSV formatconverting date-time values to R's date-time formatapplying a calibration curve to raw sensor valuesinitialization of R time series objectsseasonal decomposition of additive time series (trend extraction)plotting of decomposed time series, ACF, and cross-ACF
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Creating a custom soil attribute plot using ggmap

Creating a custom soil attribute plot using ggmap | R and Geostatistics | Scoop.it
Cusby Ryan Garner Senior Data Scientist, Revolution Analytics I love creating spatial data visualizations in R. With the ggmap package, I can easily download satellite imagery which serves as a base layer for the data I want to represent. In the code below, I show you how to visualize sampled soil attributes among 16 different rice fields in Uruguay. library(ggmap) library(plyr) library(gridExtra) temp <- tempfile() download.file("http://www.plantsciences.ucdavis.edu/plant/data.zip", temp) conne
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Circle packing in R (again)

Circle packing in R (again) | R and Geostatistics | Scoop.it
Back in 2010 I posted some R code for circle packing. Now, just five years later, I've ported the code to Rcpp and created a little package which you can find at GitHub.The main function is circleLayout which takes a set of overlapping circles and tries to find a non-overlapping arrangement for them. Here's an example: And here's the code: # Create some random circles, positioned within the central portion# of a bounding square, with smaller circles being more common than# larger ones.ncircles
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The caret Package

The caret Package | R and Geostatistics | Scoop.it
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The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for:

 

data splittingpre-processingfeature selectionmodel tuning using resamplingvariable importance estimation

as well as other functionality.

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Spatial structure of soil texture and its influence on spatial variability of nitrate leaching

Spatial structure of soil texture and its influence on spatial variability of nitrate leaching | R and Geostatistics | Scoop.it
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Field scale variability of soil texture can influence crop yield and movement of soil water in the field. The objective of this study was to investigate the spatial structure of soil texture in relation to the variability of nitrate-N leaching using geostatistics. Soil textural fractions showed strong spatial autocorrelations from surface to 60 cm depth. Random variability of soil texture increased with depth. Soil water content, as well as total carbon, total nitrogen and soil organic carbon of top 15 cm, also showed spatial autocorrelations similar to soil texture. Elevation, relative slope position and vertical distance to channel network showed significant influence on the distribution of soil texture. Soil texture at 90 cm depth correlated best with cumulative percolated water and cumulative nitrate leached in field lysimeters. Our results showed that soil layers with low hydraulic conductivity control the water and nitrate movement through the soil profile.

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T-9: Variogram Modeller Part 3 - Geostatistics Kitchen

T-9: Variogram Modeller Part 3 - Geostatistics Kitchen | R and Geostatistics | Scoop.it
“In this website you will find a set of Mathematica Notebooks & Packages developed by grad students of the portuguese research center CERENA.”
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ESTUDO SOBRE A UTILIZAÇÃO ADEQUADA DA KRIGAGEM NA REPRESENTAÇÃO COMPUTACIONAL DE SUPERFÍCIES BATIMÉTRICAS

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Model generalization of two different drainage patterns by self-organizing maps

Model generalization of two different drainage patterns by self-organizing maps | R and Geostatistics | Scoop.it

In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.

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Influence of incorporating geometric anisotropy on the construction of thematic maps of simulated data and chemical attributes of soil

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Spatial Variability of Heavy Metals in the Soils of Ahwaz Using Geostatistical Methods

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PLOS ONE: Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

PLOS ONE: Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China | R and Geostatistics | Scoop.it

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0–20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km2) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed.

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WHAT IS GEOSTATISTICS? | statistics for all

WHAT IS GEOSTATISTICS? | statistics for all | R and Geostatistics | Scoop.it
You need not be a statistician to make good use of geostatistics, but you may need the assistance, support, guidance of a (geo?)statistician. A good engineer, ecologist, biologist, plant scientist, hydrologist, soil physicist already has a good ...
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USDA Textural Soil Classification

USDA Textural Soil Classification | R and Geostatistics | Scoop.it
Included in the next version (1.0.3.1) of ggtern, will be a new data set, taken from the USDA Textural Soil Classification[1] which we can use to reproduce the original diagram published by the United Stated Department of Agriculture. Firstly let us load the packages and data: Reconstructing the USDA’s Original Plot Now let us construct…
The post USDA Textural Soil Classification appeared first on ggtern: ternary diagrams in R.
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Using apply, sapply, lapply in R

Using apply, sapply, lapply in R | R and Geostatistics | Scoop.it
This is an introductory post about using apply, sapply and lapply, best suited for people relatively new to R or unfamiliar with these functions. There is a part 2 coming that will look at density plots with ggplot, but first I thought I would go on a tangent to give some examples of the apply family, as they come up a lot working with R.I have been comparing three methods on a data set. A sample from the data set was generated, and three different methods were applied to that subset. I wanted t
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RStudio v0.99 Preview: Graphviz and DiagrammeR

RStudio v0.99 Preview: Graphviz and DiagrammeR | R and Geostatistics | Scoop.it
Soon after the announcement of htmlwidgets, Rich Iannone released the DiagrammeR package, which makes it easy to generate graph and flowchart diagrams using text in a Markdown-like syntax. The package is very flexible and powerful, and includes: Rendering of Graphviz graph visualizations (via viz.js) Creating diagrams and flowcharts using mermaid.js Facilities for mapping R objects into graphs, diagrams, and flowcharts. […]
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Introduction to text mining in R

Introduction to text mining in R | R and Geostatistics | Scoop.it
Introduction to text mining in R. Learn how to perform text mining by cleaning text as input and make analysis such as comparison and commonality wordclouds
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An Introduction on How to Make Beautiful Charts With R and ggplot2

An Introduction on How to Make Beautiful Charts With R and ggplot2 | R and Geostatistics | Scoop.it
Adding a touch of color and design can help make more compelling visualizations, thanks to ggplot2 syntax and chaining capabilities.
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Spatial Variability Analysis of Soil Nutrients Based on GIS and Geostatistics: A Case Study of Yisa Township, Yunnan, China

Spatial Variability Analysis of Soil Nutrients Based on GIS and Geostatistics: A Case Study of Yisa Township, Yunnan, China | R and Geostatistics | Scoop.it
Li Jing, Min Qingwen, Li Wenhua, Bai Yanying, Dhruba Bijaya G. C, and Yuan Zheng (2014) Spatial Variability Analysis of Soil Nutrients Based on GIS and Geostatistics: A Case Study of Yisa Township, Yunnan, China. Journal of Resources and Ecology: Vol. 5, No. 4, pp. 348-355. doi: http://dx.doi.org/10.5814/j.issn.1674-764x.2014.04.010
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GEOSTAT 2014 Bergen Day 4: Spatio-temporal geostatistics (screen 3)

“lecture by Ben Gräler http://www.geostat-course.org/Bergen_2014.&rdquo;
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Covariate selection with iterative principal component analysis for predicting physical soil properties

Covariate selection with iterative principal component analysis for predicting physical soil properties | R and Geostatistics | Scoop.it

Abstract

Local and regional soil data can be improved by coupling new digital soil mapping techniques with high resolution remote sensing products to quantify both spatial and absolute variation of soil properties. The objective of this research was to advance data-driven digital soil mapping techniques for the prediction of soil physical properties at high spatial resolution using auxiliary data in a semiarid ecosystem in southeastern Arizona, USA. An iterative principal component analysis (iPCA) data reduction routine of reflectance and elevation covariate layers was combined with a conditioned Latin Hypercube field sample design to effectively capture the variability of soil properties across the 6250 ha study area. We sampled 52 field sites by genetic horizon to a 30 cm depth and determined particle size distribution, percent coarse fragments, Munsell color, and loss on ignition. Comparison of prediction models of surface soil horizons using ordinary kriging and regression kriging indicated that ordinary kriging had greater predictive power; however, regression kriging using principal components of covariate data more effectively captured the spatial patterns of soil property–landscape relationships. Percent silt and soil redness rating had the smallest normalized mean square error and the largest correlation between observed and predicted values, whereas soil coarse fragments were the most difficult to predict. This research demonstrates the efficacy of coupling data reduction, sample design, and geostatistical techniques for effective spatial prediction of soil physical properties in a semiarid ecosystem. The approach applied here is flexible and data-driven, allows incorporation of wide variety of numerically continuous covariates, and provides accurate quantitative prediction of individual soil properties for improved land management decisions and ecosystem and hydrologic models.

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short announcement: new R learning material

short announcement: new R learning material | R and Geostatistics | Scoop.it

Coursera, hail to Coursera. Despite the uprising criticism on MOOCs and their footprint in the educational landscape at universities Coursera created an interesting R learning course. It is divided and scheduled for 4 weeks and has video-tutorials as well as written material. The guys over at RevolutionAnalytics packed it all together:

Content:
Setting working directory and getting helpHow to get helpData TypesSubsettingVectorized OperationsReading/Writing DataControl Structures in RWriting FunctionsAvoiding loops using xapplyPlottingRegular expressionsRegular expressions in RClasses and methods in R
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Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China | R and Geostatistics | Scoop.it
PLOS One, Published Online 31 December 2013 By Gao Peng, Wang Bing, Geng Guangpo, and Zhang Guangcan "The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is importa...
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On the estimation of scale of fluctuation in geostatistics

On the estimation of scale of fluctuation in geostatistics | R and Geostatistics | Scoop.it
(2014). On the estimation of scale of fluctuation in geostatistics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. ???aop.label???.
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