Home About us Contact
|
Home About us Contact | ||
|
|
||
Spatial Datasets (spatial + dataset)
Selected AbstractsInterpreting variability in global SST data using independent component analysis and principal component analysisINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 3 2010Seth Westra Abstract Component extraction techniques are used widely in the analysis and interpretation of high-dimensional climate datasets such as global sea surface temperatures (SSTs). Principal component analysis (PCA), a frequently used component extraction technique, provides an orthogonal representation of the multivariate dataset and maximizes the variance explained by successive components. A disadvantage of PCA, however, is that the interpretability of the second and higher components may be limited. For this reason, a Varimax rotation is often applied to the PCA solution to enhance the interpretability of the components by maximizing a simple structure. An alternative rotational approach is known as independent component analysis (ICA), which finds a set of underlying ,source signals' which drive the multivariate ,mixed' dataset. Here we compare the capacity of PCA, the Varimax rotation and ICA in explaining climate variability present in globally distributed SST anomaly (SSTA) data. We find that phenomena which are global in extent, such as the global warming trend and the El Niño-Southern Oscillation (ENSO), are well represented using PCA. In contrast, the Varimax rotation provides distinct advantages in interpreting more localized phenomena such as variability in the tropical Atlantic. Finally, our analysis suggests that the interpretability of independent components (ICs) appears to be low. This does not diminish the statistical advantages of deriving components that are mutually independent, with potential applications ranging from synthetically generating multivariate datasets, developing statistical forecasts, and reconstructing spatial datasets from patchy observations at multiple point locations. Copyright © 2009 Royal Meteorological Society [source] A method to generate soilscapes from soil mapsJOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, Issue 2 2010Karsten Schmidt Abstract Digital soil mapping for large areas is challenging if mapping resolution should be as high as possible and sampling should be as sparse as possible. Generally, the more complex the soil associations in a landscape, the more samples are required to systematically cover the entire feature space. Moreover, regions should be modeled separately if the patterns of spatial variation vary on subregion level. A systematic segmentation of a landscape into soilscapes is additionally important for a feasible application of soil-sensing approaches. In this paper, we introduce a semiautomated approach to segment nominal spatial datasets based on the local spatial frequency distribution of the mapping units. The aim is to provide homogeneous and nonfragmented segments with smoothed boundaries. The methodological framework for the segmentation comprises different spatial and nonspatial techniques and focuses mainly on a moving-window analysis of the local frequency distribution and a k-means cluster analysis. Based on an existing soil map (1:50,000), we derived six segments for the Nidda catchment (Central Hesse, Germany), comprising 1600 km2. As segmentation is based on a soil map, soilscapes are derived. In terms of the feature space, these soilscapes show a higher homogeneity compared to the entire landscape. Advantages compared to an existing map of landscape units are discussed. Segmenting a landscape as introduced in this study might also be of importance for other disciplines and can be used as a first step in biodiversity analysis or setting up environmental-monitoring sites. [source] Landscape-scale detection and mapping of invasive African Olive (Olea europaea L. ssp. cuspidata Wall ex G. Don Ciferri) in SW Sydney, Australia using satellite remote sensingAPPLIED VEGETATION SCIENCE, Issue 2 2009P. Cuneo Abstract Question: Is satellite imagery an effective tool for mapping and examining the distribution of the invasive species Olea europaea L. ssp. cuspidata at a regional landscape scale? Location: Southwest Sydney, Australia. Methods: Remote sensing software was used to classify pixels of Olea europaea L. ssp. cuspidata (African Olive) and major vegetation types from satellite imagery, using a "supervised classification" technique across a 721 km2 study area in the Cumberland Plain region of western Sydney. A map of African Olive distribution was produced from the image analysis and checked for accuracy at 337 random locations using ground observation and comparison with existing vegetation maps. The African Olive distribution data were then used in a GIS analysis with additional spatial datasets to investigate the relationship between the distribution of African Olive and environmental factors, and to quantify the conservation threat to endangered native vegetation. Results: A total area of 1907 ha of dense African Olive infestation was identified, with an omission error of 7.5% and a commission error of 5.4%. African Olive was found to occur on the steepest slopes (mean slope 14.3°) of the vegetation classes examined, with aspect analysis identifying a high prevalence on south- and southwest-facing slopes. The analysis also quantified the level of African Olive infestation in endangered ecological communities, with Western Sydney Dry Rainforest (25% affected) and Moist Shale Woodland (28% affected) identified as most vulnerable to African Olive invasion. Conclusion: The distribution of African Olive can be efficiently mapped at a landscape scale. This technique, used in association with additional spatial datasets, identified African Olive as a significant environmental weed in SW Sydney, occupying a greater area than previously recognised and threatening several endangered native vegetation communities. [source] Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial DatasetsBIOMETRICS, Issue 2 2009Andrew O. Finley Summary This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large spatial datasets are, however, computationally infeasible because of cubic-order matrix algorithms involved in estimation. The situation is even worse in Markov chain Monte Carlo (MCMC) contexts where such computations are performed for several iterations. Here, we discuss approaches that help obviate these hurdles without sacrificing the richness in modeling. For genetic effects, we demonstrate how an initial spectral decomposition of the relationship matrices negate the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects, we outline two approaches for circumventing the prohibitively expensive matrix decompositions: the first leverages analytical results from Ornstein,Uhlenbeck processes that yield computationally efficient tridiagonal structures, whereas the second derives a modified predictive process model from the original model by projecting its realizations to a lower-dimensional subspace, thereby reducing the computational burden. We illustrate the proposed methods using a synthetic dataset with additive, dominance, genetic effects and anisotropic spatial residuals, and a large dataset from a Scots pine (Pinus sylvestris L.) progeny study conducted in northern Sweden. Our approaches enable us to provide a comprehensive analysis of this large trial, which amply demonstrates that, in addition to violating basic assumptions of the linear model, ignoring spatial effects can result in downwardly biased measures of heritability. [source] | ||||||||||