Disaggregation and scientific visualization of earthscapes considering trends and spatial dependence structures

S Grunwald
2008 New Journal of Physics  
Earth attributes show complex, heterogeneous spatial patterns generated by exogenous environmental factors and formation processes. This study investigates various strategies to quantify the underlying spatial patterns of simulated fields resembling real earthscapes and to compare their performance for describing them. The approach is to disaggregate the variability of earth attributes into two components, deterministic trend m(x i ) and spatial dependence ε(x i ), and determine the effects of
more » ... ine the effects of m(x i ) and ε(x i ) on prediction accuracy under various combinations of spatial fields of earth attributes encountered in different earthscapes. We illustrate that cross-dependencies exist between spatial and feature accuracy. Scientific visualization is used to transpose quantitative results into visual space. Scientific visualization (SciVis) has been employed abundantly using high-resolution x-ray computer-assisted tomography (CAT) to map fractures in clay-rich soils [1], macropores [2] and microstructure in heterogeneous media [3] at fine spatial scales. Such scanning methods create dense datasets to visualize earth features, but are limited to small areas (centimetre to metre scale) of earthscapes. At landscape scales, striking models were presented reconstructing terrain patterns [4] and soilscapes [5]-[7] in three dimensions. Bellamy et al [8] demonstrated how soil carbon loss/gains across a whole country (England and Wales) can be combined with SciVis methods. Extension of such visual spatial representations of earth patterns into the temporal dimension is still rare. Spatio-temporal simulations and visualization models of soil carbon change and water table fluctuations for large landscapes were presented in [9, 10] . Earthscapes (or soilscapes) show highly complex patterns with diverse earth/soil attributes in geographic space and through time. Earthscape (or soilscape) is a term derived from earth that describes the land surface of the world, especially soils, whereas the term scape refers to the spatial extent. Landscape is the fundamental trait of a specific geographic area, including its composition, physical environment and anthropogenic or social patterns [11] . Environmental soil-landscape modeling is a science devoted to understanding the spatial distribution of soils and coevolving landscapes as part of ecosystems that change dynamically through time [12] . Biogeochemical processes as well as natural and anthropogenic induced forcing functions generate spatial and temporal patterns observable in earthscapes. Earth (soil) properties entail a suite of physical (e.g. bulk density and hydraulic conductivity), chemical (e.g. soil phosphorus, nitrogen, or carbon) and biological (e.g. microbial biomass phosphorus and peptidase activity) attributes. In this study, we focus on the geospatial aspect of disaggregating the variability of earth attributes to better understand their behavior. Grunwald [13] suggested an ontology-based approach to describe earthscapes involving: (i) conceptualization of a system; (ii) reconstruction using quantitative methods; and (iii) SciVis addressing the physical, logical, implementation and cognitive universes. SciVis has been suggested to improve our understanding of a complex ecosystem and associated bio-, topo-, pedo-and lithospheres [13, 14] . Barraclough and Guymer [15] argued that advanced visualization techniques communicate complex spatial information intuitively. Maps can visually enhance the spatial and temporal understanding of phenomena of earthscapes. According to [16] , visual interfaces maximize our natural perception abilities, improve the comprehension of huge amounts of data, allow the perception of emergent properties that were not anticipated and facilitate understanding of both large-scale and small-scale earth features. SciVis relies on accurate spatial description of earthscapes. At regional scales, observation sets of earth attributes are limited due to labor and costs. Thus, prediction models have been used extensively to fill this data gap [17] . Statistical methods, such as multivariate regression, classification and regression trees, or neural networks, are used to predict earth attributes at unsampled locations using exogenous factors (e.g. land use and topographic attributes) [18] . These factorial models are focused on establishing deterministic linkages between earth attributes and environmental variables that can be measured more rapidly and at much higher density. Remote sensing has been valuable in providing dense layers of land use, land cover, terrain patterns and other environmental attributes complementing sparse, site-specific earth attribute datasets [19] . Other prediction models explicitly consider the spatial autocorrelation structure among observed earth attributes and interpolate them taking spatial dependence
doi:10.1088/1367-2630/10/12/125011 fatcat:54cpnkyyerhq5pvosgxwapsi6q