Relationship between Remotely Sensed Vegetation Change and Fracture Zones Induced by the 2008 Wenchuan Earthquake, China

The Wenchuan earthquake triggered cascading disasters of landslides and debris flows that caused severe vegetation damage. Fracture zones can affect geodynamics and spatial pattern of vegetation damage. A segment tracing algorithm method was applied for identifying the regional fracture system through lineament extractions from a shaded digital elevation model with 25 m mesh for southern Wenchuan. Remote sensing and geographic information system techniques were used to analyze the spatiotemporal vegetation pattern. The relationship between vegetation type identified from satellite images and lineament density was used to characterize the distribution patterns of each vegetation type according to fracture zones. Broad-leaved forest, mixed forest, and farmland persist in areas with moderate lineament density. Deciduous broad-leaved and coniferous forest persists in less frac- tured areas. Shrub and meadow seem to be relatively evenly distributed across all lineament densities.Meadow, farmland, and shrub persist in the fractured areas. Changes of spatial structure and correlation between vegetation patterns before and after the earthquake were examined using semivariogram analysis of normalized difference vegetation indices derived from Landsat enhanced thematic mapper images. The sill values of the semivariograms show that the spatial heterogeneity of vegetation covers increased after the earthquake. Moreover, the anisotropic behaviors of the semivariograms coincide with the vegetation changes due to the strikes of fracture zones.

Spatial Modeling Techniques for Characterizing Geomaterials: Deterministic vs.Stochastic Modeling for Single-Variable and Multivariate Analyses

Sample data in the Earth and environmental sciences are limited in quantity and sampling location and therefore,sophisticated spatial modeling techniques are indispensable for accurate imaging of complicated structures and properties of geomaterials.This paper presents several effective methods that are grouped into two categories depending on the nature of regionalized data used.Type I data originate from plural populations and type II data satisfy the prerequisite of stationarity and have distinct spatial correlations.For the type I data,three methods are shown to be effective and demonstrated to produce plausible results: (1) a spline-based method,(2) a combination of a spline-based method with a stochastic simulation,and (3) a neural network method.Geostatistics proves to be a powerful tool for type II data.Three new approaches of geostatistics are presented with case studies: an application to directional data such as fracture,multi-scale modeling that incorporates a scaling law,and space-time joint analysis for multivariate data.Methods for improving the contribution of such spatial modeling to Earth and environmental sciences are also discussed and future important problems to be solved are summarized.