Influence of Coastal Vegetation on the 2004 Tsunami Wave Impact in West Aceh

Proceedings of the National Academy of SciencesPNAS, 15 November 2011 vol. 108 no. 46 18612-18617

Juan Carlos Laso Bayas, Carsten Marohn, Gerd Dercon, Sonya Dewi, Hans Peter Piepho, Laxman Joshi, Meine van Noordwijk, and Georg Cadisch

“In a tsunami event human casualties and infrastructure damage are determined predominantly by seaquake intensity and offshore properties. On land, wave energy is attenuated by gravitation (elevation) and friction (land cover). Tree belts have been promoted as “bioshields” against wave impact. However, given the lack of quantitative evidence of their performance in such extreme events, tree belts have been criticized for creating a false sense of security. This study used 180 transects perpendicular to over 100 km on the west coast of Aceh, Indonesia to analyze the influence of coastal vegetation, particularly cultivated trees, on the impact of the 2004 tsunami. Satellite imagery; land cover maps; land use characteristics; stem diameter, height, and planting density; and a literature review were used to develop a land cover roughness coefficient accounting for the resistance offered by different land uses to the wave advance.

Schematic transect showing the variables used in the models.

Schematic transect showing the variables used in the models. MD = maximum flood distance (m), CASU = casualties (%), STD = structural damage (%), IWH = initial water height (m), D = distance from the shore line to the settlement (m) ET = maximum elevation over the whole transect (m a.s.l.), EF = maximum elevation at the settlement level (m a.s.l.), LCRT = weighted average land cover roughness in the transect (up to the maximum flood distance), LCRF = weighted average land cover roughness in front of the settlement and LCRB5 = weighted average land cover roughness from the settlement up to 500 m behind.

“Applying a spatial generalized linear mixed model, we found that while distance to coast was the dominant determinant of impact (casualties and infrastructure damage), the existing coastal vegetation in front of settlements also significantly reduced casualties by an average of 5%. In contrast, dense vegetation behind villages endangered human lives and increased structural damage. Debris carried by the backwash may have contributed to these dissimilar effects of land cover. For sustainable and effective coastal risk management, location of settlements is essential, while the protective potential of coastal vegetation, as determined by its spatial arrangement, should be regarded as an important livelihood provider rather than just as a bioshield.”

New Book Presents Latest Crime Mapping and Analysis Methods

This workbook is designed for both classroom instruction and self-study.

This workbook is designed for both classroom instruction and self-study.

GIS Tutorial for Crime Analysis, the new workbook from Esri Press, combines step-by-step tutorials with independent exercises to teach readers the skills needed to apply mapping and analysis to police work.

Designed for both classroom instruction and self-study, the book includes a 180-day trial version of ArcGIS 10 for Desktop software and real crime data obtained from the Pittsburgh Police Bureau and the Allegheny County 911 Center in Pennsylvania. An instructor resource DVD is available from Esri Press by request.

GIS Tutorial for Crime Analysis is an invaluable resource to those in law enforcement who are involved in crime mapping and analysis,” says Lew Nelson, Esri’s director of global law enforcement solutions. “It provides practical exercises that are beneficial to both novice and experienced GIS specialists.”

The book is coauthored by Wilpen L. Gorr and Kristen S. Kurland. Gorr is a professor of public policy and management information systems; Kurland is a professor of architecture, information systems, and public policy. Both teach at the H. John Heinz III College at Carnegie Mellon University in Pittsburgh, Pennsylvania.

GIS Tutorial for Crime Analysis (ISBN: 978-1-58948-214-2, 296 pages, US$79.95) is available at online retailers worldwide, at, or by calling 1-800-447-9778. Outside the United States, visit for complete ordering options, or visit to contact your local Esri distributor. Interested retailers can contact Esri Press book distributor Ingram Publisher Services.

[Source: Esri press release]

Bayesian Multimodel Inference for Geostatistical Regression Models

PLoS ONE 6(11): November 10, 2011

Devin S. Johnson1 and Jennifer A. Hoeting

“The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC) method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs). The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike’s information criterion (AIC). The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance.”