Using geospatial business intelligence paradigm to design a multidimensional conceptual model for efficient coastal erosion risk assessment

One of the main challenges in Coastal Erosion Risk Assessment (CERA) is integrating and analysis of conflicting data in various time periods and spatial scales through dissimilar environmental, social, and economic criteria. Currently, Geographical Information Systems (GIS) are widely used in risk assessment despite their drawbacks and limitations as transactional systems for multi-scales, multi-epochs, and multi-themes analysis. Hence, an analytical conceptual framework is proposed in this paper based on geospatial business intelligence paradigm to develop a Spatial Multidimensional Conceptual Model (SMCM) to assess coastal erosion risk. The model is designed based on Spatial On-Line Analytical Processing (SOLAP) platform, on the top of both analytical and transactional paradigms, to allow fast synthesis of cross-tabulated data and easy comparisons over space, scales, epochs, and themes. This objective is achieved through a comprehensive integration of multiple environmental, social, and economic criteria as well as their interactions at various scales. It also takes into account multiple elements at risk such as people, infrastructure, and built environment as different dimensions of analysis. Using this solution allows decision makers to benefit from on-demand, interactive, and comprehensive information in a way that is not possible using GIS alone. The developed model can easily be adapted for any other coastal region through the proposed framework to perform risk assessment. The advantages and drawbacks of the proposed framework are also discussed and new research perspectives are presented.     

Status report on flood warning systems in the United States

One of the major changes in flash-flood mitigation in the past decade is the number of communities that have implemented warning systems. The authors conducted a survey of 18 early-warning systems in the United States developed by communities or regions to provide protection against flash floods or dam failures. Problems revealed by the study included the following: equipment malfunctions, inadequate maintenance funding, inconsistent levels of protection and expenditure, inconsistent levels of expectations and formalization, varying levels of local commitment to the systems, underemphasis on response capability, and a tendency to over-rely on warning systems. The study also revealed some unanticipated benefits experienced by the survey communities: the warning systems serve as valuable data collection tools, a great deal of interagency cooperation has been demonstrated, and warning systems offer increased alternatives to structural modification projects. The interjurisdictional nature of drainage basins, the evolving roles of the various federal agencies involved in flood mitigation, and the lack of governmental standards of operations for flood warning systems are issues that must be considered as communities make decisions regarding the adoption of warning systems. The record on these systems is too short for a precise assessment of how successful they are; however, results of the study indicate that if the goal of reducing loss of life and property from flooding is to be achieved, warning systems must be only one part of a comprehensive flood loss reduction program.     

Applying Satellite Imagery to Triage Assessment of Ecosystem Health

Considerable evidence documents that certain changes in vegetation and soils result in irreversibly degraded rangeland ecosystems. We used Advanced Very High Resolution Radiometer (AVHRR) imagery to develop calibration patterns of change in the Normalized Difference Vegetation Index (NDVI) over the growing season for selected sites for which we had ground data and historical data characterizing these sites as irreversibly degraded. We used the NDVI curves for these training sites to classify and map the irreversibly degraded rangelands in southern New Mexico. We composited images into four year blocks: 1988–1991, 1989–1992, and 1990–1993. The overlap in pixels classified as irreversibly degraded ranged from 42.6% to 84.3% in year block comparisons. Quantitative data on vegetation composition and cover were collected at 13 sites within a small portion of the study area. Wide coverage reconnaissance of boundaries between vegetation types was also conducted for comparisons with year block maps. The year block 1988–1991 provided the most accurate delineation of degraded areas. The rangelands of southern New Mexico experienced above average precipitation from 1990–1993. The above average precipitation resulted in spatially variable productivity of ephemeral weedy plants on the training sites and degraded rangelands which resulted in much smaller areas classified as irreversibly degraded. We selected imagery for a single year, 1989, which was characterized by the absence of spring annual plant production in order to eliminate the confounding effect of reflectance from annual weeds. That image analysis classified more than 20% of the rangelands as irreversibly degraded because areas with shrub-grass mosaic were included in the degraded classification. The single year image included more than double the area classified as irreversibly degraded by the year blocks. AVHRR imagery can be used to make triage assessments of irreversibly degraded rangeland but such assessment requires understanding productivity patterns and variability across the landscapes of the region and careful selection of the years from which imagery is chosen.     

An introduction to decision science for conservation

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.     

Satellite monitoring of desert plant community response to moisture availability

Our study demonstrates the utility of coarse spatial-resolution satellite spectra for analysis of vegetation phenophases and response to moisture availability in an arid ecosystem. We show the feasibility of deriving information on vegetation parameters such as stress and growth patterns in arid regions through the use of satellite-derived vegetation indices, despite the usual problems associated with a high ratio of soil to vegetation cover. Vegetation in our study area consists of Chihuahuan Desert grassland and scrub, including extensive zones of mixed desert scrub and grassland. Historic vegetation change has been well documented and is exemplified by decreasing grass cover and increasing shrub cover, a general trend of desertification. Our analysis suggests that satellite-based inputs can be used to improve our understanding of the spatial dynamics of climatic impacts on natural vegetation and to help us distinguish these processes from human-caused desertification.