George T. Raber

Applied Climate-Change Analysis: The Climate Wizard Tool

Background Although the message of “global climate change” is catalyzing international action, it is local and regional changes that directly affect people and ecosystems and are of immediate concern to scientists, managers, and policy makers. A major barrier preventing informed climate-change adaptation planning is the difficulty accessing, analyzing, and interpreting climate-change information. To address this problem, we developed a powerful, yet easy to use, web-based tool called Climate Wizard (http://ClimateWizard.org) that provides non-climate specialists with simple analyses and innovative graphical depictions for conveying how climate has and is projected to change within specific geographic areas throughout the world. Methodology/Principal Findings To demonstrate the Climate Wizard, we explored historic trends and future departures (anomalies) in temperature and precipitation globally, and within specific latitudinal zones and countries. We found the greatest temperature increases during 1951–2002 occurred in northern hemisphere countries (especially during January–April), but the latitude of greatest temperature change varied throughout the year, sinusoidally ranging from approximately 50°N during February-March to 10°N during August-September. Precipitation decreases occurred most commonly in countries between 0–20°N, and increases mostly occurred outside of this latitudinal region. Similarly, a quantile ensemble analysis based on projections from 16 General Circulation Models (GCMs) for 2070–2099 identified the median projected change within countries, which showed both latitudinal and regional patterns in projected temperature and precipitation change. Conclusions/Significance The results of these analyses are consistent with those reported by the Intergovernmental Panel on Climate Change, but at the same time, they provide examples of how Climate Wizard can be used to explore regionally- and temporally-specific analyses of climate change. Moreover, Climate Wizard is not a static product, but rather a data analysis framework designed to be used for climate change impact and adaption planning, which can be expanded to include other information, such as downscaled future projections of hydrology, soil moisture, wildfire, vegetation, marine conditions, disease, and agricultural productivity.

An Evaluation of Lidar-derived Elevation and Terrain Slope in Leaf-off Conditions

The effects of land cover and surface slope on lidar-derived elevation data were examined for a watershed in the piedmont of North Carolina. Lidar data were collected over the study area in a winter (leaf-off) overflight. Survey-grade elevation points (1,225) for six different land cover classes were used as reference points. Root mean squared error (RMSE) for land cover classes ranged from 14.5 cm to 36.1 cm. Land cover with taller canopy vegetation exhibited the largest errors. The largest mean error (36.1 cm RMSE) was in the scrub-shrub cover class. Over the small slope range (0° to 10°) in this study area, there was little evidence for an increase in elevation error with increased slopes. However, for low grass land cover, elevation errors do increase in a consistent manner with increasing slope. Slope errors increased with increasing surface slope, under-predicting true slope on surface slopes � 2°. On average, the lidarderived elevation under-predicted true elevation regardless of land cover category. The under-prediction was significant, and ranged up to � 23.6 cm under pine land cover.

Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation

Lidar data have become a major source of digital terrain information for use in many applications including hydraulic modeling and flood plane mapping. Based on established relationships between sampling intensity and error, nominal post-spacing likely contributes significantly to the error budget. Post-spacing is also a major cost factor during lidar data collection. This research presents methods for establishing a relationship between nominal post-spacing and its effects on hydraulic modeling for flood zone delineation. Lidar data collected at a low post-spacing (approximately 1 to 2 m) over a piedmont study area in North Carolina was systematically decimated to simulate datasets with sequentially higher post-spacing values. Using extensive first-order ground survey information, the accuracy of each DEM derived from these lidar datasets was assessed and reported. Hydraulic analyses were performed utilizing standard engineering practices and modeling software (HEC-RAS). All input variables were held constant in each model run except for the topographic information from the decimated lidar datasets. The results were compared to a hydraulic analysis performed on the un-decimated reference dataset. The sensitivity of the primary model outputs to the variation in nominal post-spacing is reported. The results indicate that base flood elevation does not statistically change over the post-spacing values tested. Conversely, flood zone boundary mapping was found to be sensitive to variations in post-spacing.

Spatial scale management experiments using optical aerial imagery and LIDAR data synergy.

Computational trends toward shared services suggest the need to automatically manage spatial scale for overlapping applications. In three experiments using high-spatial-resolution optical imagery and LIDAR data to extract impervious, forest, and herbaceous classes, this study optimized C5.0 rule sets according to: (1) spatial scale within an image tile; (2) spatial scale within spectral clusters; and (3) stability of predicted accuracies based on cross validation. Alteration of the image segmentation scale parameter affected accuracy as did synergy with LIDAR derivatives. Within the tile examined, forest and herbaceous areas benefited more from optical and LIDAR synergy than did impervious surfaces.

No Reef Is an Island: Integrating Coral Reef Connectivity Data into the Design of Regional-Scale Marine Protected Area Networks

We integrated coral reef connectivity data for the Caribbean and Gulf of Mexico into a conservation decision-making framework for designing a regional scale marine protected area (MPA) network that provides insight into ecological and political contexts. We used an ocean circulation model and regional coral reef data to simulate eight spawning events from 2008–2011, applying a maximum 30-day pelagic larval duration and 20% mortality rate. Coral larval dispersal patterns were analyzed between coral reefs across jurisdictional marine zones to identify spatial relationships between larval sources and destinations within countries and territories across the region. We applied our results in Marxan, a conservation planning software tool, to identify a regional coral reef MPA network design that meets conservation goals, minimizes underlying threats, and maintains coral reef connectivity. Our results suggest that approximately 77% of coral reefs identified as having a high regional connectivity value are not included in the existing MPA network. This research is unique because we quantify and report coral larval connectivity data by marine ecoregions and Exclusive Economic Zones (EZZ) and use this information to identify gaps in the current Caribbean-wide MPA network by integrating asymmetric connectivity information in Marxan to design a regional MPA network that includes important reef network connections. The identification of important reef connectivity metrics guides the selection of priority conservation areas and supports resilience at the whole system level into the future.

Estimating vascular plant species richness on Horn Island, Mississippi using small-footprint airborne LIDAR

Most remote sensing studies of species diversity have been based on the use of passive imagery representing the horizontal dimensions of ecosystems. However, LIDAR (light detection and ranging), provides a means to accurately quantify vertical structure. The goal of this study was to evaluate vascular plant species richness on a coastal barrier island using indicators of community vertical structure derived from airborne, multiple-return LIDAR data. Returns from a 3 m buffer area surrounding each of 90, 15 m vegetation line transects were extracted from LIDAR data of Horn Island, Mississippi, acquired in April, 2004. LIDAR indices did not correlate with richness when data for all habitats were combined. When habitats were considered separately, several LIDAR indices correlated significantly (p ⩽ 0.05) with richness in marsh, meadow and woodland habitats. Best-fit indices indicated the importance of vegetation height and structural complexity in estimating plant species richness.

Temporal Modeling of Bidirectional Reflection Distribution Function (BRDF) in Coastal Vegetation

The bidirectional reflection distribution function (BRDF) is a theoretical concept that describes the relationship between a targets irradiance geometry and the viewing angle of the sensor relative to the target. The BRDF can significantly affect the radiometric quality of remotely sensed data, particularly in off-nadir views. This research used a NASA Sandmeier Field Goniometer (SFG) to collect hourly canopy spectral reflectance at 76 hemispherical angles at two study sites within the North Inlet-Winyah Bay National Estuarine Research Reserve during late winter (March 2000—low live biomass and high dead biomass) and late summer (October 2000—high live biomass and low dead biomass). The objective of this research was to compare and quantify the temporal differences of high spectral and angular resolution BRDF diurnal data for smooth cordgrass (Spartina alterniflora) communities. These data were collected to model and quantify BRDF canopy patterns as they relate to in situ biophysical measurements and phenological change. The hypothesis tested was that temporal changes in LAI, biomass, height, geometry, understory, and tide levels throughout the phenological cycle can be spectrally quantified to provide insight into BRDF research. These data were used to create graphic plots to provide a quantitative assessment of temporal BRDF patterns and biophysical characteristics. This research identified bands that are least impacted by the BRDF, recognized optimal Sun/sensor angles-of-view, and provided insight into radiometrically adjusting remotely sensed data to minimize BRDF effects. Once scientists understand the nature of BRDF in relation to phenological changes within the vegetation canopy, they can begin to apply models to improve the accuracy of information extracted from remotely sensed data.

Remote Sensing of Impervious Surfaces and Building Infrastructure

The rapidly expanding urban surfaces of today are generally impervious to water and are a key environmental indicator (Arnold and Gibbons 1996) that can be measured with remote sensing. Roads, sidewalks, parking lots, and rooftops are usually constructed of materials that repel almost all incident precipitation. In some cases, precipitation events can result in flash flooding that is similar to the flash floods occurring in rock canyons. The ability to detect, monitor, and analyze changes in the extent of impervious surfaces is important for many other aspects in the quality of environment, such as urban heat islands and pollution. This capability is in high demand for water quality engineering purposes (Zug et al. 1999) and for the assessment of stormwater taxes (Kienegger 1992).

Intuitive simulation, querying, and visualization for river basin policy and management

Sustainable use of the freshwater resources of the world is an urgent challenge. The World Health Organization recently estimated that 1.1 billion people lack access to safe drinking water, a problem the United Nations highlights in its Millennium Development Goals. To address the scale and urgency of this challenge, IBM, The Nature Conservancy, and the Center for Sustainability and the Global Environment at the University of Wisconsin-Madison are collaborating to develop innovative, technology-based decision-support tools for improved management of water resources worldwide. The Water for Tomorrow modeling framework and decision support system (DSS) is designed to help policy makers and a variety of stakeholders to assess, come to consensus, and act on land-use decisions that balance human use, ecosystem preservation, and ecosystem restoration. Such stakeholders include farmers, fish and wildlife managers, foodprocessing plant managers, and hydropower operators. Initially focused on the Paraguay-Parana Basin of Brazil, in partnership with local academic and public-sector collaborators, the DSS integrates data and models from a wide variety of environmental sectors, including water balance, water quality, carbon balance, crop production, and proxies for biodiversity. Intuitive interfaces and complex query support allow users to reach a rich understanding of the effect of changes in management on freshwater ecosystems.

A Systematic Framework for Spatial Conservation Planning and Ecological Priority Design: An Example from St. Lucia, Eastern Caribbean

Rare and endangered species attract attention everywhere; this is certainly true within the Western world. During any inventory work at the stations, questions about rare and endangered species come up quickly. It is a very popular topic and many individuals and organizations are interested in trying to save and study these species, or at least to claim doing so in public. It must be seen as a phenomenon why this topic ranks so high on the agenda for the public, in the commercial media, and with many conservationists and students? Perhaps rare species are indicative of how we as a society, perceive, interact with, and understand nature?