Modeling projected changes of mangrove biomass in different climatic scenarios in the Sunda Banda Seascapes

Mangroves are critical in the ecological, economic and social development of coastal rural and urban communities. However, they are under threat by climate change and anthropogenic activities. The Sunda Banda Seascape (SBS), Indonesia, is among the world’s richest regions of mangrove biomass and biodiversity. To inform current and future management strategies, it is critical to provide estimates of how mangroves will respond to climate change in this region. Therefore, this paper utilized spatial analysis with model-based climatic indicators (temperature and precipitation) and mangrove distribution maps to estimate a benchmark for the mangrove biomass of the SBS in six scenarios, namely the Last Inter-glacial Period, the current scenario (1950–2000) and all four projected Representative Concentration Pathways in 2070 due to climate change. Despite mangroves gaining more biomass with climate change (the increase in CO2 concentration), this paper highlighted the great proportion of below-ground biomass in mangrove forests. It also showed that the changes in spatial distribution of mangrove biomass became more variable in the context of climate change. As mangroves have been proposed as an essential component of climate change strategies, this study can serve as a baseline for future studies and resource management strategies

Geospatial Analysis for the Determination of Hydro-Morphological Characteristics and Assessment of Flash Flood Potentiality in Arid Coastal Plains: A Case in Southwestern Sinai, Egypt

Coastal plains with a unique geographic setting and renewable natural resources are promising for sustainable development; however, these areas may be subjected to some environmental hazards due to their geological setting. One of those hazards is the seasonal flash flood that can threaten existing and future development projects in such critical areas. Southwestern Sinai, Egypt, is a coastal plain that is characterized by complex geological setting an arid climate with seasonal rainfall which can result in a high runoff. The aim of this work is to model spatially the runoff amount and density related to flash flood development and to create a flash flood hazard map of the plain as an example of coastal plain in a desert environment with large and complex hydrologic setting. In this research, ASTER images are used to develop a digital elevation model (DEM) and land use/land cover (LULC) data sets of the study area. Geographic information system (GIS) was used to perform runoff and ash potential flood analyses of the created databases and to show distributed runoff and flooding potential in spatial maps. A module was created in a GIS environment to develop a flash flood potential index map. It was clear that the main two factors controlling runoff amounts and flash flood potential in such kinds of areas are the slope and soil types. The final dataset map procedure by this work can be very helpful in land use planning by highlighting the areas subjected to flash floods.  Análisis Geoespacial para Determinar las Características Hidromorfológicas y Evaluar las Inundaciones Potenciales en Llanuras Costeras Áridas: Caso de Estudio en el Suroccidente de Sinaí, Egipto ResumenLas llanuras costeras que poseen recursos naturales renovables y una configuración geográfíca única son promisorias para el desarrollo sostenible. Estas áreas, sin embargo, son objeto de algunas amenazas ambientales debido a su escenario geológico. Una de estas amenazas es la temporada de inundaciones rápidas que pueden poner en riesgo los proyectos de desarrollo existentes y los futuros en estas zonas críticas. El suroccidente de Sinaí, Egipto, es una llanura costera que se caracteriza por su compleja configuración geológica de clima árido, con temporadas de lluvia que pueden resultar en una gran escorrentía. El objetivo de este trabajo fue modelar espacialmente la cantidad y densidad de escorrentía relacionada al desarrollo de inundaciones rápidas y elaborar un mapa de amenazas de inundación rápida en este valle, como un ejemplo de llanura costera en un ambiente desértico con un escenario hidrológico grande y complejo. En este trabajo se utilizaron imágenes ASTER para desarrollar un Modelo de Elevación Digital (DEM, en inglés) y establecer la información de uso del suelo/cobertura del suelo (LULC, en inglés) en el área de estudio. A partir del Sistema de Información Geográfica (GIS) se analizaron la escorrentía y el potencial de inundación de las bases de datos creadas, y se mostró la escorrentía y el potencial de inundación en mapas espaciales. Se creó un módulo en un ambiente del GIS para desarrollar un mapa del índice inundación rápida potencial. Se estableció que los dos factores que controlan la cantidad de escorrentía y el potencial de inundaciones rápidas en estas áreas son la inclinación y los tipos de suelo. El mapa final de procemiento con el conjunto de datos de este trabajo es de gran ayuda en la planeación del uso de suelos, ya que evidencia las áreas con posibilidad de inundaciones rápidas

The Spectral Ocean Color Imager (SPOC) – An Adjustable Multispectral Imager

SPOC (SPectral Ocean Color) is a 3U small satellite mission that will use an adjustable multispectral imager to map sensitive coastal regions and off coast water quality of Georgia and beyond. SPOC is being developed by the University of Georgia’s (UGA) Small Satellite Research Laboratory (SSRL) through NASA’s Undergraduate Student Instrument Project (USIP). UGA is working with Cloudland Instruments to develop a small scale (\u3c 1000 \u3ecm3) multispectral imager, ranging from 400-850nm, for Earth science applications which will fly as part of the NASA CubeSat Launch Initiative. The project is UGA’s first satellite mission and is built by a team of undergraduates from a wide range of backgrounds and supervised by a multidisciplinary team of graduate students and faculty. Development, assembly, testing, and validation of the multispectral imager, as well integrating it into the satellite are all being done in house. At an orbit of 400 km the resulting images will be 90 km x 100 km in size, with a default spatial resolution and spectral resolution of 130 m and 4 nm, respectively

Development of Vapor Sensors for Volatile Museum Contaminants by Surface Enhanced Raman Spectroscopy (SERS)

Detection and identification of pesticide residues on objects of cultural heritage is a serious and urgent challenge that currently faces many museums, Native American communities, and private collections worldwide. Organic artifact materials, such as wood, animal hide, basketry, textiles, paper, horn and bone, have traditionally been treated with pesticides to eradicate and prevent infestation by insects, rodents, and mold. These poisonous substances can persist for years in the controlled environment of a museum storeroom and present a potential poisoning risk to people who come in contact with the objects. Surface-enhanced Raman spectroscopy (SERS) has the potential to detect volatile organic pesticides in this context. The technique can overcome the insensitivity of normal Raman spectroscopy and fluorescence interference, and make possible detection of many organic compounds in parts per million concentration. This investigation is aimed at evaluating SERS for the detection and identification of volatiles in museums, with emphasis on naphthalene vapor. The potential of several SERS-active materials; Tollens mirrors, gold film over nanosphere arrays, citrate-stabilized colloidal silver, and nanoporous gold; to detect Rhodamine B and naphthalene is investigated. The research also highlights the mechanisms that underlie SERS, and the relationship between substrate nanostructure and SERS performance.Dissertation not available (per author's request

Bridging the Gap Between Geography and Marketing: Opportunities for CyberGIS

No abstract available

Predicting Effects of Climate Change on Habitat Suitability of Red Spruce (Picea rubens Sarg.) in the Southern Appalachian Mountains of the USA: Understanding Complex Systems Mechanisms through Modeling

Alpine, subalpine and boreal tree species, of low genetic diversity and adapted to low optimal temperatures, are vulnerable to the warming effects of global climate change. The accurate prediction of these species’ distributions in response to climate change is critical for effective planning and management. The goal of this research is to predict climate change effects on the distribution of red spruce (Picea rubens Sarg.) in the Great Smoky Mountains National Park (GSMNP), eastern USA. Climate change is, however, conflated with other environmental factors, making its assessment a complex systems problem in which indirect effects are significant in causality. Predictions were made by linking a tree growth simulation model, red spruce growth model (ARIM.SIM), to a GIS spatial model, red spruce habitat model (ARIM.HAB). ARIM.SIM quantifies direct and indirect interactions between red spruce and its growth factors, revealing the latter to be dominant. ARIM.HAB spatially distributes the ARIM.SIM simulations under the assumption that greater growth reflects higher probabilities of presence. ARIM.HAB predicts the future habitat suitability of red spruce based on growth predictions of ARIM.SIM under climate change and three air pollution scenarios: 10% increase, no change and 10% decrease. Results show that suitable habitats shrink most when air pollution increases. Higher temperatures cause losses of most low-elevation habitats. Increased precipitation and air pollution produce acid rain, which causes loss of both low- and high-elevation habitats. The general prediction is that climate change will cause contraction of red spruce habitats at both lower and higher elevations in GSMNP, and the effects will be exacerbated by increased air pollution. These predictions provide valuable information for understanding potential impacts of global climate change on the spatiotemporal distribution of red spruce habitats in GSMNP

Land Use/Land Cover and Soil Type Covariation in a Heterogeneous Landscape for Soil Moisture Studies Using Point Data

This research investigates the spatial covariation of soil and land use/land cover (LULC) at the Little River watershed to assess landscape heterogeneity and the spatial extent to which point data from the USDA hydrological network near Tifton, Georgia can be used for regional representations of environmental variables and to validate remote sensing data. Analyses were performed at the landscape scale and within square areas equivalent to four pixel units of environmental satellites commonly reported in soil moisture studies. The results showed a highly heterogeneous landscape with greater landscape fragmentation caused by LULC than by soil types, and even greater fragmentation when the two variables were considered together. We found a 23:1 ratio of soil types to LULC and few combinations of soil type/LULC dominating the landscape. We conclude that the stations are better suited for site-specific hydrologic studies and to validate high-spatial-resolution remote sensors