Spatio-temporal morphological variability of a tropical barrier island derived from the Landsat collection

Barrier islands are low-lying elongated, narrow sandy deposits, usually parallel to the coastline, separated from the continent by a lagoon. Due to their low elevation above sea level, barrier islands are environments susceptible to drastic morphological changes depending on the meteo-oceanographic conditions to which they are subjected. This work presents the morphological changes between 1985 and 2021 in “Restinga da Marambaia”—a 40 km long barrier island on Brazil’s Southeastern coast. One hundred thirty-four scenes from the Landsat collection were processed, enabling the quantification of the barrier island area. Additionally, the rates of change in the position of the shorelines facing the Atlantic Ocean, Sepetiba Bay, and Marambaia Bay were computed. The barrier island’s total area and the central sector’s width present significant seasonal variability, which is maximum during the austral fall and winter seasons. On the shores facing the Atlantic Ocean and Sepetiba Bay, it is noted that the central and far eastern sectors show an erosional trend. In contrast, the coastline is more stable on the shore facing Marambaia Bay. The seasonal variations of the barrier island area occur during a period of low rainfall and more energetic waves associated with local winds, which produce coastal currents, transporting the available sediments

Applications of Photogrammetry for Environmental Research

ISPRS International Journal of Geo-Information: special issue entitled "Applications of Photogrammetry for Environmental Research

Elephant space use in relation to ephemeral surface water availability in the eastern Okavango Panhandle, Botswana

The movement and distribution of elephants can be influenced by environmental factors over time (Foley, 2002). Examining how features in the landscape such as vegetation productivity, water sources and anthropogenic activities drive the movement of elephants can help in understanding patterns of movement. It can also help to inform the establishment and alignment of protected areas, wildlife corridors and identification of tourism hotspots as well as policy interventions to manage Human-Elephant Conflict (HEC). The Okavango Panhandle in Botswana is a HEC hotspot and the focus of My study. A number of strategies to address HEC are underway in the area, however one longer term strategy that has been proposed in this area involves provision of artificial water sources to influence elephant movements and keep animals away from fields during the cropping season. However, an improved understanding of how elephants utilize their habitats in relation to natural ephemeral surface water and other factors that influence their movements from dryland habitats to the Okavango Delta resources is needed to inform such management decisions. My study seeks to establish the role of ephemeral surface water on elephant distribution in the eastern Okavango Panhandle, Botswana as well as assess the movement distribution of elephants in relation to the seasonality, proximity and spatial extent of water presence represented by ephemeral surface water. Time series analysis of water extent on ephemeral surface water of the eastern Okavango panhandle will be developed and overlaid with elephant movement datasets. Elephant collar data from 15 elephants (5 males and 10 females) in the eastern Okavango Panhandle, Botswana have been analysed and Home Range (HR) sizes estimated using Kernel Density Estimation (KDE). The relative importance/probability of environmental variables in determining elephants' movement based on the Utilization Distribution (UD) were computed using Generalized Linear Mixed Models (GLMMs). I utilized a remote sensing spectral index, namely the Automated Water Extraction Index (AWEI) to delineate ephemeral surface water in dryland (excluding permanent waters) of the study area. The results reveal that during the wet season, elephants were evenly spread out all over the study area until the early dry season (April-June) when the ephemeral waterholes dried up. Elephants moved southwards towards the permanent waters of the Okavango River, where there are many human settlements and farms. Male HR sizes were found to be bigger than those of female elephants. Wet season (early and late) home range sizes were also bigger when compared to dry season (early and late) HR size. Mean daily distances were computed to investigate the effect of season on elephant daily distances and the distances ranged between 5km and 6.8km in the late wet and in the early wet and late dry season respectively. The Resource Selection Function (RSF) analysis shows that water adjacent sites are preferred over distant ones and both sexes prefer areas with high NDVI, with this preference being more pronounced in males. The seasonal variation of water use is notable in that it affirms the importance of proximity to water for elephants and has implications for their management and HEC. For example, I found that ephemeral surface water has a significant role in influencing elephant spatial use in the area, particularly during the early and late wet season. As ephemeral pans dried and NDVI (vegetation greenness) decreased, elephants started to move closer to the Okavango Delta and consequently human settlements and fields. However, further investigations into the timing of movements away from ephemeral waterholes and the influence of other environmental factors on elephant movements in the area would be needed before any recommendations can be made regarding artificial water provision in this area

A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

Do bacteria thrive when the ocean acidifies? Results from an off-­shore mesocosm study

Marine bacteria are the main consumers of the freshly produced organic matter. In order to meet their carbon demand, bacteria release hydrolytic extracellular enzymes that break down large polymers into small usable subunits. Accordingly, rates of enzymatic hydrolysis have a high potential to affect bacterial organic matter recycling and carbon turnover in the ocean. Many of these enzymatic processes were shown to be pH sensitive in previous studies. Due to the continuous rise in atmospheric CO2 concentration, seawater pH is presently decreasing at a rate unprecedented during the last 300 million years with so-far unknown consequences for microbial physiology, organic matter cycling and marine biogeochemistry. We studied the effects of elevated seawater pCO2 on a natural plankton community during a large-scale mesocosm study in a Norwegian fjord. Nine 25m-long Kiel Off-Shore Mesocosms for Future Ocean Simulations (KOSMOS) were adjusted to different pCO2 levels ranging from ca. 280 to 3000 µatm by stepwise addition of CO2 saturated seawater. After CO2 addition, samples were taken every second day for 34 days. The first phytoplankton bloom developed around day 5. On day 14, inorganic nutrients were added to the enclosed, nutrient-poor waters to stimulate a second phytoplankton bloom, which occurred around day 20. Our results indicate that marine bacteria benefit directly and indirectly from decreasing seawater pH. During both phytoplankton blooms, more transparent exopolymer particles were formed in the high pCO2 mesocosms. The total and cell-specific activities of the protein-degrading enzyme leucine aminopeptidase were elevated under low pH conditions. The combination of enhanced enzymatic hydrolysis of organic matter and increased availability of gel particles as substrate supported higher bacterial abundance in the high pCO2 treatments. We conclude that ocean acidification has the potential to stimulate the bacterial community and facilitate the microbial recycling of freshly produced organic matter, thus strengthening the role of the microbial loop in the surface ocean