Search for: All records

Abstract BackgroundThe southeastern United States consists of diverse ecosystems, many of which are fire-dependent. Fires were common during pre-European times, and many were anthropogenic in origin. Understanding how prescribed burning practices in use today compare to historic fire regimes can provide perspective and context on the role of fire in critical ecosystems. On the Aransas National Wildlife Refuge (ANWR), prescribed burning is conducted to prevent live oak (Quercus fusiformis) encroachment and preserve the openness of the herbaceous wetlands and grasslands for endangered whooping cranes (Grus americana) and Aplomado falcons (Falco femoralis). This field note builds a digital fire atlas of recent prescribed burning on the refuge and compares it to the historical fire ecology of ANWR. ResultsFindings indicate that the refuge is maintaining fire-dependent ecosystems with an extensive burn program that includes a fire return interval between 2 and 10 years on a majority of the refuge, with some locations experiencing much longer intervals. These fire return intervals are much shorter than the historic burn regime according to LANDFIRE. ConclusionsFollowing the fire return intervals projected by LANDFIRE, which project longer intervals than the prescribed fire program, would likely be detrimental to endangered species management by allowing increased woody plant encroachment and loss of open habitat important to whooping cranes and Aplomado falcons. Since prescribed fire is part of the management objectives on many national wildlife refuges in the United States, quantifying recent and historical fire ecology can provide useful insights into future management efforts, particularly in cases where endangered species are of special concern and management efforts may be counter to historical disturbance regimes. 

In this paper, we discuss how data-driven approaches using emerging IoT and machine learning based analytics can revolutionize the resilience and ef=iciency of urban water systems. Key challenges in creating a next generation water infrastructure includes issues of how and where to place instruments to gather a wide variety of information useful for improving operational ef=iciencies and for damage detection after major disasters. We discuss how an understanding of deployed infrastructure in diverse geographies and the dynamics of interconnected systems can help design more effective placement of technology solutions. We showcase recent work illustrating how knowledge of network structures and their behavior can help to more effectively instrument and gather operational data and how AI-based approaches utilizing geospatial data more effectively can help to maintain real-time awareness of system states which allows decision makers to more effectively monitor and control their systems. 

Following the Ridgecrest Earthquake Sequence, consisting of a M6.4 foreshock and M7.1 mainshock along with many other foreshocks and aftershocks, the Geotechnical Extreme Events Reconnaissance (GEER) Association deployed a team to gather perishable data. The team focused their efforts on documenting ground deformations including surface fault rupture south of the Naval Air Weapons Station China Lake, and liquefaction features in Trona and Argus. The team published a report within two weeks of the M7.1 mainshock. This paper presents data products gathered by the team, which are now published and publicly accessible. The data products presented herein include ground-based observations using GPS trackers, digital cameras, and hand measuring devices, as well as UAV-based imaging products using Structure from Motion to create point clouds and digital surface models. The paper describes the data products, as well as tools available for interacting with the products.