Landsat survey of near-shore ice conditions along the Arctic coast of Alaska

The author has identified the following significant results. Comparison of late season U-2 color infrared sea ice photography and early ice season LANDSAT sea ice imagery has made possible the identification of subtle features seen on early season LANDSAT imagery in the near shore areas. The U-2 imagery positively linked these features to ice conditions generally not observable by LANDSAT because of the time of year when they take place. Ice formed in place largely as single sheets appears light while ice deformed by considerable rafting appears darker when viewed on LANDSAT imagery. Because the ice is snow-covered at the time this imagery is obtained, this underlying structure must be revealed by the topography of the snow surface, and the resulting light scattering characteristics

Feasibility study for locating archaeological village sites by satellite remote sensing techniques

There are no author-identified significant results in this report

LANDSAT survey of near-shore ice conditions along the Arctic coast of Alaska

The author has identified the following significant results. Winter and spring near-shore ice conditions were analyzed for the Beaufort Sea 1973-77, and the Chukchi Sea 1973-76. LANDSAT imagery was utilized to map major ice features related to regional ice morphology. Significant features from individual LANDSAT image maps were combined to yield regional maps of major ice ridge systems for each year of study and maps of flaw lead systems for representative seasons during each year. These regional maps were, in turn, used to prepare seasonal ice morphology maps. These maps showed, in terms of a zonal analysis, regions of statistically uniform ice behavior. The behavioral characteristics of each zone were described in terms of coastal processes and bathymetric configuration

LANDSAT survey of near-shore ice conditions along the Arctic coast of Alaska

The author has identified the following significant results. On the basis of analysis of late winter 1973, 1974, and 1975 LANDSAT imagery of the Beaufort Sea coast of Alaska, the following conclusions regarding near-shore ice conditions were made: (1) by March, the seaward limit of contiguous ice is often beyond the 10 fathom contour. (2) During March, shearing can and does take place along a line roughly coincident with the 10 fathom contour. (3) Ice motions during these shearing events are not extremely great, generally on the order of 10 km. (4) Many large ice features have already been formed by late February. (5) Based on look-ahead at later LANDSAT imagery, it seems apparent that Beaufort Seas shore-fast ice was already formed by late February and may well be safe for exploratory activities from this data forward until the melt season

LANDSAT survey of near-shore ice conditions along the Arctic coast of Alaska

There are no author-identified significant results in this report

LANDSAT survey of near-shore ice conditions along the Arctic coast of Alaska

There are no author-identified significant results in this report

LANDSAT survey of near-shore ice conditions along the Arctic coast of Alaska

There are no author-identified significant results in this report

Application of satellite remote-sensing data to land selection and management

A pilot project conducted to demonstrate the utility and economy of satellite data in preparing thematic maps of a wilderness area emphasizing those resources of greatest interest to the potential owner is described. Vegetation maps delineating potential commercial timber and maps of suggested mineral prospecting areas of seven scattered regions were prepared by interpretation of LANDSAT images, coupled with a limited amount of ground truth. Images acquired both in winter and summer seasons were registered to township maps and used in making interpretations of the areal extent of commercial timber potentials. The amount of snow cover visible through the forest canopies was found to be a useful indicator of timber potentials. Identification was made of characteristic topographic features which are typical of flood plain deposits or of the well developed trellis drainage patterns which can indicate the strike of structural grain of underlying Cretaceous sedimentary rocks. The presence of igneous and mixed igneous and metamorphic rocks were indicated by combinations of spectral differences and anomalous interruptions of local radial drainage patterns

A mixed-method process evaluation of an East Midlands county summer 2021 holiday activities and food programme highlighting the views of programme co-ordinators, providers, and parents

BACKGROUND: The Holiday Activities and Food (HAF) Programme is a UK Government initiative created to alleviate food insecurity and promote health and well-being among children and their families, who are eligible for Free School Meals (FSM), during the school holidays. This process evaluation investigated factors that facilitated and acted as a barrier to the delivery of the HAF Programme from the perspectives of key stakeholders (Co-ordinators, Providers, and Parents) involved in the HAF Programme across an East Midlands county. METHODS: This evaluation utilized a mixed-methods approach, incorporating focus groups and online surveys to gain rich, multifaceted data. The focus groups were analyzed using a hybrid inductive-deductive thematic analysis and the online surveys were analyzed using mixed-methods approach due to the variation in question type (i.e., quantitative, Likert scale and open response) to align themes to the Government Aims and Standards of the HAF Programme. FINDINGS: The stakeholders highlighted several factors that facilitated and acted as a barrier to the delivery of the HAF Programme. Facilitating factors included existing and maintaining relationships between Co-ordinators, Providers, and facilities/schools/communities as this improved communication and attendance. Additionally, transport provision for those attending the Programme helped overcome barriers to attendance. The primary barrier of the Programme was the late awarding of the Programme contract as this limited the time available to prepare and organize the Programme. This in turn, had several “knock on” effects that created more barriers and resulted in some of the Government Aims and Standards not being met such as, nutrition education for children and parents. Despite the challenges faced, Co-ordinators and Providers were able to deliver the Programme and positively impact upon the children and their families that attended the Programme. CONCLUSION: Following the facilitators and barriers that were highlighted in this evaluation, several recommendations have been made to enhance the delivery of the HAF Programme and ensure Government Aims and Standards, to improve children and family's health and well-being, are attained

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie