Production of semi real time media-GIS contents using MODIS imagery

[Abstract]: Delivering environmental disaster information, swiftly, attractively, meaningfully, and accurately, to public is becoming a competitive task among spatial data visualizing experts. Basically, the data visualization process has to follow basics of spatial data visualization to maintain the academic quality and the spatial accuracy of the content. Here, “Media-GIS”, can be promoted as a one of the latest sub-forms of GIS, which targets mass media. Under Media-GIS, “Present” or the fist component of three roles of data visualization takes the major workload compare to other two, “Analysis” and “Explore”. When present contents, optimizing the main graphical variables like, size, value, texture, hue, orientation, and shape, is vital with regard to the target market (age group, social group) and the medium (print, TV, WEB, mobile). This study emphasizes on application of freely available MODIS true colour images to produce near real time contents on environmental disasters, while minimizing the production cost. With the brake of first news of a significant environmental disaster, relevant MODIS (250m) images can be extracted in GeoTIFF and KLM (Keyhole Markup Language) formats from MODIS website. This original KML file can be overlayed on Google Earth, to collect more spatial information of the disaster site. Then, in ArcGIS environment, GeoTIFF file can be transferred into Photoshop for production of the graphics of the target spot. This media-friendly Photoshop file can be used as an independent content without geo-references or imported into ArcGIS to convert into KLM format, which has geo-references. The KLM file, which is graphically enhanced content with extra information on environmental disaster, can be used in TV and WEB through Google Earth. Also, sub productions can be directed into print and mobile contents. If the data processing can be automated, system will be able to produce media contents in a faster manner. A case study on the recent undersea oil spill occurred in Gulf of Mexico included in the report to highlight main aspects discussed in the methodology

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena