Artificial neural networks in geospatial analysis

Artificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and forecasting or prediction. There are many types of neural networks based on learning paradigm and network architectures. Their use is expected to grow with increasing availability of massive data from remote sensing and mobile platforms

Trends in Planetary Data Analysis. Executive summary of the Planetary Data Workshop

Planetary data include non-imaging remote sensing data, which includes spectrometric, radiometric, and polarimetric remote sensing observations. Also included are in-situ, radio/radar data, and Earth based observation. Also discussed is development of a planetary data system. A catalog to identify observations will be the initial entry point for all levels of users into the data system. There are seven distinct data support services: encyclopedia, data index, data inventory, browse, search, sample, and acquire. Data systems for planetary science users must provide access to data, process, store, and display data. Two standards will be incorporated into the planetary data system: Standard communications protocol and Standard format data unit. The data system configuration must combine a distributed system with those of a centralized system. Fiscal constraints have made prioritization important. Activities include saving previous mission data, planning/cost analysis, and publishing of proceedings

Develping a Methodology for the Mapping and Characterization of the Nigerian Coastline Using Remote Sensing

Coastline delineation is important in maritime boundary determination, as well as for analyzing coastline change rates due to coastal erosion, sea level change, storms, and other causes. Coastline change rate estimates depend on the uncertainty of the current and historical coastlines used in the analysis, which, in turn, depend on the surveying technologies and techniques that were originally used. Current techniques for coastline mapping include photogrammetric delineation using tide-coordinated aerial imagery. However, in many developing countries, the charted coastlines may have been inadequately and inconsistently mapped largely due to inadequate resources. This paper describes the use of an automated technique for coastline mapping and classification that is based on satellite imagery. A spectral analysis using different image bands can be used to define the land/water boundary and characterize the coastal area around the coastline. A first-order uncertainty analysis was also performed. The satellite-derived coastlines were compared to charted coastlines to evaluate the adequacy and consistency of the charted coastlines. The satellite-derived coastlines were also compared to coastlines derived from historical maps to assess changes and change rates. The results of the coastline uncertainty analysis were then used to compute propagated uncertainties in coastline change rate estimates and to gain greater insight into actual changes. The procedure was developed in a GIS environment using study sites along the Nigerian coastline. However, this procedure can be applied to other poorly charted/mapped coastal areas as well

Distributed environmental monitoring

With increasingly ubiquitous use of web-based technologies in society today, autonomous sensor networks represent the future in large-scale information acquisition for applications ranging from environmental monitoring to in vivo sensing. This chapter presents a range of on-going projects with an emphasis on environmental sensing; relevant literature pertaining to sensor networks is reviewed, validated sensing applications are described and the contribution of high-resolution temporal data to better decision-making is discussed

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

Opportunities and challenges of using ion-selective electrodes in environmental monitoring and wearable sensors

Great opportunities exist for Ion-Selective Electrodes (ISEs) in the fields of environmental monitoring and of wearable applications for example as the sensing part in wireless networks. In this review special attention is given to the recent results obtained with Solid Contact Ion-Selective Electrodes and Solid Contact Reference Electrodes. Their combination as disposable sensing platform may offer the best solution to eliminate issues commonly experienced with ISEs and lead in a short term to their commercialization. Future research will likely focus on the miniaturization of the current devices and on the further development of non conventional potentiometric methods, e.g., controlled potential thin-layer coulometry

Assessment and mitigation of droughts in South-West Asia: issues and prospects

Drought / Monitoring / Assessment / Risks / Analysis / Decision support tools / Policy / Institutions / Social aspects / Economic aspects / Water harvesting / Asia