Program on Earth Observation Data Management Systems (EODMS)

An assessment was made of the needs of a group of potential users of satellite remotely sensed data (state, regional, and local agencies) involved in natural resources management in five states, and alternative data management systems to satisfy these needs are outlined. Tasks described include: (1) a comprehensive data needs analysis of state and local users; (2) the design of remote sensing-derivable information products that serve priority state and local data needs; (3) a cost and performance analysis of alternative processing centers for producing these products; (4) an assessment of the impacts of policy, regulation and government structure on implementing large-scale use of remote sensing technology in this community of users; and (5) the elaboration of alternative institutional arrangements for operational Earth Observation Data Management Systems (EODMS). It is concluded that an operational EODMS will be of most use to state, regional, and local agencies if it provides a full range of information services -- from raw data acquisition to interpretation and dissemination of final information products

Program on Earth Observation Data Management Systems (EODMS), appendixes

The needs of state, regional, and local agencies involved in natural resources management in Illinois, Iowa, Minnesota, Missouri, and Wisconsin are investigated to determine the design of satellite remotely sensed derivable information products. It is concluded that an operational Earth Observation Data Management System (EODMS) will be most beneficial if it provides a full range of services - from raw data acquisition to interpretation and dissemination of final information products. Included is a cost and performance analysis of alternative processing centers, and an assessment of the impacts of policy, regulation, and government structure on implementing large scale use of remote sensing technology in this community of users

Accessing earth observation data using JPEG2000

Applications like, change detection, global monitoring, disaster detection and management have emerging requirements that need the availability of large amounts of data. This data is currently being captured by a multiplicity of instruments and Earth Observation (EO) sensors originating large volumes of data that needs to be stored, processed and accessed in order to be useful. The authors of this paper have been involved on an ESA-founded project, called HICOD2000 to study the applicability of the new image encoding standard JPEG2000 - to EO products. This paper presents and describes the system that was developed for HICOD2000 project, which allows, not only the encoding and decoding of several EO products, but also supports some of the security requirements identified previously that allows ESA to define and apply efficient EO data access security policies and even to exploit some EO products electronic commerce over the Internet. This system was integrated with the existing ESA Ground Segment systems specifically the Services Support Environment (SSE).info:eu-repo/semantics/acceptedVersio

Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones

This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs). Feature stacking is the most commonly-used method of data fusion but does not consider the heterogeneity of multimodal optical images and OSM data, which becomes its main drawback. The proposed framework processes two data sources separately and then combines them at the model level through two fusion models (the landuse fusion model and building fusion model), which aim to fuse optical images with landuse and buildings layers of OSM data, respectively. In addition, a new approach to detecting building incompleteness of OSM data is proposed. The proposed framework was trained and tested using data from the 2017 IEEE GRSS Data Fusion Contest, and further validated on one additional test set containing test samples which are manually labeled in Munich and New York. Experimental results have indicated that compared to the feature stacking-based baseline framework the proposed framework is effective in fusing optical images with OSM data for the classification of LCZs with high generalization capability on a large scale. The classification accuracy of the proposed framework outperforms the baseline framework by more than 6% and 2%, while testing on the test set of 2017 IEEE GRSS Data Fusion Contest and the additional test set, respectively. In addition, the proposed framework is less sensitive to spectral diversities of optical satellite images and thus achieves more stable classification performance than state-of-the art frameworks.Comment: accepted by TGR

Data management for earth observations

The management of NASA earth observation data is discussed. User requirements are identified, as well as means to facilitate data acquisition. It is shown that LANDSAT data can be preprocessed to condense data into a more accessible format, thus reducing data acquisition costs

The NERC Earth Observation Data Centre (NEODC) (2006)

A poster describing the airborne and satellite data, website, community and projects provided by the NEOD

Wildfire monitoring using satellite images, ontologies and linked geospatial data

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When Earth Observation data is coupled with other data sources many pioneering applications can be developed. In this paper we show how Earth Observation data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various Earth Observation data centers. The service has been developed in the context of European project TELEIOS that faces the challenges of extracting knowledge from Earth Observation data head-on, capturing this knowledge by semantic annotation encoded using Earth Observation ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications

The Earth Observation Data for Habitat Monitoring (EODHaM) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India