Special UNISPACE III volume

Marsteller, Deborah

Bibliographic data

Monograph

Persistent identifier:: 856485039

Author:: Marsteller, Deborah

Title:: Special UNISPACE III volume

Sub title:: including: ISPRS Workshop on "Resource Mapping from Space", ISPRS-EARSeL Workshop on "Remote Sensing for the Detection, Monitoring and Mitigation of Natural Disasters", ISPRS-NASA Seminar on "Environment and Remote Sensing for Sustainable Development", July 1999, Vienna, Austria

Scope:: IV, 170 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856485039

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: ISPRS Workshop on "Resource Mapping from Space"

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: DATA FUSION FOR A BETTER EXPLOITATION OF DATA IN ENVIRONMENT AND EARTH OBSERVATION SCIENCES. Lucien WALD

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing. Vol. XXXII Part 7C2, UNISPACE III, Vienna, 1999 27 I5PR5 UNISPACE HI - ISPRS Workshop on “Resource Mapping from Space ” 9:00 am -12:00 pm, 22 July 1999, VIC Room B Vienna, Austria I5PR5 DATA FUSION FOR A BETTER EXPLOITATION OF DATA IN ENVIRONMENT AND EARTH OBSERVATION SCIENCES Lucien WALD Ecole des Mines de Paris, Groupe Télédétection & Modélisation BP 207, 06904 Sophia Antipolis cedex, France Phone: +33 (0)4 93 95 74 49 - Fax: +33 (0)4 93 95 75 35 - e-mail: lucien.wald@cenerg.cma.fr ABSTRACT Earth Observation (EO) data are currently used in several applications. Some achievements are readied which are fairly satisfactory' from the point of view of customers. How'ever it is recognised that each application only uses a small amount of the available EO data. Numerous EO missions exist. The number of sensors and of possible observations is huge. According to the instruments, the observations can be done in several wavelengths, (visible and infrared, thermal infrared, microwave domains), with different modes: passive (radiometer, spectrometer) or active (imaging radar, scatterometer, altimeter), with different spatial resolution (from 1 to 50 km), and with various observation frequencies (from 1/4 hour to 30 days). A judicious use of the whole capabilities of EO missions and data should lead to an increase of the quality of the product or information delivered to the customer or of the decision taken by the customer. This wealth of data is presently not fully exploited by the customer, mostly because methods, techniques and tools are not yet available. Data fusion lias been defined by a group of European experts as "a formal framework in which are expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of 'greater quality' will depend upon the application". Three examples will be given, illustrating the benefits of data fusion relative to standard approaches. The first example will demonstrate how mapping features in the cities can be enhanced by the fusion of images of different spatial and spectral resolutions. A panchromatic image of Marseille (France) downtown with a spatial resolution close to 2 m is merged with a inultispectral image of lower spatial resolution. The synthesised high resolution multispectral image provides a much better description of the city features. Ground measurements are usually the only information of the distribution of pollutants. In the second example, these measurements are fused with satellite images in order to map the concentration in pollutants in the city of Nantes (France). Local authorities, in charge of pollution are supplied with a more accurate knowledge of the spatial distribution of the pollutants. The tliird example shows how accurate digital elevation models can be constructed from multi-sources satellite images. For each point, the best source is selected according to the associated error and taking into account the overall coherency of the digital elevation model.

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