A geospatial database model for the management of remote sensing datasets at multiple spectral, spatial and temporal scales

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1117/12.2278510
AuthorSearch for: 1; Search for: ; Search for: 1; Search for: 1; Search for: 1
Name affiliation
  1. National Research Council of Canada. Aerospace
FormatText, Article
Proceedings titleEarth Resources and Environmental Remote Sensing/GIS Applications VIII
Series titleProceedings of SPIE; no. 10428
ConferenceEarth Resources and Environmental Remote Sensing/GIS Applications, 12-14 September, 2017, Warsaw, Poland
ISSN0277-786X
1996-756X
Article number10428-62
Abstract
In this study the development and implementation of a geospatial database model for the management of multiscale datasets encompassing airborne imagery and associated metadata is presented. To develop the multi-source geospatial database we have used a Relational Database Management System (RDBMS) on a Structure Query Language (SQL) server which was then integrated into ArcGIS and implemented as a geodatabase. The acquired datasets were compiled, standardized, and integrated into the RDBMS, where logical associations between different types of information were linked (e.g. location, date, and instrument). Airborne data, at different processing levels (digital numbers through geocorrected reflectance), were implemented in the geospatial database where the datasets are linked spatially and temporally. An example dataset consisting of airborne hyperspectral imagery, collected for inter and intra-annual vegetation characterization and detection of potential hydrocarbon seepage events over pipeline areas, is presented. Our work provides a model for the management of airborne imagery, which is a challenging aspect of data management in remote sensing, especially when large volumes of data are collected.
Publication date
PublisherSociety of Photo-Optical Instrumentation Engineers (SPIE)
LanguageEnglish
Peer reviewedYes
NPARC number23002294
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Record identifier50487621-e175-48cf-ac41-c3556808bb2c
Record created2017-10-11
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