GI_Forum 2021, Volume 9, Issue 1 12th International Symposium on Digital Earth
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2021, Volume 9, Issue 1 12th International Symposium on Digital Earth
ISSN 2308-1708 Online Edition ISBN 978-3-7001-8947-3 Online Edition
Alexander Ariza,
Hannah Kemper,
Gerhard Kemper
S. 24 - 32 doi:10.1553/giscience2021_01_s24 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2021_01_s24
Abstract: Until now, most severity products are generated from a reclassification of dNBR index ranges. In this study, we focused on an automated global burn severity mapping approach. Using the catalogue of satellite imagery and the high-performance computing power of GoogleEarthEngine we propose an automated pipeline to generate severity maps of burned areas at a medium scale of 30 and 10m from the time series of Landsat and Sentinel2 images. Landsat-8 images available during 2020 and the dNBR spectral index were used to calculate the severity level of each pixel using a calibration model and linear regression adjustments, which were taken in the field from the CBI index in an app developed for field capture. A calibration approach was carried out to give the severity level of the final burned areas after several carefully designed logic filters on the normalized burn rate (NBR). This script focuses on the fires that occurred in Honduras in 2020. The regression model found a similar spatial distribution and strong correlation between the areas analyzed in the field and those generated from the dNBR. The preliminary global validation showed that the overall accuracy reached 53.85%. However, the adjustments through the correlation models im-proved the results, yielding an R2 of 0.93 for the quadratic model, 0.79 for the Exponential model and 0.72 for the linear model. Keywords: burn severity, Composite Burn Index (CBI), GEE, disaster management, regression models Published Online: 2021/06/29 09:20:49 Object Identifier: 0xc1aa5576 0x003c9b4a Rights:https://creativecommons.org/licenses/by-nd/4.0/
GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |