Tracking Burned Area Progression in an Unsupervised Manner Using Sentinel-1 SAR Data in Google Earth Engine
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F24%3A10487224" target="_blank" >RIV/00216208:11310/24:10487224 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ULWvSSkGCj" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ULWvSSkGCj</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JSTARS.2024.3427382" target="_blank" >10.1109/JSTARS.2024.3427382</a>
Alternative languages
Result language
angličtina
Original language name
Tracking Burned Area Progression in an Unsupervised Manner Using Sentinel-1 SAR Data in Google Earth Engine
Original language description
The frequency of wildfires is increasing worldwide, contributing to a third of forest loss over the last two decades. Tracking burned area progression using traditional optical remote sensing is hindered by cloud and smoke coverage. Therefore, this research employs multitemporal synthetic aperture radar (SAR) satellite data, which are not susceptible to atmospheric effects. Focusing on four Greek wildfires in 2021, the research utilizes unsupervised k-means clustering on bitemporal and multitemporal SAR polarimetric features. The impact of input feature smoothing with varying moving kernel window sizes was assessed to improve accuracy. The use of these smoothed features led to a substantial improvement in accuracy across all four areas examined, while a window size of 19 x 19 was chosen as the right balance between preserving fine details and minimizing speckle. Furthermore, adding a filter after clustering to remove areas smaller than 2 ha led to additional improvements in accuracy, especially in commission error. The results using the defined settings revealed F1 scores of 0.75-0.88, overall accuracy of 81%-94%, and omission/commission errors of 33%-16% and 14%-3%, respectively. Challenges were observed in regions characterized by a substantial share of agricultural areas, while terrain effects revealed no substantial effects on the results. The assumption that the SAR will be sensitive mainly to bigger structural changes was proved in the visual validation using high-resolution imagery. In addition, a Google Earth Engine toolbox "Sentinel-1 Burned Area Progression" was developed using the presented methodology and is freely available for the scientific community on GitHub.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10508 - Physical geography
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ISSN
1939-1404
e-ISSN
2151-1535
Volume of the periodical
17
Issue of the periodical within the volume
July
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
15612-15634
UT code for WoS article
001311230400018
EID of the result in the Scopus database
2-s2.0-85198719065