UNSUPERVISED BURNED AREA MAPPING IN GREECE: INVESTIGATING THE IMPACT OF PRECIPITATION, PRE- AND POST-PROCESSING OF SENTINEL-1 DATA IN GOOGLE EARTH ENGINE

被引：2

作者：

Paluba, Daniel ^{[1
]}

Papale, Lorenzo Giuliano ^{[2
]}

Perivolioti, Triantafyllia-Maria ^{[3
,5
]}

Stych, Premysl ^{[1
]}

Lastovicka, Josef ^{[1
]}

Kalaitzis, Panagiotis ^{[4
]}

Karadimou, Georgia ^{[3
,5
]}

Papageorgiou, Elena ^{[3
,5
]}

Mouratidis, Antonios ^{[3
,5
]}

机构：

[1] Charles Univ Prague, Dept Appl Geoinformat & Cartog, Fac Sci, Prague, Czech Republic

[2] Tor Vergata Univ Rome, Dept Civil & Comp Sci Engn, Rome, Italy

[3] Aristotle Univ Thessaloniki, Fac Sci, Sch Geol, Thessaloniki, Greece

[4] Univ Aegean, Dept Geog, Mitilini, Greece

[5] Aristotle Univ Thessaloniki, CIRI AUTH, Ctr Interdisciplinary Res & Innovat, Balkan Ctr, Thessaloniki, Greece

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

Sentinel-1; SAR; Google Earth Engine; burned area mapping; unsupervised clustering;

D O I：

10.1109/IGARSS52108.2023.10283130

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Wildfires are one of the most significant threats to ecosystems and are increasing in frequency globally. The aim of this study is to monitor the evolution of selected wildfires in Greece that occurred during August 2021 using Sentinel-1 SAR data and unsupervised k-means clustering in Google Earth Engine. First, changes in time series after the start of the fire and the influence of precipitation were investigated. In this study, the influence of different speckle filters and post-classification filters on clustering results was tested. The difference Normalized Burn Ratio Index (dNBR) derived from Sentinel-2 data was used as a validation dataset to assess accuracy using the F1-score, overall accuracy, omission and commission error. The best achieved F1-scores were higher than 0.70 with omission error lower than 35% in all selected areas, where the Lee speckle filter with an 11x11 kernel window size and a 2 ha post-classification filter performed the best.

引用

页码：2520 / 2523

页数：4

共 15 条

[1] Tracking Burned Area Progression in an Unsupervised Manner Using Sentinel-1 SAR Data in Google Earth Engine
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Lastovicka, Josef
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Kalaitzis, Panagiotis
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[2] Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine
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[7] Unsupervised Burned Area Mapping in a Protected Natural Site. An Approach Using SAR Sentinel-1 Data and K-mean Algorithm
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[9] UNSUPERVISED GEOSPATIAL DOMAIN ADAPTATION FOR LARGE-SCALE WILDFIRE BURNED AREA MAPPING USING SENTINEL-2 MSI AND SENTINEL-1 SAR DATA
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[10] Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine
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