Unsupervised Burned Area Mapping in a Protected Natural Site. An Approach Using SAR Sentinel-1 Data and K-mean Algorithm

Abstract

This paper is focused on investigating the capabilities of SAR S-1 sensors for burned area mapping. To this aim, we analyzed S-1 data focusing on a fire that occurred on August 10 th, 2017, in a protected natural site. An unsupervised classification, using a k-mean machine learning algorithm, was carried out, and the choice of an adequate number of clusters was guided by the calculation of the silhouette score. The ΔNBR index calculated from optical S-2 based images was used to evaluate the burned area delimitation accuracy. The fire covered around 38.51 km 2 and also affected areas outside the boundaries of the reserve. S-1 based outputs successfully matched the S-2 burnt mapping.

Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy

“…Using combined data, as herein proposed, is possible to overcome the limits related to a single type of sensor (optical), in favor of a combined use with other sensors such as synthetic aperture radar (SAR) [ 93 , 138 ], whose data are implemented in the GEE datasets. Moreover, the setup of a user-friendly interface makes GEE also very attractive for non EO experts as site manager and end user in charge of cultural sites for managing risks.…”

On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas

“…Today, the availability of big and open data, such as datasets available from NASA (MODIS, Landsat satellites [1][2][3][4][5][6] etc.) and ESA (Sentinels [7][8][9][10][11][12][13][14][15][16]), and the developments in information and communication technologies (ICT), including cloud-based resources, have supported the massive increase of the use of satellite for change detection and analysis, based on various methods and techniques [13,[17][18][19][20][21][22][23][24][25][26] including artificial intelligence (AI) and, therefore, machine learning (ML) and deep learning (DL) [11,[27][28][29][30][31][32][33][34][35][36].…”