Academic Journal
MINDED-FBA: An Automatic Remote Sensing Tool for the Estimation of Flooded and Burned Areas.
| Title: | MINDED-FBA: An Automatic Remote Sensing Tool for the Estimation of Flooded and Burned Areas. |
|---|---|
| Authors: | Oliveira, Eduardo R., Disperati, Leonardo, Alves, Fátima L. |
| Source: | Remote Sensing; Feb2023, Vol. 15 Issue 3, p724, 28p |
| Abstract: | This paper presents the MINDED-FBA, a remote-sensing-based tool for the determination of both flooded and burned areas. The tool, freely distributed as a QGIS plugin, consists of an adaptation and development of the previously published Multi Index Image Differencing methods (MINDED and MINDED-BA). The MINDED-FBA allows the integration and combination of a wider diversity of satellite sensor datasets, now including the synthetic aperture radar (SAR), in addition to optical multispectral data. The performance of the tool is evaluated for six case studies located in Portugal, Australia, Pakistan, Italy, and the USA. The case studies were chosen for representing a wide range of conditions, such as type of hazardous event (i.e., flooding or fire), scale of application (i.e., local or regional), site specificities (e.g., climatic conditions, morphology), and available satellite data (optical multispectral and SAR). The results are compared in respect to reference delineation datasets (mostly from the Copernicus EMS). The application of the MINDED-FBA tool with SAR data is particularly effective to delineate flooding, while optical multispectral data resulted in the best performances for burned areas. Nonetheless, the combination of both types of remote sensing data (data fusion approach) also provides high correlations with the available reference datasets. The MINDED-FBA tool could represent a new near-real-time solution, capable of supporting emergency response measures. [ABSTRACT FROM AUTHOR] |
| Subject Terms: | MULTISPECTRAL imaging, SYNTHETIC aperture radar, REMOTE sensing, MULTISENSOR data fusion |
| Geographic Terms: | PAKISTAN, PORTUGAL |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| ISSN: | 20724292 |
| DOI: | 10.3390/rs15030724 |
| Database: | Complementary Index |
Availability