Testing ASTER and Sentinel-2 MSI Images to Discriminate Igneous and Metamorphic Rock Units in the Chadormalu Paleocrater, Central Iran

2021 ◽  
pp. 1-25
Author(s):  
Arsia Moghtaderi ◽  
Farid Moore ◽  
Hojatollah Ranjbar
Keyword(s):  
Metamorphic Rock ◽  
Central Iran ◽  
Sentinel 2

Estimación del Factor C de la RUSLE en la microcuenca del rio Lodana, Ecuador, usando imágenes del satélite Sentinel 2

2020 ◽  
Vol ve2020 (2) ◽  
pp. 69-75
Author(s):  
Ángel Claudio Ruiz Vélez ◽  
◽  
Henry Antonio Pacheco Gil ◽  
Keyword(s):  
Del Rio ◽  
Factor C ◽  
Sentinel 2

LATE EDIACARAN DIVERSITY AND PALEOECOLOGY FROM CENTRAL IRAN

2020 ◽  
Author(s):  
Marc Laflamme ◽  
◽  
Seyed Hamid Vaziri ◽  
Mahmoud Reza Majidifard ◽  
Simon Darroch
Keyword(s):  
Central Iran

Spatiotemporal Response of Rangeland NPP to Drought in Central Iran based on SPDI Index

2020 ◽  
Vol 13 (6) ◽  
pp. 694-707
Author(s):  
Leila Yaghmaei ◽  
Saeed Soltani Koupaei ◽  
Reza Jafari
Keyword(s):  
Central Iran ◽  
Spatiotemporal Response

Volatile constituents analysis of Nepeta cataria from central Iran

2009 ◽  
Vol 45 (6) ◽  
pp. 913-915 ◽  
Author(s):  
Javad Safaei-Ghomi ◽  
Zahra Djafari-Bidgoli ◽  
Hossein Batooli
Keyword(s):  
Central Iran ◽  
Volatile Constituents ◽  
Nepeta Cataria

Post-Fire Assessment of Burned Areas with Landsat-8 and Sentinel-2 Imagery Together with Modis and Viirs Active Fire Products

2020 ◽  
Author(s):  
Cesario Vincenzo Angelino ◽  
Luca Cicala ◽  
Sara Parrilli ◽  
Nicomino Fiscante ◽  
Silvia Liberata Ullo
Keyword(s):  
Landsat 8 ◽  
Burned Areas ◽  
Active Fire ◽  
Sentinel 2

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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