Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Miguel A. Belenguer-Plomer"'
Autor:
Mihai A. Tanase, Miguel A. Belenguer-Plomer, Ekhi Roteta, Aitor Bastarrika, James Wheeler, Ángel Fernández-Carrillo, Kevin Tansey, Werner Wiedemann, Peter Navratil, Sandra Lohberger, Florian Siegert, Emilio Chuvieco
Publikováno v:
Remote Sensing, Vol 12, Iss 2, p 334 (2020)
This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 × 100 km) in tropical and sub-tropical Africa. Depending on the site, the burned area was mappe
Externí odkaz:
https://doaj.org/article/b9d5f55a5d8247eeb58ef82622c2cd52
In this paper, we present an in-depth analysis of the use of convolutional neural networks (CNN), a deep learning method widely applied in remote sensing-based studies in recent years, for burned area (BA) mapping combining radar and optical datasets
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f603e6bb289c3f5047548e5ac8e8468
https://hdl.handle.net/11572/322983
https://hdl.handle.net/11572/322983
Publikováno v:
IGARSS
This study evaluated the relationship between mapping accuracy and computing time when detecting burned areas from Sentinel-1 C-band backscatter coefficient images processed at different pixel spacing (i.e., 20, 30, 40 and 50 m). The analysis was car
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f295a0cdbab6a6968de723ba6951411f
https://doi.org/10.1109/igarss39084.2020.9323400
https://doi.org/10.1109/igarss39084.2020.9323400
Autor:
Miguel A. Belenguer-Plomer, Ignacio Borlaf, Gheorghe Marin, Ovidiu Badea, Flaviu Popescu, Mihai A. Tanase
Publikováno v:
IGARSS
The aim of this study was to evaluate the utility of deep learning (DL) approaches to estimate forest growing stock volume from L-band SAR data over areas characterized by diverse species composition. For comparison, parametric models were also used.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d8684867269c9935e5e9442fc2a8b2b
https://doi.org/10.1109/igarss39084.2020.9324710
https://doi.org/10.1109/igarss39084.2020.9324710
Autor:
Angel Fernandez-Carrillo, Peter Navratil, Sandra Lohberger, Ekhi Roteta, Werner Wiedemann, Miguel A. Belenguer-Plomer, James Wheeler, Emilio Chuvieco, Mihai A. Tanase, Florian Siegert, Kevin Tansey, Aitor Bastarrika
Publikováno v:
Remote Sensing, Vol 12, Iss 2, p 334 (2020)
Remote Sensing; Volume 12; Issue 2; Pages: 334
Remote Sensing; Volume 12; Issue 2; Pages: 334
This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 × 100 km) in tropical and sub-tropical Africa. Depending on the site, the burned area was mappe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4031540cef28ef7b8796e1ee439f419c
https://www.mdpi.com/2072-4292/12/2/334
https://www.mdpi.com/2072-4292/12/2/334
Publikováno v:
Remote Sensing
Volume 11
Issue 22
Pages: 2661
Volume 11
Issue 22
Pages: 2661
Burned area algorithms from radar images are often based on temporal differences between pre- and post-fire backscatter values. However, such differences may occur long past the fire event, an effect known as temporal decorrelation. Improvements in r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5fe9d9805577d79b65cf9d4dd4be1bf
Publikováno v:
Active and Passive Microwave Remote Sensing for Environmental Monitoring III.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::614d5bd92d482d3e95b803ba48c22a48
https://doi.org/10.1117/12.2532832
https://doi.org/10.1117/12.2532832
Publikováno v:
Earth Resources and Environmental Remote Sensing/GIS Applications IX.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c13a2ae27e6016a694dee4657e6a8615
https://doi.org/10.1117/12.2325686
https://doi.org/10.1117/12.2325686
Publikováno v:
Active and Passive Microwave Remote Sensing for Environmental Monitoring II.
Fire is considered an essential climate variable (ECV) by the Global Climate Observing System (GCOS). Remote sensing is often used to detect the burned areas and subsequently estimate CO2 emissions from wildfires. Most burned area mapping approaches
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9bce5dfddf0fdb19f67f2b430b284126
https://doi.org/10.1117/12.2325650
https://doi.org/10.1117/12.2325650
Publikováno v:
Active and Passive Microwave Remote Sensing for Environmental Monitoring II.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3a15dd08facfa4cadb351d8d17b9e89
https://doi.org/10.1117/12.2325672
https://doi.org/10.1117/12.2325672