Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data

Systematic Sentinel-1 acquisitions provide an unprecedented stream of SAR data which allows to describe forest temporal dynamics in detail, a powerful tool for phenological studies and forest type classification. Several studies have explored the temporal variation of backscatter intensity in this c...

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Authors: Borlaf Mena, Ignacio, Garcia-Duro, Juan, Santoro, Maurizio, Villard, Ludovic, Badea, Ov., Tanase, Mihai Andrei|||0000-0002-0045-2299
Format: article
Publication Date:2023
Country:España
Institution:Universidad de Alcalá (UAH)
Repository:e_Buah Biblioteca Digital Universidad de Alcalá
Language:English
OAI Identifier:oai:ebuah.uah.es:10017/59717
Online Access:http://hdl.handle.net/10017/59717
https://dx.doi.org/10.1016/j.rse.2023.113728
Access Level:Open access
Keyword:SAR
Sentinel-1
Radiometry
Forest type
Classification
Geografía
Geography
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spelling Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 dataBorlaf Mena, IgnacioGarcia-Duro, JuanSantoro, MaurizioVillard, LudovicBadea, Ov.Tanase, Mihai Andrei|||0000-0002-0045-2299SARSentinel-1RadiometryForest typeClassificationGeografíaGeographySystematic Sentinel-1 acquisitions provide an unprecedented stream of SAR data which allows to describe forest temporal dynamics in detail, a powerful tool for phenological studies and forest type classification. Several studies have explored the temporal variation of backscatter intensity in this context, but none considered that scattering directionality of canopies may vary. Said directionality is related to target-sensor geometry (incidence angle), forest height, and optical depth, associated with leaf dynamics. This study explicitly models backscatter dependance on incidence angle by fitting a regression model for each Sentinel-1 image and forest type. Residuals are accumulated across the time series and used to classify pixels into the most likely forest type using the smallest accumulated residual. This modelling and classification strategy has been applied over a North-South transect across the Carpathian Mountains, including forests with different physiognomies, from deciduous broadleaf forest, to mixed broadleaf-needleleaf and pure perennial needleleaf forests. These forests were classified with increasing detail, assessing the results against in-situ forest stand data and satellite-based land cover classification products (Copernicus Forest type layer). The accuracy of our classification was K > 0.8, OA > 90% when separating broadleaf from needleleaf forest types. The accuracy decreased (K > 0.6, OA > 79%) when also separating mixed forest types. Our results suggest that incorporating directional effects into classification models can improve SAR-based forest classification of temperate forest over mountainous terrain. Furthermore, models fitted between backscatter and incidence angle provide an estimate of n, a parameter related to optical depth that has been shown to vary with leaf dynamics. n could be used to improve image normalization in studies aiming at the estimation of biomass, or to aid the estimation of fast-changing parameters such as leaf area index or leaf moisture content.Romanian National Agency for Scientific Research and Innovation AuthorityComunidad de Madrid20232023-10-0120232023-10-0120252025-10-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/59717https://dx.doi.org/10.1016/j.rse.2023.113728reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengANCS Not available P_37_651%2F105058 Prototyping an Earth-Observation based monitoring and forecasting system for the Romanian forestsComunidad de Madrid http://dx.doi.org/10.13039/100012818 Not available CM%2FJIN%2F2019-011 Synthetic Aperture Radar (SAR) enabled Analysis Ready Data (ARD) cubes for efficient monitoring of agricultural and forested landscapesopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/597172026-06-18T11:13:07Z
dc.title.none.fl_str_mv Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
title Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
spellingShingle Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
Borlaf Mena, Ignacio
SAR
Sentinel-1
Radiometry
Forest type
Classification
Geografía
Geography
title_short Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
title_full Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
title_fullStr Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
title_full_unstemmed Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
title_sort Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
dc.creator.none.fl_str_mv Borlaf Mena, Ignacio
Garcia-Duro, Juan
Santoro, Maurizio
Villard, Ludovic
Badea, Ov.
Tanase, Mihai Andrei|||0000-0002-0045-2299
author Borlaf Mena, Ignacio
author_facet Borlaf Mena, Ignacio
Garcia-Duro, Juan
Santoro, Maurizio
Villard, Ludovic
Badea, Ov.
Tanase, Mihai Andrei|||0000-0002-0045-2299
author_role author
author2 Garcia-Duro, Juan
Santoro, Maurizio
Villard, Ludovic
Badea, Ov.
Tanase, Mihai Andrei|||0000-0002-0045-2299
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv SAR
Sentinel-1
Radiometry
Forest type
Classification
Geografía
Geography
topic SAR
Sentinel-1
Radiometry
Forest type
Classification
Geografía
Geography
description Systematic Sentinel-1 acquisitions provide an unprecedented stream of SAR data which allows to describe forest temporal dynamics in detail, a powerful tool for phenological studies and forest type classification. Several studies have explored the temporal variation of backscatter intensity in this context, but none considered that scattering directionality of canopies may vary. Said directionality is related to target-sensor geometry (incidence angle), forest height, and optical depth, associated with leaf dynamics. This study explicitly models backscatter dependance on incidence angle by fitting a regression model for each Sentinel-1 image and forest type. Residuals are accumulated across the time series and used to classify pixels into the most likely forest type using the smallest accumulated residual. This modelling and classification strategy has been applied over a North-South transect across the Carpathian Mountains, including forests with different physiognomies, from deciduous broadleaf forest, to mixed broadleaf-needleleaf and pure perennial needleleaf forests. These forests were classified with increasing detail, assessing the results against in-situ forest stand data and satellite-based land cover classification products (Copernicus Forest type layer). The accuracy of our classification was K > 0.8, OA > 90% when separating broadleaf from needleleaf forest types. The accuracy decreased (K > 0.6, OA > 79%) when also separating mixed forest types. Our results suggest that incorporating directional effects into classification models can improve SAR-based forest classification of temperate forest over mountainous terrain. Furthermore, models fitted between backscatter and incidence angle provide an estimate of n, a parameter related to optical depth that has been shown to vary with leaf dynamics. n could be used to improve image normalization in studies aiming at the estimation of biomass, or to aid the estimation of fast-changing parameters such as leaf area index or leaf moisture content.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-10-01
2023
2023-10-01
2025
2025-10-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/59717
https://dx.doi.org/10.1016/j.rse.2023.113728
url http://hdl.handle.net/10017/59717
https://dx.doi.org/10.1016/j.rse.2023.113728
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv ANCS Not available P_37_651%2F105058 Prototyping an Earth-Observation based monitoring and forecasting system for the Romanian forests
Comunidad de Madrid http://dx.doi.org/10.13039/100012818 Not available CM%2FJIN%2F2019-011 Synthetic Aperture Radar (SAR) enabled Analysis Ready Data (ARD) cubes for efficient monitoring of agricultural and forested landscapes
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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