Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data
Authors
Borlaf Mena, Ignacio; Garcia-Duro , Juan; Santoro , Maurizio; Villard , Ludovic; Badea , Ov.; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/59717DOI: 10.1016/j.rse.2023.113728
ISSN: 0034-4257
Date
2023-10-01Embargo end date
2025-10-01Funders
Romanian National Agency for Scientific Research and Innovation Authority
Comunidad de Madrid
Bibliographic citation
Remote Sensing of Environment, 2023, v. 296
Keywords
SAR
Sentinel-1
Radiometry
Forest type
Classification
Project
info:eu-repo/grantAgreement/ANCS//P_37_651%2F105058/RO/Prototyping an Earth-Observation based monitoring and forecasting system for the Romanian forests/EO-ROFORMON
info:eu-repo/grantAgreement/CAM// CM%2FJIN%2F2019-011/ES/Synthetic Aperture Radar (SAR) enabled Analysis Ready Data (ARD) cubes for efficient monitoring of agricultural and forested landscapes/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2023 Elsevier
Access rights
info:eu-repo/semantics/embargoedAccess
Abstract
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.
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seasonality_borlaf_RSE_2023.pdf | 4.261Mb |
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seasonality_borlaf_RSE_2023.pdf | 4.261Mb |
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