MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY

Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23 cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g.,...

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Main Authors: R. J. L. Argamosa, A. C. Blanco, A. B. Baloloy, C. G. Candido, J. B. L. C. Dumalag, L. L. C. Dimapilis, E. C. Paringit
Format: Article
Language:English
Published: Copernicus Publications 2018-04-01
Series:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-3/13/2018/isprs-annals-IV-3-13-2018.pdf
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spelling doaj-9816466c8c024d20839d666ac50cd8922020-11-25T01:46:55ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502018-04-01IV-3132010.5194/isprs-annals-IV-3-13-2018MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERYR. J. L. Argamosa0A. C. Blanco1A. C. Blanco2A. B. Baloloy3C. G. Candido4J. B. L. C. Dumalag5L. L. C. Dimapilis6E. C. Paringit7Training Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1001, PhilippinesTraining Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1001, PhilippinesDepartment of Geodetic Engineering, University of the Philippines, Diliman, 1001, PhilippinesTraining Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1001, PhilippinesTraining Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1001, PhilippinesTraining Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1001, PhilippinesTraining Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1001, PhilippinesDepartment of Geodetic Engineering, University of the Philippines, Diliman, 1001, PhilippinesMany studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23&thinsp;cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75&thinsp;cm to 7.5&thinsp;cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a&minus;c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r<sup>2</sup> of 0.79 and an RMSE of 0.44&thinsp;Mg using only four features, namely, &sigma;<sup>&deg;</sup><sub><i>VH</i></sub> GLCM variance, &sigma;<sup>&deg;</sup><sub><i>VH</i></sub> GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-3/13/2018/isprs-annals-IV-3-13-2018.pdf
collection DOAJ
language English
format Article
sources DOAJ
author R. J. L. Argamosa
A. C. Blanco
A. C. Blanco
A. B. Baloloy
C. G. Candido
J. B. L. C. Dumalag
L. L. C. Dimapilis
E. C. Paringit
spellingShingle R. J. L. Argamosa
A. C. Blanco
A. C. Blanco
A. B. Baloloy
C. G. Candido
J. B. L. C. Dumalag
L. L. C. Dimapilis
E. C. Paringit
MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet R. J. L. Argamosa
A. C. Blanco
A. C. Blanco
A. B. Baloloy
C. G. Candido
J. B. L. C. Dumalag
L. L. C. Dimapilis
E. C. Paringit
author_sort R. J. L. Argamosa
title MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY
title_short MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY
title_full MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY
title_fullStr MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY
title_full_unstemmed MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY
title_sort modelling above ground biomass of mangrove forest using sentinel-1 imagery
publisher Copernicus Publications
series ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 2194-9042
2194-9050
publishDate 2018-04-01
description Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23&thinsp;cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75&thinsp;cm to 7.5&thinsp;cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a&minus;c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r<sup>2</sup> of 0.79 and an RMSE of 0.44&thinsp;Mg using only four features, namely, &sigma;<sup>&deg;</sup><sub><i>VH</i></sub> GLCM variance, &sigma;<sup>&deg;</sup><sub><i>VH</i></sub> GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.
url https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-3/13/2018/isprs-annals-IV-3-13-2018.pdf
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