Discovery - University of Dundee - Online Publications

Library & Learning Centre

Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data

Standard

Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data : an assessment of predictions between regions. / Cutler, M. E. J.; Boyd, D. S.; Foody, G. M.; Vetrivel, Anand.

In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 70, 2012, p. 66-77.

Research output: Contribution to journalArticle

Harvard

Cutler, MEJ, Boyd, DS, Foody, GM & Vetrivel, A 2012, 'Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions' ISPRS Journal of Photogrammetry and Remote Sensing, vol 70, pp. 66-77., 10.1016/j.isprsjprs.2012.03.011

APA

Cutler, M. E. J., Boyd, D. S., Foody, G. M., & Vetrivel, A. (2012). Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 66-77. 10.1016/j.isprsjprs.2012.03.011

Vancouver

Cutler MEJ, Boyd DS, Foody GM, Vetrivel A. Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions. ISPRS Journal of Photogrammetry and Remote Sensing. 2012;70:66-77. Available from: 10.1016/j.isprsjprs.2012.03.011

Author

Cutler, M. E. J.; Boyd, D. S.; Foody, G. M.; Vetrivel, Anand / Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data : an assessment of predictions between regions.

In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 70, 2012, p. 66-77.

Research output: Contribution to journalArticle

Bibtex - Download

@article{bfb5dead88ab4dcbbce600a209e9ce45,
title = "Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: an assessment of predictions between regions",
keywords = "Biomass, SAR, Artificial neural network, Wavelets, Allometry",
author = "Cutler, {M. E. J.} and Boyd, {D. S.} and Foody, {G. M.} and Anand Vetrivel",
year = "2012",
doi = "10.1016/j.isprsjprs.2012.03.011",
volume = "70",
pages = "66--77",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
issn = "0924-2716",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data

T2 - an assessment of predictions between regions

A1 - Cutler,M. E. J.

A1 - Boyd,D. S.

A1 - Foody,G. M.

A1 - Vetrivel,Anand

AU - Cutler,M. E. J.

AU - Boyd,D. S.

AU - Foody,G. M.

AU - Vetrivel,Anand

PY - 2012

Y1 - 2012

N2 - Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r = 0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r = 0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). (C) 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

AB - Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r = 0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r = 0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). (C) 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

KW - Biomass

KW - SAR

KW - Artificial neural network

KW - Wavelets

KW - Allometry

U2 - 10.1016/j.isprsjprs.2012.03.011

DO - 10.1016/j.isprsjprs.2012.03.011

M1 - Article

JO - ISPRS Journal of Photogrammetry and Remote Sensing

JF - ISPRS Journal of Photogrammetry and Remote Sensing

SN - 0924-2716

VL - 70

SP - 66

EP - 77

ER -

Documents

Library & Learning Centre

Contact | Accessibility | Policy