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Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions

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Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. / Foody, Giles M.; Boyd, Doreen S.; Cutler, Mark E. J.

In: Remote Sensing of Environment, Vol. 85, No. 4, 2003, p. 463-474.

Research output: Contribution to journalArticle

Harvard

Foody, GM, Boyd, DS & Cutler, MEJ 2003, 'Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions' Remote Sensing of Environment, vol 85, no. 4, pp. 463-474.

APA

Foody, G. M., Boyd, D. S., & Cutler, M. E. J. (2003). Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 85(4), 463-474doi: 10.1016/S0034-4257(03)00039-7

Vancouver

Foody GM, Boyd DS, Cutler MEJ. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment. 2003;85(4):463-474.

Author

Foody, Giles M.; Boyd, Doreen S.; Cutler, Mark E. J. / Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions.

In: Remote Sensing of Environment, Vol. 85, No. 4, 2003, p. 463-474.

Research output: Contribution to journalArticle

Bibtex - Download

@article{bd2075ed85f142e09ecb644a6e8b1487,
title = "Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions",
author = "Foody, {Giles M.} and Boyd, {Doreen S.} and Cutler, {Mark E. J.}",
note = "dc.publisher: Elsevier",
year = "2003",
volume = "85",
number = "4",
pages = "463--474",
journal = "Remote Sensing of Environment",
issn = "0034-4257",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions

A1 - Foody,Giles M.

A1 - Boyd,Doreen S.

A1 - Cutler,Mark E. J.

AU - Foody,Giles M.

AU - Boyd,Doreen S.

AU - Cutler,Mark E. J.

PY - 2003

Y1 - 2003

N2 - The full realization of the potential of remote sensing as a source of environmental information requires an ability to generalize in space and time. Here, the ability to generalize in space was investigated through an analysis of the transferability of predictive relations for the estimation of tropical forest biomass from Landsat TM data between sites in Brazil, Malaysia and Thailand. The data sets for each test site were acquired and processed in a similar fashion to facilitate the analyses. Three types of predictive relation, based on vegetation indices, multiple regression and feedforward neural networks, were developed for biomass estimation at each site. For each site, the strongest relationships between the biomass predicted and that measured from field survey was obtained with a neural network developed specifically for the site (r>0.71, significant at the 99% level of confidence). However, with each type of approach problems in transferring a relation to another site were observed. In particular, it was apparent that the accuracy of prediction, as indicated by the correlation coefficient between predicted and measured biomass, declined when a relation was transferred to a site other than that upon which it was developed. Part of this problem lies with the observed variation in the relative contribution of the different spectral wavebands to predictive relations for biomass estimation between sites. It was, for example, apparent that the spectral composition of the vegetation indices most strongly related to biomass differed greatly between the sites. Consequently, the relationship between predicted and measured biomass derived from vegetation indices differed markedly in both strength and direction between sites. Although the incorporation of test site location information into an analysis resulted in an increase in the strength of the relationship between predicted and actual biomass, considerable further research is required on the problems associated with transferring predictive relations.

AB - The full realization of the potential of remote sensing as a source of environmental information requires an ability to generalize in space and time. Here, the ability to generalize in space was investigated through an analysis of the transferability of predictive relations for the estimation of tropical forest biomass from Landsat TM data between sites in Brazil, Malaysia and Thailand. The data sets for each test site were acquired and processed in a similar fashion to facilitate the analyses. Three types of predictive relation, based on vegetation indices, multiple regression and feedforward neural networks, were developed for biomass estimation at each site. For each site, the strongest relationships between the biomass predicted and that measured from field survey was obtained with a neural network developed specifically for the site (r>0.71, significant at the 99% level of confidence). However, with each type of approach problems in transferring a relation to another site were observed. In particular, it was apparent that the accuracy of prediction, as indicated by the correlation coefficient between predicted and measured biomass, declined when a relation was transferred to a site other than that upon which it was developed. Part of this problem lies with the observed variation in the relative contribution of the different spectral wavebands to predictive relations for biomass estimation between sites. It was, for example, apparent that the spectral composition of the vegetation indices most strongly related to biomass differed greatly between the sites. Consequently, the relationship between predicted and measured biomass derived from vegetation indices differed markedly in both strength and direction between sites. Although the incorporation of test site location information into an analysis resulted in an increase in the strength of the relationship between predicted and actual biomass, considerable further research is required on the problems associated with transferring predictive relations.

KW - Biomass

KW - Landsat TM data

KW - Remote sensing

U2 - 10.1016/S0034-4257(03)00039-7

DO - 10.1016/S0034-4257(03)00039-7

M1 - Article

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

IS - 4

VL - 85

SP - 463

EP - 474

ER -

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