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Predicting forest structural attributes using ancillary data and ASTER satellite data

delete2010-02-01
delete27
PRE
AI
M
Michael Gebreslasie *
F
Fethi Ahmed
J
J. A. N. van Aardt
DOI:10.1016/j.jag.2009.11.006delete
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摘要

摘要

En 中文
This Study assessed the Suitability of both visible and shortwave infrared ASTER data and vegetation indices for estimating forest structural attributes of Eucalyptus species in the southern KwaZulu Natal. South Africa The empirical relationships between forest structural attributes and ASTER data were derived using stepwise Multiple regression analysis. Modified Soil Adjusted Vegetation Index (MSVI) and band 3 were selected for analysis as it showed best relationships with forest structural attributes The ancillary data Such as age and site index were also Included in the analysis Although the results Of this study have indicated statistically significant relationships between the forest Structural attributes and the ASTER data in the plantation forests stands with adjusted R(2)-Values lot volume, basal at ea (BA), stein per hectare (SPHA). and tree height of 0 51, 0 67. 0 65, and 0 52, respectively. but these results are hot Suitable for operational purpose in a forest company However. the structural forest attribute predictions were markedly improved after incorporating age and site index as predictor variable R(2)-values for the stands increased by 42%, 20 2%, 16 8%, and 42 2% for Volume, basal area, SPHA, and tree height, respectively These results Imply that ASTER satellite data alone are not applicable to forest Structural attribute estimation, however, ASTER data can provide useful Information if It IS Used in conjunction with age and site index data for forest Structural attribute estimation in plantation forests (C) 2009 Elsevier B V All rights reserved
Keyword:
ASTER dataset
Spectral vegetation indices
Forest structural attributes
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期刊

International Journal of Applied Earth Observation and Geoinformation 封面图
International Journal of Applied Earth Observation and Geoinformation
IF:
8.6
论文数:
5.3K
被引数:
2.4W

机构

U
university of kwazulu natal
学者数:
1.0W
论文数: 9.0K
被引数: 11
R
Rochester Institute of Technology
学者数:
3.8K
论文数: 3.3K
被引数: 45
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