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6 March 2014 Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons
Dengqiu Li, Weimin Ju, Wenyi Fan, Zhujun Gu
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Journal of Applied Remote Sensing, Vol. 8, Issue 1, 083670 (March 2014). https://doi.org/10.1117/1.JRS.8.083670
Abstract
This study investigated the ability of Landsat Enhanced Thematic Mapper Plus data acquired in leaf-on and leaf-off seasons to estimate stand age of Larix gmelinii and Betula platyphylla in northeast China. The relationships of six band reflectances, nine vegetation indices, and six texture measures with stand age were examined. Linear and multivariable regression models and multilayer perceptron neural network (MLP NN) were employed to estimate forest age based on these variables. The results indicate that reflectance in short-wave infrared bands and wetness are more significantly correlated with stand age in the leaf-on image, while reflectance in blue and green bands and greenness are more sensitive to stand age in leaf-off image. The MLP NN model can be effectively used to retrieve the stand age; the highest coefficient of determination and minimum root mean square error values of retrieved age are 0.47 and 21.3 years for Larix gmelinii, and 0.60 and 10.1 years for Betula platyphylla, respectively. The predicted age errors increased significantly when stand ages were <100 and <50 years for Larix gmelinii and Betula platyphylla, respectively. Remote sensing data acquired in the leaf-on season is more suitable for estimating forest age than that acquired in the leaf-off season over the study area.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Dengqiu Li, Weimin Ju, Wenyi Fan, and Zhujun Gu "Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons," Journal of Applied Remote Sensing 8(1), 083670 (6 March 2014). https://doi.org/10.1117/1.JRS.8.083670
Published: 6 March 2014
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Cited by 10 scholarly publications.
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KEYWORDS
Reflectivity
Vegetation
Remote sensing
Data acquisition
Data modeling
Earth observing sensors
Landsat
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