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.
Highlights
The carbon flux from the atmosphere to forests plays an important role in retarding the increase in atmospheric CO2 concentration and climate change.[1]
The present study shows the distinct trajectories of reflectance, vegetation indexes, and texture measures changing with the forest age, and the potential of mapping forest stand age over a large area with a snow-covered Landsat image
We used Landsat ETMþ imageries acquired in different seasons to estimate the stand age of two typical forest species in northeastern China
Summary
The carbon flux from the atmosphere to forests plays an important role in retarding the increase in atmospheric CO2 concentration and climate change.[1] Forest age, which is related to time since disturbance or plantation, is a dominant determinant for the long-term trend of carbon exchange between forests and the atmosphere due to the age-related change in forest growth rate.[2] This phenomenon is caused by the changes in forest fundamental structure, which influences the ways of forest space occupied and carbon allocated.[3] forest age is a crucial variable for quantifying carbon fluxes between the atmosphere and forest ecosystems.[4] It plays an important role in estimating forest biomass with remote sensing data.[5] Forest age maps with high quality are urgently required for reliably quantifying regional and global forest carbon budget
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
