Leaf Area Index (LAI) is an important biophysical variable for vegetation. Compared with vegetation indexes like NDVI
and EVI, LAI is more capable of monitoring forest canopy growth quantitatively. GLASS LAI is a spatially complete
and temporally continuous product derived from AVHRR and MODIS reflectance data. In this paper, we present the
approach to build dynamic LAI growth models for young and mature Larix gmelinii forest in north Daxing’anling in
Inner Mongolia of China using the Dynamic Harmonic Regression (DHR) model and Double Logistic (D-L) model
respectively, based on the time series extracted from multi-temporal GLASS LAI data. Meanwhile we used the dynamic
threshold method to attract the key phenological phases of Larix gmelinii forest from the simulated time series. Then,
through the relationship analysis between phenological phases and the meteorological factors, we found that the annual
peak LAI and the annual maximum temperature have a good correlation coefficient. The results indicate this forest
canopy growth dynamic model to be very effective in predicting forest canopy LAI growth and extracting forest canopy
LAI growth dynamic.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.