Forest canopy growth dynamic modeling based on remote sensing prodcuts and meteorological data in Daxing'anling of Northeast China

Qiaoli Wu; Jinling Song; Jindi Wang; Zhiqiang Xiao

doi:10.1117/12.2069111

8 November 2014 Forest canopy growth dynamic modeling based on remote sensing prodcuts and meteorological data in Daxing'anling of Northeast China

Qiaoli Wu, Jinling Song, Jindi Wang, Zhiqiang Xiao

Author Affiliations +

Proceedings Volume 9260, Land Surface Remote Sensing II; 92601P (2014) https://doi.org/10.1117/12.2069111
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

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.

Citation Download Citation

Qiaoli Wu, Jinling Song, Jindi Wang, and Zhiqiang Xiao "Forest canopy growth dynamic modeling based on remote sensing prodcuts and meteorological data in Daxing'anling of Northeast China", Proc. SPIE 9260, Land Surface Remote Sensing II, 92601P (8 November 2014); https://doi.org/10.1117/12.2069111

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