NDLI: Improving space-time forest canopy LAI simulation by fusing forest growth model (3-PG) with remote sensing data

Content Provider	IEEE Xplore Digital Library
Author	Qiaoli Wu Jinling Song Jindi Wang
Copyright Year	2015
Description	Author affiliation: State Key Lab. of Remote Sensing Sci., Beijing Normal Univ. Beijing, Beijing, China (Qiaoli Wu; Jinling Song; Jindi Wang)
Abstract	Leaf area index (LAI) is an important biophysical variable indicating forest growth. A major challenge is to improve the LAI estimates for large forest-covered areas. One way to obtain LAI value is using current LAI products. Current LAI products contain many uncertainties and need improvement. This paper aims to improve forest LAI estimates by combining satellite reflectance derived LAI with forest growth model (physiological principals predicting growth, 3-PG) estimates of LAI. 3-PG can give an accurate estimation of forest inter-annual growing trend, while remote sensing data can provide long time series observation of seasonal variations of forest phenology. We applied this method to Chinese fir forest in China, where the detailed data are available. The combined results were more accurate than either the satellite or the 3-PG estimates. We conclude that we can improve the space-time forest canopy LAI estimates by combining forest growth model with satellite imagery.
Starting Page	1933
Ending Page	1936
File Size	807686
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326173
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Satellites Data models MODIS Remote sensing Time series analysis Physiology remote sensing (RS) Forest growth leaf area index (LAI) physiological principals predicting growth (3-PG) model
Content Type	Text
Resource Type	Article

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