Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model

  • 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

Received date: 2012-03-15

  Revised date: 2012-05-03

  Online published: 2012-06-25


Global leaf area index (LAI) products such as MODIS LAI product have relatively low spatial resolution (250 m-7 km) and thus can not meet the needs of high spatial resolution remote sensing applications. It is necessary to explore the feasibility of the algorithm based on physical model for LAI retrieval using high spatial resolution remote sensing imagery. This study utilizes the algorithm based on 4-scale model to retrieve LAI in planted forest from TM imagery. The bidirectional reflectance distribution function (BRDF) and clumping representation at canopy scale are both considered in the algorithm. A validation study is conducted with in-situ measurements of LAI in planted forest from Zhangye City, Gansu Province. For comparison, the empirical model using NDVI as predicted variable is also considered for LAI estimation. The results show that better fit was found between the LAI produced by the algorithm based on 4-scale model and measured LAI (R2=0.67,RMSE=0.50) than that between LAI predicted by NDVI and measured LAI (R2=0.59,RMSE=0.67). The accuracy of the algorithm based on 4-scale model is evidently better than that of empirical model when LAI>2. Better resistance to saturation limits of vegetation index is observed for the algorithm. Moreover, the sensitivity analysis of inversed LAI to band reflectance is carried out. For red band, LAI produced from the algorithm is more influenced by the decreasing reflectance than by the increasing condition. And LAI was more sensitive to reflectance at red band (ρred) than that at near infrared band (ρnir), with uncertainty value of reflectance range from -10% to -30%. This study proved the effectiveness of the algorithm based on 4-scale model in LAI estimation from TM imagery in planted forest and will be helpful in further development of physical models for high spatial resolution LAI retrieval.

Cite this article

CHEN Hanyue, HUANG Wenjiang, NIU Zheng, GAO Shuai . Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model[J]. Journal of Geo-information Science, 2012 , 14(3) : 358 -365 . DOI: 10.3724/SP.J.1047.2012.00358


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