Journal of Geo-information Science >
Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model
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.
Key words： 4-scale model; TM; leaf area index; remote sensing; planted forest
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
[1] Aber J D, Reich P B, Goulden M L. Extrapolating leaf CO2 exchange to the canopy: A generalized model of forest photosynthesis compared with measurements by eddy correlation[J]. Oecologia, 1996(106): 257-265.
[2] Pocewicz A, Vierling L A, Lentile L B, et al. View angle effects on relationships between MISR vegetation indices and leaf area index in a recently burned ponderosa pine forest[J]. Remote Sensing of Environment, 2007(107): 322-333.
[3] 万华伟,王锦地,梁顺林,等. 联合MODIS与MISR遥感数据估算叶面积指数[J]. 光谱学与光谱分析, 2009, 11: 3106-3111.
[4] 江东,付晶莹,黄耀欢,等. 地表环境参数时间序列重构的方法与应用分析[J]. 地球信息科学学报, 2011, 13(4): 439-446.
[5] 靳华安,王锦地,肖志强,等. 遥感反演时间序列叶面积指数的集合卡尔曼平滑算法[J]. 光谱学与光谱分析, 2011, 11: 2485-2490.
[6] 方秀琴,张万昌. 叶面积指数(LAI)的遥感定量方法综述[J]. 国土资源遥感, 2003, 3: 58-62.
[7] Chen J M, Pavlic G, Brown L, et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements[J]. Remote Sensing of Environment, 2002(80): 165-184.
[8] Baret F, Hagolle O, Geiger B, et al. LAI, Fapar and fcover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm[J]. Remote Sensing of Environment, 2007(110): 275-286.
[9] Chen J M, Leblanc S G. A four-scale bidirectional reflectance model based on canopy architecture[J]. IEEE Transactions on Geoscience and Remote Sensing of Environment, 1997(35): 1316-1337.
[10] Deng F, Chen J M, Plummer S, et al. Algorithm for global leaf area index retrieval using satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing of Environment, 2006, 44(8): 2219-2229.
[11] Pisek J, Chen J M, Deng F. Assessment of global leaf area index product from SPOT-4 vegetation data over selected sites in Canada[J]. Canadian Journal of Remote Sensing, 2007, 33(4): 341-358.
[12] Liu R, Chen J M, Liu J, et al. Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research[J]. Journal of Environmental Management, 2007(85): 649-658.
[13] Li X F, Ju W M, Zhou Y L, et al. Retrieving leaf area index of forests in red soil hilly region using remote sensing data[J]. Proceedings of the Second International Conference on Earth Observation for Global Changes, 2009.
[14] 李淑敏,李红,孙丹峰,等. PROSAIL冠层光谱模型遥感反演区域叶面积指数[J]. 光谱学与光谱分析, 2009(10): 2725-2729.
[15] 蔡博峰,绍霞. 基于PROSPECT+SAIL模型的遥感叶面积指数反演[J]. 国土资源遥感, 2007, 2: 39-43.
[16] 刘洋,刘荣高,刘斯亮,等. 基于物理模型训练神经网络的作物叶面积指数遥感反演研究[J]. 地球信息科学学报, 2010, 12(3): 426-435.
[17] Chen J M and Cihlar J. A hotspot function in a simple bidirectional reflectance model for satellite applications[J]. Journal of Geophysical Research, 1997(102):25907-25913.
[18] 李显风,居为民,陈姝,等. 地表覆盖分类数据对区域森林叶面积指数反演的影响[J]. 遥感学报, 2010, 14(5): 974-989.
/
| 〈 |
|
〉 |