叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,反演LAI是植被遥感的重要研究内容之一.根据在内蒙古呼伦贝尔和锡林浩特草原利用LA1200()观测的草地LAI,比较了不同植被指数(SR、RSR、EVI、NDVI、SAVI和ARVI)估算不同类型草地LAI的能力,建立了基于Landsat-5 TM遥感数据的LAI估算模型,并利用LAI观测数据对模型进行了检验,生成了研究区内草地LAI分布图,据此对MODIS LAI产品一致性进行了评价.结果表明,在呼伦贝尔和锡林浩特两个研究区,RSR与LAI的相关性最高(r分别为0.628,0.728,RMSE分别为0.512、0.490),在低密度草地,RSR的优势更为明显;验证表明,根据RSR建立的LAI估算模型的精度可达70%;利用TM数据生成的两个地区的L41(TMLAI)空间变化明显,锡林浩特草地的LAI值整体上低于呼伦贝尔草地;在呼伦贝尔和锡林浩特,MODIS LAI产品与TM LAI一致性分别为0.566,0.323,MODIS LAI产品高估了呼伦贝尔草地LAI值,而在锡林浩特研究区则存在低估现象.
Retrieval of leaf area index for different grasslands in Inner Mongolia prairie using remote sensing data
Leaf area index (LAI) , defined as one half of the total green leaf area per unit ground surface area, is a crucial parameter of vegetation structure. It provides key quantitative information on the exchange of mass, energy, and momentum between the atmosphere and the land surface. Its retrieval is an important research focus in remote sensing of vegetation. LAI of grasslands in Huliinbuir prairie; and Xilinhot prairie; in Inner Mongolia was acquired using the; LAI 2000 instrument from June 21 to 26 and June 28 to July 3, 2010, respectively. Six vegetation indices including Simple; Ratio (SR) , Reduced Simple; Ratio (RSR) , Normalized Difference Ve;ge;tation Inele;x (NDVI) , Soil Adjusted Ve;ge;tation Inele;x (SAVI) , Atmospherically Resistant Vegetation Index (ARVI) , and Enhanced Vegetation Index (EVI) obtained from Landsat-5 TM elata we're; correlated with measured LAI. LAI re;trie;ve;el from TM elata was then used as a be;ne-hmark for assessing the; accuracy of MODIS LAI products. The; measured LAI values of grasslands over the; two study areas range; from 0.46 to 4. 06 in Huliinbuir and from 0. 65 to 4. 70 in Xilinhot. The; average LAI value; in Huliinbuir is 1.81, 11 % higher than that in Xilinhot ( 1. 63). Since grasses in these areas are; short, we; elug a small trench at each measurement location to place the; LAI 2000 sensor head at the; same; level as the; soil surface; to e;nsure; the; total LAI is included in the; measurement. Results show that RSR has the; highest correlation with LAI in the; two grasslands, with R2 ecpial to 0. 628 and 0. 728 , respectively.The Root Mean Square Error (RMSE) values of estimated LAI from RSR are 0. 512 and 0. 490, respectively. RSR outperforms other Vis more obviously at lower LAI. Validation using 15 measured LAI values (not used in algorithm development) in both Hulunbuir and Xilinhot shows that RSR-derived LAI can capture 70% of LAI variations. Combined with the surface reflectance images of the grassland, the formulae LAI = 0. 764×RSR0.675 and LAI= 0. 462×RSR + 0. 582 were developed to generate LAI maps at 30 m resolution for the Hulunbuir and Xilinhot study areas. The retrieved LAI is lower in Xilinhot than in Hulunbuir. LAI values in the mountainous areas at these two locations are significantly overestimated using the RSR based inversion model when compared with ground measurements. The overestimation exceeds 1.0 in several areas with large topographical variations. This may be caused by the topographical sensitivity of RSR. Although RSR has an advantage in retrieving LAI over flat regions, its application to grassland in the mountains requires further study. The level of agreement between MODIS LAI and LAI retrieved using TM data differs in these two study areas, with R2 equal to 0. 566 and 0. 323 in Hulunbuir and Xilinhot, respectively. The average of the MODIS LAI is higher in Hulunbuir and lower in Xilinhot than the corresponding TM LAI. The inconsistency of MODIS LAI in comparison with TM LAI derived based on ground measurements in these two study areas suggests the need to earn' out MODIS LAI validation over more grassland areas.
leaf area index of grasslandvegetation indexLAI 2000Inner Mongolia prairie
国家科技期刊平台
NSTL
万方数据
维普
