利用遥感估测地上生物量是国内外生态学与地理学的研究热点。但基于植被指数的生物量回归模型结果差异较大,究竟哪种植被指数与哪种模型更适合典型草原的生物量反演,是现代草地遥感急需解决的问题之一。该文基于TM影像数据的不同植被指数(VI)差异性,分别选取了RVI(比值植被指数)、NDVI(归一化差异植被指数)、SAVI(土壤调节植被指数)、MASVI(修改型土壤调整植被指数)和RSR(简化比率植被指数)5种植被指数,与同期的内蒙古典型草原区地面实测地上生物量做相关分析,分别建立了5种植被指数与地上生物量的线性及3种非线性(对数、二次多项式、三次多项式)回归模型。研究结果表明:对于中国北方典型草原区而言,地上生物量与5种植被指数(RVI、NDVI、SAVI、MSAVI和RSR)均呈现出显著相关,但地上生物量与后4种植被指数是正相关,与RVI为负相关;利用5种植被指数(RVI、NDVI、SAVI、MSAVI和RSR)监测草地植被生物量的复相关系数均大于0.6,充分说明利用植被指数检测典型草原生物量是一种简单可行的方法;NDVI建立的生物量回归模型,其复相关系数大于其它4类植被指数(RVI、SAVI、MSAVI和RSR),说明NDVI-生物量模型优于植被指数RVI、SAVI、MSAVI和RSR 模型,其模拟地表生物量的效果好;对于TM影像来说,植被生物量的线性模型与3种非线性模型(三次多项式生物量模型、二次多项式生物量模型、对数模型)都表现出较好的模拟效果,都通过了0.01的显著性检验,而且该研究的结果显示出三次多项式生物量回归模型最优,其次是二次多项式生物量模型,再次是线性模型,相对较差的是对数模型。通过NDVI-生物量三次多项式回归模型模拟锡林浩特草原的生物量,可以看出整个研究区的地上生物量基本上是东高西低、东南高西北低的趋势,这与研究区的地形、气候及土地利用等多种因素有关。

Aims There is a crucial need in grassland study for a vegetation index (VI)-biomass model simulating steppe biomass based on remote sensing.

Methods Thematic mapper (TM) images (spatial resolution of 30 m× 30 m) for the research area in 2005 and 1991 were rectified so that geometric errors were less than one pixel, then extracted the image of the research region in the soft of ERDAS. We used five vegetation indexes:RVI (ratio vegetation index), NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MASVI (modified soil-adjusted vegetation index) and RSR (reduced simple ratio index). They were correlated to plant biomass sampled on the ground at the same time as the TM images. We developed four kinds of regression models: linear, logarithm, second-degree polynomial and cubic polynomial.

Important findings The correlations between sampled biomass and the five VIs were highly significant, with four (NDVI, SAVI, MSAVI, RSR) being positive and one (RVI) negative. Multiple correlation coefficients (R²) of the 15 regression models were >0.6, indicating that a VI-biomass regression model was a simple method to monitor the biomass of steppe grassland. The R² of the NDVI-biomass model was the highest, indicating that it was better suited to simulate the biomass of typical steppe than the other VIs. For TM image, all four kinds of models were significant at the 0.01 level, with the cubic polynomial model as the best to simulate the biomass, followed by the second-degree polynomial, linear and logarithm models. Therefore, the cubic polynomial regression model based on NDVI-biomass was the best model, and was used to simulate the biomass of the research region. Simulated biomass was higher in the east than in the west of the research region and higher in the southeast than in the northwest. Simulated biomass was consistent with sampled biomass in 2005.