内蒙古草原植被覆盖度遥感估算

以内蒙古锡林浩特市南部中国科学院草原生态系统定位研究站周围的草场为研究对象, 分析比较了统计模型和亚像元分解模型进行草地植被覆盖度(vegetation coverage, VC)遥感估算的适用性。结果表明, 根据Landsat-5 TM影像数据计算的比值植被指数(simple ratio vegetation index, SR)与观测的VC的相关性最高(R² = 0.761); 统计模型和亚像元分解模型生成的VC空间分布特征相似, 但亚像元分解模型得到的VC平均值比统计模型的结果高0.091; 在VC的低值和高值区, 两种方法得到的VC结果相似; 但在VC的中值区, 亚像元模型得到的结果较统计模型的结果偏高。

Remote-sensing estimation of grassland vegetation coverage in Inner Mongolia, China

Aims Our objective was to estimate grassland vegetation coverage (VC) in Inner Mongolia prairie, China, using a statistical model and a sub-pixel model and determine which model was more applicable in this area.
Methods Field experiments were conducted around three experimental stations in the Inner Mongolia prairie using a digital camera and a LAI-2000 plant canopy analyzer. A spectrum information model was used to extract the measured VC value from the photos. Statistical models were built between those VC values and six vegetation indexes. Then the VC map was made by using the model with the highest R². The measured leaf area index (LAI) values were used in the sub-pixel model to make another VC map.
Important findings The simple ratio vegetation index extracted from the Landsat-5 TM (thematic mapper) image had the higher correlation with VC values calculated from the photos taken in the field (R² = 0.761). The VC spatial distribution maps generated by the two models were generally similar, but the VC average value gained from the statistical model was 0.09 lower than that of VC values retrieved by the sub-pixel model. These two methods had similar results in the areas with higher and lower VC values, but the results from the sub-pixel model were higher than the statistical model with mid VC values.