北京山区森林叶面积指数季相变化遥感监测

森林叶面积指数(LAI)遥感反演,对于区域环境生态监测具有重要意义。该文以北京市西北山区鹫峰国家森林公园为研究区,获取多时相Landsat5 TM数据,并利用半球形照相机(Hemispherical Photography)同步获取森林LAI。使用3种植被指数(归一化植被指数NDVI、增强植被指数EVI和三梯度差植被指数TGDVI),分别建立单个观测时期及整个时期的LAI反演模型,通过相关性分析筛选出最佳模型。研究表明利用整个时期的LAI建立的模型精度较高,其中最好的是基于NDVI的LAI指数模型。利用该模型反演森林LAI,生成基于时间序列的北京山区森林LAI分布图。该研究进一步分析了阔叶林、针叶林和混交林3种情况,结果表明,与不分植被类型的LAI反演模型精度比较,阔叶林和混交林有所提高,而针叶林稍微下降,但模型精度均达到显著水平。

Remote sensing of seasonal variability monitoring of forest LAI over mountain areas in Beijing

Leaf area index (LAI) is an important parameter of vegetation ecosystems, which can reflect the growth status of vegetation. In this study, multi-temporal Landsat5 TM images covering Jiufeng forest in the northwest mountain areas of Beijing were acquired. In-situ forest LAI values were measured synchronously using Hemispherical Photography. By correlation analysis of three vegetation indexes (NDVI, EVI and TGDVI) and LAI, it was found that the correlation between LAI and NDVI in exponential form behaved a good relativity. This model was applied in mapping forest LAI distributions in different time. Moreover, compared with the previous finding regardless of vegetation type, the sensitivity of the models between vegetation indexes and LAI can be improved both in broadleaf and mixed forests, while a little decrease in conifer stands. But, the accuracy of all models reached a significant level.