林下植被对遥感估算马尾松LAI的影响

叶面积指数是一项极其重要的描述植被冠层结构的植被特征参量。根据植被物候规律,利用中国环境卫星CCD多光谱影像和野外马尾松样区调查数据,通过建立不同季节和不同郁闭度样区马尾松LAI和影像NDVI经验回归模型,并利用一个新的LAI观测方式定量比较乔木层LAI和生态系统总LAI(包括草本层、灌木层和乔木层)的差异,研究林下植被对马尾松反演的影响程度。结果表明:(1)由于林下植被的物候变化,冬季林下植被对马尾松LAI反演影响最小,马尾松NDVI和LAI线性关系R2维持在0.65;夏季林下植被影响最大,线性关系R2只有0.25;春季和秋季影响居中,NDVI和LAI线性关系R2在0.47附近。但是,受林下植被影响较小的A类样区4个季节内NDVI和LAI线性关系基本都在0.60以上(夏季略低于0.60);(2)乔木层LAI和总LAI差距非常大,最大差距达到2.93,相差的比例最大达到了2.45倍;(3)总LAI和NDVI相关关系显著,其中线性关系R2达到0.66,对数关系R2可达到0.68,而乔木层LAI和NDVI相关关系较差,线性关系R2只有0.30。分别建立冬季和其它季节实测总LAI和NDVI的关系,可以估算出林下植被对马尾松LAI反演的影响程度。

Impact of the understory on estimation of leaf area index of Pinus massoniana using remote sensing technology

Leaf area index (LAI), defined as half the total developed area of green leaves per unit ground horizontal area, is an extremely important vegetation characteristic parameter that describes the construction of vegetation canopy. For the past few years, LAI has been estimated operationally at a regional or even global scale by means of various retrieval methods using remotely sensed optical imagery. Understory vegetation (e.g., grasses, herbs, shrubs, etc.) is the layer of foliage below the forest canopy. As a background signal source in remote sensing, understory has had a serious impact on estimating many forest canopy parameters by remote sensing techniques for two reasons. on the one hand, it has very similar characteristics with those of the forest canopy because they are all vegetation. on the other hand, it has greater spatial-temporal heterogeneity than other backgrounds in remote sensing, e.g., soil, water, rock and litter, etc. The purpose of this paper is to determine the impact of understory on LAI inversion of forests with different canopy closures in different seasons. In this study, data from five field investigations and corresponding Chinese HJ-1 CCD remote sensing images were collected and analyzed to study the impact of understory on LAI inversion of Pinus massoniana during the period September 2012 to October 2013 in Chuzhou, Anhui province. By building empirical models of LAI of Pinus massoniana with different canopy closures and Normalized Difference Vegetation Indices (NDVI) in different seasons based on the phenology of the understory and comparing the difference of LAI of tree layers and the LAI of all layers of the ecosystem using a new way of LAI measurement, the impact of understory on the calculation of LAI of Pinus massoniana with different canopy closures and in different seasons was found. The results show that: (1) Understory had minimal impact on LAI inversion of Pinus massoniana in winter; R² of the linear relationship between NDVI and LAI was 0.65. Understory had the most serious impact in summer; R² of the linear relationship was only 0.25. The impact of understory in spring and autumn was greater than in winter and lower than in summer, R² of linear relationship was about 0.47. However, R² of linear relationship in the quadrats A, which were less affected by understory, was almost higher than 0.60 in all seasons (slightly less than 0.60 in summer). The reason was that the phenology of the understory caused different impacts in different seasons; (2) A significant difference between tree layer LAI and LAI of all layers in the same season was found, and the biggest gap was 2.93 and the maximum multiple was 2.45; (3) the R² of the linear relationship between LAI of all layers and NDVI was about 0.66, and R² of the logarithmic relationship was more than 0.68. However, the correlation between tree layers and NDVI was poor (R² was only 0.30).These findings indicate that the impact of understory on LAI inversion of Pinus massoniana can be calculated when relationships between the LAI of all layers and NDVI in winter and other seasons are determined. Finally, the difficulties of studying understory impact on forest LAI retrieval are discussed, and several suggestions are proposed for future studies.