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Optical instruments for measuring leaf area index in low vegetation: Application in Arctic ecosystems

机译:测量低植被叶面积指数的光学仪器:在北极生态系统中的应用

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Leaf area index (LAI) is a powerful diagnostic of plant productivity. Despite the fact that many methods have been developed to quantify LAI, both directly and indirectly, leaf area index remains difficult to quantify accurately, owing to large spatial and temporal variability. The gap-fraction technique is widely used to estimate the LAI indirectly. However, for low-stature vegetation, the gap-fraction sensor either cannot get totally underneath the plant canopy, thereby missing part of the leaf area present, or is too close to the individual leaves of the canopy, which leads to a large distortion of the LAI estimate. We set out to develop a methodology for easy and accurate nondestructive assessment of the variability of LAI in low-stature vegetation. We developed and tested the methodology in an arctic landscape close to Abisko, Sweden.The LAI of arctic vegetation could be estimated accurately and rapidly by combining field measurements of canopy reflectance (NDVI) and light penetration through,the canopy (gap-fraction analysis using a LI-COR LAI-2000). By combining the two methodologies, the limitations of each could be circumvented, and a significantly increased accuracy of the LAI estimates was obtained. The combination of an NDVI sensor for sparser vegetation and a LAI-2000 for denser vegetation could explain 81% of the variance of LAI measured by destructive harvest. We used the method to quantify the spatial variability and the associated uncertainty of leaf area index in a small catchment area.
机译:叶面积指数(LAI)是对植物生产力的有力诊断。尽管已经开发了许多直接或间接地量化LAI的方法,但是由于大的时空变化,叶面积指数仍然难以准确地量化。间隙分数技术被广泛用于间接估计LAI。但是,对于低矮的植被,间隙分数传感器要么无法完全进入植物冠层之下,从而会丢失部分叶面积,要么太靠近冠层的各个叶片,从而导致冠层的较大变形。 LAI估算值。我们着手开发一种方法,可以轻松,准确地对低矮植被中LAI的变异性进行无损评估。我们在靠近瑞典阿比斯库的北极景观中开发和测试了该方法。通过结合冠层反射率(NDVI)和穿过树冠的光穿透力的实地测量(使用LI-COR LAI-2000)。通过将两种方法结合起来,可以避免每种方法的局限性,并且可以显着提高LAI估计的准确性。用于稀疏植被的NDVI传感器和用于较茂密植被的LAI-2000的结合可以解释破坏性收获所测得的LAI变化的81%。我们使用该方法量化了小流域面积的空间变异性和叶面积指数的相关不确定性。

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