Leaf Area Index of Scandinavian Forests Methods Using in situ and Remotely Sensed Data

Detta är en avhandling från Department of Physical Geography and Ecosystems Analysis Daculty of Science Lund University

Sammanfattning: This thesis deals with methods of obtaining the leaf area index (LAI) from remotely sensed satellite data. Initially, in situ estimation methods were investigated, since the accuracy of the remotely sensed estimates is dependent on the quality of these estimates. The results of the present studies indicate that some methods commonly used as a reference for more complicated methods could result in overestimated LAI values. Furthermore, it was concluded that estimates of the LAI from optical methods could be simplified when applied to forests of similar character to those studied here (Swedish mixed, deciduous and coniferous forests). The remotely sensed data were related to the in situ estimates based on an empirical approach. One drawback of this approach is that the understorey vegetation could be a disturbing factor. Therefore, the understorey LAI was estimated in situ and the possible effect of understorey vegetation on the spectral signal from the forest canopy was analysed. A radiative transfer model was employed to simulate the spectral signal from canopies, given detailed information on the forest characteristics. Model results confirmed that the effect of the understorey vegetation could be substantial, particularly for less dense stands (LAI ? 3). Strong relationships were found between the understorey and overstorey vegetation, indicating that the understorey LAI could be determined from information on the overstorey vegetation. Strong empirical relationships were established between the remotely sensed satellite data and the in situ estimates, suggesting that the LAI of the forests could be predicted using the empirical approach. Various attempts were made to develop a method that could take the influence of the understorey vegetation into consideration. The results indicate that the remote sensing data may not be sufficiently sensitive to fully reflect the complex effects of the understorey vegetation on the reflectance from a forest.

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