Considering both nonrandomness within canopies and woody components is necessary in indirect LAI measurements. Ignoring woody components underestimates the degree of spatial heterogeneity or clumping in forests. These two factors exhibit opposing effects, which may be misleading and may thus complicate the quantification and validation of the effect of each factor. Results show that spatial heterogeneity within canopies underestimates the LAI by 16–25%, whereas woody components overestimate LAI by 14–28% in four forest sites. Indirect and direct in situ measurements were conducted in broadleaf and coniferous forests. This study combined the path length distribution model and multispectral canopy imager for the first time to improve the accuracy of indirect LAI measurements. Spatial heterogeneity within canopies and woody components are two factors that limit the accuracy of indirect leaf area index (LAI) measurements, but they have not been fully considered because of the limitations of commercial instruments. The model is expected to facilitate the consistent retrieval of the forest leaf area index using ground and airborne data. This method is found to improve the individual tree measurement in urban areas and LAI mapping in natural forest, and its results at consistent at different scales. The method of obtaining the path length distribution of different platforms is studied, and the consistent retrieval model is established. In this thesis, the path length distribution model is proposed to model the clumping effect, and it is applied to the TLS and ALS data. However, the three-dimensional information of laser scanning is not fully explored in current methods and the traditional theories require adaptation. Active laser scanning provides an opportunity for consistent LAI retrieval at multiple scales because terrestrial laser scanning (TLS) and airborne laser scanning (ALS) have the similar physical mechanism. Leaf Area Index (LAI), defined as one half of the total leaf area per unit ground surface area, is a key parameter of vegetation structure for modeling Earth's ecological cycle and its acquisition accuracy always has the need and opportunity for improvement.
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