4th week of september

Brodrick, P. G., Davies, A. B., & Asner, G. P. (2019). Uncovering Ecological Patterns with Convolutional Neural Networks. Trends in ecology & evolution.

When extracting foliar information from high resolution image, it would be quite hard work to detect the leaf, which one wants to know in the map. In the map, the pixel information varies depending on individual leaf angle. And it can change depending on solar zenith angle, which changes every observation. Though one is interested in just the information of a certain leaf, spatial pattern have to be considered.

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4th week of june

Asner, G. P., Martin, R. E., Anderson, C. B., & Knapp, D. E. (2015). Quantifying forest canopy traits: Imaging spectroscopy versus field survey. Remote Sensing of Environment , 158 , 15-27. They use canopy sunlit reflectance at plot level and the trait samples from sunlit. The plot averaged refletance minimize canopy architectural effect. However actual field samples cover only 5% of a plot, the plot reflectance well explains canopy traits.

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4th week of november

Bousquet, L., Lachérade, S., Jacquemoud, S. and Moya, I., 2005. Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sensing of Environment, 98(2-3): 201-211. Specular reflectance originates from leaf surface. On the other hand, diffuse reflectance originates from leaf intrastructure. To focus on the target traits, the two reflectance needs to be separated.

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AGU 2019

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