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Integrating Hyperspectral and LiDAR Data in the Study of Vegetation

Authored by: Jessica J. Mitchell , Nancy F. Glenn , Kyla M. Dahlin , Nayani Ilangakoon , Hamid Dashti , Megan C. Maloney

Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation

Print publication date:  December  2018
Online publication date:  December  2018

Print ISBN: 9781138058545
eBook ISBN: 9781315164151
Adobe ISBN:


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Integrating hyperspectral and LiDAR (Light Detection and Ranging) data has allowed scientists to derive quantitative metrics about plant structure and biochemistry. For example, understanding and mapping species identification, functional diversity, and biomass have all benefited from coupling hyperspectral and LiDAR data. New ground-based, airborne, and satellite-based platforms are available for multi-scale mapping. For example, point clouds from unmanned aerial systems (UAS) to full waveform LiDAR available from satellite missions, along with new spaceborne imaging spectroscopy systems are transforming how we analyze vegetation at local to global scales. New computational methods, including machine learning, are allowing novel advances in coupling these data across space and time. Approaches such as statistical analysis, physical-based modeling using radiative transfer concepts, and ecological modeling at ecosystem and global scales, allow scientists to leverage these datasets at multiple scales. In this chapter, we provide an overview of available data and sensors and recent advances in the methods used to couple these data. We also provide examples and details on plant structural and functional diversity mapping with hyperspectral and LiDAR, and demonstrate the value of combining these technologies to advance particular application areas, such as biodiversity mapping and vegetation characterization of dryland ecosystems. Finally, we provide an overview of the value of ground-based LiDAR for deriving allometry to improve coarser-scale mapping.

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