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    Author(s): Yanjun Su; Qinghua Guo; Danny L. Fry; Brandon M. Collins; Maggi Kelly; Jacob P. Flanagan; John J. Battles
    Date: 2016
    Source: Canadian Journal of Remote Sensing
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Southwest Research Station
    PDF: Download Publication  (777.0 KB)


    Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites.

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    Su, Yanjun; Guo, Qinghua; Fry, Danny L.; Collins, Brandon M.; Kelly, Maggi; Flanagan, Jacob P.; Battles, John J. 2016.A vegetation mapping strategy for conifer forests by combining airborne LiDAR data and aerial imagery. Canadian Journal of Remote Sensing. 42(1): 1-15.


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    Vegetation mapping, lidar, aerial imagery, forest structure, mixed conifer forest

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