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Author (up) Aubry-Kientz, Mélaine ; Laybros, Anthony ; Weinstein, Ben ; Ball, James G. C. ; Jackson, Toby ; Coomes, David ; Vincent, Grégoire doi  openurl
  Title Multisensor data fusion for improved segmentation of individual tree crowns in dense tropical forests Type Journal Article
  Year 2021 Publication IEEE Journal of Selected topics in Applied Earth Observations and Remote Sensing Abbreviated Journal  
  Volume 14 Issue Pages 3927-3936  
  Abstract Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number EcoFoG @ webmaster @ Serial 1008  
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