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  • 标题:A GEOBIA APPROACH TO ESTIMATE LARGE AREA FOREST CANOPY HEIGHT USING LIDAR TRANSECTS AND QUICKBIRD IMAGERY
  • 本地全文:下载
  • 作者:G. Chen ; G. J. Hay
  • 期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • 印刷版ISSN:2194-9042
  • 电子版ISSN:2194-9050
  • 出版年度:2010
  • 卷号:XXXVIII - 4/C7
  • 出版社:Copernicus Publications
  • 摘要:Numerous applications of small footprint airborne lidar (light detection and ranging) have provided highly accurate results for estimating forest height. However, the associated acquisition cost remains high, which limits its use for wall-to-wall large area mapping. In this study, we developed a novel framework by integrating GEOBIA (GEOgraphic Object-Based Image Analysis), lidar transects and Quickbird imagery to estimate large area canopy height. Model results (from eight different lidar transect combinations in two different directions, N-S and W-S) were compared with the corresponding canopy height from the full lidar scene. Results show that the highest correlation (R = 0.85) was achieved using a lidar transect cover of 7.6% of the full scene (i.e., two transects in N-S direction), while the lowest correlation (R = 0.75) was obtained from a lidar transect cover of 3.8%
  • 关键词:GEOBIA; Canopy height; Lidar transect; Machine learning; Forest
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