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Extraction of individual tree information based on remote sensing images from an Unmanned Aerial Vehicle

  • role： First author第一作者
  • Affiliation：

    College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China

    The Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation in Fujian, Fujian Agricultrue and Froestry University, 350002, China

  • Email：dsammy@126.com
  • Introduction：董新宇，1994年生，男，硕士研究生，研究方向为农业资源保护、资源环境遥感。E-mail：dsammy@126.com
DONG Xinyu124,  
  • role： Corresponding author通信作者
  • Affiliation：

    The Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：jacoli@126.com
  • Introduction：李家国，1982年生，男，副研究员，研究方向为陆表环境遥感。E-mail：jacoli@126.com
LI Jiaguo2*,  
  • Affiliation：

    College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China

    Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation in Fujian, Fujian Agricultrue and Froestry University, 350002, China

CHEN Hanyue14,  
  • Affiliation：

    Shenhua Geological Exploration Co., Ltd, Beijing 100022, China

ZHAO Lei3,  
  • Affiliation：

    College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China

    Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation in Fujian, Fujian Agricultrue and Froestry University, 350002, China

ZHANG Liming14,  
  • Affiliation：

    College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China

    Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation in Fujian, Fujian Agricultrue and Froestry University, 350002, China

XING Shihe14

реферат

Extraction of individual tree information is significant in managing forestry resources and protecting the ecological environment. In addition, this strategy has been extensively applied to forests using high spatial resolution satellite images or Lidar data in recent years. The rapid development of unmanned aerial vehicle remote sensing makes itself play an important role in this field. This study uses high spatial resolution aerial remote sensing images to extract individual tree information automatically. The individual tree information includes individual tree extraction and crown delineation. A novel extraction method of individual tree information was proposed in this study. First, the spectral information of the original images was enhanced through decorrelation stretch. Spectral information enhancement aimed to expand the coupling degree of image information by stretching the principal component information of the bands that are correlated, thus increasing the color saturation of the image. Second, the optimal cluster number of K-means clustering algorithm was obtained by introducing a DBI index, then the image pixels were marked based on the K-means clustering algorithm. On this basis, a GMRF model was constructed for image segmentation. Finally, the segmentation results were post-processed through mathematical morphology to complete individual tree crown delineation, and individual tree detection was based on the position of individual tree calculated by image geometric moment. This study acquired the reference data through visual interpretation to evaluate the result. The verification result showed that the overall accuracies of individual tree detection through this method are 89.52% and 95.65% for medium and low canopy density forests, correspondingly. The accuracies of individual tree crown delineation achieved 81.90% and 95.65% for medium and low canopy density forests, respectively. Therefore, the proposed method is better than the object-oriented method in terms of extraction effect through comparison and analysis. The proposed method can be used to obtain the individual tree information of forestry management rapidly and has high extracting precision based on the verification of results. The proposed method does not require considerable manual intervention and prior knowledge, thus significantly improving the degrees of automation.

ключеви́че слова́

remote sensing;individual tree detection;individual tree crown delineation;Pinus sylvestris;Pinus tabulaeform;Unmanned Aerial Vehicle (UAV) remote sensing;GMRF

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