欢迎访问林业科学,今天是

林业科学 ›› 2019, Vol. 55 ›› Issue (11): 117-125.doi: 10.11707/j.1001-7488.20191113

• 论文与研究报告 • 上一篇    下一篇

基于两期无人机影像的针叶林伐区蓄积量估算

周小成1, 何艺1, 黄洪宇1, 许雪琴2   

  1. 1. 福州大学卫星空间信息技术综合应用国家地方联合工程研究中心 空间数据挖掘与信息共享教育部重点实验室 福州 350108;
    2. 福建金森林业股份有限公司 三明 353300
  • 收稿日期:2017-12-14 修回日期:2018-07-28 出版日期:2019-11-25 发布日期:2019-12-21
  • 基金资助:
    国家自然科学基金项目(41201427);中央引导地方科技发展专项(2017L3012)。

Estimation of Forest Stand Volume on Coniferous Forest Cutting Area Based on Two Periods Unmanned Aerial Vehicle Images

Zhou Xiaocheng1, He Yi1, Huang Hongyu1, Xu Xueqin2   

  1. 1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University Fuzhou 350108;
    2. Fujian Jinsen Forestry Co. Ltd. Sanming 353300
  • Received:2017-12-14 Revised:2018-07-28 Online:2019-11-25 Published:2019-12-21

摘要: [目的] 提出一种基于两期无人机影像的针叶林伐区蓄积量估算方法,为促进无人机数据在多类型林业样地资源调查中的深度应用提供依据。[方法] 以福建省三明市将乐县金森林业股份有限公司伐区森林小班为试验区,首先,利用无人机遥感获取分辨率优于10 cm的两期影像,经Pix4D软件处理得到点云数据,在此基础上将小班区域未采伐前的林冠点云匹配到采伐后的小班地形点云上;然后,通过布料模拟滤波算法(CSF)分离匹配后的林冠点云和地形点云,采用自然领域插值法分别将林冠点云数据插值生成数字表面模型(DSM)、地形点云数据插值生成数字高程模型(DEM),二者相减获得冠层高度模型(CHM);接着,基于改进的局域最大值法搜索冠层高度模型中的林冠顶点,提取树高;最后,根据野外采集的400株马尾松和杉木树高、胸径数据,建立5个适用于福建省马尾松和杉木的胸径-树高模型,选择相关系数最高的模型推算胸径,并利用福建省单木材积公式估算小班区域蓄积量。[结果] 1)两期无人机数据的点云匹配能较好消除陡峭地形对树高提取的影响;2)改进的局域最大值法可有效减少固定窗口搜索林冠顶点时出现的多提和漏提错误;3)小班区域估算株数为339株,实测株数为366株,估算的平均树高为18 m,实测平均树高为19 m,估算蓄积量为182 m3,实测蓄积为199 m3,株数、树高和蓄积量的估算精度均较高。[结论] 借助无人机遥感技术,可实现森林蓄积量自动化估算,降低传统野外调查成本,推动森林资源的快速调查和更新。

关键词: 针叶林, 采伐蓄积量, 局域最大值法, 无人机遥感, 胸径

Abstract: [Objective] This study proposes a method for estimating the stem volume based on UAV images before and after cutting, and provides reference for UAV remote sensing estimation of forest stem volume.[Method] Based on the state-owned forest of Jinsen Forestry Co. Ltd. in Jiangle county, Sanming city, Fujian Province, the first step in this dissertation was to use unmanned aerial vehicle remote sensing to get images whose resolution was more than 10 cm, and got point cloud after Pix4D processing. Based on it, the point cloud of before cutting canopy was matched to the point cloud of surface cloud after cutting. Secondly, the forest canopy and the surface cloud were separated by the cloth simulation filtering algorithm, the digital surface model(DSM)and digital elevation model(DEM)was generated by natural neighbour method, canopy height model(CHM)was generated by the two model subtraction. Then, the tree height was extracted by the improved local maximum algorithm to searched the top of tree in canopy height model. Finally,according to the tree high and diameter at breast height(DBH)of 400 Pinus massoniana and Cunninghamia lanceolata, five DBH estimation equation in Fujian Province were established. The highest correlation coefficient model was selected to calculate the DBH, then using single wood produce volume formula in Fujian Province to estimating sub-compartment stem volume.[Result] 1) The matching of two-stage UAV point cloud can better eliminate the impact of large terrain slope on tree height extraction. 2) The improved local maximum algorithm effectively reduces the errors that usually happen in the fixed window searching for canopy vertex. 3) The estimated number of tree is 339, the measured number of tree is 366, the estimated average height of stand is 18 m, the measured average height of stand is 19 m, the estimated volume of sub-compartment is 182 m3 and the measured volume is 199 m3, the estimation accuracy of the number of tree average height of stand and volume are higher.[Conclusion] With the technology of UAV remote sensing, automated estimation of forest stem volume can be achieved, thus greatly reducing the cost of traditional field investigation and promoting the rapid investigation and updating of the forest resources.

Key words: coniferous forest, forest volume, local maximum algorithm, UAV remote sensing, diameter at breast height(DBH)

中图分类号: