生物多样性 ›› 2018, Vol. 26 ›› Issue (8): 892-904.DOI: 10.17520/biods.2018039

• 研究报告 • 上一篇    下一篇

轻小型无人机航摄技术辅助的热带森林样地测量精度问题探讨

邓云1,3,4,*(), 王彬2, 李强2, 张志明2, 邓晓保1,3, 曹敏1, 林露湘1,3   

  1. 1 中国科学院西双版纳热带植物园热带森林生态学重点实验室, 云南勐腊 666303
    2 云南大学生态学与环境科学学院, 昆明 650091
    3 中国科学院西双版纳热带植物园云南西双版纳森林生态系统国家野外科学观测研究站, 云南勐腊 666303
    4 中国科学院大学, 北京 100049
  • 收稿日期:2018-02-06 接受日期:2018-08-05 出版日期:2018-08-20 发布日期:2018-09-27
  • 通讯作者: 邓云
  • 基金资助:
    国家重点研发专项(2016YFC0500202)

An analysis of lightweight-drone-assisted mapping accuracy in tropical forest plot

Deng Yun1,3,4,*(), Wang Bin2, Li Qiang2, Zhang Zhiming2, Deng Xiaobao1,3, Cao Min1, Lin Luxiang1,3   

  1. 1 CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303
    2 School of Ecology and Environmental Sciences, Yunnan University, Kunming 650091
    3 National Forest Ecosystem Research Station at Xishuangbanna, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Yunnan 666303
    4 University of Chinese Academy of Sciences, Beijing 100049
  • Received:2018-02-06 Accepted:2018-08-05 Online:2018-08-20 Published:2018-09-27
  • Contact: Deng Yun

摘要:

准确的样地坐标位置是无人机航摄数据与地面调查数据融合使用的必要前提, 但是在森林样地的具体实践中, 会有许多因素制约着样地位置的测量精度, 这有可能影响后期的数据融合过程甚至得出错误的结论, 研究者们需要对此予以足够的重视。本文通过对比西双版纳地区10个热带森林样地及周围区域无人机航摄过程中的地面控制点测量精度、Photoscan摄影测量软件所得点云解算精度和照片曝光点重投影精度, 发现: (1)即使使用性能相对较好的实时差分(real time kinematic, RTK)式GNSS系统进行定位, 在林内也很难获得很好的定位精度, 林窗处的地面控制点均方根误差(root mean square error, RMSE)在水平和垂直方向分别为0.167 ± 0.158 m和0.297 ± 0.170 m, 林下样地顶点桩处分别为0.392 ± 0.368 m和0.657 ± 0.412 m; (2)软件的全局解算精度主要受控制点地面测量精度和控制点数量的影响; (3)若仅依托普通的单站式GPS对无人机位置进行定位, 则照片曝光点的重投影坐标位置可能存在较大误差(RMSE在水平和垂直方向上分别为18.434 ± 5.252 m和34.042 ± 6.920 m); (4)估测地形与实测地形间的高差标准差与林冠平均高度正相关(r = 0.713, P < 0.05), 估测地形模型在20 ha样地尺度下的验证结果优于1 ha样地。基于以上结果, 我们建议: (1)在对热带森林进行无人机航摄的过程中, 必须有足够数量和质量的分布相对均匀的地面控制点对测量误差进行控制; (2)摄影测量法的优势在于能够以相对简单的前端设备建立数字表面模型, 但该方法可能很难在森林样地中建立准确的数字地形模型。在使用无人机获取数据之前, 研究者应预先考虑到适合自己的恰当方法以应对以上的精度控制问题。

关键词: 轻小型无人机, 全球导航卫星系统, 定位精度, 森林样地

Abstract:

Accurate coordinate position is a prerequisite for combining drone-assisted remotely sensed data and ground survey data. However, in the practice of surveying forests, many factors prevent accurate measurement of coordinate position and inaccurate coordinates may lead to incorrect conclusions. Therefore, researchers must pay attention to factors effecting accuracy of position. In this study, we compared location error of ground control points (GCPs), model error of photogrammetric point cloud (estimated by Photoscan software) and reprojection error of camera exposure position. First, we found that real time kinematic (RTK) global navigation satellite system (GNSS) cannot locate position in tropical forest with high accuracy. The root mean square error (RMSE) of GCPs in canopy gaps were 0.167 ± 0.158 m and 0.297 ± 0.170 m in the horizontal and vertical axes respectively. In comparison, RMSE of GCPs within forests were 0.392 ± 0.368 m and 0.657 ± 0.412 m respectively for horizontal and vertical axes. Second, the number and measurement accuracy of GCPs influenced model error of photogrammetric point cloud. Third, reprojection error of camera exposure position (18.434 ± 5.252 m and 34.042 ± 6.920 m in horizontal and vertical axes respectively) was much greater than location error of GCPs when the drone acquired position with a single-station GPS system. Fourth, standard deviation of difference between estimated digital terrain model (DTMestimated) and measured digital terrain model (DTMmeasured) was positively correlated with mean canopy height (r = 0.713, P < 0.05). DTMestimated was better estimated at 20 ha scale than at 1 ha scale. Based on these results, we suggest that uniform distribution and sufficient numbers of GCPs can improve drone-assisted mapping accuracy. Lightweight-drone-based photogrammetry has an advantage in requiring fewer equipment and enabling creation of accurate DSM (digital surface model), but remains incapable of estimating ground elevation. Researchers should consider these factors related to accuracy before using drones for surveys.

Key words: lightweight drone, global navigation satellite system, location accuracy, forest plot