Biodiversity Science ›› 2018, Vol. 26 ›› Issue (8): 892-904.doi: 10.17520/biods.2018039

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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:2018-09-27
  • Deng Yun E-mail:dy@xtbg.org.cn

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

Fig. 1

Distribution of sample plots. 1, Bubeng; 2, Guomenshan; 3, Chaditou; 4, Dapingzhang; 5, Shihuishan; 6, Xinkaidi; 7, Chachanghoushan; 8, 44 Gongli; 9, Menglunshuiku; 10, Jiangbianzhan."

Table 1

Basic information of sample plots"

序号
No.
样地名称
Plot name
植被类型
Vegetation type
北纬
North
latitude
东经
East
longitude
海拔
Elevation
(m)
飞行高度
Flying altitude
(m)
地面分辨率
Ground
resolution
(cm/pix)
航测面积
Coverage
area (km2)
点云密度
Point cloud density
(points/m2)
1 补蚌
Bubeng
季节雨林
Seasonal rainforest
21.613° 101.580° 730 362 5.52 1.85 82
2 过门山
Guomenshan
山地雨林
Montane rainforest
22.246° 100.599° 1,120 300 4.47 1.87 31
3 茶地头
Chaditou
常绿阔叶林
Evergreen broad-leaved forest
22.250° 100.612° 1,284 310 4.18 0.58 143
4 大平掌
Dapingzhang
常绿阔叶林
Evergreen broad- leaved forest
22.230° 100.574° 1,750 193 2.46 0.25 103
5 石灰山
Shihuishan
石灰山季雨林
Limestone monsoon forest
21.911° 101.283° 606 258 3.36 0.90 56
6 新开地
Xinkaidi
季节雨林次生林
Secondary forest of seasonal rainforest
21.903° 101.275° 556 223 2.88 0.78 75
7 茶厂后山
Chachanghoushan
季节雨林
Seasonal rainforest
22.155° 100.675° 784 169 2.21 0.49 129
8 44公里
44 Gongli
季节雨林
Seasonal rainforest
21.971° 101.148° 806 287 3.58 1.07 49
9 勐仑水库
Menglunshuiku
季节雨林
Seasonal rainforest
21.935° 101.179° 656 361 4.56 0.72 30
10 江边站
Jiangbianzhan
季节雨林
Seasonal rainforest
22.219° 100.734° 640 407 5.24 1.03 23

Fig. 2

Ground control point coordinate measurement (left) and mark (right) in forest"

Fig. 3

Mark of ground control point in aerial photograph"

Fig. 4

Aerial photograph processing flow with Photoscan software"

Fig. 5

Example of measured digital terrain model (a), estimated digital terrain model (b), digital surface model (c) and canopy height model (d)"

Table 2

RMSE of ground control point and corner stake in sample plots"

样地名称
Plot name
地面控制点RMSE RMSE of ground control point 样地顶点RMSE RMSE of corner stake
数量
Number
垂直
Vertical (m)
水平
Horizontal (m)
全局
Total (m)
数量
Number
垂直
Vertical (m)
水平
Horizontal (m)
全局
Total (m)
补蚌 Bubeng 10 0.581 0.454 0.476 4 0.891 0.659 0.846
过门山 Guomenshan 6 0.247 0.057 0.076 4 0.833 0.634 0.648
茶地头 Chaditou 8 0.404 0.324 0.368 4 0.125 0.007 0.012
大平掌 Dapingzhang 10 0.333 0.202 0.260 4 0.633 0.336 0.532
石灰山 Shihuishan 7 0.086 0.005 0.007 4 0.671 0.311 0.560
新开地 Xinkaidi 7 0.205 0.090 0.091 4 0.284 0.022 0.100
茶厂后山
Chachanghoushan
7 0.104 0.006 0.009 4 0.293 0.020 0.104
44公里 44 Gongli 5 0.131 0.007 0.013 4 1.430 1.101 1.700
勐仑水库
Menglunshuiku
5 0.487 0.292 0.406 4 1.082 0.691 0.994
江边站 Jiangbianzhan 7 0.397 0.228 0.310 4 0.325 0.139 0.161
最大 Maximum 10 0.581 0.454 0.476 4 1.430 1.101 1.700
最小 Minimum 5 0.086 0.005 0.007 4 0.125 0.007 0.012
平均值 ± 标准差
Mean ± SD
7 ± 2 0.297 ± 0.170 0.167 ± 0.158 0.202 ± 0.182 4 ± 0 0.657 ± 0.412 0.392 ± 0.368 0.566 ± 0.523

Table 3

Stepwise regression analysis result of ground control point amount (x1), flying height (x2), and root mean square error of RTK in related direction (x3) to root mean square error (RMSE) in estimated model (point cloud based)"

y 模型垂直均方根
误差
Vertical RMSE in estimated model
模型水平均方根
误差
Horizontal RMSE
in estimated model
模型整体均方根
误差
Total RMSE in estimated model
截距
Intercept
-3.336E-16 4.430E-17 -1.540E-16
x1 -0.309* 0.082 -0.176
x2 -0.005 0.003*
x3 4.964* 3.157**
VIF_x1 2.022 1.026 1.237
VIF_x2 3.285 1.026
VIF_x3 3.775 1.237
R2 0.757 0.493 0.650
F-统计量
F-statistic
6.245 3.405 6.504
P 0.028 0.093 0.025

Table 4

Reprojection error of camera exposure position in sample plots"

样地名称 Plot name 照片数量 Number of photos RMSE
垂直 Vertical (m) 水平 Horizontal (m) 全局 Total (m)
补蚌 Bubeng 1,142 28.924 19.230 34.733
过门山 Guomenshan 1,632 21.951 12.690 25.356
茶地头 Chaditou 625 32.228 22.554 39.336
大平掌 Dapingzhang 858 36.709 13.466 39.101
石灰山 Shihuishan 1,536 39.100 23.704 45.724
新开地 Xinkaidi 1,293 37.688 25.509 45.510
茶厂后山 Chachanghoushan 2,088 47.508 18.102 50.839
44公里 44 Gongli 2,013 34.682 12.663 36.922
勐仑水库 Menglunshuiku 675 32.660 12.659 35.028
江边站 Jiangbianzhan 1,132 28.973 23.759 37.469
最大 Maximum 2,088 47.508 25.509 50.839
最小 Minimum 625 21.951 12.659 25.356
平均值 ± 标准差 Mean ± SD 1,299 ± 515 34.042 ± 6.920 18.434 ± 5.252 39.002 ± 7.108

Table 5

Linear equation test result and structure characteristics of sample plots"

样地名称
Plot name
面积
Area (ha)
林冠覆盖度
Canopy cover
林冠最大高度
Maximum height of
canopy (m)
林冠平均高度
Average height
of canopy (m)
估测地形与实测地形高差均值
Mean difference between DTMestimated and DTMmeasured
估测地形与实测地形高差标准差
Standard deviation between DTMestimated and DTMmeasured
估测地形模型(y)与实测地形模型(x)的检验方程参数对比
Linear equation test result of DTMestimated (y) and DTMmeasured (x)
k b R2
补蚌
Bubeng
20 0.90 54.67 22.43 10.06 6.09 0.995 14.187 0.981
过门山
Guomenshan
20 0.97 52.10 22.90 2.10 6.70 1.005 -2.782 0.992
茶地头
Chaditou
1 0.95 35.63 16.79 4.55 5.90 0.979 30.783 0.761
大平掌
Dapingzhang
1 0.93 37.66 18.37 -2.91 5.28 0.888 188.821 0.920
石灰山
Shihuishan
1 0.75 22.82 8.22 0.50 2.27 0.982 11.895 0.978
新开地
Xinkaidi
1 0.90 20.37 11.68 2.79 3.94 1.199 -108.789 0.788
茶厂后山
Chachanghoushan
1 0.98 48.98 24.44 7.62 6.37 0.839 125.731 0.834
44公里
44 Gongli
1 0.99 44.80 25.36 -3.60 8.58 1.237 -196.197 0.761
仑水库
Menglunshuiku
1 0.89 45.79 23.82 -26.48 7.42 0.973 -7.650 0.779
江边站
Jiangbianzhan
1 0.93 52.46 26.43 0.18 17.12 1.245 -159.057 0.638
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