Chin J Plan Ecolo ›› 2015, Vol. 39 ›› Issue (7): 694-703.doi: 10.17521/cjpe.2015.0066

• Orginal Article • Previous Articles     Next Articles

Inversion of subtropical forest stand characteristics by integrating very high resolution imagery acquired from UAV and LiDAR point-cloud

XU Zi-Qian1,2, CAO Lin1,2, RUAN Hong-Hua1,2,*(), LI Wei-Zheng3, JIANG Sheng4   

  1. 1College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China
    2Co-innovation Center for Sustainable Forestry in Southern China, Nanjing 210037, China
    3Advanced Analysis and Testing Center of Nanjing Forestry University, Nanjing 210037, China
    4College of Geography Science, Nanjing Normal University, Nanjing 210046, China
  • Online:2015-07-22 Published:2015-07-01
  • Contact: Hong-Hua RUAN E-mail:hhruan@njfu.edu.cn
  • About author:

    # Co-first authors

Abstract: Aims We applied the integrated very high resolution imagery acquired from Unmanned Aerial Vehicles (UAV) and Light Detection and Ranging (LiDAR) point-loud data to estimate the stand characteristics of a naturally- regenerated forest in a subtropical area. Methods The high precision digital elevation model (DEM) of the forest was constructed base on LiDAR point-cloud and the inverse distance weighted interpolation method. The 3D point-cloud of forest canopy layer was constructed from UAV image pairs, with information from DEM height information normalization, for canopy height and density. With the above effort, we developed a prediction model to estimate Lorey’s height, stand density, basal area, and volume. Important findings The quantitative metrics generated from this study appeared very sensitive to Lorey’s height, followed by volume and basal area. Using UAV as a flexible and rapid method for generating forest canopy characteristics, combined with topographic information from high precision LiDAR data, seems a viable, rapid, inexpensive, and flexible method in canopy research.

Key words: LiDAR, point cloud, stand characteristics, UAV

Fig. 1

UAV image of Niushan forest, with yellow circles showing the spatial locations of the sample plots. DEM, digital elevation model."

Table 1

One-variable volume equation for stand volume in Jiangsu Province"

树种 Tree species 公式 Formula 备注 Remark
杉木 Cunninghamia lanceolata V = A × DB × (E × D + G × lgD)C A = 0.000058777042, B = 1.9699831
C = 0.89646157, E = -2.2426
F = 0.2021, G = 6.6922
水杉 Metasequoia glyptostroboides V = A × DB × ((E + F × e (G × D))H)C A = 0.000058777042, B = 1.9699831
C = 0.89646157, E = 1.000438
F = -0.00024755, G = -0.07897864
H = 7101.252
侧柏 Platycladus orientalis A = 0.000091972184, B = 1.8639778
C = 0.83156779, E = 1.000084
F = -0.0000671125, G = -0.1223273
H = 29416.66
林分阔1 Broadleaf 1 (杨 Populus、栎 Quercus) A = 0.000050479055, B = 1.9085054
C = 0.99076507, E = 0.9236004
F = 0.0502109, G = -0.09686479
H = -37.80742
林分阔2 Broadleaf 2 (刺槐 Robinia pseudoacacia、刺桐Erythrina variegata、柳 Salix、杂 Other) A = 0.000050479055, B = 1.9085054
C = 0.99076507, E = 6.569053
F = -4.565682, G = -0.03200782
H = 1.697762

Table 2

Summary of plot-level characteristics"

样地林分特征
Plot-level characteristics
统计量 Statistics (n = 30)
变化范围
Range of variation
平均值
Average
Lorey’s树高
Lorey’s height (m)
7.00-31.65 22.99
林分密度
Stand density (plant·hm-2)
1 146-3 950 2 560
胸高断面积
Basal area (m2·hm-2)
2.48-13.23 6.41
蓄积量
Volume (m3·hm-2)
66.36-488.97 274.36

Fig. 2

Technical flow chart for this study."

Fig. 3

Procedures for processing the UAV data."

Table 3

List of independent variables for this study"

自变量(归一化点云提取参数)
Independent variable
因变量(实测参数)
Dependent variable
高度分位数
Height percentile
(h10, h25, h30, h40, h60, h75, h85, h90)
Lorey’s树高 Lorey’s height (m)
林分密度 Stand density (plant·hm-2)
胸高断面积 Basal area (m2·hm-2)
蓄积量 Volume (m3·hm-2)
点云密度变量
Point cloud density variables
(d10, d25, d30, d40, d60, d75, d85, d90)
高度均值 Average height (havg)
高度最值 Maximum/minimal height (hmax, hmin)

Fig. 4

Analysis of coefficients between point-cloud metrics and stand characteristics. h10, h25…h90, height percentile; havg, average height; hmax, maximum height; hmin, minimal height; d10, d25… d90, point cloud density."

Table 4

The integrated models and their accuracy assessments"

林分特征变量 Stand characteristics 联合提取估算模型 Combined extraction estimation models R2 RMSE rRMSE (%)
Lorey’s树高 Lorey’s height (H)(m) H = 0.23 + 0.579havg + 0.346h90 0.86 0.13 6.47
林分密度 Stand density (N)(plant·hm-2) N = 596.552 + 414.135h60 - 346.586h30 0.29 0.69 27.04
胸高断面积 Basal area (G)(m2·hm-2) lnG = 2.752lnh60 - 1.841lnh10 - 1.126 0.53 0.28 16.38
蓄积量 Volume (V)(m3·hm-2) lnV = 2.499 + 1.429lnh90 + 0.7lnd90 0.59 0.40 6.93

Fig. 5

Comparison of field-measured characteristics and the estimates from model (the dotted line is 1:1 validation line)."

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