ZY3立体像对和机载LiDAR抽样数据协同估测森林平均高

doi:10.11707/j.1001-7488.20210907

摘要/Abstract

摘要：

目的: 探索一种适用于已具备林下地形，可协同利用少量实测样地数据、抽样式采集的机载激光雷达(LiDAR)条带数据和区域全覆盖的资源三(ZY3)立体像对数据有效估测区域森林平均高的方法，为提高森林资源调查效率和精度提供技术支撑。方法: 以广西高峰林场2个分场为研究区，2018年获取覆盖整个研究区的机载LiDAR、ZY3立体像对和少量实测样地数据。将LiDAR数据提取的DEM作为历史已存在的林下地形，从全覆盖的LiDAR数据中抽取12条飞行条带的LiDAR数据"模拟"抽样式采集的LiDAR数据，形成"林下地形+LiDAR抽样+ZY3立体像对+样地"数据集；以样地和LiDAR数据提取出LiDAR抽样数据对应的森林平均高为模型建立的参考数据(因变量Y)，以ZY3立体像对提取的数字表面模型(DSM)减去数字高程模型(DEM)得到的CHM_ZY3为自变量(X)，采用普通最小二乘(OLS)模型、k-邻近(KNN)模型和回归克里格(RK)模型估测森林平均高，并对其估测效果进行比较评价。结果: OLS和KNN模型的均方根误差(RMSE)分别为1.88和1.96 m，估测精变(EA)分别为87.18%和86.64%；RK模型估测精度相对较高，RK_OLS模型的RMSE=1.84 m，EA=87.42%；RK_KNN模型的RMSE=1.86 m，EA=87.32%。结论: 本研究中2类4种模型均可有效估测森林平均高，回归克里格模型(RK_OLS、RK_KNN)优于非空间模型(OLS、KNN)，RK_OLS模型估测精度最高；在林下地形已知时，协同利用少量实测样地数据、抽样式采集的机载LiDAR条带数据和区域全覆盖的ZY3立体像对数据能够实现区域森林平均高的高效、高精度估测。

关键词: 激光雷达, 资源三号, 森林平均高, 回归克里格

Abstract:

Objective: In order to provide technical support for improving the efficiency and accuracy of forest resources investigation, this paper was implemented to explore an efficient method to map the mean height of forest(MHF) with few field plot survey data, airborne light direction and ranging(LiDAR) sampling data and ZY3 stereo imaging pairs covering the full target region. Method: In Guangxi Zhuang Autonomous Region, two subfarms(Jiepai and Dongsheng) of Gaofeng forest farm were selected as our study areas. Full covered airborne LiDAR and ZY3 stereo imaging pairs combined with few field plot data were collected in 2018. DEM(digital elevation model) extracted from airborne LiDAR was treated as a historical under canopy topography, and twelve strips sampled from the airborne LiDAR data were simulated as the real airborne LiDAR sampling flight areas. DEM, twelve strips of LiDAR data and field plot data were used in the following processes. First of all, the MHF for these sampled airborne LiDAR areas were produced with field plot data, which was used as reference data(Y) for modeling. After that, an independent variable(CHM_ZY3) was obtained by subtracting the DEM from the DSM(digital surface model) extracted from ZY3 stereo imaging pairs. Finally, MHF estimation results got from ordinary least squares(OLS), k-nearest neighbor(KNN) and regression Kriging(RK) models were compared, which were used to estimate MHF combined with CHM_ZY3 data. Result: It was showed that root mean square error(RMSE) of OLS and KNN was 1.88 m and 1.96 m, the estimate accuracy(EA) was 87.18% and 86.64%, respectively. The RMSE and EA of RK_KNN was 1.86 m and 87.32%, respectively, however these parameters for RK_OLS reached to 1.84 m and 87.42%, correspondingly. Conclusion: It could be concluded that all the 2 categories and 4 types of models tested can effectively estimate MHF, and among these models, RK based models(RK_OLS and RK_KNN) are better than non-spatial models(OLS and KNN), while RK_OLS has the best performance. With known under canopy topography information, synergy utilization of the few field plot data, ZY3 stereo imaging pairs(fully covering the target area) and airborne LiDAR strips(acquired by sampling the target area) can estimate the MHF efficiently and precisely. The method proposed can provide important supports for improving the efficiency and accuracy of forest resource inventory in the future.

Key words: light direction and ranging(LiDAR), ZY3, mean height of forest, regression Kriging(RK)

中图分类号:

S771.8

赵俊鹏,赵磊,陈尔学,万祥星,徐昆鹏. ZY3立体像对和机载LiDAR抽样数据协同估测森林平均高[J]. 林业科学, 2021, 57(9): 66-75.

Junpeng Zhao,Lei Zhao,Erxue Chen,Xiangxing Wan,Kunpeng Xu. Synergy Application of ZY3 Stereo Imaging Pairs and airborne LiDAR Sampling Data for Estimating Mean Height of Forest[J]. Scientia Silvae Sinicae, 2021, 57(9): 66-75.

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参数Parameter	值Value	参数Parameter	值Value
波长Wavelength/nm	1 550	飞行高度Flying height/m	1 000
扫描角Scan angle/(°)	±30	脉冲长度Pulse length/ns	3
垂直精度Vertical accuracy/cm	15	脉冲频率Pulse frequency/kHz	400
平均点密度Mean point density/m^-2	8.51	光束发散度Beam divergence/(m·rad)	0.5

残差来源Source of residulas	拟合模型Models	块金值Nugget	基台值Sill	变程Range/m
OLS	指数Exponential	0.03	0.92	95.49
OLS	Stable	0	0.92	105.01
KNN	指数Exponential	0.39	0.94	164.98
KNN	Stable	0	0.95	148.25

模型Models	相关系数r	平均误差ME/m	均方根误差RMSE/m	估测精度EA(%)
OLS	0.40	-0.21	1.88	87.18
KNN	0.50	-1.15	1.96	86.64
RK_OLS	0.46	-0.16	1.84	87.42
RK_KNN	0.57	-1.07	1.86	87.32

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