The Inversion of Average Stand Height and Stock Volume based on Normalized Vegetation Point Cloud

doi:10.13466/j.cnki.lyzygl.2021.06.007

Abstract

Abstract:

This paper proposed a normalized vegetation point cloud computing method,which used vertical elevation difference characterization between vegetation point cloud and ground point cloud to remove the absolute height value of forest influenced by topography,on this basis,it extracted forest characteristics variables,and used the random forest algorithm to invert and estimate the average tree height and the forest stock volume within the study area. The result showed that this method can effectively improve the estimation accuracy of forest factors,and the fitting accuracy of average tree height and forest stock volume were 0.946 and 0.936,respectively.

Key words: LiDAR, normalized vegetation point cloud, forest factor inversion, reserve forest

CLC Number:

S758.5

WANG Zhaoli, WANG Haowei, YANG Jiale, DUAN Mengqi, MA Shengli. The Inversion of Average Stand Height and Stock Volume based on Normalized Vegetation Point Cloud[J]. FOREST RESOURCES WANAGEMENT, 2021, 0(6): 37-42.

Figures/Tables 11

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References 10

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特征变量	详细描述
最大高程	NVPC中高程最大值
最小高程	NVPC中高程最小值
平均高程	NVPC中高程平均值
高程四分位间距	NVPC中上四分位数与下四分位数高程之差
方差	NVPC中高程方差
标准差	NVPC中高程标准差
上四分位数高程	NVPC中高程上四分位数

特征变量	详细描述
上四分位密度	上四分位数高程以上点在植被点中所占比
下四分位密度	下四分位数高程以上点在植被点中所占比
密度四分位间距	上四分位密度与下四分位密度之差

序号	实测树高	反演结果
序号	实测树高	改进前	差值1	改进后	差值2
1	9.4	10.442	1.042	9.439	0.039
2	10.6	10.879	0.279	10.387	0.213
3	11.1	12.561	1.461	11.563	0.463
4	13.0	12.346	0.654	12.863	0.137
5	16.5	16.846	0.346	16.575	0.075
6	9.8	9.865	0.065	9.880	0.080
7	12.4	12.660	0.260	12.339	0.061
8	11.8	12.120	0.320	11.809	0.009
9	10.1	10.367	0.267	10.010	0.090
10	8.9	9.285	0.385	9.100	0.200

序号	实测蓄积	反演结果
序号	实测蓄积	改进前	差值1	改进后	差值2
1	3.8	4.671	0.871	3.778	0.022
2	5.3	4.595	0.705	5.194	0.106
3	6.2	6.536	0.336	5.831	0.369
4	7.6	6.819	0.781	7.658	0.058
5	8.4	9.132	0.732	8.707	0.307
6	10.4	11.098	0.698	9.765	0.635
7	11.1	12.644	1.544	11.090	0.010
8	12.5	11.543	0.957	11.950	0.550
9	13.9	13.088	0.812	12.620	1.280
10	18.0	16.245	1.755	17.594	0.406

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