基于无人机影像的天山云杉林树高提取及蓄积量的反演

doi:10.6048/j.issn.1001-4330.2021.10.009

新疆农业科学 ›› 2021, Vol. 58 ›› Issue (10): 1838-1845.DOI: 10.6048/j.issn.1001-4330.2021.10.009

• 作物遗传育种·种质资源·分子遗传学·耕作栽培·生理生化·农产品分析检测 • 上一篇下一篇

基于无人机影像的天山云杉林树高提取及蓄积量的反演

吕金城(), 王振锡(), 杨勇强, 曲延斌, 马琪瑶, 朱思明

新疆农业大学林学与园艺学院/新疆教育厅干旱区林业生态与产业技术重点实验室,乌鲁木齐 830052

收稿日期:2020-08-22 出版日期:2021-10-20 发布日期:2021-10-26
通信作者: 王振锡
作者简介:吕金城(1995-),男,新疆乌鲁木齐人,硕士研究生,研究方向为林业3s技术及应用,(E-mail) 1287460632@qq.com
基金资助:
新疆维吾尔自治区林业改革发展基金“新疆天保工程精准监测技术与评价体系研究”(XJTB20181102)

Height Extraction and Growing Stock Inversion of Picea schrenkiana var. tianshanica in Tianshan Mountain Based on UAV Image

Jincheng LÜ(), Zhenxi WANG(), Yongqiang YANG, Yanbin QU, Qiyao MA, Siming ZHU

College of Forestry and Horticulture, Xinjiang Agricultural University / Key Laboratory of Forestry Ecology and Industrial Technology in Arid Area of Xinjiang Education Department, Urumqi 830052, China

Received:2020-08-22 Online:2021-10-20 Published:2021-10-26
Correspondence author: Zhenxi WANG
Supported by:
Forestry Reform Development Fund of Xinjiang Uyghur Autonomous Region “Research on Precision Monitoring Technology and Evaluation System of Natural Forest Protection Project in Xinjiang”(XJTB20181102)

摘要/Abstract

摘要：

【目的】提取影像高程数据建立模型反演天山云杉林分蓄积量,为天然林保护工程实施后天山云杉生态恢复与科学管理提供参考依据。【方法】以新疆天山西部巩留县恰西森林公园的天山云杉(Picea Schrenkiana var. tianshanica)为研究对象,无人机航拍影像与样地每木检尺为数据源,使用点云分类与克里金插值法提取无人机影像高程数据得到天山云杉树高,根据样地实测数据建立胸径树高模型,最终根据胸径树高模型反演天山云杉林林分蓄积量。【结果】利用无人机影像提取树高与实测树高存在显著正相关关系,提取平均精度为88.42%,建立天山云杉胸径-冠幅模型的相关系数为0.696,而胸径-树高模型的相关系数为0.730;验证胸径-树高模型,计算RMSE值为12.386,拟合效果显著。基于胸径-树高模型反演林分蓄积量精度为87.66%,与实测值比对,残差值大部分落在(-2,+2)残差区间。【结论】采用局部最大值算法对天山云杉林树高信息的提取效果较好,建立胸径-树高模型弥补了无人机不能对胸径直接测量的缺陷,进而反演天山云杉林林分蓄积量。

关键词: 蓄积量; 无人机影像; 天山云杉; 树高提取

Abstract:

【Objective】 To establish the image elevation data model for inversion of forest growing stock inversion of Picea schrenkiana var. tianshanica based on the trees in Qiaxi Forest Park in Gongliu County, western Tianshan, Xinjiang, UAV aerial image and sample ground per wooden scale as data source in the hope of providing a reference for the ecological restoration and scientific management of natural forest protection project. 【Methods】 Using point cloud classification and Kriging interpolation to extract UAV image elevation data to obtain the height of Picea schrenkiana var. tianshanica, and at the same time, the DBH tree height model was established according to the measured data of the sample land, and the forest volume of Picea schrenkiana var.tianshanica. 【Results】 There was a significant positive correlation between tree height and measured tree height extracted by UAV images. the average extraction accuracy was 88.42%. The correlation coefficient of DBH-crown width model of Picea Schrenkiana var. tianshanica was 0.696, while that of DBH-tree height model was 0.730. Verify the DBH-tree height model, the calculated RMSE value is 12.386, the fitting effect is significant. The accuracy of retrieving stand volume based on DBH-tree height model is 87.66%. Compared with the measured values, most of the residual values fall in the residual range of (- 2,+2). 【Conclusion】 The local maximum algorithm is effective in extracting tree height information of Picea Schrenkiana var. tianshanica forest. The establishment of DBH-tree height model makes up for the defect that UAV can not measure DBH directly, and then inverts the stand volume ofPicea Schrenkiana var. tianshanica forest.

Key words: growing stock; UAV imaging; Picea schrenkiana var. tianshanica; tree height extraction

中图分类号:

S771

吕金城, 王振锡, 杨勇强, 曲延斌, 马琪瑶, 朱思明. 基于无人机影像的天山云杉林树高提取及蓄积量的反演[J]. 新疆农业科学, 2021, 58(10): 1838-1845.

Jincheng LÜ, Zhenxi WANG, Yongqiang YANG, Yanbin QU, Qiyao MA, Siming ZHU. Height Extraction and Growing Stock Inversion of Picea schrenkiana var. tianshanica in Tianshan Mountain Based on UAV Image[J]. Xinjiang Agricultural Sciences, 2021, 58(10): 1838-1845.

图/表 12

参考文献 19

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参数类型 Parameter type	参数数据 Parameter data
传感器类型 Sensor type	CMOS
传感器尺寸 Sensor size	全画幅
有效像素 Effective Pixels	4 240万
焦距 Focal length	35 mm
像素大小 Pixel size	4.53×4.53 (μm)

参数类型 Parameter type	参数数据 Parameter data
传感器类型 Sensor type	CMOS
传感器尺寸 Sensor size	全画幅
有效像素 Effective Pixels	4 240万
焦距 Focal length	35 mm
像素大小 Pixel size	4.53×4.53 (μm)

变量 Variable	最大值 Maximum value	最小值 Minimum value	平均值 Average value
胸径 Diameter at breast height(cm)	148.97	5.00	43.49
树高 Tree height(m)	60.40	3.00	25.76
冠幅 Crown breadth (m)	10.2	0.58	3.35

变量 Variable	最大值 Maximum value	最小值 Minimum value	平均值 Average value
胸径 Diameter at breast height(cm)	148.97	5.00	43.49
树高 Tree height(m)	60.40	3.00	25.76
冠幅 Crown breadth (m)	10.2	0.58	3.35

方程 Model	R²	F	df₁	df₂
线性Linear function	0.587	1 435.558	1	1 009
对数Logarithmic function	0.606	1 552.731	1	1 009
倒数Reciprocal function	0.504	1 027.327	1	1 009
二次Quadratic function	0.606	775.227	2	1 008
三次Cubic function	0.623	555.753	3	1 007
复合Function of function	0.580	1 395.627	1	1 009
幂Power function	0.696	2 308.688	1	1 009
S曲线S-curve	0.673	2 074.368	1	1 009
增长Increasing function	0.580	1 395.627	1	1 009
指数Exponential function	0.580	1 395.627	1	1 009

基于无人机影像的天山云杉林树高提取及蓄积量的反演

Height Extraction and Growing Stock Inversion of Picea schrenkiana var. tianshanica in Tianshan Mountain Based on UAV Image

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图/表 12

参考文献 19

相关文章 15

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Metrics

方程 Model	R²	F	df₁	df₂
线性Linear function	0.634	1 745.357	1	1 009
对数Logarithmic function	0.597	1 494.405	1	1 009
倒数Reciprocal function	0.424	743.741	1	1 009
二次Quadratic function	0.639	894.034	2	1 008
三次Cubic function	0.645	609.924	3	1 007
复合Function of function	0.689	2 233.585	1	1 009
幂Power function	0.730	2 724.231	1	1 009
S曲线S-curve	0.601	1 520.076	1	1 009
增长Increasing function	0.689	2 233.585	1	1 009
指数Exponential function	0.689	2 233.585	1	1 009

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