[1]苏迪,高心丹*.基于无人机航测数据的森林郁闭度和蓄积量估测[J].林业工程学报,2020,5(01):156-163.[doi:10.13360/j.issn.2096-1359.201904031]
 SU Di,GAO Xindan*.Estimation of forest canopy density and stock volume based on UAV aerial survey Data[J].Journal of Forestry Engineering,2020,5(01):156-163.[doi:10.13360/j.issn.2096-1359.201904031]
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基于无人机航测数据的森林郁闭度和蓄积量估测()
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《林业工程学报》[ISSN:1001-8081/CN:32-1160/S]

卷:
5
期数:
2020年01期
页码:
156-163
栏目:
森林工程与土建交通
出版日期:
2020-01-07

文章信息/Info

Title:
Estimation of forest canopy density and stock volume based on UAV aerial survey Data
文章编号:
2096-1359(2020)01-0156-08
作者:
苏迪高心丹*
东北林业大学信息与计算机工程学院,哈尔滨 150040
Author(s):
SU Di GAO Xindan*
College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
关键词:
图像处理 蓄积量 偏最小二乘 郁闭度 分水岭算法 无人机航测数据
Keywords:
image processing stock volume partial least squares canopy density watershed algorithm UAV aerial survey data
分类号:
S758.5
DOI:
10.13360/j.issn.2096-1359.201904031
文献标志码:
A
摘要:
蓄积量是评价森林资源质量或状况的重要指标,为了解决实测郁闭度和蓄积量费时费力以及无法充分利用航测原始数据生成各项数据的问题,以无人机航测数据的点云数据和正射影像为研究数据,利用冠层高度模型提取高程,通过一元线性回归分析估测平均树高和平均胸径模型; 使用改进形态学分水岭方法提取树冠个数; 通过主成分回归建立郁闭度模型; 结合提取与估测的GIS因子,用偏最小二乘法建立蓄积量模型。结果表明:平均树高模型精度为97.34%、平均胸径模型精度为91.27%,改进分水岭提取树冠精度为80.03%,郁闭度模型精度为83.18%,蓄积量模型精度可达88.43%。蓄积量模型的所有特征因子均是通过遥感方法从无人机原始航测数据中提取而来,充分利用了无人机航测数据。实验建立的树高、胸径和郁闭度模型可以有效地估测森林平均树高、胸径及郁闭度,改进后的分水岭算法减少了过分割,蓄积量模型能够有效估测蓄积量,提高了蓄积量提取效率,节省了大量的人力物力。
Abstract:
Stock volume is an important index to evaluate the quality or condition of forest resources. This study was aimed at solving the problems of the measurement of canopy density and stock volume because it was time-consuming, laborious, and the inability to make full use of the data generated from the aerial survey raw data. In the experiment, point cloud data and orthophoto images from the UAV aerial survey data were used as research data, the forest in the Laoshan industrial area of Maoershan Forest Farm was taken as the research area, the canopy height model was used to extract elevation, the average tree height and the DBH model were estimated by the univariate linear regression analysis, the crown number was extracted by the improved Morphological watershed method, and the canopy closure model was established by the principal component regression. This method can simplify the analysis process and improve the analysis efficiency, and the stock model was established by the partial least square's regression with extraction and estimation of GIS factors. The experimental results showed that the average tree height model accuracy was 97.34%, the average DBH model accuracy was 91.27%, the improved watershed algorithm extraction accuracy of crown number was 80.03%, the canopy density model accuracy was 83.18%, and the stock volume model accuracy could reach 88.43%. All the characteristic factors of the stock volume model were extracted from the original aerial survey data of UAV by the remote sensing method, which made full use of the UAV aerial survey data. The model of tree height, DBH and canopy density established in the experiment could effectively estimate the average tree height, DBH and canopy density of the forest. The improved watershed algorithm reduced over-segmentation. The volume model can effectively estimate the stock volume, improve the extraction efficiency of the stock volume, and significantly save manpower and material resources. At the same time, the aerial survey data are used as research data to improve the iteration efficiency of stock volume.

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备注/Memo

备注/Memo:
收稿日期: 2019-04-19 修回日期:2019-08-17
基金项目:国家自然科学基金(31770768,31870530); 中央高校基本科研业务费专项资助基金E类(2572017EB09)。
作者简介:苏迪,女,研究方向为遥感图像处理和地理信息系统。通信作者:高心丹,女,副教授。E-mail:gaoxd@nefu.edu.cn
更新日期/Last Update: 2019-12-10