基于探地雷达估算油松根系直径的方法

doi:10.13866/j.azr.2025.05.07

摘要/Abstract

摘要： 粗根对植株支撑与土壤固持意义重大，但在生态系统评价中仍无法做到精准快捷地确定粗根生物量。探地雷达（Ground Penetrating Radar，GPR）作为高效无损的地球物理学技术，能无损检测土壤根系信息。本研究在山西吉县蔡家川流域，利用探地雷达开展油松根系正演实验，借助EKKO-Project软件，提取探地雷达反射波参数、波速和时差值，建立根径拟合模型，综合分析反射波参数法、双层曲线法以及三维深度切片法3种根径估算方法在不同深度、不同根径大小和不同天线频率下的精准度与可靠性。结果表明：（1） 4种探地雷达反射波参数中，总时间间隔（ΣT）和根径的拟合效果最好，R²=0.7233，P<0.05。（2） 3种探地雷达根径估算方法中，天线频率为500 MHz时，双层曲线根径拟合效果最好（P<0.05）。（3）三维深度切片根径拟合结果显示，1000 MHz频率天线探测识别效果优于500 MHz，其实际根径与估算根径平均相对误差分别为3.0 cm和8.7 cm，拟合可靠性差。综上，3种基于探地雷达技术的油松根系直径估算方法精度差异较大，双层曲线法对研究区的油松根系直径估算的精准度最好，在实际应用中可优先使用双层曲线法。

关键词: 探地雷达, 油松, 粗根, 根径估算, 晋西黄土区

Abstract:

The coarse roots play a significant role in supporting the plants and holding the soil firmly. However, in the evaluation of ecosystems, it is still impossible to determine the biomass of the coarse roots precisely and quickly. Ground Penetrating Radar (GPR), as an efficient and non-destructive geophysical technique, can non-destructively detect the information of soil roots. In this study, Pinus tabulaeformis root growth simulation experiment was conducted in the Caijiachuan River Basin of Jixian County, Shanxi Province, China using ground-penetrating radar. With the aid of the EKKO-Project software, the reflection wave parameters, wave velocity and time difference values of the ground-penetrating radar were extracted to establish a root diameter fitting model. The accuracy and reliability of three root diameter estimation methods, namely the reflection wave parameter method, the double-layer curve method and the three-dimensional depth slice method, under different depths, different root diameters and different antenna frequencies were comprehensively analyzed. The results showed that: (1) Among the four reflected wave parameters of GPR, total time interval (ΣT) and root diameter have the best fitting effect, R²=0.7233, P<0.05. (2) Among the three methods for estimating root diameters of ground-penetrating radar, when the antenna frequency is 500 MHz, the double-layer curve method has the best fitting effect (P<0.05). (3) The results of the three-dimensional depth slice root diameter fitting show that the detection and identification effect of the 1000 MHz frequency antenna is better than that of the 500 MHz antenna. The average relative errors between the actual root diameters and the estimated root diameters are 3.0 cm and 8.7 cm respectively, and the fitting reliability is poor. In conclusion, the accuracy of the three root diameter estimation methods based on ground-penetrating radar technology varies greatly. The double-layer curve method has the best accuracy for estimating the root diameters of Pinus tabulaeformis in the study area, and it can be used preferentially in practical applications.

Key words: ground penetrating radar, Pinus tabulaeformis, coarse root, root diameter estimation, loess area of Western Shanxi Province

王勃, 张建军, 张海强, 赖宗锐, 赵炯昌, 杨周, 陆善洪. 基于探地雷达估算油松根系直径的方法[J]. 干旱区研究, 2025, 42(5): 840-851.

WANG Bo, ZHANG Jianjun, ZHANG Haiqiang, LAI Zongrui, ZHAO Jiongchang, YANG Zhou, LU Shanhong. Method for estimating the diameter of Pinus tabulaeformis roots based on ground penetrating radar[J]. Arid Zone Research, 2025, 42(5): 840-851.

图/表 15

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

表1

表2

图11

图12

图13

参考文献 39

[1]	Hodge A. The plastic plant: Root responses to heterogeneous supplies of nutrients[J]. The New Phytologist, 2004, 162(1): 9-24.
[2]	Isaac M E, Anglaaere L C N. An in situ approach to detect tree root ecology: Linking ground-penetrating radar imaging to isotope-derived water acquisition zones[J]. Ecology and Evolution, 2013, 3(5): 1330-1339. doi: 10.1002/ece3.543 pmid: 23762519
[3]	Pransiska Y, Triadiati T. Forest conversion impacts on the fine and coarse root systemand soil organic matter in tropical lowlands of Sumatera (Indonesia)[J]. Forest Ecology and Management, 2016, 379: 288-298.
[4]	Kaushal R, Jayaparkash J. Canopy management practices in mulberry: Impact on fine and coarse roots[J]. Agroforestry Systems, 2019, 93(2): 545-556. doi: 10.1007/s10457-017-0148-8
[5]	崔喜红, 陈晋, 关琳琳. 探地雷达技术在植物根系检测研究中的应用[J]. 地球科学进展, 2009, 24(6): 606-611. doi: 10.11867/j.issn.1001-8166.2009.06.0606
	[Cui Xihong, Chen Jin, Guan Linlin. The application of ground penetrating radar to plant root system detection[J]. Advances in Earth Science, 2009, 24(6): 606-611.]
[6]	王勃, 张建军, 赖宗锐, 等. 土壤含水量对探地雷达探测植物根系构型精度的影响[J]. 干旱区研究, 2024, 41(3): 456-466. doi: 10.13866/j.azr.2024.03.10
	[Wang Bo, Zhang Jianjun, Lai Zongrui, et al. Root detection under different soil moisture content based on ground penetrating radar[J]. Arid Zone Research, 2024, 41(3): 456-466.]
[7]	王明凯, 李文彬, 文剑. 基于探地雷达对粗根的识别技术研究[J]. 森林工程, 2020, 36(3): 21-27.
	[Wang Mingkai, Li Wenbin, Wen Jian. Study on recognition technology of coarse roots using ground-penetrating radar[J]. Forestry Engineering, 2020, 36(3): 21-27.]
[8]	Guo L, Chen J, Cui X, et al. Application of ground penetrating radar for coarse root detection and quantification: A review[J]. Plant and Soil, 2012, 362(1-2): 1-23.
[9]	李鹏, 李占斌, 澹台湛. 黄土高原退耕草地植被根系动态分布特征[J]. 应用生态学报, 2005, 16(5): 849-853.
	[Li Peng, Li Zhanbin, Tan Taizhan, et al. Dynamic distribution characters of herbaceous vegetation root systems in abandoned grasslands of Loess Plateau[J]. Chinese Journal of Applied Ecology, 2005, 16(5): 849-853.] pmid: 16110657
[10]	崔喜红, 陈晋, 沈金松, 等. 基于探地雷达的树木根径估算模型及根生物量估算新方法[J]. 中国科学: 地球科学, 2011, 41(2): 243-252.
	[Cui Xihong, Chen Jin, Shen Jinsong, et al. Modeling tree root diameter and biomass by ground-penetrating radar[J]. Science China Earth Sciences, 2011, 41(2): 243-252.]
[11]	Butnor J R, Doolittle J A, Kress L, et al. Use of ground-penetrating radar to study tree roots in the southeastern United States[J]. Tree Physiology, 2001, 21(17): 1269-1278. doi: 10.1093/treephys/21.17.1269 pmid: 11696414
[12]	Isaac M E, Anglaaere L C N. Intraspecific root plasticity in agroforestry systems across edaphic conditions[J]. Agriculture Ecosystems and Environment, 2014, 185: 16-23.
[13]	Hirano Y, Yamamoto R. Detection frequency of Pinus thunbergii roots by ground-penetrating radar is related to root biomass[J]. Plant and Soil, 2012, 360: 363-373.
[14]	Bi L, Xing L, Liang H, et al. Estimation of coarse root system diameter based on ground-penetrating radar forward modeling[J]. Forests, 2023, 14: 1370.
[15]	Liu Q, Cui X, Liu X, et al. Detection of root orientation using ground-penetrating radar[J]. IEEE Transactions on Geoscience & Remote Sensing, 2017, PP(99): 1-12.
[16]	王泽鹏, 张潇巍, 薛芳秀, 等. 探地雷达树木根系定位与直径估算[J]. 农业工程学报, 2021, 37(8): 160-168.
	[Wang Zepeng, Zhang Xiaowei, Xue Fangxiu, et al. Estimating the location and diameter of tree roots using ground penetrating radar[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(8): 160-168.]
[17]	Tosti F, Ciampoli L B. GPR applications in mapping the subsurface root system of street trees with road safety-critical implications[J]. Advances in Transportation Studies, 2018, 44: 107-118.
[18]	Zhu S, Huang C, Su Y, et al. 3D Ground penetrating radar to detect tree roots and estimate root biomass in the field[J]. Remote Sensing, 2014, 6(6): 5754-5773.
[19]	Wu Y, Guo L, Cui X, et al. Ground penetrating radar based automatic reconstruction of three dimensional coarse root system architecture[J]. Plant and Soil, 2014, 383(1-2) : 155-172.
[20]	Freeland R S. Surveying the near surface Fibrous citrus root system of the orange tree with 3D GPR[J]. Applied Engineering in Agriculture, 2016, 32(2): 145-153.
[21]	Stover D B, Day F P, Butnor J R, et al. Effect of elevated CO₂ on coarse-root biomass in Florida scrub detected by ground-penetrating radar[J]. Ecology, 2007, 88: 1328-1334.
[22]	Hirano Y, Dannoura M, Aono K, et al. Limiting factors in the detection of tree roots using ground-penetrating radar[J]. Plant and Soil, 2009, 319: 15-24.
[23]	马嘉辉. 基于深度学习的树木根系探地雷达无损检测方法研究[D]. 无锡: 江南大学, 2024.
	[Ma Jiahui. Research on Nondestructive Detection Method of Tree Root Ground Penetrating Radar Based on Deep Learning[D]. Wuxi: Jiangnan University, 2024.]
[24]	李爽, 张潇巍, 谭旭, 等. 基于深度学习的树木根系探地雷达多目标参数反演识别[J]. 北京林业大学学报, 2024, 46(4): 103-114.
	[Li Shuang, Zhang Xiaowei, Tan Xu, et al. Deep learning-based inverse identification of multi-target parameters for tree rooting ground-penetrating radar[J]. Journal of Beijing Forestry University, 2024, 46(4): 103-114.]
[25]	李光辉, 徐汇, 刘敏. 基于探地雷达杂波抑制与偏移成像的树木根系定位方法[J]. 农业机械学报, 2022, 53(3): 206-214.
	[Li Guanghui, Xu Hui, Liu Min, et al. Tree-root localization method based on migration imaging with clutter suppressed in ground-penetrating radar[J]. Transactions of the Chinese Society for Agricultural Machinery, 2022, 53(3): 206-214.]
[26]	胡亚伟, 孙若修, 申明爽, 等. 晋西黄土区土地利用方式对土壤C:N:P化学计量特征及土壤理化性质的影响[J]. 干旱区研究, 2021, 38(4): 990-999. doi: 10.13866/j.azr.2021.04.10
	[Hu Yawei, Sun Ruoxiu, Shen Mingshuang, et al. Effects of land use types on the stoichiometric characteristics of soil C:N:P and the physical and chemical properties of soil in western Shanxi loess region[J]. Arid Zone Research, 2021, 38(4): 990-999.] doi: 10.13866/j.azr.2021.04.10
[27]	刘秀萍, 陈丽华, 陈吉虎. 刺槐和油松根系密度分布特征研究[J]. 干旱区研究, 2007, 24(5): 647-651.
	[Liu Xiuping, Chen Lihua, Chen Jihu. Study on the distribution of root density of Robinia pseudoacacia L. and Pinus tabulaeformis Carr.[J]. Arid Zone Research, 2007, 24(5): 647-651.]
[28]	张璐云, 崔喜红, 全振先, 等. 野外自然条件下探地雷达识别植物根系的有效性研究[J]. 地球物理学进展, 2021, 36(6): 2764-2774.
	[Zhang Luyun, Cui Xihong, Quan Zhenxian, et al. Availability of ground penetrating radar in recognizing plant roots in field[J]. Progress in Geophysics, 2021, 36(6): 2764-2774.]
[29]	李鑫俐. 探地雷达土壤水分监测的最佳天线间距确定方法研究[D]. 哈尔滨: 东北农业大学, 2023.
	[Li Xinli. Determination Method of the Optimal Antenna Separation for Soil Moisture Monitoring Based on Ground Penetrating Radar[D]. Harbin: Northeast Agricultural University, 2023.]
[30]	邱啟璜, 牛健植, 王迪, 等. 基于探地雷达识别林地粗根和石砾[J]. 北京林业大学学报, 2023, 45(7): 99-109.
	[Qiu Qihuang, Niu Jianzhi, Wang Di, et al. Identification of coarse roots and rock fragments in woodland based on ground penetrating radar[J]. Journal of Beijing Forestry University, 2023, 45(7): 99-109.]
[31]	Tanikawa T, Hidetoshi D, Masako Y, et al. Leaf litter thickness, but not plant species, can affect root detection by ground penetrating radar[J]. Plant and Soil, 2013, 408(1-2): 271-283.
[32]	张鹏, 董韬, 马彬, 等. 基于探地雷达的地下管线管径探测与判识方法[J]. 地下空间与工程学报, 2015, 11(4):1023-1032.
	[Zhang Peng, Dong Tao, Ma Bin, et al. Research on interpreting the information of underground pipeline’s diameter detected by GPR[J]. Chinese Journal of Underground Space and Engineering, 2015, 11(4): 1023-1032.]
[33]	王杰. 地下管线探地雷达模型试验及信号特征识别研究[D]. 成都: 西华大学, 2020.
	[Wang Jie. Study on GPR Model Test and Signal Feature Recognition of Underground Pipeline[D]. Chengdu: Xihua University, 2020.]
[34]	Bain J C, Day F P, Butnor J R. Experimental evaluation of several key factors affecting root biomass estimation by 1500 MHz ground-penetrating radar[J]. Remote Sensing, 2017, 9(12): 13-37.
[35]	Liu X, Dong X, Leskovar D I. Ground penetrating radar for underground sensing in agriculture: A review[J]. International Agrophysics, 2016, 30(4): 533-543.
[36]	赖娜娜, 袁承江, 唐硕, 等. 应用探地雷达探测古树根系分布[J]. 东北林业大学学报, 2011, 39(11): 124-126.
	[Lai Nana, Yuan Chengjiang, Tang Shuo, et al. Application of ground-penetrating radar to detection of root system distribution of a veteran tree[J]. Journal of Northeast Forestry University, 2011, 39(11): 124-126.]
[37]	Xiao L, Li C, Cai Y. Interactions between soil properties and the rhizome-root distribution in a 12-year Moso bamboo reforested region: Combining ground-penetrating radar and soil coring in the field[J]. Science of The Total Environment, 2021, 800: 149467.
[38]	Cui X, Liu X, Cao X, et al. Pairing dual-frequency GPR in summer and winter enhances the detection and map of coarse roots in the semi-arid shrubland in China[J]. European Journal of Soil Science, 2020, 71(2): 236-251. doi: 10.1111/ejss.12858
[39]	Cox K D, Scherm H, Serman N. Ground-penetrating radar to detect and quantify residual root fragments following peach orchard clearing[J]. Hort Technology, 2005, 15(3): 600-607.

500 MHz			1000 MHz
实际根径/cm	估算根径/cm	相对误差/cm	实际根径/cm	估算根径/cm	相对误差/cm
4.8	11.8	-7.0	3.0	4.9	-1.9
4.8	15.4	-10.6	3.7	6.1	-2.4
3.0	4.7	-1.7	4.8	7.7	-2.9
3.7	11.9	-8.2	3.0	5.2	-2.2
4.8	12.5	-7.7	3.7	6.8	-3.1
3.7	18.2	-14.5	4.8	8.2	-3.4
4.8	16.3	-11.5	3.0	5.6	-2.6
-	-	-	3.7	6.7	-3.0
-	-	-	4.8	10.3	-5.5