基于无人机影像的银杏单木胸径预估方法
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摘要
胸径是立木测定的基本因子,自动获取胸径数据是准确高效计算森林蓄积量和生物量的关键。以银杏Ginkgo biloba为研究对象,通过无人机获得影像数据,利用运动恢复结构(SFM)方法生成数字表面模型和正射影像图,进而提取单株银杏的树冠面积(A_c),冠幅(W_c)及树高(H)。3个参数分别与胸径(DBH)建立一元回归模型(A_c-D_(BH),W_c-D_(BH), H-D_(BH)),二元回归模型(A_c&W_c-D_(BH), A_c&H-D_(BH), W_c&H-D_(BH))和三元回归模型(A_c&W_c&H-D_(BH))。52组拟合样本的结果显示:A_c&W_c&H-D_(BH)模型的决定系数(R~2)最高为0.825 0,均方根误差(E_(RMS))最小为0.959 1。19组检测样本的结果显示:A_c&W_c&H-D_(BH)模型反演的胸径值误差率为4.20%,小于A类森林资源胸径因子允许的误差值(5%)。研究结果表明:通过无人机采集树冠面积、冠幅和树高3个参数,可计算得到较高精度的胸径值。
To efficiently calculate and predict forest stock and biomass, diameter at breast height(DBH), a basic factor of a tree, was used in a regression model. In this study, Ginkgo biloba was used as the research object.Image data was obtained with an unmanned aerial vehicle(UAV), and using the method of structure from motion(SFM), a digital surface model and an orthophoto map were generated. Next, the canopy area(A_C), crown width(W_C) and tree height(H) of G. biloba were extracted. Then, one-way regression models(A_C-D_(BH), W_CD_(BH), H-D_(BH)), binary regression models(A_C&W_C-D_(BH), A_C&H-D_(BH), W_C&H-D_(BH)), and a ternary regression model(A_C&W_C&H-D_(BH)) were established. Results of 52 groups of fitted samples showed that the A_C&W_C&H-D_(BH) model had the highest coefficient of determination(R~2= 0.825 0) and the lowest root mean square error(E_(RMS)= 0.959 1). Results of 19 groups of test samples showed that the DBHerror rate for the A_C&W_C&H-D_(BH)model was 4.20%, which was less than the allowable error value(5%) for the A-type forest resource DBHfactor. Thus,a high precision D_(BH)value could be calculated using the three parameters of canopy area, crown width, and tree height, thereby providing a new idea for automated forest resource surveying and monitoring.
To efficiently calculate and predict forest stock and biomass, diameter at breast height(DBH), a basic factor of a tree, was used in a regression model. In this study, Ginkgo biloba was used as the research object.Image data was obtained with an unmanned aerial vehicle(UAV), and using the method of structure from motion(SFM), a digital surface model and an orthophoto map were generated. Next, the canopy area(A_C), crown width(W_C) and tree height(H) of G. biloba were extracted. Then, one-way regression models(A_C-D_(BH), W_CD_(BH), H-D_(BH)), binary regression models(A_C&W_C-D_(BH), A_C&H-D_(BH), W_C&H-D_(BH)), and a ternary regression model(A_C&W_C&H-D_(BH)) were established. Results of 52 groups of fitted samples showed that the A_C&W_C&H-D_(BH) model had the highest coefficient of determination(R~2= 0.825 0) and the lowest root mean square error(E_(RMS)= 0.959 1). Results of 19 groups of test samples showed that the DBHerror rate for the A_C&W_C&H-D_(BH)model was 4.20%, which was less than the allowable error value(5%) for the A-type forest resource DBHfactor. Thus,a high precision D_(BH)value could be calculated using the three parameters of canopy area, crown width, and tree height, thereby providing a new idea for automated forest resource surveying and monitoring.
引文
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[13] PENA J M, TORRES-S魣NCHEZ J, SERRANO-P魪REZ A, et al. Quantifying efficacy and limits of unmanned aerial vehicle(UAV)technology for weed seedling detection as affected by sensor resolution[J]. Sensors, 2015, 15(3):5609-5626.
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[15] HERNANDEZ J G, FERREEIRO E G, SARMENTO A, et al. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal[J]. For Syst, 2016, 25(2):eSC09. doi:10.5424/fs/2016252-08895.
[16] DEMETRIOS G, JEREMYS F, VICENTES M. Challenges to estimating tree height via LiDAR in closed-canopy forests:a parable from western Oregon[J]. For Sci, 2010, 56(2):139-155.
[17]刘文萍,仲亭玉,宋以宁.基于无人机图像分析的树木胸径预测[J].农业工程学报, 2017, 33(21):99-104.LIU Wenping, ZHONG Tingyu, SONG Yining. Prediction of trees diameter at breast height based on unmanned aerial vehicle image analysis[J]. Trans Chin Soc Agric Eng, 2017, 33(21):99-104.
[2] PANAGIOTIDIS D, ABDOLLAHNEJAD A, SUROVY P. Determining tree height and crown diameter from high-resolution UAV imagery[J]. Int J Remote Sensing, 2017, 38(8/10):2392-2410.
[3] BRANDTBERG T, WARNER T A, LANDENBERGER R E, et al. Detection and analysis of individu al leaf-off tree crowns in small footprint, high sampling density LiDAR data from the eastern deciduous forest in North America[J].Remote Sensing Environ, 2003, 85(3):290-303.
[4] VASTARANTA M, LATORRE E G, LUOMA V, et al. Evaluation of a Smartphone APP for forest sample plot measurements[J]. Forests, 2015, 6(4):1179-1194.
[5]步国超,汪沛.基于单站地面激光雷达数据的自适应胸径估计方法[J].激光与光电子学进展, 2016, 53(8):278-286.BU Guochao, WANG Pei. Adaptive estimation method for diameter at breast height based on terrestrial laser scanning[J]. Laser Optoelectronics Prog, 2016, 53(8):278-286.
[6] HUANG Shaoming, TITUS S J, WIENS D P. Comparison of nonlinear height diameter functions for major Alberta tree species[J]. Can J For Res, 1992, 22(9):1297-1304.
[7] SOARES P, TOM魪M. Height-diameter equation for first rotation eucalypt plantations in Portugal[J]. For Ecol Manage, 2002, 166(1):99-109.
[8] TRINCADO G, VANDERSCHAAF C L, BURKHART H E. Regional mixedeffects height-diameter models for loblolly pine(Pinus taeda L.)plantations[J]. Eur J For Res, 2007, 126(2):253-262.
[9] DE’ATH G. Multivariate regression trees:a new technique for modeling species-environment relationships[J]. Ecology, 83(4):1105-1117.
[10]王冬至,张冬燕,张志东,等.基于非线性混合模型的针阔叶混交林树高与胸径关系[J].林业科学, 2016,52(1):30-36.WANG Dongzhi, ZHANG Dongyan, ZHANG Zhidong, et al. Height-diameter relationship for conifer mixed forest based on nonlinear mixed-effects model[J]. Sci Silv Sin, 2016, 52(1):30-36.
[11]董晨,吴保国,张翰.基于冠幅的杉木人工林胸径和树高参数化预估模型[J].北京林业大学学报, 2016, 38(3):55-63.DONG Chen, WU Baoguo, ZHANG Han. Parametric prediction models of DBH and height for Cunninghamia lanceolata plantation based on crown width[J]. J Beijing For Univ, 2016, 38(3):55-63.
[12]何游云,张玉波,李俊清,等.利用无人机遥感测定岷江冷杉单木树干生物量[J].北京林业大学学报,2016, 38(5):42-49.HE Youyun, ZHANG Yubo, LI Junqing, et al. Estimation of steam biomass of individual Abies faxoniana through unmanned aerial vehicle remote sensing[J]. J Beijing For Univ, 2016, 38(5):42-49.
[13] PENA J M, TORRES-S魣NCHEZ J, SERRANO-P魪REZ A, et al. Quantifying efficacy and limits of unmanned aerial vehicle(UAV)technology for weed seedling detection as affected by sensor resolution[J]. Sensors, 2015, 15(3):5609-5626.
[14]毕凯,李英成,丁晓波,等.轻小型无人机航摄技术现状及发展趋势[J].测绘通报, 2015(3):27-31.BI Kai, LI Yingcheng, DING Xiaobo, et al. Aerial photogrammetric technology of light small UAV:status and trend of development[J]. Bull Surv Mapp, 2015(3):27-31, 48.
[15] HERNANDEZ J G, FERREEIRO E G, SARMENTO A, et al. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal[J]. For Syst, 2016, 25(2):eSC09. doi:10.5424/fs/2016252-08895.
[16] DEMETRIOS G, JEREMYS F, VICENTES M. Challenges to estimating tree height via LiDAR in closed-canopy forests:a parable from western Oregon[J]. For Sci, 2010, 56(2):139-155.
[17]刘文萍,仲亭玉,宋以宁.基于无人机图像分析的树木胸径预测[J].农业工程学报, 2017, 33(21):99-104.LIU Wenping, ZHONG Tingyu, SONG Yining. Prediction of trees diameter at breast height based on unmanned aerial vehicle image analysis[J]. Trans Chin Soc Agric Eng, 2017, 33(21):99-104.