1. Institute of Soil and Water Conservation, CAS&MWR, 712100, Yangling, Shaanxi, China; 2. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, 712100, Yangling, Shaanxi, China; 3. College of Grassland Agriculture, Northwest A&F University, 712100, Yangling, Shaanxi, China
Abstract：[Background] Regular extraction of forest structure parameters is an important way to monitor and evaluate the quality of afforestation.Northern Shaanxi is one of the main afforestation areas of the Three-North Shelterbelt Project in China. Its artificial forest has the characteristics of low canopy density, large plant spacing and sparse distribution, which provides favorableconditions for field operation and later image processing of UAV.[Methods] Based on DJI's unmanned aerial vehicle (UAV) carrying RGB(red,green and blue) high-definition camera, this work obtained high-resolution images of the artificial poplar forest in northern Shaanxi in September 21, 2019.Basic data such as DOM(digital orthophoto model), DEM (digital elevation model)and digital point cloud in the study area were obtained by Photoscan image mosaic. Then, with eCognition Developer 6.4's multi-scale segmentation and object-oriented functionality around the DOM of the study area, the single canopy area and diameter length were obtained.CHM(canopy height model)of the study area was obtained by automatically classifying the directly generated point cloud data through Photoscan Pro and calculating based on ArcGIS 10.3.Concurrently, the point cloud data graph module in Leica Cyclone was used to visually interpret the point cloud data generated by high-resolution images and extract the height of a single tree.The automatic and rapid extraction of forest structure parameters (crown width and tree height) and CHM of artificial poplar forest in the Loess Plateau area of northern Shaanxi was achieved via this method.[Results] The overall classification accuracy of canopy coverage images is 96%, and the Kappa coefficient is 0.74.The R2 between the measured east-west crown width and the extracted value is 0.90, and the R2 between the measured south-north crown amplitude and the extracted value is 0.91.Combining with the measured data for verification, R2 after mean regression fitting of crown width in the east-west directions and the north-south directions reaches 0.95. There is an obvious linear relationship between the extracted height of a single tree and the measured value, and its determination coefficient R2=0.80. The CHM after the automatic classification of point cloud data is 82.64 m, and even after the geometric calculation of DSM (digital surface model) and DEM, the CHM height is still 15.83 m. It is found that point cloud data generated by RGB camera can directly obtain the CHM, but its accuracy and resolution are relatively rough, which is not suitable for further extraction of single tree height.[Conclusions] The extraction method of forest structure parameter based on high resolution remote sensing image of UAV is efficient and reliable, and is suitable for artificial forests and areas with relatively single stand structure in northern Shaanxi.
史洁青,冯仲科,刘金成.基于无人机遥感影像的高精度森林资源调查系统设计与试验[J].农业工程学报,2017,33(11):82. SHI Jieqing,FENG Zhongke,LIU Jincheng.Design and experiment of high precision forest resource investigation system based on UAV remote sensing[J].Transactions of the CSAE,2017,33(11):82.
[2]
YAO Huang,QIN Rongjun,CHEN Xiaoyu.Unmanned aerial vehicle for remote sensing applications-a review[J].Remote Sensing,2019,11(12):1443.
[3]
马振宇,庞勇,李增元,等.地基激光雷达森林近地面点云精细分类与倒木提取[J].遥感学报,2019,23(4):743. MA Zhenyu,PANG Yong,LI Zengyuan,et al. Fine classification of near-ground point cloud basedon terrestrial laser scanning and detection of forest fallen wood[J].Journal of Remote Sensing, 2019,23(4):743.
[4]
YIN Dameng,WANG Le.Individual mangrove tree measurement using UAV-based LiDAR data:Possibilities and challenges[J].Remote Sensing of Environment,2019(223):34.
[5]
ALMEIDA D R A,BROADBENT E N,ZAMBRANO A M A,et al. Monitoring the structure of forest restoration plantations with a drone-lidar system[J].International Journal of Applied Earth Observation and Geoinformation,2019(79):192.
[6]
NEVALAINEN O,HONKAYAARA E,TUOMINEN S,et al.Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging[J].Remote Sensing,2017,9(3):185.
[7]
XIANG Tianzhu, XIA Guisong,ZHANG Liangpei.Mini-UAV-based remote sensing:Techniques,applications and prospectives[J].IEEE Geoscience and Remote Sensing Magazine,2019,7(3):29.
[8]
AYANA F,CHIHIRO H,TAKANORI M,et al. An end to end process development for UAV-SfM based forest monitoring:Individual tree detection,species classification and carbon dynamics simulation[J].Forests,2019,10(8):680.
[9]
SOTHE C,DALPONTE M,CLAUDIA M D A,et al.Tree species classification in a highly diverse subtropical forest integrating UAV-Based photogrammetric point cloud and hyperspectral data[J].Remote Sensing,2019,11(11):1338.
[10]
LIANG Xinlian,WANG Yunsheng,PYORALA J,et al.Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements[J].Forest Ecosystems,2019(6):20.
[11]
廖凯涛,宋月君,张金生,等.无人机遥测技术在水土保持生态果园改造监测中的应用[J].中国水土保持科学,2017,15(5):135. LIAO Kaitao,SONG Yuejun,ZHANG Jinsheng,et al.UAV remote sensing technology in the application of the ecological orchard construction of soil and water conservation[J].Science of Soil and Water Conservation,2017,15(5):135.
[12]
WANG Yunsheng, PYORALA J,LIANG Xinlian,et al. In situ biomass estimation at tree and plot levelsWhat did data record and what did algorithms derive from terrestrial and aerial point clouds in boreal forest[J].Remote Sensing of Environment,2019(232):111309.
[13]
YURTERVEN H, AKGUL M,COBAN S,et al. Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques[J]. Measurement,2019(145):651.
[14]
MEYER G E, NETO J C.Verification of color vegetation indices for automated crop imaging applications[J].Computers and Electronics in Agriculture,2008,63(2):282.
[15]
GULCI S. The determination of some stand parameters using SfM-based spatial 3D point cloud in forestry studies:an analysis of data production in pure coniferous young forest stands[J].Environ Monit Assess,2019,191(8):495.
[16]
BENDIG J, BOLTEN A, BENNERTZ S,et al. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging[J].Remote Sensing,2014,6(11):10395.
[17]
汪沛,罗锡文,周志艳,等.基于微小型无人机的遥感信息获取关键技术综述[J].农业工程学报,2014,30(18):1. WANG Pei,LUO Xiwen,ZHOU Zhiyan,et al.Key technology for remote sensing information acquisition based on micro UAV[J].Transactions of the CSAE,2014,30(18):1.
[18]
吴金胜,刘红利,张锦水.无人机遥感影像面向对象分类方法估算市域水稻面积[J].农业工程学报,2018,34(1):70. WU Jinsheng,LIU Hongli,ZHANG Jinshui.Paddy planting acreage estimation in city level based on UAV images and object-oriented classification method[J].Transactions of the CSAE, 2018, 34(1):70.
[19]
王雷,龙永清,杨勤科.重采样方法对DEM数据质量的影响[J].水土保持通报,2016,36(4):72. WANG Lei,LONG Yongqing,YANG Qinke.Effects of resampling method on data quality of DEMs[J].Bulletin of Soil and Water Conservation,2016,36(4):72.
[20]
夏永杰,庞勇,刘鲁霞,等.高精度DEM支持下的多时期航片杉木人工林树高生长监测[J].林业科学,2019,55(4):108. XIA Yongjie,PANG Yong,LIU Luxia,et al.Forest height growth monitoring of Cunninghamia lanceolata plantation using multi-temporal aerial photography with the support of high accuracy DEM[J].Scientia Silvae Sinicae,2019,55(4):108.
[21]
申鑫,曹林,佘光辉.高光谱与高空间分辨率遥感数据的亚热带森林生物量反演[J].遥感学报,2016,20(6):1446. SHEN Xin,CAO Lin,SHE Guanghui.Subtropical forest biomass estimation based on hyperspectral and high-resolution remotely sensed data[J].Journal of Remote Sensing,2016,20(6):1446.
[22]
HUANG Hongyu,LI Xu,CHEN Chongcheng.Individual tree crown detection and delineation from Very-High-Resolution UAV images based on bias field and Marker-Controlled watershed segmentation algorithms[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,13(7):2253.
[23]
LI Dong,GUO Huadong,WANG Cheng,et al.Individual tree delineation in windbreaks using Airborne-Laser-Scanning data and unmanned aerial vehicle stereo images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,11(9):1330.
[24]
WALLACE L, LUCIEER A, WATSON C S,et al.Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data[J].IEEE Transactionson Geoscience and Remote Sensing,2014,52(12):7619.
[25]
BRIEGER F,HERZSCHUH U,PESTRYAKOVA L A,et al. Advances in the derivation of northeast Siberian forest metrics using High-Resolution UAV-Based photogrammetric point clouds[J].Remote Sensing,2019,11(12):1447.
[26]
KARPINA M, JARZABEK-RYCHARD M, TYMKOW P,et al.UAV-Based automatic tree growth measurement for biomass estimation[J].The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2016(XLI-B8):685.
[27]
刘鲁霞,庞勇,李增元.基于地基激光雷达的亚热带森林单木胸径与树高提取[J].林业科学,2016,52(2):26. LIU Luxia,PANG Yong,LI Zengyuan. Individual tree DBH and height estimation using terrestrial laser scanning (TLS) in a subtropical forest[J]. Scientia Silvae Sinicae,2016,52(2):26.
[28]
TIAN Jiarong, DAI Tingting,LI Haidong,et al. A novel tree height extraction approach for individual trees by combining TLS and UAV image-based point cloud integration[J].Forests,2019,10(7):537.
[29]
WALLACE L,LUCIEER A, MALENOVSKY Z,et al.Assessment of forest structure using two UAV techniques:A comparison of airborne laser scanning and structure from motion (SfM) point clouds[J].Forests,2016,7(3):62.
2021, Vol. 19 
