UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring

被引:135
|
作者
Krause, Stuart [1 ,2 ,3 ]
Sanders, Tanja G. M. [1 ]
Mund, Jan-Peter [2 ]
Greve, Klaus [3 ]
机构
[1] Thunen Inst Forest Ecosyst, Alfred Moller Str 1,Haus 41-42, D-16225 Eberswalde, Germany
[2] Eberswalde Univ Sustainable Dev, Fac Forest & Environm, Alfred Moller Str 1,Haus 11, D-16225 Eberswalde, Germany
[3] Univ Bonn, Dept Geog, Meckenheimer Allee 166, D-53115 Bonn, Germany
关键词
tree height; UAV; intensive forest monitoring; photogrammetry; precision forestry; PRECISION FORESTRY; VOLUME ESTIMATION; AIRBORNE LIDAR; PLOT-LEVEL; ATTRIBUTES; EXTRACTION; DIAMETER; IMAGERY; ERROR; AREA;
D O I
10.3390/rs11070758
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] UAV-Based LiDAR Scanning for Individual Tree Detection and Height Measurement in Young Forest Permanent Trials
    Rodriguez-Puerta, Francisco
    Gomez-Garcia, Esteban
    Martin-Garcia, Saray
    Perez-Rodriguez, Fernando
    Prada, Eva
    REMOTE SENSING, 2022, 14 (01)
  • [2] UAV Photogrammetric Surveys for Tree Height Estimation
    Vacca, Giuseppina
    Vecchi, Enrica
    DRONES, 2024, 8 (03)
  • [3] An investigation of tree extraction from UAV-based photogrammetric dense point cloud
    Polat, Nizar
    Uysal, Murat
    ARABIAN JOURNAL OF GEOSCIENCES, 2020, 13 (17)
  • [4] An investigation of tree extraction from UAV-based photogrammetric dense point cloud
    Nizar Polat
    Murat Uysal
    Arabian Journal of Geosciences, 2020, 13
  • [5] Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data
    Sothe, Camile
    Dalponte, Michele
    de Almeida, Claudia Maria
    Schimalski, Marcos Benedito
    Lima, Carla Luciane
    Liesenberg, Veraldo
    Miyoshi, Gabriela Takahashi
    Garcia Tommaselli, Antonio Maria
    REMOTE SENSING, 2019, 11 (11)
  • [6] UAV-Based Forest Health Monitoring: A Systematic Review
    Ecke, Simon
    Dempewolf, Jan
    Frey, Julian
    Schwaller, Andreas
    Endres, Ewald
    Klemmt, Hans-Joachim
    Tiede, Dirk
    Seifert, Thomas
    REMOTE SENSING, 2022, 14 (13)
  • [7] Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
    Nevalainen, Olli
    Honkavaara, Eija
    Tuominen, Sakari
    Viljanen, Niko
    Hakala, Teemu
    Yu, Xiaowei
    Hyyppa, Juha
    Saari, Heikki
    Polonen, Ilkka
    Imai, Nilton N.
    Tommaselli, Antonio M. G.
    REMOTE SENSING, 2017, 9 (03)
  • [8] UAV-based Measurement of Vegetation Indices for Environmental Monitoring
    De Biasio, Thomas Arnold Martin
    Fritz, Andreas
    Leitner, Raimund
    2013 SEVENTH INTERNATIONAL CONFERENCE ON SENSING TECHNOLOGY (ICST), 2013, : 704 - 707
  • [9] UAV-BASED AUTOMATIC TREE GROWTH MEASUREMENT FOR BIOMASS ESTIMATION
    Karpina, Mateusz
    Jarzabek-Rychard, Malgorzata
    Tymkow, Przemyslaw
    Borkowski, Andrzej
    XXIII ISPRS CONGRESS, COMMISSION VIII, 2016, 41 (B8): : 685 - 688
  • [10] PERFORMANCE TEST ON UAV-BASED PHOTOGRAMMETRIC DATA COLLECTION
    Haala, Norbert
    Cramer, Michael
    Weimer, Florian
    Trittler, Martin
    INTERNATIONAL CONFERENCE ON UNMANNED AERIAL VEHICLE IN GEOMATICS (UAV-G), 2011, 38-1 (C22): : 7 - 12