UAV RTK/PPK Method - An Optimal Solution for Mapping Inaccessible Forested Areas?

Mapping hard-to-access and hazardous parts of forests by terrestrial surveying methods is a challenging task. Remote sensing techniques can provide an alternative solution to such cases. Unmanned aerial vehicles (UAVs) can provide on-demand data and higher flexibility in comparison to other remote sensing techniques. However, traditional georeferencing of imagery acquired by UAVs involves the use of ground control points (GCPs), thus negating the benefits of rapid and efficient mapping in remote areas. The aim of this study was to evaluate the accuracy of RTK/PPK (real-time kinematic, post-processed kinematic) solution used with a UAV to acquire camera positions through post-processed and corrected measurements by global navigation satellite systems (GNSS). To compare this solution with approaches involving GCPs, the accuracies of two GCP setup designs (4 GCPs and 9 GCPs) were evaluated. Additional factors, which can significantly influence accuracies were also introduced and evaluated: type of photogrammetric product (point cloud, orthoimages and DEM) vegetation leaf-off and leaf-on seasonal variation and flight patterns (evaluated individually and as a combination). The most accurate results for both horizontal (X and Y dimensions) and vertical (Z dimension) accuracies were acquired by the UAV RTK/PPK technology with RMSEs of 0.026 m, 0.035 m and 0.082 m, respectively. The PPK horizontal accuracy was significantly higher when compared to the 4GCP and 9GCP georeferencing approach (p < 0.05). The PPK vertical accuracy was significantly higher than 4 GCP approach accuracy, while PPK and 9 GCP approach vertical accuracies did not differ significantly (p = 0.96). Furthermore, the UAV RTK/PPK accuracy was not influenced by vegetation seasonal variation, whereas the GCP georeferencing approaches during the vegetation leaf-off season had lower accuracy. The use of the combined flight pattern resulted in higher horizontal accuracy; the influence on vertical accuracy was insignificant. Overall, the RTK/PPK technology in combination with UAVs is a feasible and appropriately accurate solution for various mapping tasks in forests.

[1]  P. Gong,et al.  True Orthoimage Production for Forested Areas from Large-Scale Aerial Photographs , 2003 .

[2]  Juha Hyyppä,et al.  Geometric test field calibration of digital photogrammetric sensors , 2006 .

[3]  P. Defourny,et al.  Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery , 2006 .

[4]  Bernhard Hofmann-Wellenhof,et al.  GNSS - Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more , 2007 .

[5]  C. Strecha,et al.  The Accuracy of Automatic Photogrammetric Techniques on Ultra-light UAV Imagery , 2012 .

[6]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

[7]  Arko Lucieer,et al.  Development of a UAV-LiDAR System with Application to Forest Inventory , 2012, Remote. Sens..

[8]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[9]  K. N. Tahar.  AN EVALUATION ON DIFFERENT NUMBER OF GROUND CONTROL POINTS IN UNMANNED AERIAL VEHICLE PHOTOGRAMMETRIC BLOCK , 2013 .

[10]  Krista L. Merry,et al.  Dynamic accuracy of recreation-grade GPS receivers in oak-hickory forests , 2014 .

[11]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[12]  A. Elatawneh,et al.  Forest Cover Database Updates Using Multi-Seasonal RapidEye Data—Storm Event Assessment in the Bavarian Forest National Park , 2014 .

[13]  Feng Zhao,et al.  Deciphering the Precision of Stereo IKONOS Canopy Height Models for US Forests with G-LiHT Airborne LiDAR , 2014, Remote. Sens..

[14]  Robert S. Nuske,et al.  Using Unmanned Aerial Vehicles (UAV) to Quantify Spatial Gap Patterns in Forests , 2014, Remote. Sens..

[15]  Terje Gobakken,et al.  Inventory of Small Forest Areas Using an Unmanned Aerial System , 2015, Remote. Sens..

[16]  R. Minařík,et al.  USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS , 2016 .

[17]  Henrik Persson,et al.  Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data , 2016, Remote. Sens..

[18]  Markus Gerke,et al.  Accuracy analysis of photogrammetric UAV image blocks: influence of onboard RTK-GNSS and cross flight patterns , 2016 .

[19]  Hao Chen,et al.  Forest Canopy Height Estimation Using Tandem-X Coherence Data , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  P. Surový,et al.  Forest Stand Inventory Based on Combined Aerial and Terrestrial Close-Range Photogrammetry , 2016 .

[21]  Torsten Bumgarner.  Aerial Photography And Image Interpretation , 2016 .

[22]  M. Detto,et al.  Drones as a Tool for Monoculture Plantation Assessment in the Steepland Tropics , 2017 .

[23]  M. Mokroš,et al.  Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy , 2017 .

[24]  Lammert Kooistra,et al.  Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR , 2017, Sensors.

[25]  Norbert Pfeifer,et al.  A Case Study of UAS Borne Laser Scanning for Measurement of Tree Stem Diameter , 2017, Remote. Sens..

[26]  Gianfranco Forlani,et al.  Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation , 2017, Remote. Sens..

[27]  F. Agüera-Vega,et al.  Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle , 2017 .

[28]  C. Silva,et al.  Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest , 2017 .

[29]  M. Tomé,et al.  Use of multi-temporal UAV-derived imagery for estimating individual tree growth in Pinus pinea stands , 2017 .

[30]  Nicholas C. Coops,et al.  Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models , 2018, Remote. Sens..

[31]  Eloise G Zimbelman,et al.  Real-time positioning in logging: Effects of forest stand characteristics, topography, and line-of-sight obstructions on GNSS-RF transponder accuracy and radio signal propagation , 2018, PloS one.

[32]  Heiko Balzter,et al.  Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[33]  E. Honkavaara,et al.  Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft , 2018 .

[34]  Peter Surový,et al.  Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands , 2018, Geo spatial Inf. Sci..

[35]  V. Gond,et al.  Mapping Lorey’s height over Hyrcanian forests of Iran using synergy of ICESat/GLAS and optical images , 2018 .

[36]  C. Silva,et al.  Estimating forest uniformity in Eucalyptus spp. and Pinus taeda L. stands using field measurements and structure from motion point clouds generated from unmanned aerial vehicle (UAV) data collection , 2018, Forest Systems.

[37]  Joanne C. White,et al.  Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots , 2018 .

[38]  S. Puliti,et al.  Tree-Stump Detection, Segmentation, Classification, and Measurement Using Unmanned Aerial Vehicle (UAV) Imagery , 2018 .

[39]  José Manuel Galván Rangel,et al.  The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs , 2018, International Journal of Remote Sensing.

[40]  Norbert Pfeifer,et al.  Impact of the Acquisition Geometry of Very High-Resolution Pléiades Imagery on the Accuracy of Canopy Height Models over Forested Alpine Regions , 2018, Remote. Sens..

[41]  Chang-Wook Lee,et al.  Forest Canopy Height Estimation Using Multiplatform Remote Sensing Dataset , 2018, J. Sensors.

[42]  João P. Honrado,et al.  Ecosystem Services in a Protected Mountain Range of Portugal: Satellite-Based Products for State and Trend Analysis , 2018, Remote. Sens..

[43]  Jan Komárek,et al.  Comparison of a commercial and home-assembled fixed-wing UAV for terrain mapping of a post-mining site under leaf-off conditions , 2018, International Journal of Remote Sensing.

[44]  Mostafa Rabah,et al.  Using RTK and VRS in direct geo-referencing of the UAV imagery , 2018, NRIAG Journal of Astronomy and Geophysics.

[45]  Eija Honkavaara,et al.  Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows , 2018, Remote. Sens..

[46]  Anna Barbati,et al.  UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates? , 2018, Remote. Sens..

[47]  L. Rodriguez,et al.  Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations , 2018, International Journal of Remote Sensing.

[48]  Tian Zhou,et al.  Automated Aerial Triangulation for UAV-Based Mapping , 2018, Remote. Sens..

[49]  G. Castilla,et al.  Detection of Coniferous Seedlings in UAV Imagery , 2018, Forests.

[50]  M. Kardoš.  Methods of digital photogrammetry in forest management in Slovakia , 2018 .

[51]  Jana Müllerová,et al.  Assessing the Accuracy of Digital Surface Models Derived from Optical Imagery Acquired with Unmanned Aerial Systems , 2019, Drones.

[52]  Vítězslav Moudrý,et al.  Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success , 2019, Applied Geography.

[53]  Nicholas C. Coops,et al.  Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest , 2019, Remote. Sens..

[54]  Farid Kendoul,et al.  Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.