Operational low-cost treewise forest inventory using multispectral cameras mounted on drones

Transcription

1 Operational low-cost treewise forest inventory using multispectral cameras mounted on drones Dr. Eugene Lopatin, Natural Resources Institute Finland,

2 Key challenges/opportunities in forest inventory 1. Tree-wise data 2. Low-cost 3. On demand 4. Online 5. Fast - results within hours Source: Tuominen S., Pitkänen T., Balázs A., Kangas A. (2017). Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging. Silva Fennica vol. 51 no. 4 article id p. Is it possible to increase the accuracy by decreasing the cost? 2 Eugene Lopatin

3 New directions: switching from Lidar to Photogrammetry Species recognition: when 3D and intensity or multispectral data were used together, the accuracy of PPCs (89%) surpassed that of lidar (86%). Source: St-Onge, B.; Audet, F.-A.; Bégin, J. Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds. Forests 2015, 6,

4 Alpo Hassinen, 2010 The quality of 3D models was low

5 The impact of number of angles on quality of 3D models ,1 4,05 4 3,95 3,9 3,85 Number of points from different viewing angles 0 69,79% 75,40% 84,13% 98,29% 80, , 70 90, 80 90,80,70 80, , 70 90, 80 90,80,70 100,00% 90,00% 80,00% 70,00% 60,00% 50,00% 40,00% 30,00% 20,00% 10,00% 0,00% Testing the approach: 3 flights from Phantom 3 Pro from 3 viewing angles from 120 meters DSM calculated from different combinations of flights Spatial resolution of 3D model, cm/pixel Increasing number of viewing angles is increasing the density of the digital surface model Increasing number of viewing angles is decreasing the spatial resolution of the digital surface model 5 Eugene Lopatin

6 2017: Forest management inventory using drones for ZAO Karlis (FM Timber Oy) in Russian Karelia 6 Eugene Lopatin

7 Tree-wise forest inventory using UAV 1. Imaging: planer, 2 cameras (visual and IR), GNSS (GPS/GLONASS) 2. Sample plots: ultrasound based measurements of tree coordinares, hypsometer, accurate GPS 3. Data processing: tree wise forest inventory, stands delineation 1 flight = 25 км2 = 2500 ha Results: -Orthophotos, 1 cm/pixel - Visual and IR ranges - 4 spectral bands from Sequoia -Accuracy of georeferencing +-10 см -Al least 2 imaing angles: 90,80 Results: - Exact coordinates of all trees - Height, diameter, wood assortments for each tree - Verification of the image interpretation Results: - GIS point layer of all trees - GIS layer of forest stands - Accuracy assessment report 7 Eugene Lopatin

8 Parrot Sequoia sensor: low cost solution for UAV RGB camera is useless due to the better quality of UAV cameras The value is in 1.2 Mpix spectral cameras automatic species recognition 8 Eugene Lopatin

9 Multispectral data from Sequoia sensor 9 Eugene Lopatin

10 Big drones vs. small drones Flying time up to 4 hours 2520 ha/flight Price around euro Imaging cost 1,12 euro/ha flying in border zone of Russia (17750 ha) Possibilities for costs reduction to 0,5 euro/ha 10 Eugene Lopatin

11 Big drones vs. small drones Phantom 4 Pro + Sequoia: 1689 euro = euro Flying time 30 min 43 ha/flight from 200 m (GSD= 2 cm, 3D) Imaging cost from 0,6 euro/ha (200 flights) 11 Eugene Lopatin

12 Image processing workflow: completely automated, cloud based processing Flight planning using satellite data, e.g. Landsat or Sentinel: variation by spectral channels, variation in growing stock or or FTP Dropbox Aligning images Building dense cloud point Photogrammetric data processing: 1 ha/second Classifying points: ground, vegetation Building DEM Building DSM Tree-wise forest inventory: 3 ha/second Calculating CHM = DSM DEM Crown delineation Calculating tree height Calculating crown diameter Calculating crown length, width Species recognition from Spectral data Identification of dead trees Identification of damaged trees Calculating wood assortments Accuracy Maximum error in height measurement cm Maximum error of diameter estimation 2 cm Species recognition 99% Trees shapefile (points or polygons) Assembling trees into stands Reporting and data distribution Preparing reports Calculating tree value and procurement costs Publishing data as online GIS layer 12 Eugene Lopatin

13 Orthophoto 13 Eugene Lopatin

14 Canopy height model 14 Eugene Lopatin

15 Crowns of trees over 5 meters 15 Eugene Lopatin

16 Current customers Forest management companies Wood cost reduction solution Forest owners Environmental control offices (close range imaging, 10 m) Future customers Looking for partners in VR, AR 16 Eugene Lopatin

17 Conclusions Application of tree-wise forest inventory using the data from UAV: Reduce the costs for forest management inventory Increase accuracy of forest management planning Forest inventory on demand Scale independent, any scale project is feasible The speed of inventory is very high, less than 1 day for 1 project Switching to precise forestry (analog to precise agriculture) 17 Eugene Lopatin