Utilizing Individual Tree Information in Laser Assisted Forest Inventory

Transcription

1 Utilizing individual tree information in laser assisted forest inventory Utilizing Individual Tree Information in Laser Assisted Forest Inventory Juha Hyyppä, Finnish Geodetic Institute Research Group on Mobile Mapping and Precision Forestry

2 Some References Hugershoff 1930s Solodukin in late 1970s US and CAN lidars studies in 1980s Swedish Defence studies with ALS, late 1980, early 1990s Nelson, Aldred, Bonnor, Scheier, McLean, Krabill, Nilsson, Saab Survey Systems Similar development in profiling radar Elachi, Bernard, Hyyppä, Hallikainen, DeChambre First commercial ALS in 1994, 3D radar too complicated Næsset, 1997, first ALS and forest study Today: Topic Laser scanning&forest is one of the fastest growing research areas within remote sensing and photogrammetry

3 Laser Scanning in Forest ALS Airborne Laser Scanning TLS Terrestrial Laser Scanning MLS Mobile Laser Scanning C. Hannu Hyyppä, Antero Kukko, Harri Kaartinen, Anttoni Jaakkola

4 LS Output Output: x,y,z, intensity! C. Hannu Hyyppä, Petri Rönnholm

5 Calibrated Intensity

6 Assessing Individual Tree Information Courtesy to A. Kukko, FGI

7 Finding Tree Locations State-of-the-art Developed Reference C. J. Hyyppä, FGI

8 Individual Tree Method Experience gained in International ISPRS/EuroSDR Test

9 Test Site and Materials Espoonlahti A (2.6 ha) and B (5.8 ha) Laser scanner data 29 June 2004 (2, 4 and 8 pulses per m 2 ) and / or Aerial images 11 October 2004 Extracted trees of site B Courtesy to H. Kaartinen, FGI

10 Participants Definiens AG, Germany Iris Lingenfelder FOI, Sweden Åsa Persson Pacific Forestry Centre, Canada François A. Gougeon University of Hannover, Germany Aiko Sukdolak, Bernd-Michael Wolf, Christian Heipke Joanneum Research, Austria Manuela Hirschmugl Metla, Finland Juho Pitkänen Norwegian Forest Research Institute and University of Life Sciences, Norway Svein Solberg and Erik Næsset National Ilan University,Taiwan Jee-cheng Wu Texas A&M University, USA Sorin Popescu University of Zürich, Switzerland Felix Morsdorf ProGea Consulting, Poland Roeland de Kok and Piotr Wezyk Universita' di Udine,Italy Andrea Barilotti

11 Reference trees: green dots, size dependent on tree height Examples Courtesy to H. Kaartinen, FGI Crown delineation missing from image

12 Tree Height 6.0 Tree Height, trees over 15 m (outliers removed) Definiens FOI_2 FOI_4 FOI_8 PFC_aerial PFC_laser Hannover_2 Hannover_4 Hannover_8 Joanneum_hybrid Metla_2 Metla_4 Metla_8 Norway_2 Norway_4 Norway_8 Ilian_2 Ilian_4 Ilian_8 Texas_2_100 Texas_2_50 Texas_4_50 [m] Texas_8_50 Texas_8_25 Udine_2 Udine_4 Udine_8 Zurich_2 Zurich_4 Zurich_ Courtesy to H. Kaartinen, FGI Mean STD RMSE

13 Tree Species Classification at Individual Tree Level

14 Deciduous Trees First-Last Pulse Difference First pulses: in green; Last pulses: in red Courtesy to X. Liang, FGI

15 Coniferous First-Last Pulse Difference Courtesy to X. Liang, FGI

16 Change Detection

17 Harvested Trees Detection Courtesy to X. Yu, FGI

18 Branch Cutting Due to Power Line Construction Detection of cut trees (branch). From left to right: data of 1998, data of 2000, difference image and cut trees. Courtesy to Xiaowei Yu, FGI

19 Forest Height Growth Yellow: 2003 Red: 1998 DSM 1998 DSM 2003 Courtesy to Xiaowei Yu, FGI

20 3 LS Data Captures in 4 Years - Trees really grow Courtesy to P. Rönnholm, Aalto

21 TLS

22 Use of TLS for Forests Courtesy to H. Kaartinen, FGI

23 Modelling Individual Trees in Detail C. JP Virtanen, Aalto Univ.

24 Defoliation, Biomass Defoliation test with 10 trees 5 pines 5 spruces selected Initial biomass normalized to 1 Final biomass normalized to 0 Number of hits coming from the tree used to calculate the defoliation/ biomass TLS measurement in two directions: up and down (still tilted) Branch and needle biomass R 2 =0.98 Courtesy to P. Lyytikäinen-Saarenmaa, HU, J. Hyyppä, FGI

25 Dbh, Stem Curve Courtesy to H. Kaartinen, FGI

26 SENSEI UAV-Based Laser Scanning

27 Courtesy to A. Jaakkola, FGI

28 Tree Extraction with SENSEI Method n Horizontal mean Horiz. StDev Height mean Height StDev Manual cm 14 cm -15 cm 30 cm Automatic cm 56 cm 2 cm 34 cm Courtesy to A. Jaakkola, FGI

29 Defoliation with SENSEI Courtesy to A. Jaakkola, FGI

30 Mobile Laser Scanning

31 Collecting Reference Data on the Fly

32 Automatic Detection of Poles and Trees Courtesy to M. Lehtomäki, FGI

33 Courtesy to L. Zhu, H. Kaartinen, A. Kukko, FGI

34 System X Riegl March 2010 Elevation Benchmarking of Various MLS at Intern. Level ROAMER June 2009 Intensity Courtesy to H. Kaartinen, FGI

35 Summary Today operational standwise inventory is based on low-resolution ALS data Processing costs as low as 20 cents/ha are reported, LS data acquisition costs as low as cents/ha at national level In the future LS will be used beyond ALS to the benefit of forest owners Technology allows to go into significantly higher precision

36