Challenges of Estimating

Trees Height via LIDAR

Based on Point CloudStudy of European Larch (Larix decidua)

and Norway Spruce (Picea abies).

Adam Młodzianowski

LIDAR -Light Detection And

Ranging

The goal of the study

• Which of three based on point cloud toppercentiles (95th, 99th, 100th) the mostaccurately predict tree height?

• Check if height measurements based on 100thpercentile overestimate result.

• Investigate the accuracy of segmentationalgorithm.

Why height?

Key attribute estimated in most forest inventories.

Base for quantitative analysis of forests:• biomass, • volume,• carbon stores etc.

Effective management.

Used for calculating indicies e.g. Site Index.

Study area (1)

Góry Stołowe (Table mountains) NP

Study area (2)

Overview of the Spruce plot

Data (1)

Flight height 700 m

Width of strip 430 m

Distance from

adjacent strips

214 m

Coverage of strips 50% ≈ 215 m

Flight speed 120 kn ≈ 216

km/h

Laser pulse

repetition

frequency

100 kHz

Scanning frequency 51 kHz

Scanning angle +/- 18º

Laser scanner data were

collected in the period of

14.08 – 23.09.2007.

Altman's Optech 3100 System

Flight parameters

Data (2)710.580

LIDAR points

459.620

Spruce plot

75.761

Within crowns

1.641Used for

calculation

Methods (1)

Software:

TreesVis

ArcGIS 10

SPSS Statistics 20

Methods (2)

Automatic single tree segmentation

CHM

Median filter

Primarysegmentation

Layer selection

Final filtering

Results (1)

Norway Spruce European Larch

80%

17%

3%

84%

15%

1%

Automatic Single Tree Segmentation

Results (2)

Norway Spruce European Larch

Bias and Root Mean Square Error

-3,09

-1,27-1,01

2,3

1,31 1,21

-3,5

-2,5

-1,5

-0,5

0,5

1,5

2,5

3,5

95th 99th 100th

-1,85

-0,8-0,4

1,68

0,74 0,55

-3,5

-2,5

-1,5

-0,5

0,5

1,5

2,5

3,5

95th 99th 100th

Results (3)

Norway Spruce European Larch

Regression analysis – 100th percentile

�� Linear = 0,978 �

� Linear = 0,952

0,965

0,957

0,9

0,91

0,92

0,93

0,94

0,95

0,96

0,97

0,98

0,99

1

Own study Stephens et al.(2012)

Coefficient of determination

Results (4)

Bias and RMSE Coefficient of determination

Comparison of the results

-0,4

-1,13

-0,14

0,55 0,63

0,98

1,35

-1,5

-1

-0,5

0

0,5

1

1,5

Own study Persson etal. (2002)

Hyyppä etal (2000)

Kwak et al.(2007)

Conclusion

• Small-footprint LIDAR systems have potential forthe estimation of individual tree height of coniferspecies.

• Under given conditions maximum heightpercentile derived from ALS point cloud is themost accurate metrics in tree height estimation.

• Point cloud based metrics tend to underestimateresults.

Further research

• Other tree species have to be investigated –including hardwoods.

• Influence of the stand age on heightestimation.

• Data filtering.

Thank you for attention.

References••

• Gatziolis, D., Fried, J., Monleon, V. (2010). Challenges to Estimating Tree Height via LiDAR in Closed-Canopy Forests. Forest Science, 2010; 56, 2.

•• Hyyppä, J.; Hyyppä, H.; Yu, X.; Kaartinen, H.; Kukko, A.; Holopainen, M. (2009). Forest inventory using small-footprint

airborne LiDAR. In Topographic Laser Ranging and Scanning, Principles and Processing; Shan, J., Toth, C.K., Eds.; CRC Press: Boca Raton, FL, USA, 2009, 335-370.

•• Heurich, M., Persson, A., Holmgren, J., Kennel, E. (2004). Detecting and measuring individual trees with laser scanning in

mixed mountain forest of Central Europe using and algorithm developed for Swedish boreal forest conditions. In: Proceedings of the ISPRS Working Group part 8/2, Int Arch Photogramm Remote Sens vol 36, Freiburg, Germany, 2004, 307–312.

•• Hyyppä, J., Schardt, M., Haggrén, H., Koch, B., Lohr, U., Scherrer, H.U., Paananen, R., Luukkonen, H., Ziegler, M., Hyyppä, H.,

Pyysalo, U., Friedländer, H., Uuttera, J., Wagner, S., Inkinen, M., Wimmer, A., Kukko, A., Ahokas, E., Karjalainen, M. (2001). HIGH-SCAN: The first european-wide attempt to derive single tree information from laserscanner data. Photogramm. J. Finl. 2001, 17, 58-68.

•• Hyyppä, J., Pyysalo, U., Hyyppä, H. & Samberg, A. (2000). Elevation accuracy of laser scanning-derived digital terrain and

target models in forest environment. In: (eds.). the 4th EARSeL workshop on LIDAR Remote Sensing of Land and Sea.Dresden, Germany, 139-147.

•• Kwak, D.A., Lee, W.K., Lee, J.H., Biging, G., Gong, P. (2007). Detection of individual trees and estimation of tree height using

LiDAR data. J For Res, 2007, 12:425–434.•• Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M. (2003). LiDAR remote sensing of forest structure. Prog. Phys. Geog. 27:

88_/106.•• McCombs, J.W., Roberts, S.D., Evans, D.L. (2003). Influence of fusing Lidar and multispectral imagery on remotely sensed

estimates of stand density and mean tree height in managed Loblolly Pine plantation. For. Sci. 2003, 49(3):457-466.•• Means, J., Acker, S., Fitt, B., Renslow, M., Emerson, L., Hendrix, C. (2000). Predicting forest stand characteristics with airborne

scanning LIDAR. Photogramm. Eng. Rem. Sens. 2000, 66:1367-1371.•• Miller, D.R., Quine, C.P., Warwick, H. (2000). An investigation of the potential of digital photogrammetry to provide

measurements of forest characteristics and abiotic damage. Forest Ecology and Management 2000, 135, 279-288.•• Næsset, E. (1997). Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens. Environ. 1997,

61:246–253

• Naesset, E. (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and fielddata. Remote Sens. Environ. 2002, 80, 88-99.

•

• Naesset,. E., Okland, T. (2002). Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment 79 (2002) 105–115.

•

• Persson, Å., Holmgren J., Södermann U. (2002). Detecting and Measuring Individual Trees using an Airborne Laser Scanner. Photogrammetric Engineering & Remote Sensing. Vol. 68. No. 9., 2002, 925-932.

•

• Popescu, S.C., Wynne, R.H., Scrivani, J.A. (2004). Fusion of small-footprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA, Forest Science 2004, 50(4):551-565.

•

• Reitberger, J., Heurich, M., Krzystek, P. (2010) Estimation of Stem Volume by Using 3D Segments Derived from Full-Waveform LiDARData. In Proceedings of SilviLaser 2010, Freiburg, Germany, 2010, 14–17.

•

• Reutebuch, S., McGaughey, R., Andersen, H.E., Carson, W. (2003). Accuracy of a high resolution LIDAR-based terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing (in press).

•

• Stephens, P., Kimberley, M., Beets, P., Paul, T., Searles, N., Bell, A., Brack, C., Broadley, J. (2012). Airborne scanning LiDAR in a double sampling forest carbon inventory. Remote Sensing of Environment 2012, 117, 348–357.

•

• Stereńczak K. 2011a Wykorzystanie danych lotniczego skanowania laserowego do określania zagęszczenia drzew w jednopiętrowych drzewostanach sosnowych. PhD thesis. Forestry Faculty, Warsaw University of Life Sciences.

•

• Stereńczak K., Kozak J. 2011b. Evaluation of digital terrain models generated from airborne laser scanning data under forest conditions. Scandinavian Journal of Forest Research, 26: 374-384. DOI: 10.1080/02827581.2011.570781.

•

• Stereńczak, K., Będkowski, K., Weinacker, H. (2011c). Accuracy of crown segmentation of selected trees and forest stand parameters in order to resolution of used DSM and NDSM models generated from dense small-footprint LIDAR data. Commission VI, WG VI/5.

•

• Straub, C., Koch, B. (2011). Estimating Single Tree Stem Volume of Pinus sylvestris Using Airborne Laser Scanner and Multispectral Line Scanner Data. Remote Sens. 2011, 3, 929-944.

•

• Takahashi, T., Yamamoto, K. (2005). Estimating individual tree heights of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR. J For Res 2005, 10:135–142

•

• Thomas, V., Treitz, P., McCaughey, J.H., Morrison, I., (2006). Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Canadian Journal of Forest Research 36, 34-47.