Accuracy of tree height estimation using digital aerial imagery

Document Type : Complete scientific research article

Authors

1 Faculty of Natural Resources, University of Tehran, Karaj

2 Department of Environmental Engineering, Tabriz Branch, Islamic Azad University

Abstract

Background and objectives: Measuring tree height in forest is time and cost-consuming especially in vast areas. Therefore, using digital aerial images are proposed. The main objective of this research is evaluating capability of such images for estimating trees height.
Materials and methods: This research was done based on nine UltraCam-D aerial images with 70% end lap overlay and 60% side lap, 7 cm pixel size and scale of 1:8000 in Tehran. The four spectral visible and near infrared bands were fused with the panchromatic band. The images were geometrically corrected using triangulation method and then digital elevation model (DEM) and digital surface model (DSM) were produced using LPS and INPHO software respectively. To estimate the accuracy of DEM, the elevation of 15 points from the 1:1000 topographic map of Tehran were extracted and compared with the DEM elevation values at the same points. After that, calculating 101 trees height, were performed by subtracting values of DSM and DEM for each tree. To check the precision of calculated height, heights of these trees were measured by Sonto in the field and were compared with the estimated height. The trees include various species of Conifers and Deciduous. Comparison of estimated heights (calculated) with ground values (measured heights) was performed using simple linear regression and Coefficient of determination (R2) and root-mean-square error (RMSE). Normalized mean error (NMPE) statistics were also used to evaluate the error rate of DSMs and also the pairwise T-test was used to compare height differences in two groups of broad-leaved and needle-leaved trees.
Results:
Due to the RMSE value based on the five control points (0.22 pixels), and the exact correspondence of the corrected images with the topographic map, the reliability of the geometric correction was obtained with acceptable accuracy. The low density of forest cover in the studied area allowed the ground to be seen in the images. This point made it possible to select the right control points and the number of node points. Achieved results, high linear correlation between actual height value and calculated height (R2 = 82%), RMSE (1.35 m) and average difference between calculated and actual height (1.13 m) indicate that estimated height based, has acceptable accuracy. In addition, no significant difference was observed between precision of estimating Conifers and Deciduous height.
Conclusion: Based on the results, this approach could be used operationally to determine height of the trees in relatively flat areas such as urban forest and flat Zagros forest but its capability should be investigated at mountainous areas, high density and high-slope forests.

Keywords


1.Ali, S.S., Dareb, P., and Jonesc, S.D. 2008. Fusion of remotely sensed multispectral imagery and lidar data for forest structure assessment at the tree level. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XXXVII: 1089-1094.
2.Balenović, I., Seletković, A., Pernar, R., and Jazbec, A. 2015. Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution. Annals of Forest Research.
3.Darvishsefat, A.A., Rafieyan, O., Babaee Kafaki, S., and Mataji, A. 2010. Evaluation of UltraCam-D images capability for tree species identification using object-based method in theeven-aged mixed forestation. Iranian J. of Forest. 2: 2. 165-176. (In Persian)
4.Ghasemi, A., Fallah, A., and Shataee Joibari, Sh. 2016. Evaluation of four algorithms for estimation of canopy cover of mangrove forests by using aerial imagery. J. of RS and GIS for Natural Resources. 23: 1-16.
5.Ghasemi Rozveh, A., Shataee Joibary, Sh., and Mohamadi, J. 2017. Capability investigation of digital aerial Ultra Cam-D images in identifying tree species in the Hyrcanian mixed forests (Case study: Shastkalate forest in Gorgan), J. of Wood and Forest Sciences and Technology.24: 1. 77-89.
6.Hirschmugl, M. 2008. Derivation of forest parameters from Ultracam-DData. PhD. Thesis. Graz University of Technology. 
7.Hirschmüller, H., and Bucher, T.2010. Evaluation of digital surface models by semi-global matching. DGPF Tagungsband. 
8.Höhle, J., and Höhle, M. 2009. Accuracy assessment of digital elevation modelsby means of robust statistical methods. ISPRS J. of Photogrammetry and Remote Sensing. 64: 4. 398-406.
9.Jacobsen, K. 2005. Photogrammetry and geoinformation trends in large scale mapping. In: Proceedings of the 1st annual map middle east conference, Geospatial information and knowledge economy, Dubai, UAE. 9p.
10.Järnstedt, J., Pekkarinen, A., Tuominen, S., Ginzler, C., Holopainen, M., and Viitala, R. 2012. Forest variable estimation using a high-resolution digital surface model. ISPRS J. of Photogrammetry and Remote Sensing. 74: 78-84.
11.Khorrami, R.A., Darvishsefat, A.A., Tabari Kochaksaraei, M., and Shataee Jouybari, Sh. 2014. Potential of
LIDAR data for estimation of individual tree height of Acer velutinum and Carpinus betulus. Iranian J. of Forest.
6: 2. 127-140. (In Persian)
12.Krause, S., Sanders, T.G.M., Mund,J.P., and Greve, K. 2019. UAV-based photogrammetric tree height measurement for intensive forest monitoring. Remote Sensing. 11: 7. 758-776.
13.Mohammadi, J., Shataee Jourbary, Sh., Namiranian, M., and Næsset, E. 2017. Modeling biophysical properties of broad-leaved stands in the hyrcanian forests of Iran using fused airbornelaser scanner data and UltraCam-D images.International J. of AppliedEarth Observation and Geoinformation. 61: 32-45. (In Persian)
14.Nurminen, K., Karjalainen, M., Yu, X., Hyyppä, J., and Honkavaara, E. 2013. Performance of dense digital surface models based on image matching inthe estimation of plot-level forest variables. ISPRS J. of Photogrammetry and Remote Sensing. 83: 104-115.
15.Perko, R., Raggam, H., Gutjahr, K., and Schardt, M. 2010. The capabilities of terrasar-x imagery for retrieval of forest parameters. P 452-456, In: W. Wagner and B. Székely (eds), ISPRS TC VII Symposium - 100Years ISPRS, Vienna, Austria.
16.Poli, D., and Caravaggi, I. 2012. Digital surface modelling and 3D information extraction from spaceborne very high resolution stereo pairs. JRC Scientific and Technical Reports, Ispra, Luxembourg. 28p.
17.Rafieyan, O., Darvishsefat, A.A., and Babaee Kafaki, S. 2009. Evaluation of UltraCam-D digital aerial images classification object-based method with the aim of using in forest (investigation of images from the Northern Forests). Third National Forest Conference, 12-14 May, Iran. (In Persian)
18.Rajabpour Rahmati, M., Darvishsefat, A.A., Baghdadi, N., Namiranian, M., and Soofi Mariv, H. 2015. Estimation of forest canopy height in mountainous areas using ICESat-GLAS data. Iranian J. of Forest and Poplar Research.23: 1. 90-103. (In Persian)
19.Rezayan, F., and Erfanifard, Y. 2016. Estimating biophysical parameters of Persian oak coppice trees using UltraCam-D airborne imagery inZagros semi-arid woodlands. J. of Arid Environments. 133: 10-18.
20.Shabanipoor, M., Darvishsefat, A.A., Rafieyan, O., and Etemad, V. 2014. Investigation on the possibility of tree species identification using digital aerial images by object-based classification.J. of Forest and Wood Products.67: 1. 21-32. (In Persian)
21.Sohrabi, H., Zobeiri, M., and Hosseini, S.M. 2009. Preparation of aerial volume table using visual interpretation of digital aerial images. Iranian J. of Forest and Wood Products. 62: 3. 261-274.(In Persian)
22.Tavakol, M.H. 1977. Application of aerial photographs quantitative investigations of OAK forests Western Iran (Nozian area). M.Sc. Thesis. University of Tehran. 142p. (In Persian)