Discriminating between Brant`s oak (Quercus brantii Lindl.) and gall oak (Q. infectoria Oliv.) species using the UAV images

Document Type : Research article

Authors

1 Ph.D. Student of Forestry, Department of Forestry and Forest Economics, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran

2 Corresponding author, Associate Prof., Department of Forestry and Forest Economics, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran

3 Assistant Prof., Department of Forestry and Forest Economics, Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Iran

4 Associate Prof., Geomatics College of the National Cartographic Center, Tehran, Iran

Abstract

Today, tree species classification and mapping play an important role in decision making for sustainable forest management. The high spatial resolution of the UAV images makes them an effective tool for identifying tree species. The aim of this study was to evaluate the capability of unmanned aerial vehicle (UAV) imagery to detect Brant`s oak (Quercus brantii Lindl.) and gall oak (Q. infectoria Oliv.) species in the Kakasharaf area in the Lorestan Province, Iran. For this purpose, three stands were selected with areas of 3.6, 4.9 and 5.4 ha. The images were taken in May 2017 by a Phantom 4 UAV. Mosaic images were prepared using 10, 12 and 15 ground control points, respectively. Specifically, differentiation between two species was based on the classification of images by artificial neural network and spectral information. The reference data was prepared to evaluate the classification results by field survey, and classification was conducted by using 70% of the samples as training samples and the remaining 30% as test samples. Results showed that better performance achieved by neural network classification in all three stands with kappa coefficients of 0.77, 0.76 and 0.82 and overall accuracy of 84.03, 83.42 and 87.37 percent compared with the spectral classification method, which returned kappa coefficients of 0.7, 0.64 and 0.63 and overall accuracies 78.81, 73.4 and 72.19 percent, respectively. Conclusively, UAV data revealed to have a good ability to distinguish between Q. brantii and Q. infectoria in the study area, which suggests that those data can be used for discriminating between different tree species in similar forest areas.

Keywords

References

- Anonymous, 2006. Multifunctional forestry plan of Ghale Gol region. Forests, Range and Watershed Management Organization, Tehran, 200p.
- Chianucci, F., Disperati, L., Guzzi, D., Bianchini, D., Nardino, V., Lastri, C., … and Corona, P., 2016. Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV. International Journal of Applied Earth Observation and Geoinformation, 47: 60-68.
- Dvořák, P., Müllerová, J., Bartaloš, T. and Brůna, J., 2015. Unmanned aerial vehicles for alien plant species detection and monitoring. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W4: 83-90.
- Elvidge, C.D., Keith, D.M., Tuttle, B.T. and Baugh, K.E., 2010. Spectral identification of lighting type and character. Sensors, 10(4): 3961-3988.
- Franklin, S.E., 2018. Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles. Journal of Unmanned Vehicle Systems, 6(4): 195-211.
- Gini, R., Passoni, D., Pinto, L. and Sona, G., 2014. Use of unmanned aerial systems for multispectral survey and tree classification: A test in a park area of northern Italy. European Journal of Remote Sensing, 47(1): 251-269.
- Hemmati, Z., Ebadi, H., Hosseini Naveh Ahmadabadian, A. and Esmaeili, F., 2017. Presented a new algorithm for network design and path planning it captures drone modeling purposes archaeological sites. Journal of Geomatics Science and Technology, 7(2): 167-180 (In Persian).
- Lohrabi, Y., Abbasi, M., Soltani, A., Riyahi Bakhtyari, H.R., 2018. Determination of the most suitable method for forest type mapping in central Zagros using landsat-8 satellite Images. Journal of Forest Research and Development, 4(2): 191-205 (In Persian).
- Mlambo, R., Woodhouse, I., Gerard, F. and Anderson, K., 2017. Structure from motion (SfM) photogrammetry with drone data: A low cost method for monitoring greenhouse gas emissions from forests in developing countries. Forests, 8(3): 68-80.
- Onishi, M. and Ise, T., 2018. Automatic classification of trees using a UAV onboard camera and deep learning. arXiv preprint arXiv:1804.10390.
- Pourahmad, M., Oladi, J. and Falah, A., 2018. Detection of tree species in mixed broad-leaved stands of Caspian forests using UAV images (case study: Darabkola Forest). Ecology of Iranian Forests, 6(11): 61-75 (In Persian).
- Puliti, S., Ørka, H.O., Gobakken, T. and Næsset, E., 2015. Inventory of small forest areas using an unmanned aerial system. Remote Sensing, 7(8): 9632-9654.
- Suomalainen, J., Anders, N., Iqbal, S., Roerink, G., Franke, J., Wenting, P., … and Kooistra, L., 2014. A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles. Remote Sensing, 6(11): 11013-11030.
- Tso, B. and Mather, P.M., 2016. Classification Methods for Remotely Sensed Data, 2nd Edition. CRC Press, Boca Raton, Florida, 376p.
- Wallace, L., Lucieer, A. and Watson, C.S., 2014. Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 52(12): 7619-7628.
Iranian Journal of Forest and Poplar Research
Volume 27, Issue 3 - Serial Number 77
December 2019
Pages 245-257

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