Estimation of qualitative and quantitative characteristics of Pistacia atlantica Desf. and Amygdalus spp. in of UAV point clouds

Document Type : Research article

Authors

1 Corresponding author, Associate Prof., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

2 Assistant Prof., Department of Geomatics, Forest Research Institute, Warsaw, Poland

Abstract

    Estimation of qualitative and quantitative characteristics of plants on UAV images is considered a challenge in remote sensing. Therefore, this study aimed to present a method to estimate crown area, height, and species in a mixed Pistacia-Amygdalus stand in UAV-derived point clouds. To this aim, 100 Pistacia atlantica Desf. trees and 100 Amygdalus spp < em>. shrubs were randomly selected. Point cloud was obtained by UAV-derived imagery with 50 points per m2 in a 64-ha study area in Baneh Research Forest, Fars province. The quantitative characteristics were then estimated on the point cloud. Additionally, species type was classified using random forest and 37 quantitative attributes measured on point cloud, canopy height model, and orthomosaic. Crown area and height of Pistacia (R2= 0.91 and 0.83, PRMSE=4.7% and 3.2%, respectively) and Amygdalus (R2= 0.89 and 0.47, PRMSE=22.1% and 21.5%, respectively) were also estimated. By application of quantitative attributes and random forest, species type was classified with an accuracy of 0.92 and κ of 0.98. All in all, results indicated that UAV point clouds can be efficiently applied to estimate a set of qualitative and quantitative attributes of Pistacia and Amygdalus within the study area. However, inaccurate and imprecise results were observed for estimated heights of Amygdalus.

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References

- Anderson, K. and Gaston, K.J., 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3): 138-146.
- Barazmand, S., Sossani, J., Naghavi, H. and Sadeghyan, S., 2019. Discriminating between Brant`s oak (Quercus brantii Lindl.) and gall oak (Q. infectoria Oliv.) species using the UAV images (Case study: Kakasharaf Forest, Khorramabad, Iran). Iranian Journal of Forest and Poplar Research, 27(3): 245-257 (In Persian).
- Browning, D.M., Rango, A., Karl, J.W., Laney, C.M., Vivoni, E.R. and Tweedie, C.E., 2015. Emerging technological and cultural shifts advancing drylands research and management. Frontiers in Ecology and the Environment, 13(1): 52-60.
- Chenari, A., Erfanifard, S.Y., Dehghani, M. and Pourghasemi, H.R., 2017. Estimation of crown area of wild pistachio single trees using DSM of UAV aerial images in Baneh Research Forest, Fars province. Journal of Wood & Forest Science and Technology, 24(4): 117-130 (In Persian).
- Erfanifard, Y., Stereńczak, K., Kraszewski, B. and Kamińska, A., 2018. Development of a robust canopy height model derived from ALS point clouds for predicting individual crown attributes at the species level. International Journal of Remote Sensing, 39(23): 9206-9227.
- Giannetti, F., Chirici, G., Gobakken, T., Næsset, E., Travaglini, D. and Puliti, S., 2018. A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data. Remote Sensing of Environment, 213: 195-205.
- Gómez-Ramírez, J., Ávila-Villanueva, M. and Fernández-Blázquez, M., 2020. Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Scientific Reports, 10(1): 20630.
- Heidari Safari Kouchi, A., Moradian Fard, F., Eskandari, A. and Rostami Shahraji, T., 2015. Investigation of some quantitative and qualitative characteristics of Persian oak (Quercus brantii Lindl.) in Bazoft Forests of Chahar Mahal and Bakhtiari Province. Journal of Zagros Forests Researches, 2(1): 75-91 (In Persian).
- Jayathunga, S., Owari, T. and Tsuyuki, S., 2018. Evaluating the performance of photogrammetric products using fixed-wing UAV imagery over a mixed conifer–broadleaf forest: Comparison with airborne laser scanning. Remote Sensing, 10(2): 187.
- Kargar, M.R. and Sohrabi, H., 2019. Using canopy height model derived from UAV images for tree height estimation in Sisangan forest. RS and GIS for Natural Resources, 10(3): 106-119 (In Persian).
- Kattenborn, T., Lopatin, J., Förster, M., Braun, A.C. and Fassnacht, F.W., 2019. UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment, 227: 61-73.
- 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 Journal of Forest, 6(2): 127-140 (In Persian).
- Li, L., Chen, J., Mu, X., Li, W., Yan, G., Xie, D. and Zhang, W., 2020. Quantifying understory and overstory vegetation cover using UAV-based RGB imagery in forest plantation. Remote Sensing, 12(2): 298.
- Lohrabi, Y., Abbasi, M., Soltani, A. and 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).
- Lu, J., Wang, H., Qin, S., Cao, L., Pu, R., Li, G. and Sun, J., 2020. Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds. International Journal of Applied Earth Observation and Geoinformation, 86: 102014.
- Mellor, A., Haywood, A., Stone, C. and Jones, S., 2013. The performance of random forests in an operational setting for large area sclerophyll forest classification. Remote Sensing, 5(6): 2838-2856.
- Mohan, M., Silva, C.A., Klauberg, C., Jat, P., Catts, G., Cardil, A., ... and Dia, M., 2017. Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests, 8(9): 340.
- Mozaffarian, V., 2010. Trees and Shrubs of Iran. Farhang Moaser, Tehran, Iran, 1054 pp (In Persian).
- Naseri, M.H., Shataee Jouibari, Sh., Mohammadi, J. and Ahmadi, Sh., 2020. Capability of Rapid Eye satellite imagery to map the distribution of canopy trees in Dashtebarm forest area of Fars province. Ecology of Iranian Forests, 7(14): 58-69 (In Persian).
- Nyimbili, P., Demirel, H., Şeker, D.Z. and Erden, T., 2016. Structure from motion (SfM): Approaches and applications. Proceedings of International Scientific Conference on Applied Sciences. Antalya, Turkey, 27-30 Sep. 2016: 8p.
- Owji, M.Gh. and Hamzepour, M., 2012. Vegetation Profile of Wild Pistachio Experimental Forest. Published by Research Institute of Forests and Rangelands, Tehran, 240p (In Persian).
- Saed Mocheshei, A., Pir Bavaghar, M., Shabanian, N. and Fatehi, P., 2019. Possibility of estimating the woody species diversity using Sentinel optical imagery (Case study: Marivan forests). Forest and Wood Products, 72(2): 101-110 (In Persian).
- Sagheb Talebi, Kh., Sajedi, T. and Pourhashemi, M., 2014. Forests of Iran: A Treasure from the Past, a Hope for the Future. Springer, Dordrecht, 152 pp.
- Schönberger, J.L. and Frahm, J.M., 2016. Structure-from-Motion revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 26-30 Jun. 2016: 4104-4113.
- Seyed Mousavi, S.Z., Mohammadi, J. and Shataee, Sh., 2017. The evaluation of potential airborne laser scanner data in estimating of individual canopy area and tree heights in part of educational and research Shast-Kalate forests - Gorgan. Ecology of Iranian Forest, 5(9): 47-55 (In Persian).
- Wan Mohd Jaafar, W.S., Woodhouse, I.H., Silva, C.A., Omar, H., Abdul Maulud, K.N., Hudak, A.T., ... and Mohan, M., 2018. Improving individual tree crown delineation and attributes estimation of tropical forests using airborne LiDAR data. Forests, 9(12): 759.
- Zahawi, R.A., Dandois, J.P., Holl, K.D., Nadwodny, D., Reid, J.L. and Ellis, E.C., 2015. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation, 186: 287-295.
- Zhang, W., Cai, S., Liang, X., Shao, J., Hu, R., Yu, S. and Yan, G., 2020. Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data. Forest Ecosystems, 7: 1.
 

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