Zoning of leaf burn percentage of tree canopy using UAV and Sentinel 2 images in Deland Forest Park, Golestan provinceAbstract

Document Type : Complete scientific research article

Authors

1 Ph.D student, Gorgan University of Agricultural Sciences and Natural Resources.

2 Forestry department, Forest sciences faculty, Gorgan University of agricultural sciences and natural resources

3 Associate Professor, Department of Forestry and Ecology, Gorgan University of Agricultural Sciences and Natural Resources

Abstract

Abstract
Background and purpose: Forests are subject to a variety of disturbances that adversely affect their health. There are a number of disturbances that cause trees to become weak and dry in the long run. One of them is the stress caused by burning leaves, which typically takes place during very hot summers. The identifying and monitoring of the expansion of its level is therefore very important. As one of the important information sources, remote sensing data can be used to identify it. It is possible to capture UAV images quickly and at desired times, but they have limitations such as the captured surface and the sensor's spectral range. In identifying and preparing a map of leaf burns, tree crowns, and other damages, the combined use of UAV images and satellite images with a high resolution can be helpful. The purpose of this research is to investigate and identify leaf burn of the canopy of trees using UAV images in a part of Deland Forest Park in Golestan province and combining it with Sentinel 2 images to prepare a classification map of the percentage of leaf burn in the tree canopy of the area in a wider area.
Materials and methods: RGB UAV images were taken in Deland Forest Park and processed appropriately. Also, Sentinel 2 images were prepared and analyzed geometrically and radiometrically. Ground truth of trees with leaf burn and healthy trees was done in September 1400 selectively and with high precision differential global positioning device. The support vector machine algorithm was used to classify UAV images into three categories: trees with leaf burn, healthy trees, and clear trees. The classification of the main UAV images along with vegetation indices obtained from UAV spectral bands and with selected spectral indices was also repeated. Finally, the prepared maps were evaluated for accuracy. In the second step, for preparing a map of the classes of leaf burn percentage of the canopy cover on a wider level and in the whole park, using the image of Sentinel 2, based on the classified map of the UAV, which has the highest accuracy, as a training example in the regression model of the random forest and calibration. Finally, a map of leaf burn intensity classes of the entire Deland Forest Park area was prepared using the prepared model.Results: UAV image classification results indicated that UAV visible spectrum bands are able to distinguish healthy trees from trees with leaf burn with an accuracy of 89.62% and a Kappa coefficient of 0.84 for the classification of UAV images. By combining the main bands and 9 vegetation indices, the results of the research decreased slightly (overall accuracy 87.40% and kappa coefficient 0.81); however, when the main UAV bands were combined with the best vegetation indices (EGMRI, EGI and

Keywords

References

1.Khodaverdi, S., Amiri, M., Kartoolinejad, D., and Mohammadi, J. 2018. Characteristics of canopy gap in a broad-leaved mixed forest (Case study: District No. 2, Shast-Kalateh Forest, Golestan province). Iranian J. of Forest and Poplar Research. 26: 1. 24-35. (In Persian)
2.Hoseinpour, A. 2019. Recognizing plant tension in plantations by use of UAVs visible light detector (Case study: Nekazalemrood forestry plan). Ecology of Iranian Forest. 7: 13. 20-28. (In Persian)
3.Moshou, D., Gravalos, I., Bravo, D.K.C., Oberti, R., West, J.S., and Ramon, H. 2011. Multisensor fusion of remote sensing data for crop disease detection. In Geospatial Techniques for Managing Environmental Resources. pp. 201-219.
4.Khazaeli, P., Rezaee, S., Mirabolfathy, M., Zamanizadeh, H., and Kiadaliri, H. 2016. Distribution, specific detection, and the pathogenesis variation of Calonectria pseudonaviculata isolates, causal agent of boxwood blight disease, in the Hyrcanian forest of Iran. Applied Entomology and Phytopathology, 84: 1. 141-156. (In Persian)
5.Naseri, M.H., Shataee Jouibari, S., Mohammadi, J., and Ahmadi, S. 2019. Capability of rapideye satellite imagery to map the distribution of canopy trees in dashtebarm forest area of Fars province. Ecology of Iranian Forest. 7: 14. 58-69. (In Persian)
6.Smigaj, M., Gaulton, R., Suárez, J.C., and Barr, S.L. 2019. Combined use of spectral and structural characteristics for improved red band needle blight detection in pine plantation stands. Forest Ecology and Management. 434: 213-223.
7.Hao, Z., Lin, L., Post, C.J., Jiang, Y., Li, M., Wei, N., Yu, K., and Liu, J. 2021. Assessing tree height and density of a young forest using a consumer unmanned aerial vehicle (UAV). New Forests. 52: 5. 843-862.
8.Smigaj, M., Gaulton, R., Suárez, J.C., and Barr, S.L. 2019. Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity. Forest Ecology and Management. 433: 699-708.
9.Michez, A., Piégay, H., Lisein, J., Claessens, H., and Lejeune, P. 2016. Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environmental monitoring and assessment. 188: 3. 1-19.
10.Bagheri, N. 2020. Application of aerial remote sensing technology for detection of fire blight-infected pear trees. Computers and electronics in agriculture. 168: 105147.
11.Avtar, R., Suab, S.A., Yunus, A.P., Kumar, P., Srivastava, P.K., Ramaiah, M., and Juan, C.A. 2020. Applications of UAVs in plantation health and area management in Malaysia. In Unmanned Aerial Vehicle: Applications in Agriculture and Environment. pp. 85-100.
12.Kampen, M., Lederbauer, S., Mund, J.P., and Immitzer, M. 2019. UAV-based multispectral data for tree species classification and tree vitality analysis. Dreiländertagung der DGPF, der OVG und der SGPF in Wien, sterreich Publikationen der DGPF. 28: 01.
13.Liao, K., Yang, F., Dang, H., Wu, Y., Luo, K., and Li, G. 2022. Detection of Eucalyptus leaf disease with UAV multispectral imagery. Forests. 13: 8. 1322.
14.Zhou, X., Yang, L., Wang, W., and Chen, B. 2021. UAV data as an alternative to field sampling to monitor vineyards using machine learning based on UAV/Sentinel-2 data fusion. Remote Sensing. 13: 3. 457.
15.Kattenborn, T., Lopatin, J., Förster, M., Braun, A.C., and Fassnacht, F.E. 2019. UAV data as an 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.
16.Pilaš, I., Gašparović, M., Novkinić, A., and Klobučar, D. 2020. Mapping of the canopy openings in mixed Beech–Fir forest at sentinel-2 subpixel level using UAV and machine learning approach. Remote Sensing. 12: 23. 3925.
17.Hajizadeh, G., Kavousi, M., Afshari, A., and Shataee, S.J. 2012. Effects of ovipositing height and host tree species on some biological parameters of Gypsy moth lymantra dispar (L), IN Golestan forests (Case study: Daland park). J of Wood and Forest Science and Technology. 19: 1. 149-162. (In Persian)
18.DJI Company. 2016. DJI Phantom4 Pro, https://www.dxomark.com/Cameras/DJI/Phantom4-Pro---Specifications.
19.Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment. 8: 2. 127-150.
20.Woebbecke, D.M., Meyer, G.E., Von Bargen, K., and Mortensen, D.A. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE. 38: 1. 259-269.
21.Meyer, G.E., and Neto, J.C. 2008. Verification of color vegetation indices for automated crop imaging applications. Computers and electronics in agriculture. 63: 2. 282-293.
22.Gitelson, A.A., Kaufman, Y.J., Stark, R., and Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote sensing of Environment. 80: 1. 76-87.
23.Neto, J.C. 2004. A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems. The University of Nebraska-Lincoln.
24.Hamdi-Aissa, B., and Girard, M.C. 2004. Apport des données satellitales pour l’évaluation de l’impact sur l’environnement du risque salinisation dans l’écosystème désertique (cuvette de Ouargla, Algérie). X ème journée scientifique du réseau de télédétection de l’AUF, Géorisque et télédétection, Ottawa. pp. 177-180.
25.Gillespie, A.R., Kahle, A.B., and Walker, R.E. 1987. Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques. Remote Sensing of Environment. 22: 3. 343-365.
26.Tiwari, S., Agarwal, S., and Trang, A. 2008, July. Texture feature selection for buried mine detection in airborne multispectral imagery. In IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium. 1. I-145.
27.Louhaichi, M., Borman, M.M., and Johnson, D.E. 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International. 16: 1. 65-70.
28.Nguyen, L.H., Robinson, S., and Galpern, P. 2022. Medium-resolution multispectral satellite imagery in precision agriculture: mapping precision canola (Brassica napus L.) yield using Sentinel-2 time series. Precision Agriculture. 23: 3. 1051-1071.
Journal of Wood and Forest Science and Technology
Volume 29, Issue 4
December 2022
Pages 75-92

Files

Share

How to cite

Statistics