پهنه‌بندی درصد سوختگی برگ تاج‌پوشش درختی با استفاده از تصاویر پهپاد و سنتینل 2 در پارک جنگلی دلند استان گلستان

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.