| چکیده انگلیسی مقاله |
Extended Abstract Introduction Phenology is a key indicator in plant growth and plays an important role in monitoring vegetation. Monitoring seasonal variations in vegetation activities and crop technology over large areas is essential for many applications, including estimating initial net production time to model crop performance and supportive water supply decisions. On the other hand, extracting this important information requires a lot of time and money. The southeast of Fars Province in Iran has a very favorable climate and environmental conditions for citrus growth and thus the region is one of the most important citrus cultivation spots in Iran. Given the significance of citrus cultivation in the food production of the country as well as its important role in regional economics, planning in the field of citrus phenological information in this region solve many challenges in the agricultural sector in the region. In other words, knowing the plant phenological status in citrus orchards can play an important role in planning and managing climate change and ultimately the development of the agricultural sector of this province. In this regard, this study aims to estimate the main phenological stages of orange trees using remote sensing. Methodology In the proposed study, MODIS images (2006-2016) were employed. The images were downloaded for 10 days. The remotely sensed images were used to extract vegetation indices including NDVI, EVI, and TCI to modeling the phenology of the orange trees. Also, 1/25000 maps of Iran National Cartographic Center were used as the spatial reference for the images geo-referencing. The meteorological data including daily maximum and minimum temperature, relative humidity, and precipitation were collected from the Darab Agrometeorology station. The phenological data including the onset and end of each phonological period of orange trees which were being observed from 2006 to 2016 at the Agrometeorology Station was used in this study. In this study, the three most widely used remote sensing indices were investigated to evaluate the health and status of vegetation and temperature conditions. The normalized difference vegetation index, vegetation status, and temperature condition index were calculated to compare the results of the remote sensing and traditional harvesting of plant phenological stages. To observe the effect of moisture on vegetation, the charts of normalized maximum temperature, normalized maximum moisture, and normalized difference vegetation index were plotted for all the years. Discussion The phenological stages of citrus had 9 main phases and 97 sub-phases, out of which 6 main stages were presented to the researchers and were investigated. The 6 main phenological stages of oranges are as follows: Leaf bud and fruit formation, leaf bud and fruit flourishing, fruiting and leaf growth, fruit and leaf growth, fruit ripening, and sleep cycle. To interpret these stages, the charts of normalized maximum temperature and normalized temperature condition index obtained from the MODIS satellite images were plotted for all crop years. The variation of Tem max was correlated to the growing stages of orange trees. In the other words, the normalized temperature condition index obtained from the satellite images properly indicated the temperature variations. Moreover, the temperature change charts properly showed the changes in the duration of the phenological stages of orange trees. Conclusion To investigate the effect of temperature variations on different phenological stages of orange trees, the normalized maximum temperature and normalized difference vegetation index were plotted for all the crop years. At each point where the peak of the normalized maximum temperature was observed, the peak in the normalized difference vegetation index was also found at a very small distance. In other words, when the temperature increased, the conditions were favorable for increasing vegetation and the plant begins to grow. Finally, to evaluate the performance of remote sensing indices in expressing changes in temperature and vegetation conditions, the correlation coefficient between remote sensing indices and ground data was calculated in pairs. Since the study area was arable land and human factors were involved in plant growth, the resulting correlation coefficients were small. The results of calculating the correlation coefficients indicated that the indices obtained from remote sensing using satellite images can properly show the changes in the main phenological stages. On the other hand, these indices can be produced daily and the trend of changes can be seen without harvesting and physical observation of the product. Keywords: Phenology, Orange Trees, Remote Sensing, Vegetation Indices, Satellite Images. References: - Böttcher, K., Härmä, P., Peltoniemi, M., Tanis, C. M., Aurela, M., & Arslan, A. N. (2016). Comparison of Web-Camera and Satellite Based Observations on Vegetation Phenology in Finland. AGILE 2016 – Helsinki. - Camacho, M., & Orozco, L. (1998). Reproductive Phenology of the Oak Family (Fagaceae) in the Lowland in Forests of Borneo. Proyecto Silvicultura Bosques Naturales, CATIE, Turrialba, Costa Rica. - Dai, J., Wang, H., & Ge, Q. (2014). The Spatial Pattern of Leaf Phenology and Its Response to Climate Change in China. International Journal of Biometeorology, 58(4), 521-528. - Ferraz, D. K. (1998). Phenology of Tree Species in an Urban Forest Fragment in Southeastern, Developing an International Phenology (a) Monitoring Network. 1998Phonology Symposium, pp.132-144. - Heydari, H., Valadan Zoej, M., Maghsoudi, Y., & Dehnavi, S. (2018). An Investigation of Drought Prediction Using Various Remote Sensing Vegetation Indices for Different Time Spans. International Journal of Remote Sensing, 39(6), 1871-1889. - Jeong, S. J., Ho, C. H., Choi, S. D., Kim, J., Lee, E. J., & Gim, H. J. (2013). Satellite Data Based Phenological Evaluation of the Nationwide Reforestation of South Korea. PLoS One, 8(3), 19. - Kimball, J. S., McDonald, K. C., Running, S. W., & Frolking, S. E. (2004). Satellite Radar Remote Sensing of Seasonal Growing Seasons for Boreal and Subalpine Evergreen Forests. Journal of Remote Sensing of Environment, 90(2), 243-258. - Kogan, F. N. (1995). Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data. Bulletin of the American Meteorological Society, 76(5), 655-668. - Liu, H. Q., & Huete, A. (1995). A Feedback Based Modification of the NDVI to Minimize Canopy Background and Atmospheric Noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 457-465. - Luo, Y., Zhang, Z., Chen, Y., Li, Z., & Tao, F. (2020). ChinaCropPhen1km: A high-Resolution Crop Phenological Dataset for Three Staple Crops in China During 2000–2015 Based on Leaf Area Index (LAI) Products. Journal of Earth System Sciences Data, 12(1), 197-214. - Ma, X., Huete, A., Yu, Q., Coupe, N. R., Davies, K., Broich, M., … & Eamus, D. (2013). Spatial Patterns and Temporal Dynamics in Savanna Vegetation Phenology across the North Australian Tropical Transect. Journal of Remote Sensing of Environment, 139, 97-115. - Qiu, B., Li, W., Tang, Z., Chen, C., & Qi, W. (2015). Mapping Paddy Rice Areas Based on Vegetation Phenology and Surface Moisture Conditions. Ecological Indicators, 56, 79-86. - Rose, M. E. (1974). Immunity to Eimeria Maxima: Reactions of Antisera in Vitro and Protection in Vivo. The Journal of Parasitology, 60(3), 528-530. - Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., & Ohno, H. (2005). A Crop Phenology Detection Method Using Time-Series MODIS Data. Journal of Remote Sensing of Environment, 96(3-4), 366-374. - Tao, J. B., Wu, W. B., Yong, Z., Yu, W., & Jiang, Y. (2017). Mapping Winter Wheat Using Phenological Feature of Peak before Winter on the North China Plain Based on Time-Series MODIS Data. Journal of Integrative Agriculture, 16(2), 348-359. - The American Meteorological Society. (2012). Available in: http://glossary.ametsoc.org/wiki/Phenology, Last seen 2017/ 6/ 23. - The American Meteorological Society. (2012). Retrieved from: http://glossary.ametsoc.org/wiki/Remote_sensing, Last seen 2017/ 6/ 23. - The American Meteorological Society. (2012). Retrieved from: http://glossary.ametsoc.org/wiki/Vegetation_index, Last seen 2017/ 6/ 23. - Tucker, C. J., Elgin Jr, J. H., & Mcmurtrey III. J. E. (1979). Temporal Spectral Measurements of Corn and Soybean Crops. (n.p). - Yang, X., Mustard, J. F., Tang, J., & Xu, H. (2012). Regional-Scale Phenology Modeling Based on Meteorological Records and Remote Sensing Observations. Journal of Geophysical Research, 117(3), 1-18. - You, X., Meng, J., Zhang, M., & Dong, T. (2013). Remote Sensing Based Detection of Crop Phenology for Agricultural Zones in China Using a New Threshold Method. Journal of Remote Sensing, 5(7), 3190-3211. - Zeng, L., Wardlow, B. D., Wang, R., Shan, J., Tadesse, T., Hayes, M. J., & Li, D. (2016). A Hybrid Approach for Detecting Corn and Soybean Phenology with Time-Series MODIS Data. Journal of Remote Sensing of Environment, 181, 237-250. |