Area estimation of saffron cultivation using satellite images and time difference method (case study: Fazl Village in Nishabur County of Iran)

Document Type : Research Paper

Authors

1 Assistant Professor of Rangeland and Watershed Management Department, University of Kashan, Kashan, Iran

2 Ph.D student of Desertification Department, University of Semnan, Semnan, Iran

Abstract

Estimating crop area is a key factor for crop monitoring and agricultural management. Having annual information on crop acreage and production change is necessary for agricultural decision-makers and planners. In recent years, the cultivation of saffron in Nishabur has received more attention by farmers due to low water requirements and its good income. Planning for the marketing of this strategic product and the provision of agricultural inputs for saffron requires that the information of the area under its cultivation be estimated accurately. The aim of this study was to monitor changes in cultivated areas and to estimate the saffron area using satellite imagery with different spatial resolutions and time series normalized different vegetation indexes (NDVITs). Using Landsat 8 satellite images and time difference methods based on plant phenology estimate the areas under cultivation of saffron was estimated in Fazl Village of Nishabur County. A satellite image of June 17, 2016, related to the plant's dormant phase and another on January 11, 2017, for the vegetative growth stage of saffron was prepared. Using different vegetation indices, saffron cultivation was distinguished from other agricultural products and estimated at 497 hectares with an overall accuracy of 72% and a kappa value of 71%. Also, the results indicated that the accuracy of this method depended on the patch area of agricultural lands, such that in areas less than 2000 square meters, the user's accuracy was 44 percent, in lands with an area between 2000 and 5000 square meters, the accuracy was 66 percent, while in farms between half to one-hectare, the accuracy reached 80% and in lands more than one hectare, the accuracy was 89%. The results of this research indicated that the use method is suitable for estimating the area under the cultivation of saffron and we suggest its examination in other parts of the country.

Keywords

References

Abaszadeh, N., Beheshtefar, M. and Morabi, M. 2012. Crop Type Mapping in Qazvin by Using Multi- Temporal Satellite Images: IRSC-LISSIII DATA. Journal of Environmental Research. 2(3), 87–96.
Aghaei, M. and Rezagholizadeh, M. 2011. Iran's comparative advantage in production of saffron. Journal of  Agriculture Economy and Development. 25, 121–132.
Alavizade, S.A.M., Mirlotfi, M.R. and Naimabadi, N. 2016. The Effects of saffron economic stability of rural residents in the Darbeghazi district city of Nishabur. Journal of  Saffron Agronomy and Technology. 4 (2), 133–142.
Alipour, F., Aghkhani, M.H., Abasspour-Fard, M.H. and Sepehr, A. 2014. Demarcation and Estimation of Agricultural Lands Using ETM+ Imagery Data (Case study: Astan Ghods Razavi Great Farm). Journal of Agricultural Machinery. 4(2), 244–254.
Amirshekari, H., Sorooshzadeh, A., Modarress Sanavy, A. and Jalali Javaran, M. 2007. Study of effects of root temperature, corm size, and gibberellin on underground organs of saffron (Crocus sativus L.). Iranian Journal of   Biology. 19, 5–18. (In Persian).
Atkinson, P.M., Jeganathan, C., Dash, J. and Atzberger, C. 2012. Inter-comparison offour models for smoothing satellite sensor time-series data to estimate vegetation phenology. Journal of  Remote Sensing of Environmentm. 123, 400-417.
Bashiri, M and Salari, A. 2016. Using geostatistics for zoning areas suitable for saffron cultivation in the Khorasan Razavi Province Based on Climatological Parameters. J Journal of  Saffron Agronomy and Technology. 4 (2), 155–167.
Bouzarjmehri, k., Shikh Ahmadi, F. and Javani, K. 2016. Investigating financial impacts of cultivating saffron on rural families with an emphasis on unstainable agriculture (Case Study: Balavelayat Rural District, City of Bakharz). Journal of  Saffron Agronomy and Technology. 4 (1), 63–73.
Cao, R., Chen, J., Shen, M. and Tang, Y. 2015. An improved logistic method for detectingspring vegetation phenology in grasslands from MODIS EVI time-series data. Journal of Agricultural and Forest Meteorology. 200, 9–20.
Chemura, A., Mutanga, O. and Dube, T. 2017. Integrating age in the detection and mapping of incongruous patchesin coffee (Coffea arabica) plantations using multi-temporal Landsat 8NDVI anomalies. International  Journal of Applied Earth Observation and Geoinformation. 57,1–13.
Dehghani Bidgoli, R., Koohbanani, H. and  Bashiri, M. 2018. Preparation of Map for Lands under Saffron Cultivation Using Timely Plant's Indicator Based Agronomic Calendar (Case study: Darbeghazi Village,Neyshabur province), Journal of Saffron Research (semi-annual). 6(1), 103-113.
Epiphanio, R., Dalla, V., Formaggio, AR., Rudorff, BFT., Maeda, EE. and Luiz. AJB. 2010. Estimating soybean crop areas using spectral‑temporal surfaces derived from MODIS images in Mato Grosso, Brazil. Journal of Pesquisa Agropecuária Brasileira. 45, 72‑80.
Kandasamy, S. and Fernandes, R. 2015. An approach for evaluating the impact of gapsand measurement errors on satellite land surface phenology algorithms: application to 20 year NOAA AVHRR data over Canada. . Journal of Remte Sensing of Environment. 164, 114–129.
Khozeymehnezhad, H., Farhangfar, H., Behdani, M.A. and Hassanpour, M. 2016. Assessment of Saffron Farmers Knowledge on the Issues Associated with Irrigation (Case Study: Southern Khorasan). Journal of  Saffron Agronomy and Technology. 4 (1), 41–50.
Koocheki, A. 2013. Research on production of saffron in Iran: Past trend and future prospect. Journal of  Saffron Agronomy and Technology. 1 (1), 3–21.
Koocheki, A., and Seyyedi, S.M. 2015. Phonological stages and formation of replacement corms of saffron (Crocus sativus L.) during growing period. Journal of Saffron Research. 3(2), 134–154. (In Persian)
Kumar, R., Singh, V., Devi, K., Sharma, M., Singh, M.K. and Ahuja, P.S. 2009. State of art of saffron (Crocus sativus L.) agronomy: A comprehensive review. Journal of Food Reviwe International. 25, 44–85.
Manjunath, KR., Potdar, MB. and Purohit, NL. 2002. Large area operational wheat yield model development and validation based on spectral and meteorological data. International Journal of  Remote Sensing. 23, 3023–3038.
Masi, E., Taiti, C., Heimler, D., Vignolini, P., Romani, A. and Mancuso, S.  2016. PTR-TOF-MS and HPLC analysis in the characterization of saffron (Crocus sativus L.) from Italy and Iran. Journal of Food Chemistry. 192, 75–81.
Mohtashami, T., Karbasi, A., Zandi, B. and Gharibi, D. 2016. Economic analysis and comparison of technical efficiency in small and large saffron farms of Khorasan Razavi province. Journal of Saffron Agronomy and Technology. 4(2), 119–132.
NASA (Ed.). 2011. Landsat 7 Science Data Users Handbook Landsat Project Science Office at NASA's Goddard Space Flight Center in Greenbelt. 186. Available at Web site http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf.
Pickup, G., Chewings, VH. and Nelason, DJ. 1993. Estimating changes in vegetation cover over time in arid rangelands using Landsat MSS data. Journal of Remote Sensing of Environmentm. 43, 243‑263.
Prasad, AK., Chai, L., Singh, RP. and Kafatos, M. 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation. 8, 26–33.
Rahimzadegan, M. and Pourgholam, M. 2017. Identification of the area under cultivation of Saffron using Landsat-8 temporal satellite images (Case study: Torbat Heydarieh). Journal of RS and GIS for Natural Resources. 7(4), 97–115.
Salazar, L., Kogan, F. and Roytman, L. 2007. Use of remote sensing data for estimation of winter wheat yield in the United States.  International Journal of Remote Sensing. 28, 3795–3811.
 Tewari. S., Kulhavy. J., Rock. B.N. and Hadas. P. 2003. Remote monitoring of forest response to changed soil moisture regime due to river regulation. Journal of Forest Sciences. 49, 429–438.
Yaghoubi, F., Jami Al-Ahmadi, M., Bakhshi., M.R. and Sayyari, M.H. 2016. Comparison of indicators of technical and economic water use efficiency in saffron and wheat production systems in the Qaenat region. Journal of  Saffron Agronomy and Technology.  3(4), 277–288.
 You, X., Meng, J., Zhang, M and 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, 3190–3211.
Environmental Resources Research
Volume 8, Issue 2
July 2020
Pages 121-128

Files

Share

How to cite

Statistics