Preparation of Map for Lands under Saffron Cultivation Using Timely Plant's Indicator Based Agronomic Calendar (Case study: Darbeghazi Village in Neyshabur province)

Dehghani Bidgoli, Reza; Koohbanani, Hamid Reza; Bashiri, Mehdi

doi:10.22077/jsr.2018.1050.1045

Document Type : Original Article

Authors

¹ Department of Rangeland and Watershed Management, College of Natural Resources and Earth Sciences, University of Kashan

² PhD Student of desertification, Faculty of Desertification, Semnan University

³ Assistant Professor of Grassland and Watershed Management Department, Faculty of Natural Resources, University of Torbat Heydarieh

https://doi.org/10.22077/jsr.2018.1050.1045

Abstract

In recent years, cultivation of saffron in Nishabur city has been more attention by farmers due to low water requirements and adequate income. Planning for the marketing of this strategic product and the provision of agricultural inputs related to saffron requires that the information of area under its cultivation. In this research, using Landsat 8 satellite images and time difference methods based on plants phenology prepared to estimate the areas under cultivation of this product in Darbeghazi village of Nishabur. A satellite image of June related to the plant's dormant phase and an image in December related to vegetative growth stage prepared. Using different vegetation indices, saffron lands were distinguished from other agricultural products. In this research, the cultivated area of saffron in the studied area was 1229 hectares with a total accuracy of 82%. Also, the results of this study indicate that the accuracy of this method depends on the patch area of agricultural lands, so that in areas less than 2000 square meters, the user's precision is 62 percent, in lands with an area between 2000 and 5000 square meters, accuracy is 72 percent, in parts between half to one hectare precision is 81% and in lands more than one hectare, accuracy is 90%. The results of this research indicated that this method is suitable for estimating the area under cultivation of saffron 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. Environmental Research. 2(3), 87-96. [In Persian with English Summary].

Aghaei, M., and Rezagholizadeh, M., 2011. Iran's comparative advantage in production of saffron. Journal of Development and Agricultural Economics. 25, 121–132. [In Persian with English Summary].

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 Neyshabur. Saffron Agronomy and Technology. 4(2), 133-142. [In Persian with English Summary].

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-e Ghods Razavi great farm). Agricultural Machinery. 4(2), 244-254. [In Persian with English Summary].

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 with English Summary].

Atkinson, P.M., Jeganathan, C., Dash, J., and Atzberger, C., 2012. Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment. 123, 400–417.

Bashiri, M., and Salari, A., 2016. Using geostatistics for zoning areas suitable for saffron cultivation in the Khorasan-e Razavi province based on climatological parameters. Saffron Agronomy and Technology. 4(2), 155-167. [In Persian with English Summary].

Bouzarjmehri, K., Sheikh 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). Saffron Agronomy and Technology. 4(1), 63-73. [In Persian with English Summary].

Cao, R., Chen, J., Shen, M., and Tang, Y., 2015. An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data. Agricultural and Forest Meteorology. 200, 9–20.

Chemura, A., Mutanga, O., and Dube, T., 2017. Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies. International Journal of Applied Earth Observation and Geoinformation. 57, 1–13.

Hamzei, M., and Bouzarjmehri, K., 2015. Analysis of effects and factors influencing on increasing the cropping pattern of saffron in the City of Neyshabur Case study: Ishaq Abad district. Saffron Agronomy and Technology. 2(4), 277-288. [In Persian with English Summary].

Kandasamy, S., and Fernandes, R., 2015. An approach for evaluating the impact of gap sand measurement errors on satellite land surface phenology algorithms: application to 20 years NOAA AVHRR data over Canada. Remote 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). Saffron Agronomy and Technology. 4(1), 41-50. [In Persian with English Summary].

Koocheki, A., 2013. Research on production of saffron in Iran: Past trend and future prospect. Saffron Agronomy and Technology. 1(1), 3-21. [In Persian with English Summary].

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 with English Summary].

Koohbanani, H., Dashti Amirabad, J., Nikoo, S., and Taya, A., 2017. Desertification-intensity zoning through Fuzzy-Logic approach: (Case study: Deyhook, Tabas). Environmental Erosion Research. 7(1), 35-49. [In Persian with English Summary].

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. Food Reviews International. 25, 44–85.

Landsat-8 (L₈)., 2016. Data User Handbook, Landsat Project Science Office at NASA's Goddard Space Flight Center in Greenbelt, 1168. Available at: http://landsathandbook.gsfc.nasa.gov/pdfs

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. 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. Saffron Agronomy and Technology. 4(2), 119-132. [In Persian with English Summary].

NASA., 2011. Landsat 7 Science Data Users Handbook Landsat Project Science Office at NASA's Goddard Space Flight Center in Greenbelt, 186. http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf

Rahimzadegan, M., and Pourgholam, M., 2017. Identification of the area under cultivation of Saffron using Landsat-8 temporal satellite images (Case study: Torbat Heydarieh). RS and GIS for Natural Resources. 7(4), 97-115. [In Persian with English Summary].

Ramezankhani, M., Mokhtarzadeh, S., and Sahebi, M.R., 2011. Comparison of Separativeness of Agricultural Products in Multi- time Classification by Statistical Methods and Neural Networks, Geomatics conference, Tehran, Iran. [In Persian].

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 Science. 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. Saffron Agronomy and Technology. 3(4), 277-288. [In Persian with English Summary].

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. Remote Sensing. 5, 3190-3211.