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جانعلی پور, میلاد, Abbaszadeh teharni, Nadia, Jafari, Mohammad. (1405). Mapping Saffron Cultivation Areas Using Landsat 8/9 Time-Series and a Pixel-Based SVM Classification Method. , (), -. doi: 10.22077/jsr.2026.11019.1301
میلاد جانعلی پور; Nadia Abbaszadeh teharni; Mohammad Jafari. "Mapping Saffron Cultivation Areas Using Landsat 8/9 Time-Series and a Pixel-Based SVM Classification Method". , , , 1405, -. doi: 10.22077/jsr.2026.11019.1301
جانعلی پور, میلاد, Abbaszadeh teharni, Nadia, Jafari, Mohammad. (1405). 'Mapping Saffron Cultivation Areas Using Landsat 8/9 Time-Series and a Pixel-Based SVM Classification Method', , (), pp. -. doi: 10.22077/jsr.2026.11019.1301
جانعلی پور, میلاد, Abbaszadeh teharni, Nadia, Jafari, Mohammad. Mapping Saffron Cultivation Areas Using Landsat 8/9 Time-Series and a Pixel-Based SVM Classification Method. , 1405; (): -. doi: 10.22077/jsr.2026.11019.1301

Mapping Saffron Cultivation Areas Using Landsat 8/9 Time-Series and a Pixel-Based SVM Classification Method

پژوهش‌های زعفران
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 اردیبهشت 1405
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22077/jsr.2026.11019.1301
نویسندگان
میلاد جانعلی پور* 1؛ Nadia Abbaszadeh teharni1؛ Mohammad Jafari2
1پژوهشگاه هوافضا وزارت علوم تحقیقات و فناوری
2Islamic Azad University
چکیده
Remote sensing provides an efficient and cost-effective methodology for estimating the cultivation area of agricultural crops. In this study, the cultivation area of saffron in Roshtkhar County was estimated using Landsat 8 and 9 time-series imagery and a pixel-based Support Vector Machine (SVM) classification method. The analysis, conducted for the Persian year 1402 (2023 AD), identified approximately 450 hectares of saffron cultivation. An accuracy assessment revealed that the proposed method performed robustly. Classification of Landsat 8 imagery yielded an overall accuracy of approximately 92% and a user's accuracy of 95% for the saffron class. Results from Landsat 9 imagery were similarly high, with an overall accuracy of roughly 91% and a saffron class accuracy ranging from 90% to 93%. Consequently, while Landsat 8 demonstrated slightly superior performance in identifying saffron lands, both sensors proved highly effective for this application, confirming the suitability of the proposed method for distinguishing saffron from other land cover classes.
کلیدواژه‌ها
Precision Agriculture؛ Remote Sensing؛ Machine Learning؛ Saffron
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