توسعه و کاربرد شاخص‌های محصول و وضعیت مزرعه با استفاده از سری زمانی تصاویر ماهواره‌ای Sentinel-2

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد سنجش از دور و GIS، دانشگاه شهید بهشتی

2 استادیار مرکز مطالعات سنجش از دور و GIS، دانشگاه شهید بهشتی

3 استاد گروه GIS، دانشکده نقشه برداری (ژئودزی و ژئوماتیک)، دانشگاه صنعتی خواجه نصیرالدین طوسی

4 مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران

5 استادیار، پژوهشگاه فضایی ایران

چکیده

یکی از اهداف مهم در کشاورزی پایدار، حفظ اکوسیستم های سالم با تاکید بر مدیریت منابع زمینی، آبی و طبیعی به منظور تحقق امنیت غذایی در سطوح محلی تا جهانی است. داده های سری زمانی سنجش از دور به عنوان منبعی وسیع و ارزشمند از اطلاعات طیفی و زمانی، توانسته محققان را در پیشبرد اهداف مدیریت مزرعه کمک کند. مدیریت مزرعه همیشه با   چالش‌هایی همراه بوده و عدم دسترسی به اطلاعات کمی و کیفی محصولات زراعی از مشکلات این حوزه به شمار می‌رود. هدف از این تحقیق، توسعه و کاربرد شاخص‌های وضعیت محصول و مزرعه با استفاده از داده‌های سری زمانی NDVI ماهواره Sentinel-2)) و نقشه نوع محصول مزارع شرکت کشت و صنعت مغان در سال 1396-1395 و شرکت کشت و صنعت شهید رجایی دزفول در سال  1396-1397 است تا به وسیله آن، مناطقی که توسط عواملی همچون بیماری، هجوم آفات و علف های هرز و همچنین مشکلات خاک و عدم توزیع نامناسب آب آبیاری در مزرعه، دچار تغییر فنولوژیکی در طول زمان شده اند، شناسایی شوند. برای این منظور، داده های سری زمانی شاخص NDVI برای 4 نوع محصول (گندم، ذرت، یونجه و چغندرقند) و در مزارع مختلف محاسبه شد و برای نشان دادن وضعیت مزرعه و محصول در هر مزرعه و مزارع نسبت به هم، دو شاخص وضعیت مزرعه و محصول توسعه داده شد. ارزیابی نتایج این شاخص‌ها با مشاهدات زمینی، حاکی از آن است که محصول یونجه در کشت و صنعت مغان و گندم در کشت و صنعت شهید رجایی دزفول به ترتیب 88/88 و 11/94 درصد، بالاترین دقت (صحت کلی) را در بین محصولات منطقه داشتند.

کلیدواژه‌ها


عنوان مقاله [English]

Development and application of crop and field condition indices using time-series satellite images of Sentinel-2

نویسندگان [English]

  • Hamed nematollahi 1
  • Davoud Ashourloo 2
  • Abas Alimohammadi 3
  • Elham Khodabandehloo 4
  • Soheil Radiom 5
1 M.Sc. of RS & GIS, Shahid Beheshti University
2 Assistant Prof., Dept. of RS & GIS, Shahid Beheshti University
3 Professor., Dept. of GIS Engineering, Faculty of Geodesy & Geomatic Engineering, K.N. Toosi Uniersity of Technology
4 Space Research Institute, Iranian Space Research Center
5 Assistant Prof., Iranian Space Research Center
چکیده [English]

One of important objectives in sustainable agriculture is preservation of healthy ecosystems with focus on natural aquatic and terrestrial resources management in order to accomplish food security at local and global scales. Time-series remotely sensed datasets are precious and valuable resource of temporal and spectral information that could support researchers to access field management goals. Farm management have been always encountered some challenges such as lack of access to quantitative and qualitative information of agricultural crops. This research aims to develop crop and field condition indices using time-series of NDVI (Sentinel-2) and crop type maps of Moghan Agro-Industry (MAI) in 2016-2017 and also Shahid Rajaei Agro-Industry (SRAI) in 2017-2018. Then we tried to identify parts of the fields that are affected by Environmental factors such as disease, pest, weed, soil-related deficiencies and uneven distribution of water due to Inefficient irrigation system. To this end, Time-series of NDVI for four crops (wheat, maize, alfalfa and sugar beet) in various fields was provided. Finaly, field and crop condition indices were developed to show the variations of crop in each field and also the fields in comparison with each other. Finally, the proposed indices showed high accuracy with ground observations. The results were 88.88% for Alfalfa fields in MAI, and 94.11% for wheat fields in SRAI. After evaluation of the results of indices with ground observations, it was revealed that where field (homogeneity) index is low, growth limiting factors are activated.

کلیدواژه‌ها [English]

  • NDVI time-series
  • field condition index
  • crop condition index
  • farm management
  • Sentinel-2
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