برآورد محصول و کاه گندم دیم با استفاده از تصاویر Landsat-OLI

نوع مقاله : مقاله کامل

نویسندگان

1 دانشجوی کارشناسی ارشد سنجش از دور و سیستم‌های اطلاعات جغرافیایی، دانشکدة جغرافیا، دانشگاه تهران

2 دانشیار گروه سنجش از دور و سیستم‌های اطلاعات جغرافیایی، دانشکدة جغرافیا، دانشگاه تهران

چکیده

پیش‏بینی عملکرد محصول از مهم‌ترین ابزارهای برنامه‏ریزی به‌منظور تأمین به‌موقع محصولات زراعی، مخصوصاً محصول استراتژیک گندم، است. در این تحقیق پیش‏بینی عملکرد گندم دیم در بخشی از شهرستان گیلان‏غرب با استفاده از شاخص‏های گیاهی  NDVI و GLAI و داده‏های زمینی عملکرد گندم دیم و کاه مربوط به 35 قطعه زمین زراعی براساس ایجاد رابطة رگرسیون چندمتغیره بین شاخص‌های گیاهی و داده‌های زمینی در سال‌های زراعی 2014-2018 انجام گرفت. در بازة زمانی 2014-2018، نمودار دورة رشد محصول با استفاده از هر شاخص رسم شد و پارامتر هندسی مربوط به منحنی رشد گیاه مانند مساحت زیرنمودار از آن‌ها استخراج شد. نتایج این تحقیق نشان داد GLAI ضریب تعیین بیشتری نسبت به شاخص NDVI دارد. همچنین، رابطة رگرسیون چندمتغیره با 865 .0R2= برای برآورد میزان کاه و یک رابطه با 851 .0R2= برای گندم به‌دست آمد که درنهایت با استفاده از این روابط مقدار گندم برای کل منطقه برابر 295.606 تن و مقدار کاه برابر 705.032 تن برآورد شد. از بین مراحل مختلف رشد گیاه نیز مرحلة تشکیل گل‌آذین با 65 .0R2= بیشترین ضریب تعیین جهت برآورد میزان محصول گندم و کاه را به خود اختصاص داد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimating of biomass and wheat dry-farming using Landsat OLI imagery

نویسندگان [English]

  • milad bagheri 1
  • A., Darvishi Bloorani 2
  • saeed hamze 2
  • mohamad reza mrjelokhani 2
1 Uiniversiti of tehran
2 university of tehran
چکیده [English]

Introduction
One of the most important planning tools for timely supply of crops, especially the strategic wheat product, is to predict the performance of this product before harvest, which can be very important in planning for itself. Combining the results of observations and ground measurements with remote sensing techniques can be widely applied in all agricultural sectors and facilitate the access to precision farming. Agricultural products have always been associated with the risk of fluctuations in the climate and changes in international markets, although this risk is never completely eliminated, but it can be understood by identifying the various parameters affecting plant growth and estimating the amount of the product Before harvesting, they minimize them. The forecast of rainfed wheat yields as a strategic product, with the Earth's population reaching 7 billion now.
Materials and methods
Field data includes biomass and net weight of wheat produced per farm in kilograms. These data are obtained by direct field surveys during harvesting. The GPS was used to determine the total area of the land, and considering the time zone of the crop, the Landsat-8 satellite time series was used from mid-February to late May in the studied years. After performing the necessary pre-processing on the images, the images were classified using a multi-timed classification. Initially, both NDVI and LAI indexes were obtained for all images in each ENVI environment every 5 years. Finally, the phenolic curves of both indices for each plot of land were fitted for each year from the studied years, which cultivated the wheat field, and the time of each phonological step was obtained for the studied area.
Results
In order to evaluate the overall accuracy of the classification, the Kappa coefficient and overall accuracy for the classes defined separately were calculated using the classification error matrix. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. In this study, both indicators were affected by the multivariable regression of the product estimation, and the highest coefficient of determination was obtained for each of the indices alone. From the 5 phonological stages, the inflorescence stage with R2=0.65 has the highest correlation coefficient.
Discussion and conclusion
From the obtained coefficients, we conclude that GLAI or green leaf area index (absorption) has a higher coefficient than NDVI. GLAI, which represents the main part of the photosynthesis of the plant (leaf), which is the main factor in the production process in the plant, certainly has a greater impact on the plant's production process. This leads to the preference of this index for the NDVI for estimation, but since the main goal of the paper is to obtain a multivariate regression relationship, we can do this in addition to the effect of both the desired index, the coefficient of determination and continuity For each of the indicators, we increase individually and make estimates in the region more accurately. With the involvement of both indicators in our relationship, we obtained a significant coefficient, especially for biomass with R2=0.865, which ensures that our prediction values are close to real values and that the program On the basis of this estimate, the probability of success will be high. From the study of phonological stages with wheat yield, we also conclude that, firstly, the entire phonological stages have less regurgitation coefficients than the phonological graphs of the two vegetation indexes, which means the whole diagram of wheat growth stages relative to the phenological periods on these graphs Have more ability to estimate the yield of wheat. Two of the five phonological stages studied, the inflorescence formation stage with R2=0.65 the highest correlation coefficient. This step in time is about the peak of the graph or the maximum value of the phonological graph.
Introduction
One of the most important planning tools for timely supply of crops, especially the strategic wheat product, is to predict the performance of this product before harvest, which can be very important in planning for itself. Combining the results of observations and ground measurements with remote sensing techniques can be widely applied in all agricultural sectors and facilitate the access to precision farming. Agricultural products have always been associated with the risk of fluctuations in the climate and changes in international markets, although this risk is never completely eliminated, but it can be understood by identifying the various parameters affecting plant growth and estimating the amount of the product Before harvesting, they minimize them. The forecast of rainfed wheat yields as a strategic product, with the Earth's population reaching 7 billion now.

Results
In order to evaluate the overall accuracy of the classification, the Kappa coefficient and overall accuracy for the classes defined separately were calculated using the classification error matrix. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. According to the phenological diagrams, the parameters of the area under the charts of both indicators were calculated for all lands. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. For this reason regression relations and determination coefficients (R2) between indices and wheat and biomass were created. In this study, both indicators were affected by the multivariable regression of the product estimation, and the highest coefficient of determination was obtained for each of the indices alone. From the 5 phonological stages, the inflorescence stage with R2=0.65 has the highest correlation coefficient.
Discussion and conclusion
From the obtained coefficients, we conclude that GLAI or green leaf area index (absorption) has a higher coefficient than NDVI. GLAI, which represents the main part of the photosynthesis of the plant (leaf), which is the main factor in the production process in the plant, certainly has a greater impact on the plant's production process. This leads to the preference of this index for the NDVI for estimation, but since the main goal of the paper is to obtain a multivariate regression relationship, we can do this in addition to the effect of both the desired index, the coefficient of determination and continuity For each of the indicators, we increase individually and make estimates in the region more accurately. With the involvement of both indicators in our relationship, we obtained a significant coefficient, especially for biomass with R2=0.865, which ensures that our prediction values are close to real values and that the program On the basis of this estimate, the probability of success will be high. From the study of phonological stages with wheat yield, we also conclude that, firstly, the entire phonological stages have less regurgitation coefficients than the phonological graphs of the two vegetation indexes, which means the whole diagram of wheat growth stages relative to the phenological periods on these graphs Have more ability to estimate the yield of wheat.

کلیدواژه‌ها [English]

  • Multivariate regression wheat crop and straw estimation
  • phonological stages
  • GLAI
  • NDVI
  • Landsat-OLI
عزیزی، ‌قاسم و یار‌احمدی،  ‌داریوش. (1382). بررسی ارتباط پارامترهای اقلیمی و عملکرد گندم دیم با استفاده از مدل رگرسیونی (مطالعة موردی دشت سیلاخور)، پژوهش‌های جغرافیایی، 35(۴۴): 23-30.‎
زند، بهنام و لعل‌نیا، علی‌اکبر. (1393). کتاب زراعت غلات، تهران: انتشارات پیام نور.
نصرالهی، محمد؛ ممبنی، مریم؛ ولی‌زاده، سارا و خسروی، حسن. (1393). بررسی تأثیر روند تغییرات کاربری اراضی/ پوشش زمین بر وضعیت منابع آب زیرزمینی، با استفاده از تصاویر ماهواره‌ای (مطالعة موردی: دشت گیلان‌غرب)، فصل‌نامة پژوهش‌های اطلاعات جغرافیایی، 23(۹۱): 89-97.‎
وزارت جهاد کشاورزی (1384). راهنمای داشت گندم. سازمان تحقیقات و آموزش کشاورزی.
Azizi, Gh. and Yarmohammadi, M. (2003). Investigation of relationship between climate parameters and wheat yield using linear regression model (Silakhor plain studies), Quarterly Journal of Geographic Research, PP. 1-35.
Bazgeer, S. (2005). Land Use Change Analysis In The Submountainous Region Of Punjab Using Remote Sensing, Gis, And Agrometerological Parameters (Doctoral dissertation, Punjab Agricultural University; Ludhiana), PP. 25-39.
Bazgeer, S.; Kamali, GH. and Mortazavi, A. (2007). Wheat Yield Prediction through Agrometeorological Indices for Hamedan, Iran, Biaban Journal, 12: 33-38.
Bazgeer, S.; Kamali, GH. A.; Sedaghatkerdar, A. and Moradi, A. (2008). Pre-harvest Wheat Yield Prediction Using Agrometeorological Indices for Different Regions of Kordestan Province, Iran, Research Journal of Environmental Sciences, 2(4): 275-280.
Fensholt, R.; Rasmussen, K.; Nilson, T.T. and Mbow, C. (2009). Evaluation of Earth Observation Based Long Term Vegetation Trends-Intercomparing NDVI Time series Trend Analysis Consistency of Sahel From AVHRR GIMMS, Terra MODIS and SPOT VGT Data, Remote Sensing of Environment, PP. 1-13.
Food and Agriculture Organization of the United Nations (FAO) (2011). Global Strategy to Improve Agricultural and Rural Statistics, Report, No. 56719-GB; FAO: Rome, Italy.
Geng, Y.B.;  Dong, Y.S. and  Meng, W.Q. (2000). Progress of terrestrial carbon cycle studies. Advance in Earth Science, 19: 297-306.
Hathout, S. and Carlyle, S. (2003). The Use of Remote Sensing and Geographical Information Systems for the Forecasting of Wheat Yield by Ostan in Iran. The Arab World Geographer, 6(4)” 221-236.
Huang, J.; Sedano, F.; Huang, Y.; Ma, H.; Li, X.; Liang, S.; ... and Wu, W. (2016). Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and forest meteorology, 216: 188-202.
Huang, J.; Ma, H.; Sedano, F.; Lewis, P.; Liang, S.; Wu, Q.; ... and Zhu, D. (2019). Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST–PROSAIL model. European Journal of Agronomy, 102: 1-13.
Jihad, M. o. A. (2014). Agronomical statistics, 2012-2013 agronomical year.  (in Persan).
Jégo, G.; Pattey, E. and Liu, J. (2012). Using leaf area index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of fiel crops. Field Crops Res. 131: 63-74.
Labus, M. P.; Nielsen, G.; Alawrence, R.L.; Engeld, R. and Long, S. (2002). Wheat Yield Estimates Using Multi-temporal NDVI Satellite Imagery Int, Journal of Remote Sensing, 23(20): 4169-4180.
Morison, J. I. and Morecroft, M. D. (Eds.) (2006). Plant growth and climate change.
Rembold, F. and Maselli, F. (2004). Estimating inter-annual crop area variation using multi-resolution satellite sensor images, International Journal of Remote Sensing, 25(13): 2641-2647.
Rötter, R. P.; Carter, T. R.; Olesen, J. E. and Porter, J. R. (2011). Crop-climate models need an overhaul, Nature Climate Change, 1(4): 175-177.
Sadras, V. O. and McDonald, G. (2012). Water use efficiency of grain crops in Australia: principles, benchmarks and management. Change, 11(19): 24-33.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2): 127-150.
Tucker, C.J.; Holben, B.N.; Elgin, J.H., Jr. and McMurtrey, J.E. (1980). III Relationship of spectral data to grain yield variation. Photogramm. Eng. Remote Sensing, 46: 657-666.
UN Food and Agriculture Organization (FAO) Global Information and Early Warning System(GIEWS) (2013). Available online: http://fao.org/giews (accessed on 18 February 2013).
Zend, B. and Lal Nia, A. (2014). Crop Growing, Tehran: Payam Noor University (in Persan).