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بهبود دقت شناسایی مزارع برنج با استفاده از تصاویر سری زمانی دمای سطح زمین ماهواره لندست-8 و الگوریتم های یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی فتوگرامتری- دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، پردیس دانشکده‌های فنی، دانشگاه تهران

2 استادیار دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی - پردیس دانشکده‌های فنی - دانشگاه تهران

چکیده

برنج اصلی­ ترین محصول غذایی بیش از نیمی از مردم جهان است. نظارت بر سطح زیر کشت محصول برنج، نقش مهمی در برنامه­ ریزی­ های کشاورزی دارد. امروزه می­ توان با تکیه بر فن­آوری سنجش از دور و روش­ های یادگیری ماشین، روش­ های مدیریتی را بهبود بخشید. تحقیق فوق با هدف شناسایی برنج در سال 2020 به­ کمک نقشه­ های سری زمانی شاخص ­های NDVI و LST استخراج شده از تصاویر لندست-8، با الگوریتم SVM در ایالت کالیفرنیا، انجام گرفته است. یکی از انگیزه­ های اصلی این تحقیق، بررسی قابلیت­ های نقشه­ های سری زمانی شاخص LST در کنار نقشه ­های سری زمانی شاخص NDVI به­­ منظور بهبود دقت شناسایی مزارع برنج ، با الگوریتم SVM است. در گام اول از روش پیشنهادی پس از اخذ سری زمانی تصاویر لندست-8 و انجام تصحیحات رادیومتری و اتمسفری، نقشه ­های سری زمانی دو شاخص NDVI و LST تولید شد. در گام دوم، شناسایی مزارع برنج با الگوریتم طبقه ­بندی SVM در دو سناریوی وجود یا عدم وجود نقشه­ ی سری زمانی LST در کنار نقشه­ ی سری زمانی NDVI پیشنهاد شد. در نهایت از الگوریتم ­های طبقه ­بندی نزدیک­ترین همسایگی، درخت تصمیم گیری، رگرسیون لجستیک و پرسپکترون چند لایه برای مقایسه­ ی روش پیشنهادی استفاده شد. نتایج حاصل از شاخص پیشنهادی باعث بهبود دقت کلی به مقدار متوسط 3.572 درصد و ضریب کاپا به مقدار متوسط 7.112 درصد در روش­ های شناسایی مزارع برنج هنگام بکار گیری همزمان نقشه­ های سری زمانی شاخص LST و NDVI با کاهش خطای نوع اول به کمک استخراج ویژگی­ های فصل رشد حرارتی (حذف کلاس­ های غیر برنج همچون پنبه، لوبیای سبز و ... از کلاس برنج) شد. همچنین الگوریتم ماشین بردار پشتیبان، بالاترین دقت کلی 94.28 درصد و ضریب کاپای 88.29 درصد را در شناسایی مزارع برنج از سایر مزارع کشاورزی، در مقایسه با سایر روش ­های مقایسه ­ای نشان داد. نتایج حاصل از روش ­های مقایسه ­ای کارآیی پایین الگوریتم درخت تصمیم گیری را در شناسایی لبه­ های مزارع برنج، نشان داد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Improving the accuracy of rice fields identification using Landsat-8 Satellite Land surface temperature time series images and machine learning algorithms

نویسندگان [English]

  • Mahdiyeh Fathi 1
  • Reza Shah-Hosseini 2

1 PhD Student in Photogrammetry Engineering in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran

2 Assistant Professor in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran

چکیده [English]

Extended Abstract
Introduction
Rice is an important crop and the main food of more than half of the world’s population, which needs water and heat to grow. Thus, mapping and monitoring rice fields with efficient means such as remote sensing technology is necessary for food security and the lack of water sources. The phenology extracted from the time series of vegetation indices is used for monitoring and mapping the area under rice cultivation. In addition to the phenological curve, the LST time series map, which is calculated from Landsat 8 images and is related to the phenomenon of evaporation and transpiration of irrigated crops, can cause the separation of rice cultivation from rainfed crops, summer crops, water, etc. Therefore, in this study, the effect of the LST time series map is investigated map for improving the accuracy of rice field identification.
Materials & Methods
Since the planting to harvest period of rice is from May to October, in this study, the time-series maps of LST and NDVI for the 3rd of April, 21st of May, 6th of John, 22nd of John, 8th of July, 24th of July, 9th of August, 12th of October, and 28th of October have been calculated after download the Landsat-8 time-series in 2020 The ground truth map of the study area has been obtained from the US Department of Agriculture. To identify rice fields and calculate the LST and NDVI using the Landsat-8 images, initial pre-processing including radiometric and geometric corrections has been applied to these images first. After initial corrections and the calculation of NDVI and LST maps, to identify rice fields in the study area, machine learning algorithms such as Support Vector Machine, K-Nearest Neighborhood, Multilayer Perspective, Logistic Regression, and Decision Tree, have been proposed.
 
Results & Discussion
The results of the proposed method at the state of California showed that using the time series map of  Land surface temperature (LST) with the time-series map of  Normalized Difference vegetation Index, improved the results of identifying rice fields (the average Overall Accuracy= + 3/572% and the average kappa coefficient= +7/112%). Visual results showed that some cultivation such as tomato, corn, cucumber, fallow, and water were removed from the rice final map when using the LST time-series map with the NDVI time-series map. According to the numerical results, the Support Vector Machine algorithm (Overall Accuracy 94/28 and Kappa Coefficient 88/29), the Multilayer Perceptron algorithm  (Overall Accuracy 94/26 and Kappa Coefficient 88/21), and the K-Nearest Neighborhood algorithm (Overall Accuracy 93/71 and Kappa Coefficient 87/08) showed the highest Overall Accuracy and Kappa Coefficient compared to the Logistic Regression algorithm (overall accuracy 91/96 and kappa coefficient 83/54) and the Decision Tree algorithm (Overall Accuracy 91/34 and Kappa Coefficient 81/97), respectively.
Conclusion
Although, many methods have been proposed to identify rice fields from satellite images. But, the similarity of rice class with other classes is one of the main challenges related to rice identification. In this research, the effect of LST time series maps to improve the identification accuracy of rice fields in Landsat-8 time-series images was investigated. In this study, the effect of the time series map of land surface temperature index extracted from Landsat-8 images on improving the accuracy of identifying rice fields from other rice fields due to the evapotranspiration process using machine learning algorithms was investigated. The results showed the effectiveness of the proposed index in improving the identification accuracy of rice fields. One of the reasons for improving the accuracy of identifying rice fields is to extract the characteristics of the thermal growing season from the Earth's surface temperature time series (LST) maps along with the rice phenology curve. The results showed that due to the flooding of rice fields when using the NDVI time series map, water class and fields summer crops were identified as rice class. But, water and summer crops classes were removed from the rice final map using a land surface temperature time-series map with the extraction of thermal growth season characteristics. Therefore, the results showed that there was a direct relationship between LST time-series maps and rice cultivation.

کلیدواژه‌ها [English]

  • Rice identification
  • Landsat-8
  • Land Surface temperature map
  • Machine learning algorithms
مراجع
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فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»
دوره 32، شماره 125 - شماره پیاپی 125
خرداد 1402
صفحه 53-66
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