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برآوردتغییرات سطح زیرکشت گندم و سویا با استفاده از طبقهبندی تصاویر ماهوارهای در غرب استان گلستان | ||
| پژوهش های کاربردی زراعی | ||
| مقاله 3، دوره 31، شماره 3 - شماره پیاپی 120، مهر 1397، صفحه 41-61 اصل مقاله (1.35 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/aj.2018.121231.1268 | ||
| نویسندگان | ||
| پریسا علی زاده1؛ بهنام کامکار* 2؛ شعبان شتایی2؛ حسین کاظمی2 | ||
| 1گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان | ||
| 2دانشگاه علوم کشاورزی و منابع طبیعی گرگان | ||
| چکیده | ||
| برآورد سطح زیر کشت بر اساس شاخصهای جنبی از جمله میزان بذر، کود و سم تحویلشده به کشاورزان و همچنین برآوردهای شرکتهای مشاور کنترل میگردد که کاری زمانبر و پرهزینه میباشد. بنابراین این مطالعه با هدف برآورد سطح زیر کشت دو محصول گندم و سویا در طی سالهای 2000 تا 2016 با استفاده از تصاویر ماهواره لندست انجام شد. بعد از پیش پردازش و پردازش های لازم، و تهیه نمونه های تعلیمی مناسب از مزارع کشت سویا و گندم، طبقه بندی تصاویر با استفاده از دو روش طبقهبندی شبکه عصبی مصنوعی و ماشین بردار پشتیبان انجام شد و جهت طبقهبندی با هدف تفکیک این دو محصول زراعی دادههای واقعیت زمینی، نقشه NDVI اراضی زراعی و شناسایی رفتار طیفی نقاط آموزشی گندم و سویا بکار برده شدند. نقشه های حاصل از طبقه بندی با استفاده از نقاط واقعیت زمینی مورد ارزیابی صحت قرار گرفتند. با توجه به نتایج بهدستآمده از بررسی ضـریب کاپـا و صـحت کلـی، روش ماشینبردار پشتیبان برای طبقهبندی اراضی کشاورزی و تفکیک محصولات نسبت به روش شبکه عصبی موفقتر بود و در همه تصاویر مقدار صحت کلی محاسبهشده و ضریب کاپا به ترتیب بیشتر از 80٪ و بیش از 8/0میباشد که این نشاندهنده قابلاعتماد بودن نتایج طبقهبندیاست. طبق نتایج، حدود 93 درصد از نقاط برآوردشده سطح زیرکشت گندم و سویا در طی 16 سال مورد مطالعه در محدوده حدود اطمینان 15± درصد قرار دارند که نشان میدهد این روش، روش مطمئنی برای تفکیک این دو محصول با استفاده از تصاویر اردیبهشتماه (برای گندم) و شهریورماه (برای سویا) میباشد. | ||
| کلیدواژهها | ||
| سنجش از دور؛ طبقهبندی؛ شبکه عصبی مصنوعی؛ ماشینبردار پشتیبان | ||
| عنوان مقاله [English] | ||
| Estimation of changes in land area under wheat and soybean cultivation using satellite images classification techniques in west of Golestan province | ||
| نویسندگان [English] | ||
| Parisa Alizadeh1؛ Behnam Kamkar2؛ shaban shataee2؛ hossein kazemi posht masari2 | ||
| 1Department of Agriculture, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources | ||
| 2Agronomy Dept., Gorgan University of Agricultural Sciences and Natural Resources | ||
| چکیده [English] | ||
| Introduction:Estimation of cultivated area is controlled on the basis of some features such as seed rate, fertilizer and other delivered chemical inputs which are costly andtime consuming. In this way, using satellite data as a new solution not only reduces the drawbacks of conventional estimation methods of cultivated area , but also can be helpful for various programming aims in the agriculture scope. Land cover mapping is important for much planning and management activities. Today, satellite images and remote sensing techniques are extensively used in all sectors, including agriculture and natural resources because they provide updated data and high analyzing abilities. It is one of the fastest and most cost-effective methods to map those lands are available for researchers. In recent years, sattelite imagery and different detection methods have helped researches to detect the features at a lower cost and spend less time. Different methods are avialbale for this purpose.. Each method has some advantages and disadvantages. Artificial neural network, fuzzy logic, support vector machine, decision tree, object-oriented classification and intelligent systems can be considered as advanced classification methods (Guo et al., 2012). In this study, we want to detect wheat and soybean-grown fields with two advanced classification methods (i.e. support vector machine (SVM) and artificial neural network (ANN)) in west of Golestan Province, Iran. Cultivation areas were determined from those maps extracted from satellite images to use them as the base layer for other research goals. Materials and Methods:The current research was conducted with the aim of estimating the wheat- and soybean-grown areas during a 16-year period from 2000 to 2016 using Landsat satellite imagery. To this purpose, two classification methods, support vector machine and artificial neural network, were used. In order to classify and detect aforementioned two crops, ground control points (GCPs), the normalized difference vegetation index (NDVI) for agricultural lands, and spectral behavior of wheat and soybean training GCPs were involved. In order to validate the results of classification, the generated maps were checked by GCPs (coordinated with GPS). Results and Discussion: Wheat and soybeans were at the maximum vegetative growth in May and September, and were well-detected from other crops. So the images of these two months were used for detection in all studied years. According to the results of previous studies, support vector machine and artificial neural network could be used as two reliable image classification methods for detecting the crops vegetation cover (Rahimzadegan & Pourgholam ,2017; Mokhtari & Najafi, 2015). The accuracy of image classification was assessed using kappa coefficient and overall accuracy. Kappa coefficient and overall accuracy showed the support vector machine method was superior than artificial neural network method in classifying agricultural lands and detecting studied wheat- and soybean-grown fields. In the all images, the calculated overall accuracy coefficient was more than 80% (0.84 to 0.92 for wheat; and 0.84 to 0.90 for soybean) and Kappa coefficient was more than 0.8, indicating the reliability of the classification outputs. According to comparisons of satellite image-based estimations and real recorded ststistics, about 93% of the estimated areas of wheat - and soybean grown fields in the 16 consecutive years of study were within the range of 15% confidence level, which indicates that this method is a reliable method for detecting these two crops using images of April (for wheat) and September (for soybean). Conclusions: The support vector machine method of classification was identified as the superior method. wheat- and soybean-grown fields maps extracted from satellite images can be used as a base layer for regional modeling, providing yield gap layer, calculating the water requirement, designing crop pattern and etc. | ||
| کلیدواژهها [English] | ||
| Remote Sensing, Classification, Artificial Neural Networks, Support Vector Machine | ||
| مراجع | ||
|
Amini Bazyani, S., Akbari, M., and Zare Abyane, H. 2013. Assessment of Cropped Area, and Cropping Intensity in Hamedan–Bahar Plain Using Remote Sensing Data. Iranian Journal of lrrigation and Drainage, 7 (1), 36-48 (In Persian with English Summary). | ||
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