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İstanbul Teknik Üniversitesi (2015)

Identification Of Cotton And Corn Fields By Object Based Classification Using Multitemporal Satellite Images

ÇELİK Yaren Başak

Titre : Identification Of Cotton And Corn Fields By Object Based Classification Using Multitemporal Satellite Images

Mısır Ve Pamuk Ekili Alanların Çok Zamanlı Uydu Görüntüleri Ve Obje Tabanlı Sınıflandırma Yöntemi İle Tespiti

Auteur : ÇELİK Yaren Başak

Université de soutenance : İstanbul Teknik Üniversitesi

Grade : Master of Science (MS) 2015

Résumé partiel
Remote sensing and satellite technologies have developed significantly in the recent years. New Very High Resolution (VHR) satellites such as SPOT-6, SPOT-7, Pleiades 1A, Pleiades 1B etc. were launched to their orbits and these new systems have extensive acquisition capacity of collecting several millions km2 area per day. Extracting precise and accurate spatial information using these data sets by applying different methodologies is among the most common subject that explored by scientists and experts. With the increasing capability and usage of remote sensing systems, satellite images with different qualities have been used widely by several disciplines like environment, geology, meteorology etc, but especially in agriculture. When dealing with large agricultural areas, determination of the agricultural activities by ground based methods can be time and source consuming and sometimes become nearly impossible. On the other hand, spatial distribution, boundaries of agricultural areas and variety of the crops can be determined by using satellite images and remote sensing techniques. The accuracy of image analysis is extremely important to identify different crop types and monitor the growing stages of these crops periodically. The main analysis method for cultivated area determination is image classification. In agricultural analysis with satellite imagery, multi-temporal image analysis are mostly preferred and provides more accurate results if the phonologic development stages are not precisely determined with in situ data. If the multi-temporal data set is acquired from different satellites, spatial resolution relationships should be carefully established in order to acquire better and more accurate results, since the classification algorithm selection and analysis design are directly affected from this relationship. In multi-temporal analysis, dealing with whole image bands for multiple images is time consuming and may reduce the analysis performance. At this point, spectral vegetation indices, with their sensitivity to biophysical properties of crops, are commonly used for identifying crop types and vegetation index calculation of multi-temporal satellite data helps to emphasize time dimension as well. Normalized difference vegetation index (NDVI) is one of the most widely used indices in crop type identification, which focuses on red and near infrared portion of electromagnetic spectrum. In the NDVI images, dense vegetation is expected to have higher NDVI values therefore they appear as bright on the image and the areas with little or no vegetation are expected to have very low NDVI values therefore they appear as dark on the image. In this study, parcel based identification of cotton and corn fields was conducted using different combinations of multi-temporal SPOT6 and Landsat8 images in three selected districts (Harran, Ceylanpinar and Viransehir) within Sanliurfa which is one of the largest provinces of Turkey.


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