Uzaktan algılama (UA) birçok farklı alanda kullanıldığı gibi tarımda da etkin bir biçimde kullanılmaktadır. UA tekniklerinden faydalanarak tarım alanlarında verim, bitki su tüketimi, bitki büyüme ve gelişme gibi özelliklerin tahmin edilmesi için geliştirilmiş birçok matematiksel model vardır. Bu çalışmada modellerden Normalize Edilmiş Vejetasyon Değişim İndeksi (NDVI) ve Toprak Yansımalarını Dikkate Alan Vejetasyon İndeksi (SAVI)’den yararlanılmıştır. NDVI ve SAVI uydu görüntülerinin yakın kızılötesi (NIR) ve kırmızı (RED) ışık dalga boyundaki bantlar kullanılarak hesaplanmaktadır. Çalışma kapsamında 2018 ve 2019 yılına ait LANDSAT 8 uydu görüntüleri kullanılarak Tarım İşletme Genel Müdürlüğüne ait Sultansuyu Tarım İşletme Müdürlüğü’nün Yeniköy mevkiinde bulunan dairesel hareketli yağmurlama sulama (Center Pivot) sistemleri ile sulanan silajlık mısır üretim alanlarındaki değişim bitki gelişme dönemi boyunca analiz edilmiştir. Bu kapsamda uydu görüntülerinden elde edilen NDVI ve SAVI haritaları belirlenmiştir. Elde edilen haritalardan ekimden hasada kadar geçen süre için bitki gelişim değişmeleri değerlendirilmiştir. Çalışmadan elde edilen sonuçlara göre silajlık mısır bitkisinde bitki büyüme ve gelişmesi uydu görüntülerinin NIR ve RED bantları kullanılarak elde edilen NDVI ve SAVI bitki parametreleri ile takip edilebileceği belirlenmiştir.

This study was conducted at Transitional Zone Agricultural Research Institute in Eskisehir, in 2017-2019 growing seasons. In the study, responses of four winter wheat cultivars (Atay85, Hat 31, Yunus and Nacibey) to nitrogen fertilization under irrigation conditions were compared with vegetation indices based on spectral reflection and In- Season Estimates of Yield calculated from these indices. GreenSeekerTM (NTech Industries, Inc., Ukiah,CA) hand-held sensor was used for this purpose. The experimental layout were used 0, 4, 8, 12, 16 and 20 kg N/da nitrogen rates 2 factor factorial in randomized complete block design in the experiment. Vegetation indices (NDVI) were obtained at growth stages Zadoks2,4, Zadoks3,0, Zadoks3,1 and Zadoks3,2. Zadoks3,0 (stem elongation) was found to be the most realistic reading time. A comparison of the system with traditional farmer applications, based on the average of 3 experiment fields, the new system was shown to give similar yields with 2,8 kg/da less N in the spring (ZD3,0), showing its economically promising value. The sensor application is determined to be 2% more economically effective than farmer application. Economic nitrogen dose respectively Atay85, Hat 31, Yunus and Nacibey was determined as the nitrogen dose 12,6 kgN/da, 14,1 kgN/ da, 14,4 kg N/da and 17,9 kgN/da.

Changes in the landscape become extremely destructive and many heritage values and resources are irreversibly lost. The speed, frequency and magnitude of these changes in the landscape increased in the second half of the 20th century, especially with the impact of human activities. Remote sensing is the most widely used method for determining the change in the landscape. In the research, MNDWI, NDVI and NDMI techniques, which are frequently applied in remote sensing, were used in order to determine the landscape change in Salt Lake. Thus, the changes in the built area, water surface and land cover between the years 2000-2020 in Salt Lake were determined. According to the MNDWI and NDMI Analysis results, a decrease was observed in the water surface width and moisture content in Salt Lake between 2000 and 2020. In the steppe areas south of Salt Lake, the increase in tree cover due to the change in land use type was determined by NDVI analysis. Therefore, it is possible to say that there is an increase in the amount of moisture in these areas. In addition, it was determined that the increase in agricultural activities in the region caused a change in land use types and the amount of green space in the region changed at this rate. With the mentioned methods, negative changes in the landscape as a result of human activities on the landscape can be determined practically. Thus, it will be possible to predict the negative consequences of climate change and take precautions.