Abstract. Land use and land cover (LU/LC) detection has great significance in management of natural resources and protection of environment. Hence, monitoring LU/LC with the state-of-the-art approaches has gained importance during the recent years and free access satellite images have become valuable data source. The aim of this study is to compare classification abilities of Landsat-9 and PRISMA satellite images while applying Support Vector Machine (SVM) algorithm to distinguish different LU/LC classes. For this purpose, the study area was chosen to be of heterogeneous character that includes industrial area, roads, residential area, airport, sea, forest, vegetation and barren land. When the classification results were visually examined, it was seen that forest, industrial area and airport classes were distinguished more accurately than other classes. On the other hand, qualitative results were validated with quantitative accuracy assessment results. The overall accuracy (OA) and Kappa coefficient values were calculated as 89.33 and 0.88 for Landsat-9 satellite image and as 92.33 and 0.91 for the PRISMA satellite image, respectively. In the accuracy assessment results, although Landsat-9 and PRISMA satellite images showed similar classification performances, a slight improvement was observed by using the PRISMA satellite image. The findings indicated that although both of the Landsat-9 and PRISMA satellite images were proper data to assess the LU/LC of the complex region, a slightly more performance could be achieved by using the PRISMA satellite image.

Abstract. Water is an essential natural source for human being and environment. To conserve water sources, monitoring them by using remote sensing data and technologies is an efficient way. In this study, water quality of the Sea of Marmara (Turkey), which has lots of currents, was examined. The main aim of the study was developing a common model to monitor chlorophyll-a concentration in time by using satellite data. After, the coefficients of the OC2 (ocean chlorophyll 2) model were detected by curve fitting, it was applied to Landsat images. The bias and RMSE (Root Mean Square Error) were found as 0.73 µg/l and 5.80 µg/l, respectively. The high RMSE was stemmed from dynamic structure of the sea. Thus, the temporal resolution has a profound impact on the accuracy of estimations. The developed model was applied to the HLS (Harmonized Landsat Sentinel-2) data, which has high temporal resolution. The results of the HLS and Landsat images were compared, and HLS is found as proper to monitor the water quality. The combined data (SST (Sea Surface Temperature) daily data from 1981 to present derived from satellite observations Level-4 product) was used for the secondary aim of the study which was monitoring SST. The bias and RMSE of the data, which was acquired on 19.07.2017, were found as 0.33 °C and 1.12 °C, respectively. The bias and RMSE of the data, which was acquired on 18.07.2018, were found as −0.02 °C and 1.03 °C, respectively. The combined data is found appropriate to monitor the SST.

Abstract. Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively.

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Dünya gözlem uydularının gelişmesiyle Arazi Örtüsü/Arazi Kullanımı(AÖ/AK) sınıflandırması, ekosistemleri izlemede ve kaynak yönetiminde değerli bilgiler sağlayan önemli bir uygulama haline gelmiştir. Landsat ve Sentinel-2 gibi uydu görüntüleri ile AÖ/AK sınıfları belirli detayda çıkartılabilirken bazı uygulamalarda spektral çözünürlük nedeniyle sınıfların ayırt edilebilirliğinde problemler ortaya çıkabilmektedir. Günümüzde hiperspektral veri sağlayan uydulardan elde edilen görüntüler yüksek spektral çözünürlük sağladıklarından sınıfların ayırt edilebilirliğini arttırmaktadır. Farklı mekânsal çözünürlüklere sahip 13 spektral bandı bulunan Sentinel-2 uydusu farklı mekânsal çözünürlüğe sahip bantları ile detaylı AÖ/AK sınıflarının üretilmesine olanak sağlamaktadır. PRISMA (Precursore IperSpettrale della Missione Applicativa) uydusu ise 30 m mekânsal çözünürlük ve 240 spektral bant ile oldukça yüksek spektral çözünürlük sağlamaktadır. Bu çalışmada Marmara Denizi’ne önemli ölçüde deşarjı olan Susurluk Nehri ve çevresine ait 13.05.2021 tarihli PRISMA ve 14.05.2021 tarihli Sentinel-2 uydu görüntülerinden sınıflandırma ile ekili tarım alanı, boş arazi, orman, yerleşim, endüstri, yol, göl, akarsu, bataklık sınıfları belirlenmiş ve sonuçları karşılaştırılmıştır. Bu amaçla öncelikle PRISMA ve Sentinel-2 görüntülerine ana bileşenler dönüşümü uygulanmış ve oluşturulan veri setleri Maksimum Olabilirlik algoritması ile sınıflandırılmıştır. Tematik doğruluk analizi yapılarak sınıflandırma sonuçlarının doğrulukları belirlenmiş ve metrik sonuçları karşılaştırılarak her iki verinin sınıfları ayırt etmedeki performansları incelenmiştir. Yapılan değerlendirmede PRISMA uydu görüntüsünün sınıflandırma sonuçlarında spektral çözünürlüğün katkısı nedeniyle sınıfların büyük bölümünde Sentinel-2 uydusu sonuçlarına göre daha yüksek doğruluk elde edilmiştir.