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Prof. Elif Sertel
Department of Geomatics Engineering, Istanbul Technical University, Maslak-İstanbul, Turkey

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Research Keywords & Expertise

0 Deep Learning
0 Disaster Management
0 Remote Sensing
0 Geospatial Data analysis
0 Land cover/land use change

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Remote Sensing
Deep Learning
Land cover/land use change

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Journal article
Published: 24 July 2021 in Knowledge-Based Systems
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The pan-sharpening process aims to generate a new synthetic output image preserving the spatial details of panchromatic and spectral details of the multi-spectral image inputs. Recently, deep learning-based methods show substantial success in the remote sensing field mostly with the application of traditional Convolutional Neural Networks (CNNs). Most of the traditional CNN-based approaches treat all the channels equitably and cannot learn the correlation. Attention mechanism which can learn the correlations among the channels has been proven to be effective in super-resolution and object detection tasks. In this research, we introduced a novel deep learning framework, channel-spatial attention-based for pan-sharpening (CSAPAN), by designing a Densely residual attention module (RAM). Besides, we train our model in the high-frequency domain and up-sample the low-resolution multispectral images by using the pixel shuffle method before stacking with the panchromatic images for further feature extraction. We evaluated our proposed CSAPAN along with traditional methods and CNN-based methods in reduced and full resolution and obtained satisfactory quantitative and qualitative results on Pleiades, Worldview-2, and QuickBird-2 satellite image datasets.

ACS Style

Peijuan Wang; Elif Sertel. Channel–spatial attention-based pan-sharpening of very high-resolution satellite images. Knowledge-Based Systems 2021, 229, 107324 .

AMA Style

Peijuan Wang, Elif Sertel. Channel–spatial attention-based pan-sharpening of very high-resolution satellite images. Knowledge-Based Systems. 2021; 229 ():107324.

Chicago/Turabian Style

Peijuan Wang; Elif Sertel. 2021. "Channel–spatial attention-based pan-sharpening of very high-resolution satellite images." Knowledge-Based Systems 229, no. : 107324.

Journal article
Published: 21 July 2021 in ISPRS International Journal of Geo-Information
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Scanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distribution of transportation infrastructures and settlements and to conduct quantitative and geometrical analysis. In this research, we used the Deutsche Heereskarte 1:200,000 Türkei (DHK 200 Turkey) maps as the base geoinformation source to construct the past transportation networks using the deep learning approach. Five different road types were digitized and labeled to be used as inputs for the proposed deep learning-based segmentation approach. We adapted U-Net++ and ResneXt50_32×4d architectures to produce multi-class segmentation masks and perform feature extraction to determine various road types accurately. We achieved remarkable results, with 98.73% overall accuracy, 41.99% intersection of union, and 46.61% F1 score values. The proposed method can be implemented in DHK maps of different countries to automatically extract different road types and used for transfer learning of different historical maps.

ACS Style

Burak Ekim; Elif Sertel; M. Kabadayı. Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map. ISPRS International Journal of Geo-Information 2021, 10, 492 .

AMA Style

Burak Ekim, Elif Sertel, M. Kabadayı. Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map. ISPRS International Journal of Geo-Information. 2021; 10 (8):492.

Chicago/Turabian Style

Burak Ekim; Elif Sertel; M. Kabadayı. 2021. "Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map." ISPRS International Journal of Geo-Information 10, no. 8: 492.

Research article
Published: 16 June 2021 in International Journal of Remote Sensing
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Remote Sensing image super-resolution aims to improve the spectral and/or spatial resolution of the satellite imageries. In order to improve the performance of the CNN-based super-resolution methods, increasing the depth of the network is commonly used. However, this increases computational complexity and training difficulties only with small improvement of the performance. Meanwhile, the CNN kernels treat all the channels equally and cannot take the advantage of the abundant high-frequency information contained in the low-resolution images. To address these problems, Channel attention is one of the mechanisms and has been proven to be useful in many tasks. In this research, we proposed a channel attention-based framework for Remote Sensing Image Super-resolution (CARS) by constructing a novel residual channel attention block (RCAB) to further extract the features. In addition, a densely residual channel attention block (RCAB+) and densely residual spatial attention block (RSAB) were proposed to improve the performance. We adopted a post-upsampling architecture to reduce the computational complexity and time cost. Moreover, transfer learning strategy (CARS+T) was introduced to further improve the SR performance and proved to generate finer edge details. Experimentally, our proposed CARS, CARS_SA and CARS+T achieved competitive quantitative and qualitative results both on Data Fusion Contest Dataset and Pleiades Dataset that we created.

ACS Style

Peijuan Wang; Bulent Bayram; Elif Sertel. Super-resolution of remotely sensed data using channel attention based deep learning approach. International Journal of Remote Sensing 2021, 42, 6048 -6065.

AMA Style

Peijuan Wang, Bulent Bayram, Elif Sertel. Super-resolution of remotely sensed data using channel attention based deep learning approach. International Journal of Remote Sensing. 2021; 42 (16):6048-6065.

Chicago/Turabian Style

Peijuan Wang; Bulent Bayram; Elif Sertel. 2021. "Super-resolution of remotely sensed data using channel attention based deep learning approach." International Journal of Remote Sensing 42, no. 16: 6048-6065.

Preprint content
Published: 03 June 2021
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Land Use and Land Cover (LULC) maps derived from satellite images are a significant source of geo-information to better understand the current status of landscapes, to analyze the landscape changes, and to develop sustainable decisions for landscape and urban planning. Since the spectral and spatial resolution of satellite images directly impact the LULC classes to be identified and the accuracy of the classification, these will also affect the values of calculated landscape metrics. This research aims to propose the most appropriate functions and features to obtain highly accurate thematically extensive LULC classification results from multi-resolution satellite images and to investigate how the change in spatial resolution of satellite images would affect the landscape metrics values and landscape pattern analysis Sentinel-2, SPOT-7, Pleaides, and Worldview-4 images with respective 10 m, 1.5 m, 0.5 m, and 0.3 m spatial resolution were classified using Geographic Object-Based Image Analysis techniques to create multi-scale LULC maps with the overall classification accuracy values of 66.05%, 85.00%, 91.79%, and 95.71%, respectively. Patch Density, Total Area, Largest Patch Index, Shape Index, Euclidian Nearest Neighbor distance, Aggregation Index, and Shannon's Diversity Index metrics were found to be most appropriate metrics to analyze the impact of spatial resolution on landscape metrics calculations considering the ability of these metrics to capture the spatial details, spatial arrangement, spatial distribution and complexity of shapes of landscape and classes.Landscape metrics results obtained from different LULC maps were compared to analyze the effects of image spatial resolution on different landscape metrics.

ACS Style

Elif Sertel; Raziye Hale Topaloglu; Beril Varol; Asli Gul Aksu; Kubra Bahsi. Investigation of The Relationships Between Image Spatial Resolution And Landscape Metrics. 2021, 1 .

AMA Style

Elif Sertel, Raziye Hale Topaloglu, Beril Varol, Asli Gul Aksu, Kubra Bahsi. Investigation of The Relationships Between Image Spatial Resolution And Landscape Metrics. . 2021; ():1.

Chicago/Turabian Style

Elif Sertel; Raziye Hale Topaloglu; Beril Varol; Asli Gul Aksu; Kubra Bahsi. 2021. "Investigation of The Relationships Between Image Spatial Resolution And Landscape Metrics." , no. : 1.

Journal article
Published: 03 January 2021 in International Journal of Environment and Geoinformatics
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ACS Style

Peijuan Wang; Ugur Alganci; Elif Sertel. Comparative Analysis on Deep Learning based Pan-sharpening of Very High-Resolution Satellite Images. International Journal of Environment and Geoinformatics 2021, 8, 150 -165.

AMA Style

Peijuan Wang, Ugur Alganci, Elif Sertel. Comparative Analysis on Deep Learning based Pan-sharpening of Very High-Resolution Satellite Images. International Journal of Environment and Geoinformatics. 2021; 8 (2):150-165.

Chicago/Turabian Style

Peijuan Wang; Ugur Alganci; Elif Sertel. 2021. "Comparative Analysis on Deep Learning based Pan-sharpening of Very High-Resolution Satellite Images." International Journal of Environment and Geoinformatics 8, no. 2: 150-165.

Journal article
Published: 30 July 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Convolutional neural network (CNN)-based approaches have shown promising results in the pansharpening of the satellite images in recent years. However, they still exhibit limitations in producing high-quality pansharpening outputs. To that end, we propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem, which brings an entirely novel perspective and solution to the problem compared with the existing methods that base their solution solely on producing a super-resolution version of the multispectral image. Whereas the CNN-based methods provide a reduced-resolution panchromatic image as the input to their model along with the reduced-resolution multispectral images and, hence, learn to increase their resolution together, we instead provide the grayscale transformed multispectral image as the input and train our model to learn the colorization of the grayscale input. We further address the fixed downscale ratio assumption during training, which does not generalize well to the full-resolution scenario. We introduce a noise injection into the training by randomly varying the downsampling ratios. Those two critical changes, along with the addition of adversarial training in the proposed PanColorization generative adversarial network (PanColorGAN) framework, help overcome the spatial-detail loss and blur problems that are observed in CNN-based pansharpening. The proposed approach outperforms the previous CNN-based and traditional methods, as demonstrated in our experiments.

ACS Style

Furkan Ozcelik; Ugur Alganci; Elif Sertel; Gozde Unal. Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 3486 -3501.

AMA Style

Furkan Ozcelik, Ugur Alganci, Elif Sertel, Gozde Unal. Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (4):3486-3501.

Chicago/Turabian Style

Furkan Ozcelik; Ugur Alganci; Elif Sertel; Gozde Unal. 2020. "Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs." IEEE Transactions on Geoscience and Remote Sensing 59, no. 4: 3486-3501.

Journal article
Published: 09 February 2020 in Ecological Indicators
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Soil salinization is one of the significant soil degradation problems especially faced in arid and semi-arid regions of the world. It poses a high threat to soil productivity in agricultural lands. The demand for economic and rapid detection and temporal monitoring of soil salinity has been rising recently. Satellite imagery and remote sensing approaches are the significant tools for accurate prediction and mapping of soil salinity in various regions of the world. This study aims to compare Landsat- 8 OLI and Sentinel-2A derived soil salinity maps of the western part of Urmia Lake in Iran by applying three different salinity indices in conjunction with field measurements. Totally 70 soil samples were collected from top 20 cm of surface soil in October 2016 from an area of 18 km2. Landsat-8 OLI and Sentinel-2A images were acquired in the same month; both images were atmospherically and radiometrically corrected prior to applying soil salinity indices. After comparing Normalized Difference Vegetation Index (NDVI) value of corresponding pixel for each sample with its electrical conductivity (EC) value, 54 soil samples with various EC ranges were selected for mapping. Among them, 42 samples were used for establishing the regression model and remaining 12 samples were utilized to validate the model. Multiple and linear regression analyses were conducted to correlate the EC data with their corresponding soil salinity spectral index values derived from visible bands of satellite images. The results revealed that soil salinity indices extracted from both Landsat-8 OLI and Sentinel-2A visible bands estimated soil salinity with acceptable accuracy of R2 0.73 and 0.74, respectively. Multiple linear regression analysis using both Landsat- 8 OLI and Sentinel-2A data demonstrated higher accuracy with R2 value of 0.77 and 0.75, respectively, compared to linear regression. This study proves that various soil salinity classes with different EC ranges can be estimated by correlating ground measurement data with satellite data.

ACS Style

Taha Gorji; Aylin Yıldırım; Nikou Hamzehpour; Aysegul Tanik; Elif Sertel. Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements. Ecological Indicators 2020, 112, 106173 .

AMA Style

Taha Gorji, Aylin Yıldırım, Nikou Hamzehpour, Aysegul Tanik, Elif Sertel. Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements. Ecological Indicators. 2020; 112 ():106173.

Chicago/Turabian Style

Taha Gorji; Aylin Yıldırım; Nikou Hamzehpour; Aysegul Tanik; Elif Sertel. 2020. "Soil salinity analysis of Urmia Lake Basin using Landsat-8 OLI and Sentinel-2A based spectral indices and electrical conductivity measurements." Ecological Indicators 112, no. : 106173.

Journal article
Published: 01 February 2020 in Remote Sensing
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Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.

ACS Style

Ugur Alganci; Mehmet Soydas; Elif Sertel. Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images. Remote Sensing 2020, 12, 458 .

AMA Style

Ugur Alganci, Mehmet Soydas, Elif Sertel. Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images. Remote Sensing. 2020; 12 (3):458.

Chicago/Turabian Style

Ugur Alganci; Mehmet Soydas; Elif Sertel. 2020. "Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images." Remote Sensing 12, no. 3: 458.

Conference paper
Published: 01 July 2019 in 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
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Global warming, which triggers climatic changes, has direct effects on the phenology of plants. For a sustainable agricultural production, continuous monitoring of crops and trees is critical to have updated information and producing effective agricultural plans. Remote sensing is an efficient option for this purpose and is a very popular technique. Olive is an essential agricultural product for the economy of Mediterranean countries such as Turkey. Determination of olive trees, which are expanded all around Aegean and}{Mediterranean regions of the country, is critical to assess the production capacity and the quality of products. In this study, combinations of time series of Sentinel-1 satellite images, Sentinel-2 satellite images and NDVI products obtained from Sentinel-2 satellite images are used to investigate the classification accuracy of olive trees. According to analysis results, a significant correlation with R 2 = 0.67 found between NDVI and SAR data (sigma nought VH/VV in decibel scale). This result pointed out probable accuracy improvement in classification of fused data from different sensors. In the next step, supervised random forest classification was applied on the fused data combinations and results showed that Sentinel-1 – Sentinel-2, Sentinel-1 – NDVI and Sentinel-2 – NDVI combinations achieved the highest overall accuracy with 73 %, while standalone Sentinel-1 and Sentinel-2 image time series classification accuracies are 48 % and 68 % respectively.

ACS Style

Haydar Akcay; Sinasi Kaya; Elif Sertel; Ugur Alganci. Determination of Olive Trees with Multi-sensor Data Fusion. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 2019, 1 -6.

AMA Style

Haydar Akcay, Sinasi Kaya, Elif Sertel, Ugur Alganci. Determination of Olive Trees with Multi-sensor Data Fusion. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). 2019; ():1-6.

Chicago/Turabian Style

Haydar Akcay; Sinasi Kaya; Elif Sertel; Ugur Alganci. 2019. "Determination of Olive Trees with Multi-sensor Data Fusion." 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) , no. : 1-6.

Conference paper
Published: 01 July 2019 in 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
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Drought is one of the frequently observed natural hazard resulting from precipitation deficit and increased evapotranspiration caused by high temperatures. Remote sensing indices are used to analyze spatio-temporal distribution of drought conditions and identify drought severity. In this study, we analyzed the spatio-temporal distribution of drought conditions in Turkey from February 2000 to January 2019 by using different drought indices produced from MODIS satellite data in Google Earth Engine (GEE) platform. Vegetation Health Index (VHI), Normalized Multiband Drought Index(NMDI) and Normalized Difference Drought Index (NDDI) maps in country level for different years and months of the related years were utilized to assess the drought conditions. Time series were also created for some specific locations to deeply analyze the drought conditions in 20-year period. Our results show that MODIS derived drought indices provide useful geospatial information to assess drought conditions in country level. Moreover, GEE platform is very handy and rapid tool to reach related satellite images and conduct remote sensing analysis of huge and long term date efficiently. Geospatial big data could be successfully accessed and processed in this platform not only for drought monitoring but also for other environmental monitoring applications.

ACS Style

Samet Aksoy; Ozge Gorucu; Elif Sertel. Drought Monitoring using MODIS derived indices and Google Earth Engine Platform. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 2019, 1 -6.

AMA Style

Samet Aksoy, Ozge Gorucu, Elif Sertel. Drought Monitoring using MODIS derived indices and Google Earth Engine Platform. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). 2019; ():1-6.

Chicago/Turabian Style

Samet Aksoy; Ozge Gorucu; Elif Sertel. 2019. "Drought Monitoring using MODIS derived indices and Google Earth Engine Platform." 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) , no. : 1-6.

Conference paper
Published: 01 July 2019 in 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
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Land degradation by salinity is one of the main environmental hazards threatening soil sustainability especially in arid and semi-arid regions of the world characterized by low precipitation and high evaporation. Geo-statistical approaches and remote sensing (RS) techniques have provided fast, accurate and economic prediction and mapping of soil salinity within the last two decades. Obtaining multi-temporal data via satellite images in different spatial domains with various scales is one of the key developments of monitoring spatial variability of soil salinity. In addition, geo-statistical methods have the capability of producing prediction surfaces from limited sample data. This study aims to map spatial distribution of soil salinity in the selected pilot area which is located in the western part of Urmia Lake Basin, Iran, by applying geo-statistical methods. A kriging based map and three different co-kriging based maps were produced using electrical conductivity (EC) measurements as primary variable and three different soil salinity index values as secondary variable. Three soil salinity indices were created by using Sentinel-2A image that were acquired in the same date of field measurements to generate 3 various soil salinity prediction maps. Salinity maps obtained from geo-statistical methods were compared and validated to understand the performance of these approaches for soil salinity prediction. The results of this study demonstrated that co-kriging can provide promising estimation of spatial variability of soil salinity especially when there is relevant and abundant set of secondary data derived from satellite images.

ACS Style

Taha Gorji; Aylin Yıldırım; Nikou Hamzehpour; Elif Sertel; Aysegul Tanik. Characterizing the spatial variability of soil salinity in Lake Urmia Basin by applying geo-statistical methods. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 2019, 1 -5.

AMA Style

Taha Gorji, Aylin Yıldırım, Nikou Hamzehpour, Elif Sertel, Aysegul Tanik. Characterizing the spatial variability of soil salinity in Lake Urmia Basin by applying geo-statistical methods. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). 2019; ():1-5.

Chicago/Turabian Style

Taha Gorji; Aylin Yıldırım; Nikou Hamzehpour; Elif Sertel; Aysegul Tanik. 2019. "Characterizing the spatial variability of soil salinity in Lake Urmia Basin by applying geo-statistical methods." 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) , no. : 1-5.

Conference paper
Published: 01 July 2019 in 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
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Investigation of radar and optical data indices that contain a lot more information on landscapes and vegetation dynamics can be useful to identify opportunities and challenges in agricultural activities. In addition, the potential of synchronous implications of radar and optical data will be an effective method for agro-environmental monitoring and management to promote economic and environmental sustainability as monitoring programs. Crop discrimination as an agricultural monitoring system is a critical step regarding to estimate the area allocated to each crop type, computing statistics for crop control of area-based subsidies or crop production forecasting, environmental impact analysis and some other applications. Integrating both optical (reflectance) and Synthetic Aperture Radar (backscatter) multi-temporal features provides some advantages in terms of a more reliable crop map. We utilize multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery and Sentinel-2 optical datasets in order to investigate the performance of the sensors backscatter and reflectance for temporal crop type mapping and the sustainable management of agricultural activities. Multi-temporal Sentinel-1, C-band VV and VH polarized SAR data and Sentinel2 optical data were acquired simultaneously by in-situ measurements for the study area. As preliminary results, it is concluded that the classification accuracies were improved results (5%) with using combinations of sensors. Classification accuracies of 93% were achieved in this study with integration use of SAR and optical data.

ACS Style

Rouhollah Nasirzadehdizaji; Fusun Balik Sanli; Ziyadin Cakir; Elif Sertel. Crop Mapping Improvement by Combination of Optical and SAR datasets. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 2019, 1 -6.

AMA Style

Rouhollah Nasirzadehdizaji, Fusun Balik Sanli, Ziyadin Cakir, Elif Sertel. Crop Mapping Improvement by Combination of Optical and SAR datasets. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). 2019; ():1-6.

Chicago/Turabian Style

Rouhollah Nasirzadehdizaji; Fusun Balik Sanli; Ziyadin Cakir; Elif Sertel. 2019. "Crop Mapping Improvement by Combination of Optical and SAR datasets." 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) , no. : 1-6.

Conference paper
Published: 01 July 2019 in 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)
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This research aims to determine the flooded agricultural lands after the flood that occurred in April 2015 on the Menderes Plain. The unexpected heavy and continuous precipitation in spring season induced a flash flood on the Menderes River, which directly damaged the agricultural lands. The flooded areas are determined by geographic object based GEOBIA classification of normalized difference water index (NDWI) data calculated from after-disaster SPOT 6 satellite image and land cover type of the flooded areas are verified from pre-disaster SPOT 6 satellite image. Moreover, topographic characteristics of the flooded areas are produced from open access ALOS W3D DSM data in order to investigate the relationship between the flood and topography. Results of this research exhibited that, optical satellite images are feasible data sources in determining flooded areas due to unique reflectance responses of them especially in the green and near infrared portions of the spectrum. Both flood extent and agricultural parcels affected by the flood are accurately mapped by using SPOT 6 image and GEOBIA approach.

ACS Style

Ugur Alganci; Elif Sertel; Sinasi Kaya. Determination of the Flooded Agricultural Lands with Spot 6 High Resolution Satellite Images: A Case Study of Menderes Plain, Turkey. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 2019, 1 -4.

AMA Style

Ugur Alganci, Elif Sertel, Sinasi Kaya. Determination of the Flooded Agricultural Lands with Spot 6 High Resolution Satellite Images: A Case Study of Menderes Plain, Turkey. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). 2019; ():1-4.

Chicago/Turabian Style

Ugur Alganci; Elif Sertel; Sinasi Kaya. 2019. "Determination of the Flooded Agricultural Lands with Spot 6 High Resolution Satellite Images: A Case Study of Menderes Plain, Turkey." 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) , no. : 1-4.

Conference paper
Published: 01 June 2019 in 2019 9th International Conference on Recent Advances in Space Technologies (RAST)
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This research aims to evaluate a histogram matching based custom mosaicking process on different land cover land use characteristics using multi-temporal satellite images. Three different study regions located in different parts of Turkey are selected and SPOT 6/7 satellite images belonging to these regions are used in analysis. Reference histogram data obtained from 2018 dated Sentinel 2 mosaic of Turkey. This dataset is used for histogram matching after conducting post process to enhance the visual representation of the study region. Feathering effort is operated on the target images after histogram matching step in order to eliminate dissimilarities between geometric shapes and introduce seamless transition in overlapping areas. Accuracy assessment is performed both by visual interpretation and by calculating the weighted average of differences between reference and matched histograms. Results proved that, histogram matching based method proposed in this study provides practical and efficient mosaicking approach, which can be used for producing high-resolution and large coverage satellite image dataset.

ACS Style

Hakan Kartal; Ugur Alganci; Elif Sertel. Histogram Matching Based Mosaicking of SPOT 6/7 Satellite Images. 2019 9th International Conference on Recent Advances in Space Technologies (RAST) 2019, 451 -455.

AMA Style

Hakan Kartal, Ugur Alganci, Elif Sertel. Histogram Matching Based Mosaicking of SPOT 6/7 Satellite Images. 2019 9th International Conference on Recent Advances in Space Technologies (RAST). 2019; ():451-455.

Chicago/Turabian Style

Hakan Kartal; Ugur Alganci; Elif Sertel. 2019. "Histogram Matching Based Mosaicking of SPOT 6/7 Satellite Images." 2019 9th International Conference on Recent Advances in Space Technologies (RAST) , no. : 451-455.

Conference paper
Published: 01 June 2019 in 2019 9th International Conference on Recent Advances in Space Technologies (RAST)
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The present study aims to evaluate the land cover/use resource for high-resolution map production in a complex landscape mosaic in the central Italy. The classification methodologies are based on manual and supervised and rule-based classification of Göktürk-l very high resolution imagery. For the different classifications and for results validation the Italian land use inventory (IUTI) database were used. General spatial distributions of selected classes are successfully mapped with both classification approaches. Pixel-based classification results include some salt and pepper effects and spectral mixtures but still provides good delineation of the selected classes. Incorporating image indices, topological and textural functions into object-oriented classification improved the classification accuracy. However, the scale parameter is important to capture the detail levels obtained with Göktürk-l images. Moreover, considering the high spatial resolution of Göktürk-1 images, photointerpretation approach is also very suitable to identify objects specifically if more detailed classes will be considered. High quality spatial, spectral and radiometric characteristics of the Göktürk-1 satellite makes it possible to create thematically and geometrically detailed geo-spatial information for every local public administration or private sector without spatio-temporal limitations.

ACS Style

Marco Ottaviano; Elif Sertel; Marco Marchetti. Turkish Satellite Göktürk-1 at Work: Applications for Artificial, Natural and Semi-Natural Resources, Mapping and Inventory. 2019 9th International Conference on Recent Advances in Space Technologies (RAST) 2019, 833 -838.

AMA Style

Marco Ottaviano, Elif Sertel, Marco Marchetti. Turkish Satellite Göktürk-1 at Work: Applications for Artificial, Natural and Semi-Natural Resources, Mapping and Inventory. 2019 9th International Conference on Recent Advances in Space Technologies (RAST). 2019; ():833-838.

Chicago/Turabian Style

Marco Ottaviano; Elif Sertel; Marco Marchetti. 2019. "Turkish Satellite Göktürk-1 at Work: Applications for Artificial, Natural and Semi-Natural Resources, Mapping and Inventory." 2019 9th International Conference on Recent Advances in Space Technologies (RAST) , no. : 833-838.

Conference paper
Published: 01 June 2019 in 2019 9th International Conference on Recent Advances in Space Technologies (RAST)
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This study aims to create Land Cover Land Use (LCLU)map of a part of the Izmir metropolitan city in Turkey, based on an enhanced Urban Atlas nomenclature and object based classification approach. Multi-temporal Sentinel-2 images from different seasons are used and rule-based object oriented classification techniques are applied on the images. Totally 20 LCLU classes are identified in the study area with different accuracy values. Thematic open source data were also integrated into classification to better identify some land use classes and to increase the total classification accuracy. Our results show that 10-m bands of Sentinel-2 images are capable to produce thematically detailed LCLU map with an overall accuracy of 86% and 0.852 Kappa values.

ACS Style

Elif Ozlem Yilmaz; Beril Varol; Raziye Hale Topaloglu; Elif Sertel. Object-Based Classification of Izmir Metropolitan City by Using Sentinel-2 Images. 2019 9th International Conference on Recent Advances in Space Technologies (RAST) 2019, 407 -412.

AMA Style

Elif Ozlem Yilmaz, Beril Varol, Raziye Hale Topaloglu, Elif Sertel. Object-Based Classification of Izmir Metropolitan City by Using Sentinel-2 Images. 2019 9th International Conference on Recent Advances in Space Technologies (RAST). 2019; ():407-412.

Chicago/Turabian Style

Elif Ozlem Yilmaz; Beril Varol; Raziye Hale Topaloglu; Elif Sertel. 2019. "Object-Based Classification of Izmir Metropolitan City by Using Sentinel-2 Images." 2019 9th International Conference on Recent Advances in Space Technologies (RAST) , no. : 407-412.

Journal article
Published: 23 May 2019 in Water
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This research aimed to evaluate the impact of land cover/use changes on watershed responses and hydrological processes by applying the Soil and Water Assessment Tool (SWAT) distributed hydrologic model to the Buyukcekmece Water Basin of Istanbul Metropolitan city. SWAT model was run for two different scenarios for the 40-year period between 1973 and 2012, after completing calibration procedures under gauge-data scarce conditions. For the first scenario, 1990 dated Land cover/land use (LCLU) map and meteorological data obtained between 1973 and 2012 were used. For the second scenario, 2006 dated LCLU map and same meteorological data were used to analyze the impact of changing landscape characteristics on hydrological processes. In the selected watershed, LCLU changes started towards the end of the 1980s and reached a significant status in 2006; therefore, 1990 and 2006 dated LCLU maps are important to model human impact period in the watershed. Afterwards, LCLU changes within sub-basin level were investigated to quantify the effects of different types of land changes on the major hydrological components such as actual evapotranspiration, percolation, soil water, base flow, surface runoff and runoff. Our analysis indicated that, under the same climatic conditions, changes in land cover/use, specifically urbanization, played a considerable role in hydrological dynamics with changes on actual transpiration, base flow, surface runoff, runoff, percolation and soil water mainly due to urban and agricultural area changes. Among the different hydrological components analyzed at watershed level, percolation, ET and base flow were found to be highly sensitive to LCLU changes, whereas soil water was found as the least sensitive to same LCLU changes.

ACS Style

Elif Sertel; Mehmet Zeki Imamoglu; Gokhan Cuceloglu; Ali Erturk. Impacts of Land Cover/Use Changes on Hydrological Processes in a Rapidly Urbanizing Mid-latitude Water Supply Catchment. Water 2019, 11, 1075 .

AMA Style

Elif Sertel, Mehmet Zeki Imamoglu, Gokhan Cuceloglu, Ali Erturk. Impacts of Land Cover/Use Changes on Hydrological Processes in a Rapidly Urbanizing Mid-latitude Water Supply Catchment. Water. 2019; 11 (5):1075.

Chicago/Turabian Style

Elif Sertel; Mehmet Zeki Imamoglu; Gokhan Cuceloglu; Ali Erturk. 2019. "Impacts of Land Cover/Use Changes on Hydrological Processes in a Rapidly Urbanizing Mid-latitude Water Supply Catchment." Water 11, no. 5: 1075.

Journal article
Published: 02 April 2019 in International Journal of Environment and Geoinformatics
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Soil salinization is one of the severe land-degradation problems due to its adverse effects on land productivity. Each year several hectares of lands are degraded due to primary or secondary soil salinization, and as a result, it is becoming a major economic and environmental concern in different countries. Spatio-temporal mapping of soil salinity is therefore important to support decision-making procedures for lessening adverse effects of land degradation due to the salinization. In that sense, satellite-based technologies provide cost effective, fast, qualitative and quantitative spatial information on saline soils. The main objective of this work is to highlight the recent remote sensing (RS) data and methods to assess soil salinity that is a worldwide problem. In addition, this study indicates potential linkages between salt-affected land and the prevailing climatic conditions of the case study areas being examined. Web of Science engine is used for selecting relevant articles. "Soil salinity" is used as the main keyword for finding "articles" that are published from January 1, 2007 up to April 30, 2018. Then, 3 keywords; "remote sensing", "satellite" and "aerial" were used to filter the articles. After that, 100 case studies from 27 different countries were selected. Remote sensing based researches were further overviewed regarding to their location, spatial extent, climate regime, remotely sensed data type, mapping methods, sensing approaches together with the reason of salinity for each case study. In addition, soil salinity mapping methods were examined to present the development of different RS based methods with time. Studies are shown on the Köppen-Geiger climate classification map. Analysis of the map illustrates that 63% of the selected case study areas belong to arid and semi-arid regions. This finding corresponds to soil characteristics of arid regions that are more susceptible to salinization due to extreme temperature, high evaporation rates and low precipitation.

ACS Style

Taha Gorji; Aylin Yıldırım; Elif Sertel; Ayşegül Tanık. Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes. International Journal of Environment and Geoinformatics 2019, 6, 33 -49.

AMA Style

Taha Gorji, Aylin Yıldırım, Elif Sertel, Ayşegül Tanık. Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes. International Journal of Environment and Geoinformatics. 2019; 6 (1):33-49.

Chicago/Turabian Style

Taha Gorji; Aylin Yıldırım; Elif Sertel; Ayşegül Tanık. 2019. "Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes." International Journal of Environment and Geoinformatics 6, no. 1: 33-49.

Journal article
Published: 10 February 2019 in Remote Sensing
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In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification.

ACS Style

Paria Ettehadi Osgouei; Sinasi Kaya; Elif Sertel; Ugur Alganci. Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote Sensing 2019, 11, 345 .

AMA Style

Paria Ettehadi Osgouei, Sinasi Kaya, Elif Sertel, Ugur Alganci. Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote Sensing. 2019; 11 (3):345.

Chicago/Turabian Style

Paria Ettehadi Osgouei; Sinasi Kaya; Elif Sertel; Ugur Alganci. 2019. "Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery." Remote Sensing 11, no. 3: 345.

Journal article
Published: 01 January 2019 in International Journal of Global Warming
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Approximately one-fourth of the population in Turkey works in the agricultural sector. One common problem in agricultural waste removal is the adverse effects of crop residue burning (CRB) on public health and environment, after the harvest. In this research, a pilot study area of 225 km

ACS Style

Kubra Bahsi; Betul Salli; Doğushan Kılıç; Elif Sertel. Estimation of emissions from crop residue burning using remote sensing data. International Journal of Global Warming 2019, 19, 94 .

AMA Style

Kubra Bahsi, Betul Salli, Doğushan Kılıç, Elif Sertel. Estimation of emissions from crop residue burning using remote sensing data. International Journal of Global Warming. 2019; 19 (1/2):94.

Chicago/Turabian Style

Kubra Bahsi; Betul Salli; Doğushan Kılıç; Elif Sertel. 2019. "Estimation of emissions from crop residue burning using remote sensing data." International Journal of Global Warming 19, no. 1/2: 94.