Yazar "Esetlili M.T." seçeneğine göre listele

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    Agroecological aspect of olive cultivation
    (Scibulcom Ltd., 2014) Esetlili M.T.; Ozen F.; Kurucu Y.; Kaya U.
    Olive cultivation is very common in the Aegean region of Turkey and the sloping lands of this region face with severe erosion due to intensive cultivation practices. Erosion rapidly leads to infertile lands owing to detritions of the most productive top layer of soil and removal of soil to low lands on the slope. The level of erosion in olive-plantation zones has reached to a level that threatens even the agriculture of this crop. In this study, soil loss which results from conversion of natural vegetation such as maquis and forests into olive plantation was determined by using remote sensing technique and GIS. Results of the study showed that lands which were converted into olive plantations have a soil lose of 12.159 t ha-1 annually. If natural vegetation of the lands was protected, only 3.965 t ha-1 year soil would be lost. This result states that every year 8.194 t ha-1 of soil are lost because of land use changes into olive cultivation on sloppy lands. It is known that similar agricultural land use plans have been supported by government in the Aegean region, which results to huge amounts of soil loss. Also, this result shows ecological damage of soil practices causing accelerated erosion and olive cultivation without precautions against erosion.
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    An application of roll-invariant polarimetric features for crop classification from multi-temporal RADARSAT-2 SAR data
    (International Society for Photogrammetry and Remote Sensing, 2018) Ustuner M.; Sanli F.B.; Abdikan S.; Esetlili M.T.; Bilgin G.
    Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (?¯) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H?¯, (2) H?¯Span, (3) H?¯A, (4) H?¯ASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H?ASpan (91.43% for SVM, 92.25% for RF and 90.55% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25% by RF and H?ASpan while lowest classification accuracy was obtained as 66.99% by NB and H?. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification. © Authors 2018. CC BY 4.0 License.
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    Comparison of crop classification methods for the sustainable agriculture management
    (Scibulcom Ltd., 2016) Ustuner M.; Esetlili M.T.; Sanli F.B.; Abdikan S.; Kurucu Y.
    Accurate and reliable information regarding crop yields and soil conditions of agricultural fields are essential for the sustainable management of agricultural areas. The increasing necessity of the food due to the high population, global climate change and rapid urbanisation, the sustainable management of the agricultural resources is becoming more crucial for countries. Remote sensing technology offers a feasible solution for gathering the cost-effective, reliable and up-to-date information about crop monitoring by using high-resolution remote sensing data. Image classification is the one of most common method to obtain information from the remotely sensed images. Despite machine learning based classifiers such as Support Vector Machines (SVM) could provide high classification accuracy, the researchers have been still working to improve the classification accuracy. Recently, the utilisation of ensemble learning approaches in remote sensing classification is the research of interest for this purpose. In this study, we implemented six different supervised classification techniques and a classifier ensemble: Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Spectral Angle Mapper, Parallelepiped, Support Vector Machines and Winnertakes- all (WTA) classification which is an ensemble based classifier. In this study, we investigated the comparative performance of the classifiers within overall and corn-class category for the study area located in Aydin, Turkey. Radial Basis Function (RBF) kernel was used here for the SVM classification. Results demonstrate that WTA classification outperformed other classification methods whilst the Parallelepiped obtained the lowest classification accuracy 13.24%. Moreover SVM gave the second highest overall classification accuracy of 89.90%.
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    Creating potential erosion risk map of the Karaburun Peninsula by Geographical Information System and remote sensing technique
    (2011) Kurucu Y.; Altinbas U.; Uysal H.; Bolca M.; Esetlili M.T.; Ozen F.; Yonter G.; Ozden N.; Yolcu G.; Karakurt H.; Altun N.
    With this study, it is aimed to draw a potential erosion risk map needed to be used for planning the precautions against erosion, which is one of the biggest problems of our country, by using advanced techniques. An area of 1126 km 2 including the Cesme Karaburun Peninsula, which is located on the west coast of Turkey, has been selected as study area. RUSLE soil loss factors have been used in the study. Each factor used for detecting the soil loss has been determined geographically from different sources and recorded as layer in database according to Geographic Information System. In the study, for determining the C factor a 15-m spatial resolution ASTER image, for determining 'L' and 'S' factors numerical counter lines, for determining 'R' factor weather observation results, and for determining 'K' factor, soil order maps have been used. Afterwards, layers are united by using spatial intersection and new polygons containing all attributes have been created. After the database was created, the Rusle model has been applied and as a result soil loss has been determined for each polygon. At the end of the study, it has been determined that the soil loss in the Cesme Karaburun Peninsula, selected as study area, amounts to 1 279 548 t/ha/year.
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    Crop type classification using vegetation indices of rapideye imagery
    (International Society for Photogrammetry and Remote Sensing, 2014) Ustuner M.; Sanli F.B.; Abdikan S.; Esetlili M.T.; Kurucu Y.
    Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46% was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.
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    Determination of impact of urbanization on agricultural land and wetland land use in Balçovas' Delta by remote sensing and GIS technique
    (2007) Bolca M.; Turkyilmaz B.; Kurucu Y.; Altinbas U.; Esetlili M.T.; Gulgun B.
    Because of their intense vegetation and the fact that they include areas of coastline, deltas situated in the vicinity of big cities are areas of greet attraction for people who wish to get away from in a crowded city. However, deltas, with their fertile soil and unique flora and fauna, need to be protected. In order for the use of such areas to be planned in a sustainable way by local authorities, there is a need for detailed data about these regions. In this study, the changes in land use of the Balçova Delta, which is to the immediate west of Turkey's third largest city Izmir, from 1957 up to the present day, were investigated. In the study, using aerial photographs taken in 1957, 1976 and 1995 and an IKONOS satellite image from the year 2005, the natural and cultural characteristics of the region and changes in the coastline were determined spatially. Through this study, which aimed to reveal the characteristics of the areas of land already lost as well as the types of land use in the Balçova delta and to determine geographically the remaining areas in need of protection, local authorities were provided with the required data support. Balçova consists of flat and fertile wetland with mainly citrus-fruit orchards and flower-producing green houses. The marsh and lagoon system situated in the coastal areas of the delta provides a habitat for wild life, in particular birds. In the Balçova Delta, which provides feeding and resting for migratory birds, freshwater sources are of vital importance for fauna and flora. The settlement area, which in 1957 was 182 ha, increased 11-fold up to the year 2005 when it reached 2,141 ha. On the other hand, great losses were determined in farming land, olive groves, forest and in the marsh and lagoon system. This unsystematic and rapid urbanization occurring in the study region is not only causing the loss of important agricultural land and wetland, but also lasting water and soil pollution. © Springer Science+Business Media B.V. 2006.
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    Determination of the arsenic pollution due to geothermal sources in the agricultural lands of Alangullu-Aydin region
    (Scibulcom Ltd., 2014) Esetlili M.T.; Colak Esetlili B.; Ozen F.; Bolca M.; Kurucu Y.
    Arsenic, along with Hg, Sb, B, Li and F, is one of the important components of geothermal waters. In regions where geothermal waters are found abundantly, the arsenic content of these waters easily mixes into the watersheds and rivers. WHO reports the limit values of 10 µg/l for drinking waters, 19 µg/l for protection of aquatic life and FAO (Food and Agriculture Organisation) permissible limit for irrigation water (100 µg/l). Arsenic, which is a natural pollutant in general, is of utmost importance due to its proven adverse impacts in particular on human health. Health problems due to high levels of arsenic are encountered in many countries such as Bangladesh, India, United States of America, Argentina, Chile, Taiwan and China. In this study, the arsenic content of the water and soil samples were evaluated to determine the impact of arsenic (As) pollution from geothermal sources on agricultural lands in Aydin-Germencik Alangullu Region which is a sub-basin of Great Menderes Basin (Western Turkey), where sodium chloride type geothermal sources are found as open systems. Water samples were taken from 4 different geothermal sources with natural flow conditions into agricultural lands of the basin. The As contents of the geothermal waters were measured between 680 and 1150 µg/l. Soil samples taken from four different areas differed in relation to the impact level of the source and were determined to be between 14.88 and 48.50 mg kg-1. The distribution of the impacted agricultural lands in the sub-basin was mapped using geostatistical methods.
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    Evaluation of image fusion methods using PALSAR, RADARSAT-1 and SPOT images for land use/ land cover classification
    (Springer India, 2017) Sanli F.B.; Abdikan S.; Esetlili M.T.; Sunar F.
    This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification. © 2016, Indian Society of Remote Sensing.
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    Fusion of terrasar-X and rapideye data: A quality analysis
    (International Society for Photogrammetry and Remote Sensing, 2013) Balik Sanli F.; Abdikan S.; Esetlili M.T.; Ustuner M.; Sunar F.
    This research compares and evaluates image fusion algorithms to achieve spatially improved images while preserving the spectral information. In order to compare the performance of fusion techniques both active and passive images were used. As an active image a high resolution, X-band, VV polarized TerraSAR-X data and as a multispectral image RapidEye data were used. RapidEye provides five optical bands in the 400-850 nm range and it is the first space-borne sensor which operationally gathers the red edge spectrum (690-730 nm) besides the standard channels of multi-spectral satellite sensors. The selected study area is in the low lands of Menemen (Izmir) Plain on the west of Gediz Basin covering both agricultural fields and residential areas. For the quality analysis, Adjustable SAR-MS Fusion (ASMF), Ehlers fusion and High Pass Filtering (HPF) approaches were investigated. In this study preliminary results of selected image fusion methods were given. The quality of the fused images was assessed with qualitative and quantitative analyses. For the qualitative analysis visual comparison was applied using different band combinations of fused image and original multispectral Rapid-Eye image. In the merged images color distortions regarding to SAR-optical synergy were investigated. Statistical analysis was carried out as quantitative analyses. In this respect Correlation Coefficient (CC), Standard Deviation Difference (SDD), Universal Image Quality Index (UIQI) and Root Mean Square Error (RMSE) were performed for quality assessments. In general HPF was performed best while ASMF was performed the worst in all results.
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    Impact of land use changes on agricultural ecosystem: A case study of kemalpasa-izmir
    (Scibulcom Ltd., 2014) Ozen F.; Esetlili M.T.; Bolca M.; Kurucu Y.
    Differentiating elements of developed and developing societies are basically population, industrialisation, energy and ecological environment. This phenomenon, which is based on the four essential elements, is the key of a life where all creatures in the life cycle and their sustainable interactions exist and their negative issues are predictable. In this project, how the decisions, which were taken in order to meet growing energy needs of increasing population, residence, new working areas as a result of growing industrialisation between 1975-2013 years, have affected irrigating agriculture fields and natural habitat are researched. The Kemalpasa plain in Izmir province was chosen to be the research area as this area contains fertile agricultural fields and the environment in this area is under threat of growing industrialisation in recent years. Most of the lands in the region were determined to be unsuitable for non-Agricultural activities (LUCC I-IV) in terms of land use capability. Due to microclimatic features of the region, cherry cultivation owning high income for agricultural economy of the region is intensively performed as well and this region has the earliest harvested cherry in the local region. Land use of planted agriculture fields, residential zones, industrial zones, agricultural zones and natural vegetation were detected, and their change with respect to time was examined. By this way, the effects of growing industrialisation in time on soil, agricultural activities taking place on soil and ecological environment were presented.
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    Relationship between highway constructions and natural habitat. a case study of izmir highway
    (Scibulcom Ltd., 2014) Esetlili M.T.; Ozen F.; Kurucu Y.; Bolca M.
    Natural habitats and ecological systems provide living space for one or more living beings, and they are the source of biodiversity on Earth. Integrity of ecosystems impetuously collapse due to unplanned urbanisation, misuse of lands and excessive consumption of natural resources by human beings. Additionally, monitoring studies are not sufficient to plan preventions. The main aim of this study is to set a precedent by monitoring the damage, the pressure and time-dependent changes, which are caused by highway projects planned without analysing ecosystem flexibility, on natural habitats through the use of remote sensing technique and GIS. The effects of highway projects on habitats are investigated with respect to two different points of view. The first one is that highway projects directly invade and divide habitats. The other one is that highways become centre of attractions for residential and industrial zones, and demand for settlements increases around highways. The research area of this study is the most seriously affected natural area in Aegean region where Izmir-Aydin highway passes through. The changes in the research area in terms of land usage from 1984 to 2014 were analysed by satellite images. The size of industrial and residential zones in the research area was 138 ha in 1984. The size of industrial and residential zone has increased by 1.225% and reached to 1.690 ha as industrial and residential zones became denser throughout the highway. As a result of this study, it was determined that Izmir-Aydin highway has irreversibly split and destroyed substantial part of the natural habitat.
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    Remote sensing and geographic information system in the management of agricultural risks related to climate change
    (Scibulcom Ltd., 2018) Esetlili M.T.; Kurucu Y.; Cicek G.; Demirtas O.
    Agriculture is an important national strategic sector for the nutrition, employment, and development of all countries worldwide. The world is faced with the danger of starvation, especially due to the climatic changes in the recent years and global warming. Turkey is also in a region that is influenced by climate change. To provide food for the growing world population adequately and regularly, sustainable development of the agricultural sector needs to be promoted by minimising the existing risks. In this context, it is extremely important to monitor cultivated areas in the management of agricultural risks and to determine the level of effects the climate change would cause. Losses that may occur in agricultural production can be predicted earlier by using remote sensing techniques. In addition to climatic data, information on the topography and soil and land features can be collected in a GIS database, which would facilitate the development of an effective risk management model. The objective of this study was to examine the assessment and monitoring of crop-cultivated lands in Kayseri-Bunyan Province (Turkey), which was chosen as a pilot region, and predict the probable reduction in the yields that might occur due to dry seasons, plant diseases, and various disorders. © 2018, Scibulcom Ltd. All rights reserved.
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    Soil moisture estimation from radarsat -1, asar and palsar data in agricultural fields of menemen plane of western Turkey
    (International Society for Photogrammetry and Remote Sensing, 2008) Sanli F.B.; Kurucu Y.; Esetlili M.T.; Abdikan S.
    Due to the accelerating global warming, droughts which cause severe damages especially in the agriculture became a very recurrent phenomenon in all over the world. Monitoring the characteristics of soil moisture is very important in Turkey because the major impact of the global warming on our country appears to be climate changes. In this respect, drought has become a serious threat for the country where the agriculture is one of the major income sources. Therefore, monitoring of draughts has the highest priority among the other strategies. Although the sensitivity of microwaves towards the soil moisture is well understood, retrieving soil moisture with Synthetic Aperture radar (SAR) measurements still has difficulties due to the major impact of soil texture, surface roughness and vegetation cover. In this study, SAR data gathered by different sensors for the same area in closer dates were used to estimate the relative soil moisture. The relation between the ground soil moisture and the sigma nought/backscatter values of SAR images were investigated. Sigma nought values of C band HH polarized Radarsat Fine Beam image and C Band VV polarized ENVISAT (ASAR) images as well as backscatter values of an L band HH polarized ALOS (PALSAR) satellite images were used. RADARSAT, ASAR and PALSAR images were gathered on the 28th of May, 8th of June, and 10th of June in 2006 respectively for the alluvial lands of Menemen Town, Izmir. Ground soil moisture measurements taken using gravimetric methods showed a good agreement with the backscatter values of the images obtained from different types of SAR data. A comparison among the spatial distribution of retrieved soil moisture changes from SAR images was done. The correlations between the soil moisture content and backscattering of ASAR, RADARSAT-1 and PALSAR images were found 76%, 81% and 86 % respectively. Although the resolution of RADARSAT-1 fine beam image (6.25m × 6.25m) is closer to the resolution of PALSAR image (6.25m × 6.25m), PALSAR gives better correlation than RADARSAT-1 image. Although the resolution of RADARSAT-1 and PALSAR images is far more higher than that of the ASAR image (30m × 30m), the significance of the results produced is almost similar in such flat areas.