Correlation between UAV multispectral Imagery and spectroradiometer measurements in sunflower developmental stages Alperen Erdoğan, Ömer Mutluoğlu, Önder Gürsoy Revista De La Facultad De Agronomia, 2025 Oilseed crops are among the product groups with a supply deficit in the world. The sunflower oil crisis experienced after 2020 ha increased the importance of sunflower cultivation. The most important stages in agricultural applications are to understand whether the plant is healthy in the early stages before it is formed and to prevent negative results in harvest. With the developing technology, the use of unmanned aerial vehicles (UAVs) and multispectral cameras in agricultural applications has gained enormous importance. Thanks to UAVs, agricultural temporal resolution can be adjusted according to the user's request, and spatial resolution can be adjusted according to the ability of the sensor used and the flight altitude. Spectral resolution is directly proportional to the number of bands and the band wavelength. We performed correlation analysis in this study by comparing the accuracy of the band values with ground measurements made with a spectroradiometer. We measured the sunflower in its vegetative, R-3, and R-5 phases and found that there was a strong correlation (r=0.894) in the green band, r=0.845 in the red, r=0.789 in the red edge (RE) band, and r=0.725 in the near infrared band (NIR). The results show a strong connection between the spectral bands and the spectroradiometer measurements, especially in the green and red bands.
A Multi-Criteria Forest Fire Danger Assessment System on GIS Using Literature-Based Model and Analytical Hierarchy Process Model for Mediterranean Coast of Manavgat, Türkiye İzzet Ersoy, Emre Ünsal, Önder Gürsoy Sustainability Switzerland, 2025 Forest fires pose significant environmental and economic risks, particularly in fire-prone regions like the Mediterranean coast of Türkiye. This study presents a comprehensive Forest Fire Danger Assessment System (FoFiDAS), by integrating Geographic Information Systems (GIS), a literature-based model, the Analytical Hierarchy Process (AHP), and machine learning (ML) to improve forest fire danger classification. Both models integrate 13 key parameters identified through the literature. A comparison of these models revealed 53% overlap in fire danger classifications. While the AHP model, based on expert-weighted assessment, provided a more structured and localized classification, the literature-based model relied on broader scientific data but lacked adaptability. Pearson correlation analysis demonstrated a strong correlation between fire danger classifications and historical fire occurrences, with correlation scores of 0.927 (AHP) and 0.939 (literature-based). Further ROC analysis confirmed the predictive performance of both models, yielding AUC values of 0.91 and 0.9121 for the literature-based and AHP models, respectively. Five ML algorithms were used to validate classification performances, with Artificial Neural Network (ANN) achieving the highest accuracy (86.5%). The accuracy of the ANN algorithm exceeded 0.93 for each danger class, and the F1-Score was above 0.85. FoFiDAS offers a reliable tool for fire danger assessment, supporting early intervention and decision making.
Field Scale Soil Moisture Estimation with Ground Penetrating Radar and Sentinel 1 Data Rutkay Atun, Önder Gürsoy, Sinan Koşaroğlu Sustainability Switzerland, 2024 This study examines the combined use of ground penetrating radar (GPR) and Sentinel-1 synthetic aperture radar (SAR) data for estimating soil moisture in a 25-decare field in Sivas, Türkiye. Soil moisture, vital for sustainable agriculture and ecosystem management, was assessed using in situ measurements, SAR backscatter analysis, and GPR-derived dielectric constants. A novel empirical model adapted from the classical soil moisture index (SSM) was developed for Sentinel-1, while GPR data were processed using the reflected wave method for estimating moisture at 0–10 cm depth. GPR demonstrated a stronger correlation within situ measurements (R2 = 74%) than Sentinel-1 (R2 = 32%), reflecting its ability to detect localized moisture variations. Sentinel-1 provided broader trends, revealing its utility for large-scale analysis. Combining these techniques overcame individual limitations, offering detailed spatial insights and actionable data for precision agriculture and water management. This integrated approach highlights the complementary strengths of GPR and SAR, enabling accurate soil moisture mapping in heterogeneous conditions. The findings emphasize the value of multi-technique methods for addressing challenges in sustainable resource management, improving irrigation strategies, and mitigating climate impacts.
Investigating surface water pollution by integrated remotely sensed and field spectral measurement data: A case study Önder Gürsoy, Rutkay Atun Polish Journal of Environmental Studies, 2019 Water quality assessment using remote sensing and its terrestrial components is carried out in short time for larger areas. Another issue that is as important regarding water availability is access to quality water. It is important to investigate the availability of the analysis of remotely sensed data instead of environmental and chemical analysis that determines water quality and usability. To examine the detection of water qualities without taking water samples in situ, spectral library data was used in the Hafik Region. In this context we used spectral measurement data of water samples previously taken from İmranlı, where the Kızılırmak River originates, and used for spectral classification of water quality. Matched filtering was used for integrating spectral data and CHRIS Proba image as the spectral classification method. To conduct an accuracy analysis, chemical oxygen demand measurement was carried out at 10 points determined as 1st and 2nd water quality in the study area on the river and lakes according to the Ministry of the Environment and Urbanization. The overall accuracy of the classification was calculated as 70%. The results of this study have shown the importance of spectral classification of satellite imagery in evaluating water quality and monitoring water resources.
Detecting Clay Minerals in Hydrothermal Alteration Areas with Integration of ASTER Image and Spectral Data in Kösedag-Zara (Sivas), Turkey Oktay Canbaz, Önder Gürsoy, Ahmet Gökce Journal of the Geological Society of India, 2018 ABSTRACT Remote sensing technology and its terrestrial components are more useful than classical geological investigation in mineral exploration and mapping the hydrothermal alteration areas and help to investigate larger areas in short time. Intrusive and volcanic rocks, namely Kösedag syenite and Karatas volcanics in Kösedag (Zara) area shows argillic alteration zones. Two different test area were chosen and sampled for mineralogical studies. XRD-CF investigations showed that kaolinite and illite are the dominant clay minerals in test areas of A and B respectively. The spectroradiometer measurements were carried out 5 times on different surfaces of clayey samples with self-illuminated contact-probe lens within the wavelength range of 350-2500 nm. The spectroradiometer measurements used as endmember were resampled to ASTER short wave infrared (SWIR) bandwidths. Band ratio, principal component and decorrelation stretching analysis were performed to visualize the distribution of clay minerals. In spectral classification method, matched filtering (MF) was used for integrating the satellite image and spectroradiometer measurement data. It was concluded that co-interpretations of the band ratio, principal component, decorrelation stretching analysis, MF results and geological map are very useful in determining, classifying and mapping of the argillic alteration zones related to hydrothermal processes on ASTER image and they seem to be very useful to identify the target areas for mineral exploration.
Determining lateral offsets of rocks along the eastern part of the North Anatolian Fault Zone (Turkey) using spectral classification of satellite images and field measurements Önder Gürsoy, Şinasi Kaya, Ziyadin Çakir, Orhan Tatar, Oktay Canbaz Geomatics Natural Hazards and Risk, 2017 Fault displacements are being measured by geological observations using the method of detecting and evaluating marker rocks. Thus, the length of total displacement in a fault zone relates to position detection of marker rocks. Due to limits of human eye, we used remote sensing data and terrestrial spectral measurements at 229 locations for measuring the total offset along the Kelkit Valley segment of the North Anatolian Fault Zone (NAFZ). We examined the lithology, especially ophiolites that are older than the fault zone and can be a good marker for detecting the total offset in the region. The Advanced Spaceborne Thermal Emission and Reflection Radiometer images are subjected to Spectral Angle Mapper (SAM) method. Principal component analysis, decorrelation stretching and geological map were used to compare the SAM results. Ophiolites on either side of the fault zone were clearly classified and identified with the SAM analysis. As a result of comparison of SAM with image enhancement methods and the geological map, we measured the total fault displacement on the NAFZ in the part of the Kelkit Valley. Along the fault zone to the north and south of the ophiolites providing a right lateral offset was measured as 90 ± 5 km.
