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Öğe Accuracy assessment of direct georeferencing UAV images with onboard global navigation satellite system and comparison of CORS/RTK surveying methods(IOP Publishing Ltd, 2021) Zeybek, MustafaThe chestnut gall wasp, Dryocosmus kuriphilus Yasumatsu (Hymenoptera: Cynipidae) is one of the most important insect pests of chestnut. The aim of this study was to isolate and characterize bacteria from D. kuriphilus to obtain new microbial agents for both biological control and other biotechnological applications. D. kuriphilus larvae were collected from chestnut fields located in Bursa and Yalova provinces of Marmara Region of Turkey during May–July 2014. Four bacterial isolates were obtained from D. kuriphilus. According to their morphological, biochemical and molecular properties, these isolates were identified as Staphylococcus saprophyticus (Dk1), Paenibacillus sp. (Dk2), Pseudomonas flourescens (Dk3) and Paenibacillus sp. (Dk4). To the best of our knowledge, this is the first study on the bacterial flora of D. kuriphilus. In our study, the potential of these isolates as a biological control agent against different hazardous pests and other possible biotechnological applications of importance were discussed under the light of literature.Öğe Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey(Turkiye Klinikleri, 2020) Vatandaşlar, Can; Zeybek, MustafaForest inventory (FI) is the most challenging stage of forest management and planning process. Therefore, in situ surveys are often reinforced by modern remote sensing (RS) methods for collecting forestry-related data more efficiently. This study tests a state-of-the-art data collection method for practical use in the Turkish FI system for the first time. To this end, forest sampling plots were conventionally measured to collect dendrometric data from 437 trees in Artvin and Saçınka Forest Enterprises. Then, each plot was scanned using a handheld mobile laser scanning (HMLS) instrument. Finally, HMLS data were compared against ground measurements via basic FI measures. Based on statistical tests, no apparent differences were found between the two datasets at the plot level (P < 0.05). There were also robust correlations for diameter breast height at individual tree level (r > 0.97; P < 0.01). Residual analysis showed that both positive and negative errors had a homogeneous distribution, except for plot 8 where tree stems were in irregular shapes due to anthropogenic pressures. When all plots’ data were aggregated, average values for the number of trees, basal area, and timber volume were estimated as 535 trees/ha–1, 49.6 m2/ha–1, and 499.7 m3/ha–1, respectively. Furthermore, secondary measures such as the number of saplings and slope were successfully retrieved using HMLS method. The highest overestimation was in timber volume with less than 10% difference at the landscape level. The differences were attributed to poor data quality of conventional measurements, as well as marginal site conditions in some plots. We concluded that the HMLS method met the accuracy standards for most FI measures, except for stand height. Thus, the Turkish FI system could benefit from this novel technology, which in turn supports the implementation of sound forest management and planning.Öğe An approach for the automated extraction of road surface distress from a UAV-derived point cloud(Elsevier BV, 2021) Biçici, Serkan; Zeybek, MustafaThe condition of the road surface should be inspected to increase the service life of the road and to ensure safety and comfort. This study aims to automatically detect and measure road distress from unmanned aerial vehicle (UAV)-based images. The proposed methodology consists of three steps. First, images acquired from the UAV are used to generate the three-dimensional point cloud. Then, the road surface is extracted from the 3D point cloud. Finally, the developed algorithm is used to automatically detect and measure road distress. The accuracy assessment is conducted by comparing the analyses from point cloud data and measurements obtained from the traditional inspection method. The root mean square error values range from 2.09–6.72 cm. Finally, the outcomes of the proposed methodology are compared with those of commercial GIS software. Both produce statistically similar results for detecting road surface distress.Öğe Assessing the potential of mobile laser scanning for stand-level forest inventories in near-natural forests(Oxford University Press, 2023) Vatandaşlar, Can; Seki, Mehmet; Zeybek, MustafaRecent advances in LiDAR sensors and robotic technologies have raised the question of whether handheld mobile laser scanning (HMLS) systems can allow for the performing of forest inventories (FIs) without the use of conventional ground measurement (CGM) techniques. However, the reliability of such an approach for forest planning applications, particularly in non-uniform forests under mountainous conditions, remains underexplored. This study aims to address these issues by assessing the accuracy of HMLS-derived data based on the calculation of basic forest attributes such as the number of trees, dominant height and basal area. To this end, near-natural forests of a national park (NE Türkiye) were surveyed using the HMLS and CGM techniques for a management plan renewal project. Taking CGM results as reference, we compared each forest attribute pair based on two datasets collected from 39 sample plots at the forest (landscape) scale. Diameter distributions and the influence of stand characteristics on HMLS data accuracy were also analyzed at the plot scale. The statistical results showed no significant difference between the two datasets for any investigated forest attributes (P >0.05). The most and the least accurately calculated attributes were quadratic mean diameter (root mean square error (RMSE) = 1.3 cm, 4.5 per cent) and stand volume (RMSE = 93.7 m3 ha−1, 16.4 per cent), respectively. The stand volume bias was minimal at the forest scale (15.65 m3 ha−1, 3.11 per cent), but the relative bias increased to 72.1 per cent in a mixed forest plot with many small and multiple-stemmed trees. On the other hand, a strong negative relationship was detected between stand maturation and estimation errors. The accuracy of HMLS data considerably improved with increased mean diameter, basal area and stand volume values. Eventually, we conclude that many forest attributes can be quantified using HMLS at an accuracy level required by forest planning and management-related decision making. However, there is still a need for CGM in FIs to capture qualitative attributes, such as species mix and stem quality.Öğe Assessment of changes in masseter muscle by three-dimensional close-range photogrammetry after Botulinum toxin type-A injection: A case report with review of literature(Pakistan Medical Assoc, 2019) Bayrak, Nagehan Burcu; Zeybek, Mustafa; Şanlioğlu, İsmail; Dolanmaz, DoğanMasseter muscle hypertrophy (MMH) is a benign, unilateral or bilateral, painless enlargement. Treatment protocols include surgical excision or a non-invasive option, using botulinum toxin type A (BTX-A). There is no study in the literature that measures this dimensional change in the masseter muscle (MM). The aim of this case report is to present changes in volume and surface area in MM with three-dimensional closerange stereophotogrammetry (3DCS). For treatment 30 units of BTX-A was injected into the three points hypertrophic muscle and patient records were taken to compare with 3DCS with a non-metric Canon EOS 550 D camera before and after injection. The changes in the surface area and volume of this muscle were mapped and the objective data were obtained. This technique is useful for predicting the results of BTX-A application, and can be a useful tool for better physicianpatient communication.Öğe An automated approach for extracting forest inventory data from individual trees using a handheld mobile laser scanner(Zagreb Univ, 2021) Zeybek, Mustafa; Vatandaşlar, CanMany dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near )natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83-0.99, p<0.05). The average RMSE for tree DBHs was 1.8 cm at the forest landscape level. As for tree detection, 92.5% of 292 trunks were correctly classified on point cloud data. In general, estimation accuracy was sufficient for operational forest inventory needs. However, they could markedly decrease in >> hard plotso << located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in "easy plots". By improving the algorithmic performances, the accuracy levels can be further increased by future research.Öğe Automated extraction of road information from UAV-based data(Reed Business-Geo, 2022) Zeybek, Mustafa; Biçici, SerkanWhen it comes to monitoring the condition of roads, UAV technology can overcome many of the downsides associated with traditional methods, which can be time-consuming, labour-intensive and sometimes subjective. This article explores the opportunities for automated extraction of UAV-based data information about road construction, inventory and road environments.Öğe Classification of UAV point clouds by random forest machine learning algorithm(Murat Yakar, 2021) Zeybek, MustafaToday, unmanned aerial vehicle (UAV)-based images have become an important data sources for researchers who deals with mapping from various disciplines on photogrammetry and remote sensing. Reconstruction of an area with three-dimensional (3D) point clouds from UAV-based images are an essential process to be used for traditional 2D cadastral maps or to produce a topographic maps. Point clouds should be classified since they subjected to various analyses for extraction for further information from direct point cloud data. Due to the high density of point clouds, data processing and gathering information makes the classification of point clouds a challenging task and may take a long time. Therefore, the classification processing allows an optimal solution to acquire valuable information. In this study, random forest machine learning algorithm for classification processing is applied with radiometric features (Red band, Green band and Blue band) and geometric characteristics derived from covariance feature (curvature, omnivariance, flatness, linearity, surface variance, anisotropy and normalized terrain surface) of points. In addition, the case study is presented in order to test applicability of the proposed methodology to acquire an accuracy and performance of random forest method on the UAV based point cloud. After the classification processing, a class assigned each point from the model was compared with the reference data class. Lastly, the overall accuracy of the classification was achieved as 96% and the Kappa index was reached to 91% on data set.Öğe Comparative analysis of tree-based ensemble learning algorithms for landslide susceptibility mapping: A case study in Rize, Turkey(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Yavuz Özalp, Ayşe; Akıncı, Halil; Zeybek, MustafaThe Eastern Black Sea Region is regarded as the most prone to landslides in Turkey due to its geological, geographical, and climatic characteristics. Landslides in this region inflict both fatalities and significant economic damage. The main objective of this study was to create landslide susceptibility maps (LSMs) using tree-based ensemble learning algorithms for the Ardeşen and Fındıklı districts of Rize Province, which is the second-most-prone province in terms of landslides within the Eastern Black Sea Region, after Trabzon. In the study, Random Forest (RF), Gradient Boosting Machine (GBM), CatBoost, and Extreme Gradient Boosting (XGBoost) were used as tree-based machine learning algorithms. Thus, comparing the prediction performances of these algorithms was established as the second aim of the study. For this purpose, 14 conditioning factors were used to create LMSs. The conditioning factors are: lithology, altitude, land cover, aspect, slope, slope length and steepness factor (LS-factor), plan and profile curvatures, tree cover density, topographic position index, topographic wetness index, distance to drainage, distance to roads, and distance to faults. The total data set, which includes landslide and non-landslide pixels, was split into two parts: training data set (70%) and validation data set (30%). The area under the receiver operating characteristic curve (AUC-ROC) method was used to evaluate the prediction performances of the models. The AUC values showed that the CatBoost (AUC = 0.988) had the highest prediction performance, followed by XGBoost (AUC = 0.987), RF (AUC = 0.985), and GBM (ACU = 0.975) algorithms. Although the AUC values of the models were close to each other, the CatBoost performed slightly better than the other models. These results showed that especially CatBoost and XGBoost models can be used to reduce landslide damages in the study area.Öğe Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey(Springer, 2021) Akıncı, Halil; Zeybek, MustafaLandslide susceptibility maps provide crucial information that helps local authorities, public institutions, and land-use planners make the correct decisions when they are managing landslide-prone areas. In recent years, machine-learning techniques have become very popular for producing landslide susceptibility maps. This study aims to compare the performance of these machine learning models with the traditional statistical methods used to produce landslide susceptibility maps. The landslide susceptibility for Ardanuc, Turkey was evaluated using three models: logistic regression (LR), support vector machine (SVM), and random forest (RF). Ten parameters that are efective in landslide occurrence are used in this study. The accuracy and prediction capabilities of the models were assessed using both the receiver operating characteristic (ROC) curve and area under the curve (AUC) methods. According to the AUC method, the success rate of the LR, SVM, and RF models was 83.1%, 93.2%, and 98.3%, respectively. Further, the prediction rates were calculated as 82.9% (LR), 92.8% (SVM), and 97.7% (RF). According to the verifcation results, RF and SVM models outperformed the traditional LR model in terms of success and prediction rate. The RF model, however, performed better than the SVM model in terms of success and prediction rates. The landslide susceptibility maps produced as a result of this study can guide city planners, local administrators, and public institutions related to disaster management to prevent and reduce landslide hazards.Öğe Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye(Elsevier Ltd, 2024) Alkan Akıncı, Hazan; Akıncı, Halil; Zeybek, MustafaAntalya is one of the provinces with the highest number of forest fires in Türkiye. In 2021, 278 forest fires occurred within the administrative boundaries of Antalya Regional Directorate of Forestry. The main objective of this study is to produce forest fire susceptibility (FFS) maps of Antalya province using machine learning (ML) models. In addition to forest fire inventory data, 16 factors, including topographic, environmental, meteorological, and human-driven, were used in the study. Inventory data included 2166 fire ignition points from the General Directorate of Forestry. 70 % of the inventory dataset was used to train the ML models and 30 % to validate the models. Overall accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) approaches were considered as validation metrics. FFS maps of Antalya were produced using stand-alone ML algorithms, K-Nearest Neighbors, and Support Vector Machines, as well as tree-based Conditional Inference Trees (CTREE), Random Forest (RF), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. To the best of our knowledge, this is the first study using the CTREE algorithm for forest fire susceptibility mapping. Therefore, this study is important for the related literature. The validation results revealed that the XGBoost model outperformed other models. It is thought that the FFS map produced using the XGBoost model will guide forest engineers, wildland firefighting teams, and firefighters to minimize damage and control forest fires.Öğe Conventional air pollutant source determination using bivariate polar plot in Black Sea, Turkey(Springer Science and Business Media B.V., 2021) Demirarslan, Kazım Onur; Zeybek, MustafaThe purpose of this study is to identify and characterize individual sources of pollutants such as PM10, SO2, NOx, and CO in the urban area in Karadeniz (Turkey) using the bivariate polar plots method. In addition, the relationship between the meteorological conditions and the pollutants was determined based on correlation analysis in the region. Bivariate polar plots are a graphical method used to demonstrate the dependence of pollutant concentrations on wind direction measured at stations. Thanks to these graphics, resource types and properties can be determined. Wind flow and pollution data were used to provide information on wind and pollutant interactions in the study area. As a result of the study, it was founded that the main source of pollutants is intensive anthropogenic activities such as urban, street traffic, agricultural activities, and natural resources. It has been concluded that the highway in the region is not an important source of pollutants. In addition, the pollutant relations were examined with meteorological data, and it was discovered that temperature and relative humidity were effective for all pollutants. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.Öğe Effectiveness of training sample and features for random forest on road extraction from unmanned aerial vehicle-based point cloud(Sage Publications, 2021) Biçici, Serkan; Zeybek, MustafaThe accuracy of random forest (RF) classification depends on several inputs. In this study, two primary inputs—training sample and features—are evaluated for road classification from an unmanned aerial vehicle-based point cloud. Training sample selection is a challenging step since the machine learning stage of the RF classification depends greatly on it. That is, an imbalanced training sample might dramatically decrease classification accuracy. Various criteria are defined to generate different types of training samples to evaluate the effectiveness of the training sample. There are several point features that can be used in RF classification under different circumstances. More features might increase the classification accuracy, however, in that case, the processing time is also increased. Point features such as RGB (red/green/blue), surface normals, curvature, omnivariance, planarity, linearity, surface variance, anisotropy, verticality, and ground/non-ground class are investigated in this study. Different training samples and sets of features are used in the RF to extract the road surface. The experiment is conducted on a local road without a raised curb located on a relatively steep hill. The accuracy assessment is conducted by comparing the model classification results with the manually extracted road surface point cloud. It is found that the accuracy increases up to around 4%–13%, and 95% overall accuracy was obtained when using convenient training samples and features.Öğe El-tipi LiDAR nokta bulutundan tek ağaç gövdesinin otomatik çıkarımında istatistiksel sınıflandırma algoritmalarının performans analizi(Artvin Çoruh Üniversitesi, 2020) Zeybek, MustafaEnerjisi ışık veya lazer olan LiDAR yöntemi yoğun koordinat verisini hızlı bir şekilde ölçen bir ölçme tekniğidir. Orman alanlarında kullanımı yaygın olan bu teknik, yoğun bir veri işleme adımına sahiptir. Bu işlemlerin en başında sınıflandırma gelir. Ağaç parametrelerinin kestiriminde ağaç gövdelerinin doğru tespiti önemli bir konudur. Bu çalışma, el-tipi mobil LiDAR (EML) ile ölçülmüş nokta bulutlarında ağaç gövde modelinin sınıflandırma ile çıkarımında kullanılan yöntemlerin performanslarını değerlendirmek amacıyla yapılmıştır. Tek ağaç bazında EML nokta bulutundan gövdenin tespit edilmesi için istatistiksel sınıflandırma tekniklerinden, lojistik regresyon, doğrusal ayrıştırma analizi, rastgele orman ve destek vektör makinesi kullanılmıştır. Sadece gövdeyi temsil eden noktalar diğer dal ve yapraklardan ayrılarak sınıflandırılmış, genel doğruluk oranına göre sınıflandırma doğruluğu en yüksek yöntem rastgele orman sınıflandırıcısı olduğu tespit edilmiştir. Veri işleme performansı açısından doğrusal ayrıştırma analizi diğer yöntemlere göre daha iyi performans sergilemiştir.Öğe Extraction of forest inventory parameters using handheld mobile laser scanning: A case study from Trabzon, Turkey(Elsevier B.V., 2021) Vatandaşlar, Can; Zeybek, Mustafa; Vatandaşlar, Can; Zeybek, MustafaForest inventory (FI) surveys are cumbersome when field measurements are performed by manual means. We propose a semi-automated data collection approach using handheld mobile laser scanning (HMLS) to estimate and map key FI parameters. To this end, machine learning (e.g., random forest classifier for tree detection) and innovative algorithms (e.g., ellipse fitting for diameter estimation of noncircular trees) were used for the first time in FI surveying. After surveying nine plots, we compared HMLS-derived data against the field reference. HMLS-derived tree diameters (DBHs) were strongly correlated with the reference data at the single-tree level (r= 0.93–0.99; p< 0.001). At the plot level, HMLS slightly overestimated DBHs in complex plots due to the influence of undergrowth and creepers on trunks. Yet, no statistically significant difference was found between the two datasets (p> 0.05). Overall, HMLS was concluded as efficient and effective tool for FIs, even if used alone.Öğe Extraction of road lane markings from mobile LiDAR data(Sage Publications, 2021) Zeybek, MustafaThis study presents a method for automatic extraction of road lane markings from mobile light detection and ranging (LiDAR) data. Road lanes and traffic signs on the road surface provide safe driving for drivers and aid traffic flow movement along the highway and street. Mobile LiDAR systems acquire massive datasets very quickly in a short time. To simplify the data structure and feature extraction, it is essential for traffic management personnel to apply the right methods. Road lanes must be visible and are a major factor in road safety for drivers. In this study, a methodology is devised and implemented for the extraction of features such as dashed lines, continuous lanes, and direction arrows on the pavement from point clouds. Point cloud data was collected from the Riegl VMX-450 mobile LiDAR system. The alpha shape algorithm is implemented on a point cloud and compared with the widespread use of edge detection techniques applied for intensity-based raster images. The proposed methodology directly extracts three-dimensional and two-dimensional road features to control the quality of road markings and spatial positions with the obtained marking boundaries. State-of-the-art results are obtained and compared with manually digitized reference markings. The standard deviations were evaluated and acquired for intensity imagebased and direct point cloud-based extractions, at 1.2 cm and 1.7 cm, respectivelyÖğe Geometric feature extraction of road from UAV based point cloud data(Springer Science and Business Media Deutschland GmbH, 2021) Zeybek, Mustafa; Biçici, SerkanThis study presents a new approach to achieving the high accuracy geometric feature extraction of road surface automatically from UAV based images. The proposed methodology begins with the automatic extraction of road surface from point cloud. The extraction of road is based on point clouds and machine learning classification algorithm. Then, road boundaries are derived from extracted road surface points and are used to estimate the road centerline. The point clouds are then used to create digital elevation models to extract profile and cross-section elevations at specified intervals by referenced the estimated smooth road centerline. The accuracy of the road surface classification is evaluated by comparing manual classified points. According to the results, precise road extraction, road centerline, profile, and cross-sections are produced with high accuracy using the proposed approach.Öğe Improvements on road centerline extraction by combining voronoi diagram and intensity feature from 3D UAV-based point cloud(Springer Science and Business Media Deutschland GmbH, 2022) Biçici, Serkan; Zeybek, MustafaThis study presents an application for road data users to make it easier to identify the centerline of roads. Images obtained from high-resolution unmanned aerial vehicles (UAV) provide a reliable database for fundamental applications such as road safety, road maintenance, traffic network, city planning, and vehicle navigation systems, thanks to accurate road extraction and centerline. Road extraction methods are based on algorithms that usually classify roads from 2D images. However, such data are difficult to provide high accuracy spatial information. Moreover, there are limitations for spatial information extraction problems for the road centerline. To overcome these limitations, we present a method to extract road centerline with 3D data based on point clouds that provide reliable information from UAV images. Commonly used three measures, namely Completeness, Correctness and Quality, for the road centerline extraction are 0.905, 0.999 and 0.905, respectively.Öğe Improving the Spatial Accuracy of UAV Platforms Using Direct Georeferencing Methods: An Application for Steep Slopes(2023) Zeybek, Mustafa; Taşkaya, Selim; Elkhrachy, Ismail; Tarolli, PaoloThe spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture play a crucial role in the mapping process. Researchers are exploring solutions that use image-based techniques such as structure from motion (SfM) to produce topographic maps using UAVs while accessing locations with extremely high accuracy and minimal surface measurements. Advancements in technology have enabled real-time kinematic (RTK) to increase positional accuracy to 1–3 times the ground sampling distance (GSD). This paper focuses on post-processing kinematic (PPK) of positional accuracy to achieve a GSD or better. To achieve this, precise satellite orbits, clock information, and UAV global navigation satellite system observation files are utilized to calculate the camera positions with the highest positional accuracy. RTK/PPK analysis is conducted to improve the positional accuracies obtained from different flight patterns and altitudes. Data are collected at altitudes of 80 and 120 meters, resulting in GSD values of 1.87 cm/px and 3.12 cm/px, respectively. The evaluation of ground checkpoints using the proposed PPK methodology with one ground control point demonstrated root mean square error values of 2.3 cm (horizontal, nadiral) and 2.4 cm (vertical, nadiral) at an altitude of 80 m, and 1.4 cm (horizontal, oblique) and 3.2 cm (vertical, terrain-following) at an altitude of 120 m. These results suggest that the proposed methodology can achieve high positional accuracy for UAV image georeferencing. The main contribution of this paper is to evaluate the PPK approach to achieve high positional accuracy with unmanned aerial vehicles and assess the effect of different flight patterns and altitudes on the accuracy of the resulting topographic maps.Öğe Investigation of landslide detection using radial basis functions: a case study of the Taşkent landslide, Turkey(Springer, 2020) Zeybek, Mustafa; Şanlıoğlu, İsmailThis paper investigates landslide detection over flat and steep-slope areas with large forest cover using different radial basis function interpolation methods, which can affect the quality of a digital elevation model. Unmanned aerial vehicles have been widely used in landslide detection studies. The generation of image-based point clouds is achievable with various matching algorithms from computer vision systems. Point cloud-based analysis was performed by generating multi-temporal digital elevation models to detect landslide displacement. Interpolation methodology has a crucial task to fill the gaps in insufficient areas that result from filtered areas or sensors that do not generate spatial information. Radial basis function interpolations are the most commonly used technique for estimating the unknown values in survey areas. However, the quality of the radial basis function interpolation methods for landslide studies has not been thoroughly investigated in previous studies. In this study, radial basis function interpolation methods are investigated and compared with the global navigational satellite systems, which provide high accuracy for geodetic measurement systems. The main purpose of this study was to investigate the various radial basis function models to detect landslides using a point cloud-based digital elevation model and determine the quality of detection with global navigational satellite systems. As a result of this study, each of the radial basis function-generated digital elevation models was found to be statistically compatible with global navigational satellite systems, resulting in displacements from the ground truth data.
