The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture
play a crucia... more The 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.
Abstract Forest inventory (FI) surveys are cumbersome when field measurements are performed by ma... more Abstract Forest 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.
Heyelanlar çeşitli dinamik kuvvetlerin yeryüzünde meydana getirdiği değişimlerdir. Heyelanlar, ye... more Heyelanlar çeşitli dinamik kuvvetlerin yeryüzünde meydana getirdiği değişimlerdir. Heyelanlar, yeryüzü üzerinde önemli derecede etkiler oluşturan doğal afetlerin önde gelenlerinden bir tanesidir. Heyelanların etkilerinin azaltılması amacıyla izlenmesi Dünyada oldukça fazla sayıda ve değişik yöntemlerle çalışmalar yapılmaktadır. Teknolojinin sağladığı katkılarla bu yöntemler günden güne ilerlemekte, daha hassas, hızlı ve düşük maliyette araştırmaların sürdürülmesine imkân sağlamaktadır. GNSS ölçmelerinin yüksek doğruluğu, ekonomik olması, kıtalar arası hareketleri dahi izlenebilir hale getirmesi, klasik yersel ölçme yöntemlerine olan rağbeti bir hayli azaltmıştır. 2000'li yılların başında heyelanların GNSS yöntemleriyle araştırılması oldukça hızlı ilerlemiştir. Son on yılda teknolojik gelişmeler heyelanların izlenmesinde kullanılan tekniklerin gelişmesinde önemli katkılar sağlamıştır. Bu teknolojik gelişmeler doğrultusunda mühendislik uygulamalarına yeni bir boyut kazandıran laze...
Roads are one of the main characteristics of cities, and their data should be updated periodicall... more Roads are one of the main characteristics of cities, and their data should be updated periodically. In this study, a new automatic method is proposed for extracting road surface information and road inventory from a Mobile LiDAR System-based point cloud. The proposed method consists of four steps. First, a three-dimensional point cloud is acquired using the MLS raw data. To improve the extraction accuracy, irrelevant points are removed from the point cloud. Piecewise linear models are used in the third step to classify the road surface. Road geometric characteristics such as centerline, profile, cross-section, and cross slope are extracted in the final step. The manually obtained road boundary is compared with the extracted road boundary to assess the classification results. Completeness, correctness, quality, and accuracy measures are range from 97% to 99%. When comparing these measures with previous studies, the proposed method produces one of the highest ones.
ELAZIĞ-KARAKOCAN DEPREMININ TUSAGA-AKTIF ISTASYONLARINA ETKISI Ozet Dogu Anadolu Fay Zonu (DAFZ) ... more ELAZIĞ-KARAKOCAN DEPREMININ TUSAGA-AKTIF ISTASYONLARINA ETKISI Ozet Dogu Anadolu Fay Zonu (DAFZ) Turkiye’de cok sayida buyuk depremlere neden olan aktif fay zonlarindan birisidir. Turkiye’de 2009 yilindan beri calisan Turkiye Ulusal Sabit GNSS Istasyonlari Agi-Aktif (TUSAGA-Aktif) bulunmaktadir. Bu ag depremler neticesinde olusan deformasyonlari belirlemede etkin rol oynamaktadir. DAFZ yakininda, Elazig-Karakocan da 8 Mart 2010 tarihinde 6.0 Mw buyuklugunde Karakocan depremi meydana gelmistir. Bu calismanin amaci, TUSAGA–Aktif istasyonlarinin verileri yardimiyla Karakocan deprem merkezine yakin istasyonlarin yer degistirmelerin buyuklugunu ve yonunu belirlemektir. Istasyon verilerinin dengelenmesinde AUSPOS (Australian Online GPS Processing Service) internet tabanli veri isleme servisi kullanilmistir. Calismada Karakocan depreminden etkilendigi tahmin edilen DAFZ yakininda TUSAGA - Aktif istasyonlarinin deprem tarihinden 5 gun once ve 5 gun sonraki gunlere ait dengeleme sonuclari a...
When it comes to monitoring the condition of roads, UAV technology can overcome many of the downs... more When 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. Automated Extraction of Road Information from UAV-based Data THE BENEFITS OF AIRBORNE TECHNOLOGY IN URBAN PROJECTS data. The fl ight plan is remotely set in the interface and the UAV fl ies and acquires data automatically. However, the fl ight plan needs to be adjusted according to the characteristics of the UAV platform, which are maximum fl ight time, fl ight speed, height above ground level and horizontal distance.
Bu çalışmanın amacı; (i) orman envanterlerinde mobil lazer tarama
(LiDAR) teknolojisinden yararl... more Bu çalışmanın amacı; (i) orman envanterlerinde mobil lazer tarama
(LiDAR) teknolojisinden yararlanma olanaklarını araştırmak ve (ii)
meşcere parametrelerine ilişkin LiDAR verilerini, uygulamada tespit
edilen değerlerle karşılaştırmaktır. Bu doğrultuda, Şavşat’ta arazi ölçümleri
gerçekleştirilen örnek alanlar el tipi LiDAR cihazı ile taranmıştır.
Daha sonra örnek alanlardan elde edilen veri setleri birbiriyle
karşılaştırılarak LiDAR’ın hassasiyeti sınanmıştır. Yapılan istatistik
testler sonucunda, LiDAR ve çapölçer ile ölçülen ağaçların çapları
arasında anlamlı bir fark bulunmamıştır (p>0,05). Yersel ölçümler
referans kabul edilirse; göğüs çapı, ağaç sayısı, meşcere üst boyu ve
meşcere hacmi parametreleri LiDAR cihazıyla sırasıyla; ort. 0,68 cm
(%2,2), 14 ad/ha (%2,0), 0,8 m (%3,4) ve 155,7 m3/ha (%24,6) hata
ile tahmin edilebilmiştir. Hacimde gözlenen yüksek hata üzerine,
arazideki altı adet ağaç önce LiDAR ile dikili halde taranmış ve sonra
kesilerek, bölümleme yöntemiyle hacimlendirilmiştir. Yerde ölçülen
gövde hacimlerinin LiDAR ile ort. 0,061 m3 (%5,1) hata ile tespit edilebildiği
görülmüştür. Dolayısıyla, meşcere hacimlerindeki yüksek
hata oranlarının LiDAR yönteminden değil, envanterde kullanılan
tek girişli hacim tablolarından kaynaklandığı anlaşılmıştır. Buna
karşılık, LiDAR nokta bulutları üzerinden ağaç türü ve meşcere tipleri
belirlenememiştir. Çalışmanın sonunda, amenajman planlarındaki
birçok meşcere parametresine ait değerlerin mobil LiDAR teknolojisiyle
arazide daha az vakit harcanarak kabul edilebilir doğruluk
düzeylerinde hesaplanabildiği sonucuna ulaşılmıştır.
Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially us... more Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially used in image processing. Structure-from-Motion (SfM) based software are usually used to convert two-dimensional UAV-based images into three-dimensional (3D) data. Then, objects such as buildings, trees, and roads can be classified from the 3D data for further analysis. In this study, the road surface generated from 3D data was evaluated. There are several factors that affect the accuracy of the 3D data. In this study, two factors, namely UAV flight altitude and SfM based software, were evaluated. Two different flight altitudes, which were 35 meters and 50 meters, were used. It was found that the lower flights with closer altitudes did not make a significant difference on the results and produced similar results. Another factor is different SfM based software. Two well-known SfM based software were used in this study, which were the Agisoft Metashape and Pix4D Mapper. In this case study, i...
The purpose of this study is to identify and characterize individual sources of pollutants such a... more The 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.
JPMA. The Journal of the Pakistan Medical Association, 2019
Masseter muscle hypertrophy (MMH) is a benign, unilateral or bilateral, painless enlargement. Tre... more Masseter 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 closer an gestereophotogrammetry (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.
The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Pr... more The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Province using machine learning (ML) models and to compare the predictive performances of the models used. Tree-based ensemble learning models, including random forest (RF), gradient boosting machines (GBM), and extreme gradient boosting (XGBoost), were used in the study. A landslide inventory map consisting of 85 landslide polygons was used in the study. The inventory map comprises 32,777 landslide pixels at 30 m resolution. Randomly selected 70% of the landslide pixels were used for training the models and the remaining 30% were used for the validation of the models. In susceptibility analysis, altitude, aspect, curvature, distance to drainage network, distance to faults, distance to roads, land cover, lithology, slope, slope length, and topographic wetness index parameters were used. The validation of the models was conducted using success and prediction rate curves. The validation resu...
Transportation Research Record: Journal of the Transportation Research Board, 2021
The accuracy of random forest (RF) classification depends on several inputs. In this study, two p... more The 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 investigat...
Journal of the Indian Society of Remote Sensing, 2021
In the last decade, airborne light detection and ranging (LiDAR) scanning (ALS) technology has be... more In the last decade, airborne light detection and ranging (LiDAR) scanning (ALS) technology has become a powerful technique for remote sensing, imaging, and mapping. However, the data obtained from any measurement system can include inaccurate signals affected by systematic errors or by the external environment. De-noising to remove inaccurate outlier points is a fundamental and challenging problem for ALS-based mapping applications. The proposed method aims to recover the patterned (planar and linear) points within the assigned outlier and removed points. The method consists of 3 steps. First, statistical outlier removal (SOR) filtering is implemented, and outlier points are detected with the filtering method. Next, the machine learning system reclassifies the filtered outlier points. If the classification result is “inlier” , that point is added to the filtered inlier point cloud as an inlier point. The accuracy of outlier points was evaluated against a manually determined validation set. The results achieved 99%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99\\%$$\\end{document} and 98%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98\\%$$\\end{document} according to the highest overall accuracy criterion and kappa coefficient, respectively. These findings are a promising step to test the proposed method in three different test areas and extend it to widespread spatial dimensions. Furthermore, the findings show that many useful points are removed by SOR filtering. The developed methodology contributes to the reduction of errors caused by data losses in various modelling studies, especially for power transmission line and 3D façade modelling studies.
Abstract The condition of the road surface should be inspected to increase the service life of th... more Abstract The 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.
Landslide susceptibility maps provide crucial information that helps local authorities, public in... more Landslide 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 effective 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 verification 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.
The spatial accuracy of unmanned aerial vehicles (UAVs) and the images they capture
play a crucia... more The 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.
Abstract Forest inventory (FI) surveys are cumbersome when field measurements are performed by ma... more Abstract Forest 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.
Heyelanlar çeşitli dinamik kuvvetlerin yeryüzünde meydana getirdiği değişimlerdir. Heyelanlar, ye... more Heyelanlar çeşitli dinamik kuvvetlerin yeryüzünde meydana getirdiği değişimlerdir. Heyelanlar, yeryüzü üzerinde önemli derecede etkiler oluşturan doğal afetlerin önde gelenlerinden bir tanesidir. Heyelanların etkilerinin azaltılması amacıyla izlenmesi Dünyada oldukça fazla sayıda ve değişik yöntemlerle çalışmalar yapılmaktadır. Teknolojinin sağladığı katkılarla bu yöntemler günden güne ilerlemekte, daha hassas, hızlı ve düşük maliyette araştırmaların sürdürülmesine imkân sağlamaktadır. GNSS ölçmelerinin yüksek doğruluğu, ekonomik olması, kıtalar arası hareketleri dahi izlenebilir hale getirmesi, klasik yersel ölçme yöntemlerine olan rağbeti bir hayli azaltmıştır. 2000'li yılların başında heyelanların GNSS yöntemleriyle araştırılması oldukça hızlı ilerlemiştir. Son on yılda teknolojik gelişmeler heyelanların izlenmesinde kullanılan tekniklerin gelişmesinde önemli katkılar sağlamıştır. Bu teknolojik gelişmeler doğrultusunda mühendislik uygulamalarına yeni bir boyut kazandıran laze...
Roads are one of the main characteristics of cities, and their data should be updated periodicall... more Roads are one of the main characteristics of cities, and their data should be updated periodically. In this study, a new automatic method is proposed for extracting road surface information and road inventory from a Mobile LiDAR System-based point cloud. The proposed method consists of four steps. First, a three-dimensional point cloud is acquired using the MLS raw data. To improve the extraction accuracy, irrelevant points are removed from the point cloud. Piecewise linear models are used in the third step to classify the road surface. Road geometric characteristics such as centerline, profile, cross-section, and cross slope are extracted in the final step. The manually obtained road boundary is compared with the extracted road boundary to assess the classification results. Completeness, correctness, quality, and accuracy measures are range from 97% to 99%. When comparing these measures with previous studies, the proposed method produces one of the highest ones.
ELAZIĞ-KARAKOCAN DEPREMININ TUSAGA-AKTIF ISTASYONLARINA ETKISI Ozet Dogu Anadolu Fay Zonu (DAFZ) ... more ELAZIĞ-KARAKOCAN DEPREMININ TUSAGA-AKTIF ISTASYONLARINA ETKISI Ozet Dogu Anadolu Fay Zonu (DAFZ) Turkiye’de cok sayida buyuk depremlere neden olan aktif fay zonlarindan birisidir. Turkiye’de 2009 yilindan beri calisan Turkiye Ulusal Sabit GNSS Istasyonlari Agi-Aktif (TUSAGA-Aktif) bulunmaktadir. Bu ag depremler neticesinde olusan deformasyonlari belirlemede etkin rol oynamaktadir. DAFZ yakininda, Elazig-Karakocan da 8 Mart 2010 tarihinde 6.0 Mw buyuklugunde Karakocan depremi meydana gelmistir. Bu calismanin amaci, TUSAGA–Aktif istasyonlarinin verileri yardimiyla Karakocan deprem merkezine yakin istasyonlarin yer degistirmelerin buyuklugunu ve yonunu belirlemektir. Istasyon verilerinin dengelenmesinde AUSPOS (Australian Online GPS Processing Service) internet tabanli veri isleme servisi kullanilmistir. Calismada Karakocan depreminden etkilendigi tahmin edilen DAFZ yakininda TUSAGA - Aktif istasyonlarinin deprem tarihinden 5 gun once ve 5 gun sonraki gunlere ait dengeleme sonuclari a...
When it comes to monitoring the condition of roads, UAV technology can overcome many of the downs... more When 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. Automated Extraction of Road Information from UAV-based Data THE BENEFITS OF AIRBORNE TECHNOLOGY IN URBAN PROJECTS data. The fl ight plan is remotely set in the interface and the UAV fl ies and acquires data automatically. However, the fl ight plan needs to be adjusted according to the characteristics of the UAV platform, which are maximum fl ight time, fl ight speed, height above ground level and horizontal distance.
Bu çalışmanın amacı; (i) orman envanterlerinde mobil lazer tarama
(LiDAR) teknolojisinden yararl... more Bu çalışmanın amacı; (i) orman envanterlerinde mobil lazer tarama
(LiDAR) teknolojisinden yararlanma olanaklarını araştırmak ve (ii)
meşcere parametrelerine ilişkin LiDAR verilerini, uygulamada tespit
edilen değerlerle karşılaştırmaktır. Bu doğrultuda, Şavşat’ta arazi ölçümleri
gerçekleştirilen örnek alanlar el tipi LiDAR cihazı ile taranmıştır.
Daha sonra örnek alanlardan elde edilen veri setleri birbiriyle
karşılaştırılarak LiDAR’ın hassasiyeti sınanmıştır. Yapılan istatistik
testler sonucunda, LiDAR ve çapölçer ile ölçülen ağaçların çapları
arasında anlamlı bir fark bulunmamıştır (p>0,05). Yersel ölçümler
referans kabul edilirse; göğüs çapı, ağaç sayısı, meşcere üst boyu ve
meşcere hacmi parametreleri LiDAR cihazıyla sırasıyla; ort. 0,68 cm
(%2,2), 14 ad/ha (%2,0), 0,8 m (%3,4) ve 155,7 m3/ha (%24,6) hata
ile tahmin edilebilmiştir. Hacimde gözlenen yüksek hata üzerine,
arazideki altı adet ağaç önce LiDAR ile dikili halde taranmış ve sonra
kesilerek, bölümleme yöntemiyle hacimlendirilmiştir. Yerde ölçülen
gövde hacimlerinin LiDAR ile ort. 0,061 m3 (%5,1) hata ile tespit edilebildiği
görülmüştür. Dolayısıyla, meşcere hacimlerindeki yüksek
hata oranlarının LiDAR yönteminden değil, envanterde kullanılan
tek girişli hacim tablolarından kaynaklandığı anlaşılmıştır. Buna
karşılık, LiDAR nokta bulutları üzerinden ağaç türü ve meşcere tipleri
belirlenememiştir. Çalışmanın sonunda, amenajman planlarındaki
birçok meşcere parametresine ait değerlerin mobil LiDAR teknolojisiyle
arazide daha az vakit harcanarak kabul edilebilir doğruluk
düzeylerinde hesaplanabildiği sonucuna ulaşılmıştır.
Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially us... more Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially used in image processing. Structure-from-Motion (SfM) based software are usually used to convert two-dimensional UAV-based images into three-dimensional (3D) data. Then, objects such as buildings, trees, and roads can be classified from the 3D data for further analysis. In this study, the road surface generated from 3D data was evaluated. There are several factors that affect the accuracy of the 3D data. In this study, two factors, namely UAV flight altitude and SfM based software, were evaluated. Two different flight altitudes, which were 35 meters and 50 meters, were used. It was found that the lower flights with closer altitudes did not make a significant difference on the results and produced similar results. Another factor is different SfM based software. Two well-known SfM based software were used in this study, which were the Agisoft Metashape and Pix4D Mapper. In this case study, i...
The purpose of this study is to identify and characterize individual sources of pollutants such a... more The 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.
JPMA. The Journal of the Pakistan Medical Association, 2019
Masseter muscle hypertrophy (MMH) is a benign, unilateral or bilateral, painless enlargement. Tre... more Masseter 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 closer an gestereophotogrammetry (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.
The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Pr... more The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Province using machine learning (ML) models and to compare the predictive performances of the models used. Tree-based ensemble learning models, including random forest (RF), gradient boosting machines (GBM), and extreme gradient boosting (XGBoost), were used in the study. A landslide inventory map consisting of 85 landslide polygons was used in the study. The inventory map comprises 32,777 landslide pixels at 30 m resolution. Randomly selected 70% of the landslide pixels were used for training the models and the remaining 30% were used for the validation of the models. In susceptibility analysis, altitude, aspect, curvature, distance to drainage network, distance to faults, distance to roads, land cover, lithology, slope, slope length, and topographic wetness index parameters were used. The validation of the models was conducted using success and prediction rate curves. The validation resu...
Transportation Research Record: Journal of the Transportation Research Board, 2021
The accuracy of random forest (RF) classification depends on several inputs. In this study, two p... more The 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 investigat...
Journal of the Indian Society of Remote Sensing, 2021
In the last decade, airborne light detection and ranging (LiDAR) scanning (ALS) technology has be... more In the last decade, airborne light detection and ranging (LiDAR) scanning (ALS) technology has become a powerful technique for remote sensing, imaging, and mapping. However, the data obtained from any measurement system can include inaccurate signals affected by systematic errors or by the external environment. De-noising to remove inaccurate outlier points is a fundamental and challenging problem for ALS-based mapping applications. The proposed method aims to recover the patterned (planar and linear) points within the assigned outlier and removed points. The method consists of 3 steps. First, statistical outlier removal (SOR) filtering is implemented, and outlier points are detected with the filtering method. Next, the machine learning system reclassifies the filtered outlier points. If the classification result is “inlier” , that point is added to the filtered inlier point cloud as an inlier point. The accuracy of outlier points was evaluated against a manually determined validation set. The results achieved 99%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99\\%$$\\end{document} and 98%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98\\%$$\\end{document} according to the highest overall accuracy criterion and kappa coefficient, respectively. These findings are a promising step to test the proposed method in three different test areas and extend it to widespread spatial dimensions. Furthermore, the findings show that many useful points are removed by SOR filtering. The developed methodology contributes to the reduction of errors caused by data losses in various modelling studies, especially for power transmission line and 3D façade modelling studies.
Abstract The condition of the road surface should be inspected to increase the service life of th... more Abstract The 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.
Landslide susceptibility maps provide crucial information that helps local authorities, public in... more Landslide 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 effective 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 verification 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.
Orman ekosistemlerinin sürdürülebilir şekilde yönetimi için periyodik orman envanterleri yapılmak... more Orman ekosistemlerinin sürdürülebilir şekilde yönetimi için periyodik orman envanterleri yapılmaktadır. Orman envanteri kapsamında ağaçların çap, boy, hacim gibi yapısal özelliklerinin arazide geleneksel (yersel) ölçme teknikleriyle çıkarılması oldukça masraflı ve zahmetli olmaktadır. Lazer sensörleri ve diğer elektronik aygıtlardaki teknolojik gelişmeler, LiDAR sistemlerini küçülterek elde rahatlıkla taşınabilir hale getirmiştir. Ancak, LiDAR sistemleriyle elde edilen 3B nokta bulutlarından anlamlı envanter bilgisi çıkarımı için hala manuel müdahaleler gereklidir. Bu nedenle, büroda yapılan veri analizleri zaman almakta ve dolayısıyla el-tipi LiDAR teknolojisinin ormancılıktaki iş verimliliğini düşürmektedir. Bu çalışmada yapay zeka tekniklerinden makine öğrenme algoritmaları kullanılarak nokta bulutundan ağaç gövdelerinin otomatik tespiti ve raporlanması amaçlanmıştır. Bu amaçla, nokta bulutu öncelikle yer ve yerüstü (vejetasyon) olarak sınıflandırılmış ve daha sonra nokta bulutundan kesit alınarak gövdelerin otomatik tespiti gerçekleştirilmiştir. Son olarak, tespit edilen gövdeler daire oturtma işlemine tabi tutulmuş ve bu şekilde ağaç sayıları hesaplanmıştır. Çalışmada önerilen yaklaşımın LiDAR ile yapılan orman envanterlerinin otomasyonuna katkı sağlayacağı ve orman amenajmanı başta olmak üzere çeşitli ormancılık alanlarında etkin olarak kullanılabileceği değerlendirilmektedir.
We present the workflow to extract ground surface from low-cost Unmanned Aerial Vehicle (UAV) ima... more We present the workflow to extract ground surface from low-cost Unmanned Aerial Vehicle (UAV) images and Terrestrial Laser Scanner (TLS) point clouds. UAV systems with camera sensors provide a trustfully high resolution spatial datasets. However, the image-based 3D points are not sufficient to generate point cloud under density forest areas and often contains only maximum altitude value of tree in point cloud. Due to the lack of ground surface point under density forest, detailed digital terrain model (DTM)'s generation is not possible. Hence the point cloud has to include points from ground; we propose the ground based laser scanning technology to combine from image based point clouds with this technique. Not only to combine point clouds are important, but also filtering the point cloud from the tree or non-ground object points. In this study, we had performed UAV flight and acquired images from the multirotor platform which includes conventional Canon Power shot camera. TLS point cloud gathered from single station and 360 degree with field of interested area. Further analysis is ongoing to detect landslide monitoring from integrated and filtered point cloud data with multi-temporal data. Digital terrain models differencing and three-dimensional point cloud comparison will be able to perform after integrated point clouds. The developed method accurately integrates the point cloud from georeferenced UAV image-based and georeferenced TLS point clouds. The experimental results show that TLS derived point clouds georeferenced with Ground control points (GCPs), the accuracy of global positioning of UAV imagery can be improved with alignment methods. Simultaneously, the accuracy of GCPs which acquired from GNSS surveys were used to geo-referencing of TLS have the same to point clouds accuracy under ± 2 cm.
Nowadays, Un-manned Aerial Vehicle (UAV) platforms are useful data source for analyzing of terrai... more Nowadays, Un-manned Aerial Vehicle (UAV) platforms are useful data source for analyzing of terrain, surveying and three dimensional (3D) modeling of structures and façades of constructions. Low-cost platforms with rotary or fixed wing UAVs are capable of performing the multi view geometry and structure from motion with amateur cameras in autonomous mode. For the purpose of UAV flights gathering images are become powerful technique for many applications including change detection, deformation monitoring and forestry applications in small scale areas. Mostly procedure of gathering raw data is easier than a traditional photogrammetric pipeline gathering images on image blocks calculations of similar pixels on multi view images. Generation of very dense point clouds are possible after image alignment and estimating of camera pose. Processing of raw point clouds can result with highly accurate Digital Surface model (DSM), with further analysis digital terrain model, in other words bare earth extraction can be done. This paper presents the latest developments of UAV image processing methods for computer vision applications, surveying and 3D modeling issues for landslide area. Automation steps are mentioned for image processing, camera orientation, DSM generation and orthomosaics production stage.
Landslide area occurs on steep-slope and forestry. Generally, investigations are pursuing on the ... more Landslide area occurs on steep-slope and forestry. Generally, investigations are pursuing on the difficult topographical area for monitoring a landslide. GNSS measurements and analysis are providing very accurate, large content, scope and reliable information to monitoring movements on ground surface. Most techniques are not possible produce safe results for stability of the slope in forestry area. GNSS measurement techniques are the most reliable technique to monitoring slopes. This paper is aimed to present landslide monitoring with Global Navigation Satellite System (GNSS) measurements and to compare performance of Precise Point Positioning(PPP) method and rapid-static GNSS solution. Study area is located on the Middle of Taurus mountain chains. Three epoch GNSS campaigns for monitoring were performed on the study site from 2011 to 2012. For the purpose of detecting surface movement, ground monitoring points conducted on reachable areas and clean sky view in complex and dense forestry topography. In this study, GNSS data were processed by post-processed PPP method and rapid static GNSS solution for the purpose of comparison. Abrupt heavy rainfalls in area resulted 4m displacement top-scarp of landslide area. The landslide toe moved forward averagely 1-2 m between campaigns. Processing results show that differences between PPP-derived and rapid-static-derived displacement are within dm level. As a conclusion, in case of limited GNSS receiver, post-processed static PPP method can be used as an efficient alternative to the rapid-static GNSS method to detect displacement caused by landslide.
Heyelanların tespiti, izlenmesi, tehlikesi ve riskinin belirlenmesi, olası zararlarından korunulm... more Heyelanların tespiti, izlenmesi, tehlikesi ve riskinin belirlenmesi, olası zararlarından korunulması ile afet durumunda atlatılması-yönetilmesi pek çok karmaşık işlem adımlarını barındırmaktadır. Bu işlem adımları genellikle heyelan envanterinin oluşturulması, duyarlılık tehlike ve risk haritalarının yapılması ile gerekli analizlerin yapılması günümüzde heyelanla ilgili çalışmaların önemli bir kısmını oluşturmaktadır. Heyelanların oluşmasında etkili olan faktörlerin çok çeşitli olması onların belirlenmesi ile izlenmesinde çeşitliliğe neden olmaktadır. Heyelan alanının topoğrafyası, jeolojisi, hidrolojisi ile bu alana dışarıdan yapılan müdahaleler bu faktörlerin en önemlileri arasında yer almaktadır. Heyelan alanlarının morfolojik yapısının belirlenmesi, günümüzde ilerleyen teknolojik yazılım ve donanımlar sayesinde çeşitlenmiş, haritalama çalışmalarına yeni bakış açıları kazandırmış ve bu çalışmaları kolaylaştırmıştır. Bu çalışmada, ilerleyen teknolojiyle birlikte gelişen haritalama ve deformasyon ölçmelerinin heyelanlarda uygulanması konusu üzerinde durulmuştur. Bu çalışmada Taşkent (Konya) ilçe merkezinin güneyinde oluşan heyelan üzerinde Yersel lazer tarama (YLT) sistemleri, Küresel konumlama sistemleri (GNSS) yöntemleri, İnsansız hava aracı (İHA) ve Mobil lazer tarama (MLT) teknikleri ile yapılan çalışmalar değerlendirilmiştir. Heyelan hareketi 2010 yılından bu yana yapılan gözlem, ölçüm, analiz ve değerlendirmelerle izlenmiştir. Bu çalışmada hacimsel karşılaştırma analizleri ile izleme yapılmış ve sonuçları sunulmuştur. Aktif sensör sistemleri sayesinde (Lidar, yersel lazer tarama, mobil lidar vb.) heyelan alanlarında farklı zamanlarda elde edilen verilerin karşılaştırılması ile hareket eden kütlenin yer değiştirme miktarları ve hızları belirlenebilmektedir. Bunun yanında yersel, klasik haritalama teknikleriyle yapılan kontrol ölçmeleri sayesinde üretilen bilgilerin doğruluğu denetlenebilmektedir. Yüksek çözünürlükte, hassas, doğru, güvenirliği yüksek ve özelliklede düşük maliyetle elde edilmiş verilerle heyelan bölgelerinin haritalarının üretilmesi kısa zamanda yapılabilmektedir. Ulaşımın kolay olduğu alanlarda ise klasik haritalama teknikleri geometrik nivelman, GNSS ölçmeleri, EDM ölçmeleri yeterli bilgiyi sağlayabilmektedir.
Anahtar Kelimeler: Heyelan izleme, modern jeodezi, lazer tarama, GNSS, İnsansız hava aracı.
Bu bildiri heyelanların izlenmesinde insansız hava araçlarından (İHA) elde edilen yüksek çözünürl... more Bu bildiri heyelanların izlenmesinde insansız hava araçlarından (İHA) elde edilen yüksek çözünürlüklü verilerin kullanılmasında yöntem ve metotların araştırılmasını kapsamaktadır. İHA’lar ile elde edilen yüksek çözünürlüklü verilerin deformasyon analizleri kullanılarak heyelanların hareketlerinin izlenmesini, boyutlarını ve hacimsel olarak hareket eden kütle karakteristiğini ortaya çıkarılmasındaki araştırmalarda kullanılmaktadır. Bu çalışmada Konya, Taşkent ilçesinde bir bölgede meydana gelen heyelan alanındaki araştırmalar yer almaktadır.
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Papers by Mustafa Zeybek
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.
(LiDAR) teknolojisinden yararlanma olanaklarını araştırmak ve (ii)
meşcere parametrelerine ilişkin LiDAR verilerini, uygulamada tespit
edilen değerlerle karşılaştırmaktır. Bu doğrultuda, Şavşat’ta arazi ölçümleri
gerçekleştirilen örnek alanlar el tipi LiDAR cihazı ile taranmıştır.
Daha sonra örnek alanlardan elde edilen veri setleri birbiriyle
karşılaştırılarak LiDAR’ın hassasiyeti sınanmıştır. Yapılan istatistik
testler sonucunda, LiDAR ve çapölçer ile ölçülen ağaçların çapları
arasında anlamlı bir fark bulunmamıştır (p>0,05). Yersel ölçümler
referans kabul edilirse; göğüs çapı, ağaç sayısı, meşcere üst boyu ve
meşcere hacmi parametreleri LiDAR cihazıyla sırasıyla; ort. 0,68 cm
(%2,2), 14 ad/ha (%2,0), 0,8 m (%3,4) ve 155,7 m3/ha (%24,6) hata
ile tahmin edilebilmiştir. Hacimde gözlenen yüksek hata üzerine,
arazideki altı adet ağaç önce LiDAR ile dikili halde taranmış ve sonra
kesilerek, bölümleme yöntemiyle hacimlendirilmiştir. Yerde ölçülen
gövde hacimlerinin LiDAR ile ort. 0,061 m3 (%5,1) hata ile tespit edilebildiği
görülmüştür. Dolayısıyla, meşcere hacimlerindeki yüksek
hata oranlarının LiDAR yönteminden değil, envanterde kullanılan
tek girişli hacim tablolarından kaynaklandığı anlaşılmıştır. Buna
karşılık, LiDAR nokta bulutları üzerinden ağaç türü ve meşcere tipleri
belirlenememiştir. Çalışmanın sonunda, amenajman planlarındaki
birçok meşcere parametresine ait değerlerin mobil LiDAR teknolojisiyle
arazide daha az vakit harcanarak kabul edilebilir doğruluk
düzeylerinde hesaplanabildiği sonucuna ulaşılmıştır.
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.
(LiDAR) teknolojisinden yararlanma olanaklarını araştırmak ve (ii)
meşcere parametrelerine ilişkin LiDAR verilerini, uygulamada tespit
edilen değerlerle karşılaştırmaktır. Bu doğrultuda, Şavşat’ta arazi ölçümleri
gerçekleştirilen örnek alanlar el tipi LiDAR cihazı ile taranmıştır.
Daha sonra örnek alanlardan elde edilen veri setleri birbiriyle
karşılaştırılarak LiDAR’ın hassasiyeti sınanmıştır. Yapılan istatistik
testler sonucunda, LiDAR ve çapölçer ile ölçülen ağaçların çapları
arasında anlamlı bir fark bulunmamıştır (p>0,05). Yersel ölçümler
referans kabul edilirse; göğüs çapı, ağaç sayısı, meşcere üst boyu ve
meşcere hacmi parametreleri LiDAR cihazıyla sırasıyla; ort. 0,68 cm
(%2,2), 14 ad/ha (%2,0), 0,8 m (%3,4) ve 155,7 m3/ha (%24,6) hata
ile tahmin edilebilmiştir. Hacimde gözlenen yüksek hata üzerine,
arazideki altı adet ağaç önce LiDAR ile dikili halde taranmış ve sonra
kesilerek, bölümleme yöntemiyle hacimlendirilmiştir. Yerde ölçülen
gövde hacimlerinin LiDAR ile ort. 0,061 m3 (%5,1) hata ile tespit edilebildiği
görülmüştür. Dolayısıyla, meşcere hacimlerindeki yüksek
hata oranlarının LiDAR yönteminden değil, envanterde kullanılan
tek girişli hacim tablolarından kaynaklandığı anlaşılmıştır. Buna
karşılık, LiDAR nokta bulutları üzerinden ağaç türü ve meşcere tipleri
belirlenememiştir. Çalışmanın sonunda, amenajman planlarındaki
birçok meşcere parametresine ait değerlerin mobil LiDAR teknolojisiyle
arazide daha az vakit harcanarak kabul edilebilir doğruluk
düzeylerinde hesaplanabildiği sonucuna ulaşılmıştır.
Anahtar Kelimeler: Heyelan izleme, modern jeodezi, lazer tarama, GNSS, İnsansız hava aracı.