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Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning methods require the collection of both training and testing data to produce and evaluate the classification results. The collection of good quality landslide ground truths to train classifiers and detect landslides in other regions is a challenge, with a significant impact on classification accuracy. Taking this into account, the following research question arises: What is the appropriate training–testing dataset split ratio in supervised classification to effectively detect landslides in a testing area based on DEMs? We investigated this issue for both the pixel-based approach (PBA) and object-based image analysis (OBIA). In both approaches, the random forest (RF) classification was implemented. The experiments were performed in the most landslide-affected area in Poland in the Outer Carpathians-Rożnów Lake vicinity. Based on the accuracy assessment, we found that the training area should be of a similar size to the testing area. We also found that the OBIA approach performs slightly better than PBA when the quantity of training samples is significantly lower than the testing samples. To increase detection performance, the intersection of the OBIA and PBA results together with median filtering and the removal of small elongated objects were performed. This allowed an overall accuracy (OA) = 80% and F1 Score = 0.50 to be achieved. The achieved results are compared and discussed with other landslide detection-related studies.
Kamila Pawluszek-Filipiak; Andrzej Borkowski. On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification. Remote Sensing 2020, 12, 3054 .
AMA StyleKamila Pawluszek-Filipiak, Andrzej Borkowski. On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification. Remote Sensing. 2020; 12 (18):3054.
Chicago/Turabian StyleKamila Pawluszek-Filipiak; Andrzej Borkowski. 2020. "On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification." Remote Sensing 12, no. 18: 3054.
Surface subsidence is a dominant component of the displacement vector triggered by underground mining. Over the last few decades, Differential Interferometry Synthetic Aperture Radar (DInSAR) has been used to efficiently monitor this phenomenon with great spatial and temporal coverage. More advanced multi-temporal DInSAR (MTInSAR) algorithms have been proposed to overcome some of the limitations of conventional DInSAR. However, advanced MTInSAR approaches are also not perfect in terms of measuring mining subsidence (e.g., temporal decorrelation, ambiguity, nonlinearity). For this reason, we propose a fusion of the Persistent Scatterer Interferometry (PSInSAR) and DInSAR results. By combining these complementary techniques, the atmospheric errors in PSInSAR data are reduced and larger deformation rates could have been detected more accurately (thanks to DInSAR) than by an approach solely based on PS-InSAR. This allows to measure areas with fast-moving subsidence (1 m/year) due to ongoing underground coal exploitation. Data from ascending and descending orbits of Sentinel-1A\B were used to obtain the vertical deformation component. The resulting integrated vertical deformation map was compared with the results from levelling benchmarks. The Root Mean Square Error (RMSE) calculated based on this comparison was 22 mm. Moreover, the maximal vertical cumulative subsidence detected in the study area was 1.05 m/year.
Kamila Pawluszek-Filipiak; Andrzej Borkowski. Monitoring mining-induced subsidence by integrating differential radar interferometry and persistent scatterer techniques. European Journal of Remote Sensing 2020, 54, 18 -30.
AMA StyleKamila Pawluszek-Filipiak, Andrzej Borkowski. Monitoring mining-induced subsidence by integrating differential radar interferometry and persistent scatterer techniques. European Journal of Remote Sensing. 2020; 54 (sup1):18-30.
Chicago/Turabian StyleKamila Pawluszek-Filipiak; Andrzej Borkowski. 2020. "Monitoring mining-induced subsidence by integrating differential radar interferometry and persistent scatterer techniques." European Journal of Remote Sensing 54, no. sup1: 18-30.
Underground coal exploitation often results in land-surface subsidence, the rate of which depends on geological characteristics, the mechanical properties of the rocks, and the applied extraction technology. Since mining-related subsidence is characterized by “fast” displacement and high nonlinearity, monitoring this process by using Interferometric Synthetic Aperture Radar (InSAR) is very challenging. The Small BAseline Subset (SBAS) approach needs to predefine an a priori deformation model to properly estimate an interferometric component related to displacements. As a consequence, there is a lack of distributed scatterers (DS) when the selected a priori deformation model deviates from the real deformation. The conventional differential SAR interferometry (DInSAR) approach does not have this limitation, since it does not need any deformation model. However, the accuracy of this technique is limited by factors related to spatial and temporal decorrelation, signal delays due to the atmospheric artifacts, and orbital or topographic errors. Therefore, this study presents the integration of DInSAR and SBAS techniques in order to leverage the advantages and overcome the disadvantages of both methods and to retrieve the complete deformation pattern over the investigated study area. The obtained results were evaluated internally and externally with leveling data. Results indicated that the Kriging-based integration method of DInSAR and SBAS can be effectively applied to monitor mining-related subsidence. The root-mean-square Error (RMSE) between modeled and measured deformation by InSAR was found to be 11 and 13 mm for vertical and horizontal displacements, respectively. Moreover, DInSAR technique as a cost-effective and complementary method to conventional geodetic techniques can be applied for effective monitoring fast mining subsidence. The minimum and maximum RMSE between DInSAR displacement and specific leveling profiles were found to be 0.9 and 3.2 cm, respectively. Since the SBAS processing failed in subsidence estimation in the area of maximum deformation rate, the deformation estimates outside the maximum rate could only be compared. In these areas, the good agreement between SBAS and DInSAR indicates that the SBAS technique could be reliable for monitoring the residual subsidence that surrounds the subsidence trough. Using the proposed approach, we detected subsidence of up to −1 m and planar displacements (east–west) of up to 0.24 m.
Kamila Pawluszek-Filipiak; Andrzej Borkowski. Integration of DInSAR and SBAS Techniques to Determine Mining-Related Deformations Using Sentinel-1 Data: The Case Study of Rydułtowy Mine in Poland. Remote Sensing 2020, 12, 242 .
AMA StyleKamila Pawluszek-Filipiak, Andrzej Borkowski. Integration of DInSAR and SBAS Techniques to Determine Mining-Related Deformations Using Sentinel-1 Data: The Case Study of Rydułtowy Mine in Poland. Remote Sensing. 2020; 12 (2):242.
Chicago/Turabian StyleKamila Pawluszek-Filipiak; Andrzej Borkowski. 2020. "Integration of DInSAR and SBAS Techniques to Determine Mining-Related Deformations Using Sentinel-1 Data: The Case Study of Rydułtowy Mine in Poland." Remote Sensing 12, no. 2: 242.
Grzegorz Mutke; Andrzej Kotyrba; Adam Lurka; Dorota Olszewska; Przemysław Dykowski; Andrzej Borkowski; Andrzej Araszkiewicz; Adam Barański. Upper Silesian Geophysical Observation System A unit of the EPOS project. Journal of Sustainable Mining 2019, 18, 198 -207.
AMA StyleGrzegorz Mutke, Andrzej Kotyrba, Adam Lurka, Dorota Olszewska, Przemysław Dykowski, Andrzej Borkowski, Andrzej Araszkiewicz, Adam Barański. Upper Silesian Geophysical Observation System A unit of the EPOS project. Journal of Sustainable Mining. 2019; 18 (4):198-207.
Chicago/Turabian StyleGrzegorz Mutke; Andrzej Kotyrba; Adam Lurka; Dorota Olszewska; Przemysław Dykowski; Andrzej Borkowski; Andrzej Araszkiewicz; Adam Barański. 2019. "Upper Silesian Geophysical Observation System A unit of the EPOS project." Journal of Sustainable Mining 18, no. 4: 198-207.
Fluvial transport is a natural process that shapes riverbeds and the surrounding terrain surface, particularly in mountainous areas. Since the traditional techniques used for fluvial transport investigation provide only limited information about the bed load transport, recently, laser scanning technology has been increasingly incorporated into research to investigate this issue in depth. In this study, a terrestrial laser scanning technique was used to investigate the transport of individual boulders. The measurements were carried out annually from 2011 to 2016 on the Łomniczka River, which is a medium-sized mountain stream. The main goal of this research was to detect and determine displacements of the biggest particles in the mountain riverbed. The methodology was divided into two steps. First, the change zones were detected using two strategies. The first strategy was based on differential digital elevation model (DEM) creation and the second involved the calculation of differences between point clouds instead of DEMs. The experiments show that the second strategy was more efficient. In the second step, the displacements of the boulders were determined based on the detected areas of change. Using the proposed methodology, displacements for individual stones in each year were determined. Most of the changes took place in 2012–2014, which correlates well with the hydrological observations. During the six-year period, movements of individual particles with diameters less than 0.8 m were observed. Maximal displacements in the observed period reached 3 m. Therefore, it is possible to determine both vertical and horizontal displacement in the riverbed using multitemporal TLS.
Agata Walicka; Grzegorz Jóźków; Marek Kasprzak; Andrzej Borkowski. Terrestrial Laser Scanning for the Detection of Coarse Grain Size Movement in a Mountain Riverbed. Water 2019, 11, 2199 .
AMA StyleAgata Walicka, Grzegorz Jóźków, Marek Kasprzak, Andrzej Borkowski. Terrestrial Laser Scanning for the Detection of Coarse Grain Size Movement in a Mountain Riverbed. Water. 2019; 11 (11):2199.
Chicago/Turabian StyleAgata Walicka; Grzegorz Jóźków; Marek Kasprzak; Andrzej Borkowski. 2019. "Terrestrial Laser Scanning for the Detection of Coarse Grain Size Movement in a Mountain Riverbed." Water 11, no. 11: 2199.
Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas.
Kamila Pawłuszek; Sylwia Marczak; Andrzej Borkowski; Paolo Tarolli. Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features. ISPRS International Journal of Geo-Information 2019, 8, 321 .
AMA StyleKamila Pawłuszek, Sylwia Marczak, Andrzej Borkowski, Paolo Tarolli. Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features. ISPRS International Journal of Geo-Information. 2019; 8 (8):321.
Chicago/Turabian StyleKamila Pawłuszek; Sylwia Marczak; Andrzej Borkowski; Paolo Tarolli. 2019. "Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features." ISPRS International Journal of Geo-Information 8, no. 8: 321.
Knowledge about the number of trees in an orchard and their geometric parameters is beneficial in precise farming and together with other information may be used to predict the yield. These parameters can be obtained based on time-consuming field measurements or more effectively, from very high resolution 3D data collected with Unmanned Aerial Vehicles (UAV). Numerous UAV experiments have been conducted in agricultural areas; however, most of studies are limited to the use of a passive optical sensor (camera). This study demonstrates an experiment on the novel remote sensing approach of determining selected geometric parameters of trees in an apple orchard, based on a high-density point cloud obtained from a Velodyne HDL-32E laser scanner mounted on a small UAV platform Leica Aibot X6 V2. Reference data of selected geometric parameters of trees was obtained from orthophotomap and with geodetic surveying methods. Original and robust methodology is proposed for the point cloud processing, which is the inventive combination of an alpha-shape algorithm, principal component analysis and detection of local minima on crown profiles. The developed approach allowed for the correct identification of 99% of the trees in the test orchard. The root mean square error of determined crown areas was equal to 0.98 m2. The accuracy of tree top identification, tree height and crown base height determination was equal to 0.38, 0.09 and 0.09 m, respectively.
Edyta Hadas; Grzegorz Jóźków; Agata Walicka; Andrzej Borkowski. Apple orchard inventory with a LiDAR equipped unmanned aerial system. International Journal of Applied Earth Observation and Geoinformation 2019, 82, 101911 .
AMA StyleEdyta Hadas, Grzegorz Jóźków, Agata Walicka, Andrzej Borkowski. Apple orchard inventory with a LiDAR equipped unmanned aerial system. International Journal of Applied Earth Observation and Geoinformation. 2019; 82 ():101911.
Chicago/Turabian StyleEdyta Hadas; Grzegorz Jóźków; Agata Walicka; Andrzej Borkowski. 2019. "Apple orchard inventory with a LiDAR equipped unmanned aerial system." International Journal of Applied Earth Observation and Geoinformation 82, no. : 101911.
The Sentinel-1 constellation provides an effective new radar instrument with a short revisit time of six days for the monitoring of intensive mining surface deformations. Our goal is to investigate in detail and to bring new comprehension of the mine life cycle. The dynamics of mining, especially in the case of horizontally evolving longwall technology, exhibit rapid surface changes. We use the classical approach of differential radar interferometry (DInSAR) with short temporal baselines (six days), which results in deformation maps with a low decorrelation between the satellite images. For the same time intervals, we compare the radar results with prediction models based on the Knothe–Budryk theory for mining subsidence. The validation of the results with ground levelling measurements reveals a high level of resemblance of the DInSAR subsidence maps (−0.04 m bias with respect to the levelling). On the other hand, aside from the explicable exaggeration, the location of the subsidence trough needs improvement in the forecasted deformations (0.2 km shift in location, a deformation velocity four times higher than in DInSAR). In addition, a time lag between DInSAR (compatible with extraction) and prediction is revealed. The model improvement can be achieved by including the DInSAR results in the elaboration of the model parameters.
Maya Ilieva; Piotr Polanin; Andrzej Borkowski; Piotr Gruchlik; Kamil Smolak; Andrzej Kowalski; Witold Rohm. Mining Deformation Life Cycle in the Light of InSAR and Deformation Models. Remote Sensing 2019, 11, 745 .
AMA StyleMaya Ilieva, Piotr Polanin, Andrzej Borkowski, Piotr Gruchlik, Kamil Smolak, Andrzej Kowalski, Witold Rohm. Mining Deformation Life Cycle in the Light of InSAR and Deformation Models. Remote Sensing. 2019; 11 (7):745.
Chicago/Turabian StyleMaya Ilieva; Piotr Polanin; Andrzej Borkowski; Piotr Gruchlik; Kamil Smolak; Andrzej Kowalski; Witold Rohm. 2019. "Mining Deformation Life Cycle in the Light of InSAR and Deformation Models." Remote Sensing 11, no. 7: 745.
The thin plate spline (TPS) is an interpolation approach that has been developed to investigate a frequently occurring problem in geosciences: the modelling of scattered data. In this paper, we carry over the concept of the thin plate spline from the plane to the sphere. To develop the spherical TPS, we utilize the idea of an elastic shell that is attributed with the bending energy and the external energy. The bending energy describes the shape of the membrane, while the external energy reflects deviations between the shell and the data to be modelled. Minimizing both energy terms leads to the variational problem with the solution in the form of the Euler–Lagrange equations. We provide the solution of the variational problem for two cases: (1) total energy minimization over the whole sphere and (2) total energy minimization over a closed region of the sphere. In case (1) we found a closed analytical solution in the form of collocation in a reproducing kernel Hilbert space. The local case (2) solution is based on a discretization of the corresponding Euler–Lagrange equation using the spherical Laplace operator. The performance of the introduced spherical TPS is demonstrated on two real world data sets. It is shown quantitative that the thin plate approach is significantly more effective than Gaussian filter in terms of the GRACE data de-striping. We also show that the TPS can be used effectively for the modelling of the vertical total electron content. It allows the reduction of the computational effort in comparison with well-established planar TPS approximation. Moreover, the harmonicity property of the TPS can be utilized to solve various issues related to Earth gravity modelling.
Wolfgang Keller; Andrzej Borkowski. Thin plate spline interpolation. Journal of Geodesy 2019, 93, 1251 -1269.
AMA StyleWolfgang Keller, Andrzej Borkowski. Thin plate spline interpolation. Journal of Geodesy. 2019; 93 (9):1251-1269.
Chicago/Turabian StyleWolfgang Keller; Andrzej Borkowski. 2019. "Thin plate spline interpolation." Journal of Geodesy 93, no. 9: 1251-1269.
The paper presents an efficient methodology of water body extent estimation based on remotely sensed data collected with UAV (Unmanned Aerial Vehicle). The methodology includes the data collection with selected sensors and processing of remotely sensed data to obtain accurate geospatial products that are finally used to estimate water body extent. Three sensors were investigated: RGB (Red Green Blue) camera, thermal infrared camera, and laser scanner. The platform used to carry each of these sensors was an Aibot X6—a multirotor type of UAV. Test data was collected at 6 sites containing different types of water bodies, including 4 river sections, an old river bed, and a part of a lake shore. The processing of collected data resulted in 2.5-D and 2-D geospatial products that were used subsequently for water body extent estimation. Depending on the type of used sensor, the created geospatial product, and the type of the water body and the land cover, three strategies employing image processing tools were developed to estimate water body range. The obtained results were assessed in terms of classification accuracy (distinguishing the water body from the land) and geometrical planar accuracy of the water body extent. The product identified as the most suitable in water body detection was four bands RGB+TIR (Thermal InfraRed) ortho mosaic. It allowed to achieve the average kappa coefficient of the water body identification above 0.9. The planar accuracy of water body extent varied depending on the type of the sensor, the geospatial product, and the test site conditions, but it was comparable with results obtained in similar studies.
Przemysław Tymków; Grzegorz Jóźków; Agata Walicka; Mateusz Karpina; Andrzej Borkowski. Identification of Water Body Extent Based on Remote Sensing Data Collected with Unmanned Aerial Vehicle. Water 2019, 11, 338 .
AMA StylePrzemysław Tymków, Grzegorz Jóźków, Agata Walicka, Mateusz Karpina, Andrzej Borkowski. Identification of Water Body Extent Based on Remote Sensing Data Collected with Unmanned Aerial Vehicle. Water. 2019; 11 (2):338.
Chicago/Turabian StylePrzemysław Tymków; Grzegorz Jóźków; Agata Walicka; Mateusz Karpina; Andrzej Borkowski. 2019. "Identification of Water Body Extent Based on Remote Sensing Data Collected with Unmanned Aerial Vehicle." Water 11, no. 2: 338.
The fluvial transport is an important aspect of hydrological and geomorphologic studies. The knowledge about the movement parameters of different-size fractions is essential in many applications, such as the exploration of the watercourse changes, the calculation of the river bed parameters or the investigation of the frequency and the nature of the weather events. Traditional techniques used for the fluvial transport investigations do not provide any information about the long-term horizontal movement of the rocks. This information can be gained by means of terrestrial laser scanning (TLS). However, this is a complex issue consisting of several stages of data processing. In this study the methodology for individual rocks segmentation from TLS point cloud has been proposed, which is the first step for the semi-automatic algorithm for movement detection of individual rocks. The proposed algorithm is executed in two steps. Firstly, the point cloud is classified as rocks or background using only geometrical information. Secondly, the DBSCAN algorithm is executed iteratively on points classified as rocks until only one stone is detected in each segment. The number of rocks in each segment is determined using principal component analysis (PCA) and simple derivative method for peak detection. As a result, several segments that correspond to individual rocks are formed. Numerical tests were executed on two test samples. The results of the semi-automatic segmentation were compared to results acquired by manual segmentation. The proposed methodology enabled to successfully segment 76 % and 72 % of rocks in the test sample 1 and test sample 2, respectively.
A. Walicka; G. Jóźków; A. Borkowski. INDIVIDUAL ROCKS SEGMENTATION IN TERRESTRIAL LASER SCANNING POINT CLOUD USING ITERATIVE DBSCAN ALGORITHM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, XLII-2, 1157 -1161.
AMA StyleA. Walicka, G. Jóźków, A. Borkowski. INDIVIDUAL ROCKS SEGMENTATION IN TERRESTRIAL LASER SCANNING POINT CLOUD USING ITERATIVE DBSCAN ALGORITHM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; XLII-2 ():1157-1161.
Chicago/Turabian StyleA. Walicka; G. Jóźków; A. Borkowski. 2018. "INDIVIDUAL ROCKS SEGMENTATION IN TERRESTRIAL LASER SCANNING POINT CLOUD USING ITERATIVE DBSCAN ALGORITHM." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2, no. : 1157-1161.
The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately. In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200 points/m2) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92 % of trees in the test area. 6 % of trees in orchard were not separated from each other and 2 % were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09 m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.
E. Hadas; G. Jozkow; A. Walicka; A. Borkowski. DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, XLII-2, 407 -410.
AMA StyleE. Hadas, G. Jozkow, A. Walicka, A. Borkowski. DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; XLII-2 ():407-410.
Chicago/Turabian StyleE. Hadas; G. Jozkow; A. Walicka; A. Borkowski. 2018. "DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2, no. : 407-410.
The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification.
Kamila Pawluszek; Andrzej Borkowski; Paolo Tarolli. Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution. Landslides 2018, 15, 1851 -1865.
AMA StyleKamila Pawluszek, Andrzej Borkowski, Paolo Tarolli. Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution. Landslides. 2018; 15 (9):1851-1865.
Chicago/Turabian StyleKamila Pawluszek; Andrzej Borkowski; Paolo Tarolli. 2018. "Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution." Landslides 15, no. 9: 1851-1865.
The ionosphere is still considered one of the most significant error sources in precise Global Navigation Satellite Systems (GNSS) positioning. On the other hand, new satellite signals and data processing methods allow for a continuous increase in the accuracy of the available ionosphere models derived from GNSS observables. Therefore, many research groups around the world are conducting research on the development of precise ionosphere products. This is also reflected in the establishment of several ionosphere-related working groups by the International Association of Geodesy. Whilst a number of available global ionosphere maps exist today, dense regional GNSS networks often offer the possibility of higher accuracy regional solutions. In this contribution, we propose an approach for regional ionosphere modelling based on un-differenced multi-GNSS carrier phase data for total electron content (TEC) estimation, and thin plate splines for TEC interpolation. In addition, we propose a methodology for ionospheric products self-consistency analysis based on calibrated slant TEC. The results of the presented approach are compared to well-established global ionosphere maps during varied ionospheric conditions. The initial results show that the accuracy of our regional ionospheric vertical TEC maps is well below 1 TEC unit, and that it is at least a factor of 2 better than the global products.
Anna Krypiak-Gregorczyk; Pawel Wielgosz; Andrzej Borkowski. Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation. Remote Sensing 2017, 9, 1221 .
AMA StyleAnna Krypiak-Gregorczyk, Pawel Wielgosz, Andrzej Borkowski. Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation. Remote Sensing. 2017; 9 (12):1221.
Chicago/Turabian StyleAnna Krypiak-Gregorczyk; Pawel Wielgosz; Andrzej Borkowski. 2017. "Ionosphere Model for European Region Based on Multi-GNSS Data and TPS Interpolation." Remote Sensing 9, no. 12: 1221.
The Velodyne HDL-32E laser scanner is used more frequently as main mapping sensor in small commercial UASs. However, there is still little information about the actual accuracy of point clouds collected with such UASs. This work evaluates empirically the accuracy of the point cloud collected with such UAS. Accuracy assessment was conducted in four aspects: impact of sensors on theoretical point cloud accuracy, trajectory reconstruction quality, and internal and absolute point cloud accuracies. Theoretical point cloud accuracy was evaluated by calculating 3D position error knowing errors of used sensors. The quality of trajectory reconstruction was assessed by comparing position and attitude differences from forward and reverse EKF solution. Internal and absolute accuracies were evaluated by fitting planes to 8 point cloud samples extracted for planar surfaces. In addition, the absolute accuracy was also determined by calculating point 3D distances between LiDAR UAS and reference TLS point clouds. Test data consisted of point clouds collected in two separate flights performed over the same area. Executed experiments showed that in tested UAS, the trajectory reconstruction, especially attitude, has significant impact on point cloud accuracy. Estimated absolute accuracy of point clouds collected during both test flights was better than 10 cm, thus investigated UAS fits mapping-grade category.
G. Jozkow; P. Wieczorek; M. Karpina; A. Walicka; A. Borkowski. PERFORMANCE EVALUATION OF sUAS EQUIPPED WITH VELODYNE HDL-32E LiDAR SENSOR. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-2/W6, 171 -177.
AMA StyleG. Jozkow, P. Wieczorek, M. Karpina, A. Walicka, A. Borkowski. PERFORMANCE EVALUATION OF sUAS EQUIPPED WITH VELODYNE HDL-32E LiDAR SENSOR. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-2/W6 ():171-177.
Chicago/Turabian StyleG. Jozkow; P. Wieczorek; M. Karpina; A. Walicka; A. Borkowski. 2017. "PERFORMANCE EVALUATION OF sUAS EQUIPPED WITH VELODYNE HDL-32E LiDAR SENSOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6, no. : 171-177.
The aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A key issue of the alpha-shape algorithm is to define the α parameter, as it directly affects the crown delineation results. We propose to adjust this parameter based on a group of representative trees in an orchard for which the classical field measurements were performed. The best value of the α parameter is one whose correlation coefficient of dendrometric parameters between field measurements and estimated values is the highest. We determined crown diameters as principal components of ALS points representing a delineated crown. The method was applied to a test area of an olive orchard in Spain. The tree dendrometric parameters estimated from ALS data were compared with field measurements to assess the quality of the developed approach. We found the method to be equally good or even superior to previously investigated semi-automatic methods. The average error is 19% for tree height, 53% for crown base height, and 13% and 9% for the length of the longer diameter and perpendicular diameter, respectively.
Edyta Hadas; Andrzej Borkowski; Javier Estornell; Przemyslaw Tymkow. Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis. GIScience & Remote Sensing 2017, 54, 898 -917.
AMA StyleEdyta Hadas, Andrzej Borkowski, Javier Estornell, Przemyslaw Tymkow. Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis. GIScience & Remote Sensing. 2017; 54 (6):898-917.
Chicago/Turabian StyleEdyta Hadas; Andrzej Borkowski; Javier Estornell; Przemyslaw Tymkow. 2017. "Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis." GIScience & Remote Sensing 54, no. 6: 898-917.
Terrestrial laser scanning is an efficient technique in providing highly accurate point clouds for various geoscience applications. The point clouds have to be transformed to a well-defined reference frame, such as the global Geodetic Reference System 1980. The transformation to the geocentric coordinate frame is based on estimating seven Helmert parameters using several GNSS (Global Navigation Satellite System) referencing points. This paper proposes a method for direct point cloud georeferencing that provides coordinates in the geocentric frame. The proposed method employs the vertical deflection from an external global Earth gravity model and thus demands a minimum number of GNSS measurements. The proposed method can be helpful when the number of georeferencing GNSS points is limited, for instance in city corridors. It needs only two georeferencing points. The validation of the method in a field test reveals that the differences between the classical georefencing and the proposed method amount at maximum to 7 mm with the standard deviation of 8 mm for all of three coordinate components. The proposed method may serve as an alternative for the laser scanning data georeferencing, especially when the number of GNSS points is insufficient for classical methods.
Edward Osada; Krzysztof Sośnica; Andrzej Borkowski; Magdalena Owczarek-Wesołowska; Anna Gromczak. A Direct Georeferencing Method for Terrestrial Laser Scanning Using GNSS Data and the Vertical Deflection from Global Earth Gravity Models. Sensors 2017, 17, 1489 .
AMA StyleEdward Osada, Krzysztof Sośnica, Andrzej Borkowski, Magdalena Owczarek-Wesołowska, Anna Gromczak. A Direct Georeferencing Method for Terrestrial Laser Scanning Using GNSS Data and the Vertical Deflection from Global Earth Gravity Models. Sensors. 2017; 17 (7):1489.
Chicago/Turabian StyleEdward Osada; Krzysztof Sośnica; Andrzej Borkowski; Magdalena Owczarek-Wesołowska; Anna Gromczak. 2017. "A Direct Georeferencing Method for Terrestrial Laser Scanning Using GNSS Data and the Vertical Deflection from Global Earth Gravity Models." Sensors 17, no. 7: 1489.
GNSS signals have become a valuable tool in studying the Earth's ionosphere as their dual-frequency observables allow for calculation ionospheric total electron content (TEC). Recent studies estimated relative error of popular GNSS-TEC maps at the level of 20-30 %. This motivates research community to develop new modelling and interpolation methods. In this paper, we demonstrate new approach to GNSS-TEC estimation. In the new approach we use solely carrier phase multi-GNSS observables and thin plate splines (TPS) for accurate ionosphere modeling. The model allows for providing TEC maps for Europe with high spatial and temporal resolutions - 0.2x0.2 degrees and 2.5 minutes, respectively. In addition, we present the performance of our approach during one the most intense ionospheric storms of 24th Solar cycle that took place in March 2015. The results showed that the accuracy of our maps was better with residuals lower by at least 50 % comparing the widely used product.
Pawel Wielgosz; Anna Krypiak-Gregorczyk; Andrzej Borkowski. Regional Ionosphere Modeling Based on Multi-GNSS Data and TPS Interpolation. 2017 Baltic Geodetic Congress (BGC Geomatics) 2017, 287 -291.
AMA StylePawel Wielgosz, Anna Krypiak-Gregorczyk, Andrzej Borkowski. Regional Ionosphere Modeling Based on Multi-GNSS Data and TPS Interpolation. 2017 Baltic Geodetic Congress (BGC Geomatics). 2017; ():287-291.
Chicago/Turabian StylePawel Wielgosz; Anna Krypiak-Gregorczyk; Andrzej Borkowski. 2017. "Regional Ionosphere Modeling Based on Multi-GNSS Data and TPS Interpolation." 2017 Baltic Geodetic Congress (BGC Geomatics) , no. : 287-291.
Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1 m, 2 m, 5 m and 10 m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1 m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5 m DEM-resolution for FFNN and 1 m DEM resolution for results. The best performance was found to be using 5 m DEM-resolution for FFNN and 1 m DEM resolution for ML classification.
K. Pawłuszek; A. Borkowski; P. Tarolli. TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-1/W1, 83 -90.
AMA StyleK. Pawłuszek, A. Borkowski, P. Tarolli. TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-1/W1 ():83-90.
Chicago/Turabian StyleK. Pawłuszek; A. Borkowski; P. Tarolli. 2017. "TOWARDS THE OPTIMAL PIXEL SIZE OF DEM FOR AUTOMATIC MAPPING OF LANDSLIDE AREAS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1, no. : 83-90.
The availability of digital elevation model (DEM) delivered by airborne laser scanning (ALS) opens new horizons in the geomorphological research, especially in the landslide studies. This detailed geomorphological information allows for mapping of landslide affected areas using DEM data only. In order to map landslide areas in the automatic manner using machine learning classification algorithms and only DEM, generation of several DEM derivatives is needed. These first and second order derivatives provide information about specific properties of the terrain. However, involving a set of topographic features in the machine learning process increases significantly time of computations. Moreover, the topographic features are correlated since they are generated using the same DEM. The objective of this study is an in-depth exploration of the topographic information provided by the DEM data as well as the reduction of the computational time while the automatic landslide mapping. For this reason, a set of DEM derivatives have been generated and transformed into the principal component domain. The Principal Component Analysis (PCA) is a procedure that converts the set of correlated features into a set of linearly uncorrelated components using the orthogonal transformation. For the automatic landslide detection, the support vector machine (SVM) algorithm was used. The achieved results were compared with the existing landslide inventory map and overall accuracy and kappa coefficient were calculated. For the non-reduced original topographic model, we received 73% of overall accuracy. For the PCA-reduced models, accuracy parameters are not significantly worse. For instance, using only 7 principal components, which provide 90% of the total variability of the original topographic features, we received the overall accuracy of 72% while the computation time was reduced.
Kamila Pawłuszek; Andrzej Borkowski. Automatic Landslides Mapping in the Principal Component Domain. Advancing Culture of Living with Landslides 2017, 421 -428.
AMA StyleKamila Pawłuszek, Andrzej Borkowski. Automatic Landslides Mapping in the Principal Component Domain. Advancing Culture of Living with Landslides. 2017; ():421-428.
Chicago/Turabian StyleKamila Pawłuszek; Andrzej Borkowski. 2017. "Automatic Landslides Mapping in the Principal Component Domain." Advancing Culture of Living with Landslides , no. : 421-428.