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Dr. Kamila Pawluszek-Filipiak
Wroclaw University of Environmental and Life Sciences

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0 OBIA
0 Remote Sensing
0 landslides
0 Radar interferometry
0 DEM exploration

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Preprint content
Published: 04 March 2021
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Crops are of the fundamental food sources for humanity. Due to the population growth as well as climate change, monitoring of the crops is important to sustain agriculture and conserve natural resources. Development of the remote sensing techniques especially in terms of revisiting time opens new avenues to study crops temporal behaviors from space. Moreover, thanks to the Copernicus program, which guarantees optical as well as radar data to be freely available, there are opportunities to utilize them in an operative way. Additionally, utilization of spectral as well as radar data allows for the synergetic application of both datasets. However, to utilize this data in the operational crop monitoring, it is very important to understand the temporal variations of the remote sensing signal. Therefore, we make an attempt to understand spectral as well as radar remote sensing temporal behavior and its relation with phonological stages.

For the analysis, 14 cloud-free Sentinel-2 (S-2) acquisitions as well as 34 Sentinel-1 (S-1) acquisitions are utilized. S-2 data were collected with 2A-level while S-1 data was captured in the format of Single Look Complex (SLC) in the Interferometric Wide (IW) swath mode. SLC products consist of complex SAR data preserving phase information which allows studying polarimetric indicators. All remote sensing (spectral as well as SAR) data cover the time period from 04/05/2020 to 07/11/2020. During this time, also 14 field visits were carried out to capture information about phonological stages of corn and wheat according to the BBCH scale (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie). Additionally, to better understand the temporal behavior of S-1/S-2 signal, weather information from the Institute of Meteorology and Water Management (IMGW) was captured.

Based on various spectral bands of S-2 data, 12 spectral indices were calculated e.g., GNDVI (Green Normalized Vegetation Index), IRECI (Inverted Red-Edge Chlorophyll Index), MCARI (Modified Chlorophyll Absorption in Reflectance Index), MSAVI (Modified Soil-Adjusted Vegetation Index), MTCI (MERIS Terrestrial Chlorophyll Index), NDVI (Normalized Difference Vegetation Index), PSSRa (Pigment Specific Simple Ratio) and others. After radiometric calibration and the Lee speckle filtering, backscattering coefficients (σVVoVHo) of S-1 images were calculated as well as its backscattering ratio (σVHo/ σVVo).  All images were then converted from linear to decibel (dB). Additionally, 2 × 2 covariance matrix delivered from S-1 was extracted from the scattering matrix of each SLC image using PolSARpro version 6.0.2 software. After speckle filtration, total scattered power was derived which allows calculating the Shannon Entropy. This value measures the randomness of the scattering within a pixel.

Time series of many S-2 indices reveal the strong correlation between the development of phenology stages of corn and wheat and the increase of S2 delivered values of spectral indices. However, such a strong correlation cannot be observed within many of S-1 indices. Some of them very poorly indicate the correlation between the development of phenology stages of corn and wheat and increase of S-1 indices values. Additionally, it was observed that values of S1/S2 indices for the same phenology stage very between corn and winter wheat.

 

ACS Style

Marta Pasternak; Kamila Pawluszek-Filipiak. An attempt to understand corn and winter wheat temporal behavior by means of Sentinel-1 and Sentinel-2 data and its relation with phonological stages. 2021, 1 .

AMA Style

Marta Pasternak, Kamila Pawluszek-Filipiak. An attempt to understand corn and winter wheat temporal behavior by means of Sentinel-1 and Sentinel-2 data and its relation with phonological stages. . 2021; ():1.

Chicago/Turabian Style

Marta Pasternak; Kamila Pawluszek-Filipiak. 2021. "An attempt to understand corn and winter wheat temporal behavior by means of Sentinel-1 and Sentinel-2 data and its relation with phonological stages." , no. : 1.

Preprint content
Published: 04 March 2021
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Landslide identification is the fundamental step to reduce the potential damaging effects of landslide activities. A variety of techniques and approaches has been developed to detect landslides. Conventional landslide identification is a complex and laborious task due to a large amount of the field work and materials that have to be investigated. Additionally, the conventional geomorphological mapping mainly provides a subjective representation of landscape complexities at different scales. Sometimes, in certain conditions, such as densely-vegetated terrain, conventional landslide mapping is ineffective or even impossible.

Therefore, innovative methods that allow for the reduction of subjectivism, time, and effort have increasingly become the subject of interest in landslide research. These methods mainly focus on semi-automated or automatic landslide mapping and include analysis of remote sensing data, such as optical images, Digital Elevation Models (DEMs) derived by Light Detection and Ranging etc. Among them, the pixel-based approach (PBA) and the object-based image analysis (OBIA) methods can be distinguished, for which supervised classification methods are usually utilized.

The accuracy of supervised classification methods strongly corresponds to the training samples - its quality and amount. Supervised classification methods require the collection of training as well as testing data to generate and assess the accuracy of the classification results. It is a challenging task, especially in forested areas, to capture ground truths of the good quality to train the classifier and to identify landslides. Considering this, we decided to investigate the following research question: What is the appropriate training–testing dataset split ratio in supervised classification to detect landslides in a testing area based on DEMs? Since PBA and OBIA approaches are nowadays widely utilized, we investigated this issue for both methods. The Random Forest classifier was implemented for both methods. The experiments were performed in Poland in the Outer Carpathians.

Accuracy measures calculated for the region growing validation indicated that the training area should be similarly large to the testing area in DEM-based automatic landslide detection. Additionally, we found that the OBIA approach performs slightly better than PBA when the quantity of training samples is lower. Besides this, we also attempted to increase the detection performance and to generate final landslide inventory. For this purpose, the intersection of the OBIA and PBA results together with median filtering and the removal of small elongated objects were carried out. We achieved the Overall Accuracy of 80% and F1 Score of 0.50.

ACS Style

Kamila Pawluszek-Filipiak; Andrzej Borkowski. Automatic landslide detection using the Random Forest classification - the importance of the train-test split ratio. 2021, 1 .

AMA Style

Kamila Pawluszek-Filipiak, Andrzej Borkowski. Automatic landslide detection using the Random Forest classification - the importance of the train-test split ratio. . 2021; ():1.

Chicago/Turabian Style

Kamila Pawluszek-Filipiak; Andrzej Borkowski. 2021. "Automatic landslide detection using the Random Forest classification - the importance of the train-test split ratio." , no. : 1.

Preprint content
Published: 04 March 2021
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The EPOS-PL project is the Polish realization of the European Plate Observing System (EPOS) initiative, which aims at the integration of existing and newly created research infrastructures to facilitate the use of multidisciplinary data and products in the field of Earth sciences in Europe. Within the EPOS, one of the tasks aims at SAR data utilization for deformation monitoring in the area of Rydułtowy mine. The Rydułtowy mine is the oldest active mining in the Upper Silesia Coal Basin in Poland. In the area of this mine, five Corner Reflectors (CRs) have been deployed in the framework of the EPOS- PL. Additionally, in the area of interest one high-frequency GNSS receiver working permanently has been placed. This GNSS permanent station (RES100POL) enables estimating of deformation time-series based on multi-GNSS observation in post-processing.

In this study, we use Sentinel-1A/B TOPSAR images acquired between 25 June 2018 and 14 July 2019 in one ascending and two descending geometries with revisiting time of 6-days. Additionally, we use ground truths of two leveling and GNSS measurement campaigns carried out to precisely estimate deformations on five CRs (2nd-4th of July 2018 and 28th-30th of June 2019). GNSS static measurements were carried out via three independent measurement sessions. Coordinates of the station RES100POL and static GNSS and leveling measurements ware were used for validation of SAR measurements.

SAR data has been processed by means of classical consecutive Differential Interferometry (DInSAR) as well as Persistent Scattering (PSInSAR) approach. During SAR data processing, snow coverage accumulated on the CRs caused that some Sentinel-1 images from the winter season have been removed from DInSAR as well as PSInSAR processing. Results from ascending and descending orbits allow the estimation of vertical as well as east-west deformation components. Root Mean Square Error (RMSE) between CRs measured by conventional geodetic techniques and DInSAR was estimated as 31mm and 38mm for east-west and vertical deformation components, respectively. RMSE measured between PSInSAR and GNSS was estimated as 5mm and 7mm for east-west and vertical components, respectively. RMSE of 15mm and 3mm was estimated for DInSAR with respect to GNSS from RES100POL station for east-west and vertical components, respectively. Subsequently, RMSE of 4mm and 5mm was estimated as deformation time variations between PSInSAR and GNSS from RES1 station for east-west and vertical components, respectively. These measures indicate clearly the advantage of the PSInSAR method. However,  the PSInSAR approach was able to estimate deformations only for three CRs due to the fast and non-linear deformation pattern observed on other two CRs.

ACS Style

Natalia Wielgocka; Kamila Pawluszek-Filipiak; Damian Tondaś; Andrzej Borkowski. Satellite Radar Interferometry on corner reflectors in the area of mining region in Poland. 2021, 1 .

AMA Style

Natalia Wielgocka, Kamila Pawluszek-Filipiak, Damian Tondaś, Andrzej Borkowski. Satellite Radar Interferometry on corner reflectors in the area of mining region in Poland. . 2021; ():1.

Chicago/Turabian Style

Natalia Wielgocka; Kamila Pawluszek-Filipiak; Damian Tondaś; Andrzej Borkowski. 2021. "Satellite Radar Interferometry on corner reflectors in the area of mining region in Poland." , no. : 1.

Journal article
Published: 22 February 2021 in Procedia Computer Science
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Satellite differential interferometry has proven to be a useful method for the deformation estimation caused by underground mining. This underground human intervention may influence mining-induced tremors which can even affect human lives. Having considered the importance of the problem, the goal of the presented study is to check whether DInSAR estimated deformations can be correlated with mining tremors. For analyses, 51 differential interferograms have been processed using Sentinel 1A/B ascending images. Seismic events of the biggest magnitude have been selected for detailed investigation. Time series deformations in the centre of the subsidence basins as well as the time series deformation profiles have been used to search for evidence of the mining-induced tremors. Unfortunately, achieved results for these specific events did not present characteristic changes in the deformation behaviour which can be attributed to particular seismic events. This can be due to the small magnitude of the tremors (Mw≈2.8), geological settings, properties of the study are as well as the depth of the extraction (1km). Achieved results portray that finding any sign or evidence of the induce tremors in time series deformations is a complex and challenging task even if such an effective satellite interferometric methods are available.

ACS Style

Kamila Pawłuszek-Filipiak; Andrzej Borkowski. Mining-induced tremors in the light of deformations estimated by satellite SAR interferometry in the Upper Silesian Coal Basin, Poland. Procedia Computer Science 2021, 181, 685 -692.

AMA Style

Kamila Pawłuszek-Filipiak, Andrzej Borkowski. Mining-induced tremors in the light of deformations estimated by satellite SAR interferometry in the Upper Silesian Coal Basin, Poland. Procedia Computer Science. 2021; 181 ():685-692.

Chicago/Turabian Style

Kamila Pawłuszek-Filipiak; Andrzej Borkowski. 2021. "Mining-induced tremors in the light of deformations estimated by satellite SAR interferometry in the Upper Silesian Coal Basin, Poland." Procedia Computer Science 181, no. : 685-692.

Conference paper
Published: 23 December 2020 in Understanding and Reducing Landslide Disaster Risk
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Landslide detection and characterisation are the fundamental activities performed to reduce economic losses caused by hazardous landslide events. Since existing landslide databases have to be updated, different techniques are used for this purpose. Last decades, satellite radar interferometry has increasingly been involved in this context. In this study, a sophisticated variant of radar interferometry, namely, the persistent scatterers interferometry (PSI) technique, was used to update the landslide activity state and landslide intensity in the area of Rożnów Lake in Poland. The study area is located in the Western Carpathians. Sentinel-1 A and B images covering almost the whole year 2017 were used. Ascending and descending images were processed separately by means of the PSI approach, and afterwards, PSI velocities were projected onto the slope direction. Then the results for ascending and descending orbits were merged into the database, and the landslide activity and intensity were assessed. The landslide state assessment was performed by means of a PSI-based matrix approach. The activity state of 205 landslides has been evaluated. As a result, the majority of the landslides have been assessed as active. The results are supported by field evidence. Moreover, the results suggest the need for updating the existing landslide inventory maps.

ACS Style

Kamila Pawluszek-Filipiak; Andrzej Borkowski. Updating Landslide Activity State and Intensity by Means of Persistent Scatterer Interferometry. Understanding and Reducing Landslide Disaster Risk 2020, 119 -126.

AMA Style

Kamila Pawluszek-Filipiak, Andrzej Borkowski. Updating Landslide Activity State and Intensity by Means of Persistent Scatterer Interferometry. Understanding and Reducing Landslide Disaster Risk. 2020; ():119-126.

Chicago/Turabian Style

Kamila Pawluszek-Filipiak; Andrzej Borkowski. 2020. "Updating Landslide Activity State and Intensity by Means of Persistent Scatterer Interferometry." Understanding and Reducing Landslide Disaster Risk , no. : 119-126.

Conference paper
Published: 23 December 2020 in Understanding and Reducing Landslide Disaster Risk
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One of the remedies for reducing the negative effects of landslide activity is landslide mapping. Landslide detection, carried out by using historical data analysis, stereoscopic photo interpretation and/or field works, is expensive, time-consuming and requires expert knowledge and experience. Automatic approaches for landslide detection can provide benefits such as increased efficiency and reduced costs and time. Many attempts have been made to automate the process of landslide identification but the key information for this process is provided by the high-resolution Digital Elevation Model (DEM) delivered from Airborne Laser Scanning (ALS) data. Having considered this, the objective of this study is to utilise the Object-Oriented Approach (OOA) and DEM for the detection of landslides. In this study, we use the results archived from Pawluszek et al. (ISPRS Int J Geo-Inf 8:321, 2019). The challenges and opportunities of automatic approaches are discussed, based on an investigation conducted in an area heavily affected by landslides. The study area is located close to Rożnów Lake, in Poland and stands out by various land uses. The automatic detection results achieved (OA = 85% and K = 0.6) indicate that there is a huge potential in automatic approaches. However, these approaches face difficulties in landslide detection due to the smoothing of typical landslide features. This situation appears for old landslides and landslides located in areas of active agricultural treatments. Besides the fuzzy delineation of the landslide extent, landslide amalgamation in the OOA results can be observed. Thus, automatic approaches still need to be developed and improved. At the current stage of the development, automatic approaches cannot replace validation based on field reconnaissance but can support an interpreter in their work.

ACS Style

Kamila Pawluszek-Filipiak; Andrzej Borkowski. Object-Oriented Automatic Landslide Detection from High Resolution Digital Elevation Model—Opportunities and Challenges Based on a Case Study in the Polish Carpathians. Understanding and Reducing Landslide Disaster Risk 2020, 75 -80.

AMA Style

Kamila Pawluszek-Filipiak, Andrzej Borkowski. Object-Oriented Automatic Landslide Detection from High Resolution Digital Elevation Model—Opportunities and Challenges Based on a Case Study in the Polish Carpathians. Understanding and Reducing Landslide Disaster Risk. 2020; ():75-80.

Chicago/Turabian Style

Kamila Pawluszek-Filipiak; Andrzej Borkowski. 2020. "Object-Oriented Automatic Landslide Detection from High Resolution Digital Elevation Model—Opportunities and Challenges Based on a Case Study in the Polish Carpathians." Understanding and Reducing Landslide Disaster Risk , no. : 75-80.

Journal article
Published: 18 September 2020 in Remote Sensing
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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.

ACS Style

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 Style

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 (18):3054.

Chicago/Turabian Style

Kamila 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.

Journal article
Published: 11 September 2020 in Applied Sciences
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To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.

ACS Style

Kamila Pawluszek-Filipiak; Natalia Oreńczak; Marta Pasternak. Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping. Applied Sciences 2020, 10, 6335 .

AMA Style

Kamila Pawluszek-Filipiak, Natalia Oreńczak, Marta Pasternak. Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping. Applied Sciences. 2020; 10 (18):6335.

Chicago/Turabian Style

Kamila Pawluszek-Filipiak; Natalia Oreńczak; Marta Pasternak. 2020. "Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping." Applied Sciences 10, no. 18: 6335.

Letter
Published: 15 May 2020 in Remote Sensing
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On 29 January 2019, the collapse of a mine roof resulted in a significant surface deformation and generated a tremor with a magnitude of 4.6 in Rudna Mine, Poland. This study combines the seismological and geodetic monitoring of the event. Data from local and regional seismological networks were used to estimate the mechanism of the source and the ground motion caused by the earthquake. Global Navigation Satellite System data, collected at 10 Hz, and processed as a long-term time-series of daily coordinates solutions and short-term high frequency oscillations, are in good agreement with the seismological outputs, having detected several more tremors. The range and dynamics of the deformed surface area were monitored using satellite radar techniques for slow and fast motion detection. The radar data revealed that a 2-km2 area was affected in the six days after the collapse and that there was an increase in the post-event rate of subsidence.

ACS Style

Maya Ilieva; Łukasz Rudziński; Kamila Pawłuszek-Filipiak; Grzegorz Lizurek; Iwona Kudłacik; Damian Tondaś; Dorota Olszewska. Combined Study of a Significant Mine Collapse Based on Seismological and Geodetic Data—29 January 2019, Rudna Mine, Poland. Remote Sensing 2020, 12, 1570 .

AMA Style

Maya Ilieva, Łukasz Rudziński, Kamila Pawłuszek-Filipiak, Grzegorz Lizurek, Iwona Kudłacik, Damian Tondaś, Dorota Olszewska. Combined Study of a Significant Mine Collapse Based on Seismological and Geodetic Data—29 January 2019, Rudna Mine, Poland. Remote Sensing. 2020; 12 (10):1570.

Chicago/Turabian Style

Maya Ilieva; Łukasz Rudziński; Kamila Pawłuszek-Filipiak; Grzegorz Lizurek; Iwona Kudłacik; Damian Tondaś; Dorota Olszewska. 2020. "Combined Study of a Significant Mine Collapse Based on Seismological and Geodetic Data—29 January 2019, Rudna Mine, Poland." Remote Sensing 12, no. 10: 1570.

Digital earth observation
Published: 07 May 2020 in European Journal of Remote Sensing
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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.

ACS Style

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 Style

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 (sup1):18-30.

Chicago/Turabian Style

Kamila 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.

Preprint content
Published: 17 April 2020
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The main goal of this research is the activity state verification of existing landslide inventory maps using Persistent Scatterer Interferometry (PSI). The study was conducted in Małopolskie municipality, a rural setting with a sparse urbanization in Polish Flysch Carpathians. PSI have been applied using Synthetic Aperture Radar (SAR) data from ALOS PALSAR, and Sentinel 1A/B from different acquisition geometry (ascending and descending orbit) to increase PS coverage and overcome geometric effects due to layover and shadowing. The Line-Of-Sight PSI measurements were projected to the steepest slope, which allows to homogenize the results from diverse acquisition modes and to compare displacement velocities with different slope orientations. Additionally, landslide intensity (motion rate) and expected damages maps were generated and verified during filed investigations. High correlation between PSI results and in-situ damage observations has been confirmed. Activity state and landslide-related expected damage map have been confirmed for 43 out of a total of 50 landslides investigated in the field. The short temporal baseline provided by Sentinel satellite 1A/B data allows increasing of the PS density significantly. The study substantiates usefulness of SAR based landslide activity monitoring for land use and land development, even in rural areas.

ACS Style

Kamila Pawluszek-Filipiak; Mahdi Motagh; Andrzej Borkowski. Multi-temporal landslide activity investigation by spaceborne SAR interferometry: Polish Carpathians case study. 2020, 2020, 1 -39.

AMA Style

Kamila Pawluszek-Filipiak, Mahdi Motagh, Andrzej Borkowski. Multi-temporal landslide activity investigation by spaceborne SAR interferometry: Polish Carpathians case study. . 2020; 2020 ():1-39.

Chicago/Turabian Style

Kamila Pawluszek-Filipiak; Mahdi Motagh; Andrzej Borkowski. 2020. "Multi-temporal landslide activity investigation by spaceborne SAR interferometry: Polish Carpathians case study." 2020, no. : 1-39.

Preprint content
Published: 23 March 2020
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Since launching Sentinel 1 satellites, the European Space Agency has been providing a huge amount of repeated SAR data. Thanks to 6-days revisiting time, it creates a perfect possibility for the monitoring of ground deformation, caused by underground mining activity, by using Differential SAR interferometry (DInSAR).

Because, DInSAR exploits single interferometric SAR pairs, the accuracy of this technique is limited by spatial and temporal decorrelation and atmospheric artifacts. To minimize the atmospheric influence on DInSAR results, we investigated precipitation and relative humidity data acquired from the Institute of Meteorology and Water Management (IMGW). Theoretically, the summed atmospheric LOS errors due to relative humidity for 106 ascending and 112 descending images are -3.5 cm and 7,5 cm, respectively.  In fact, we observed that there is a moderate correlation between precipitation/relative humidity and “bad” acquisition in relatively small study area. Nevertheless, we were able to remove 33 ascending and 15 descending images from the queue of consecutive DInSAR. Finally, it allowed to estimate up to 1m subsidence in the period of 1 Jan 2017–8 Oct 2018 in the Rydułtowy mine located in the southwest part of the Upper Silesian Coal Basin (USCB), Poland.

To evaluate our DInSAR accuracy due to atmospheric artefacts, we decided to compare the results with “atmospheric-free” results acquired by SBAS technique. SBAS separates diverse interferometric components that correspond to deformation, topographic error, atmospheric error, and orbital errors.

The Root-Mean-Square Error (RMSE) has been calculated between SBAS and DInSAR for selected subsidence profiles. The maximal RMSE was found to be 3.6 cm and 4.1cm for ascending and descending LOS displacements, respectively. This shows that DInSAR cannot be used for monitoring millimeter-level deformation. On the contrary, it can be effectively used to assess quick nonlinear deformations reaching several decimeters /year such as in the presented study case.

ACS Style

Kamila Pawłuszek-Filipiak; Andrzej Borkowski. InSAR techniques to determine mining-related deformations using Sentinel-1 data: the case study of Rydułtowy mine in Poland. 2020, 1 .

AMA Style

Kamila Pawłuszek-Filipiak, Andrzej Borkowski. InSAR techniques to determine mining-related deformations using Sentinel-1 data: the case study of Rydułtowy mine in Poland. . 2020; ():1.

Chicago/Turabian Style

Kamila Pawłuszek-Filipiak; Andrzej Borkowski. 2020. "InSAR techniques to determine mining-related deformations using Sentinel-1 data: the case study of Rydułtowy mine in Poland." , no. : 1.

Journal article
Published: 10 January 2020 in Remote Sensing
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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.

ACS Style

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 Style

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 (2):242.

Chicago/Turabian Style

Kamila 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.

Journal article
Published: 24 July 2019 in ISPRS International Journal of Geo-Information
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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.

ACS Style

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 Style

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 (8):321.

Chicago/Turabian Style

Kamila 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.

Original paper
Published: 03 December 2018 in Natural Hazards
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In the last decade, development in remote sensing techniques has opened new avenues for studying the evolution of landscapes dominated by mass wasting processes. Conventional methods including field reconnaissance are time-consuming and resource-intensive. Thus, it is worth taking advantage of the high-resolution digital elevation model (HRDEM) to identify landslide features remotely and investigate landslide morphology. This research proposes a new technique of landslide feature identification and morphology mapping using computer-aided methods to enhance the visual interpretation of HRDEM. These computer-aided methods involve deep exploration of topographic information provided by HRDEM. In addition to the HRDEM, nine diverse HRDEM derivatives were used to maximise the morphological information captured by HRDEM. To compact and to better understand the morphological information, original HRDEM derivatives were transformed into the principal component (PC) domain. Based on PC composition provided by three initial PCs, it was possible to identify morphological signatures of landslides and represent them as the detailed landslide surface morphology maps. The presented methodology serves as an alternative means of landslide characterisation. It permitted the evaluation of slope morphology and the ability to reassess recent and future landslide activity on a comparative basis.

ACS Style

Kamila Pawluszek. Landslide features identification and morphology investigation using high-resolution DEM derivatives. Natural Hazards 2018, 96, 311 -330.

AMA Style

Kamila Pawluszek. Landslide features identification and morphology investigation using high-resolution DEM derivatives. Natural Hazards. 2018; 96 (1):311-330.

Chicago/Turabian Style

Kamila Pawluszek. 2018. "Landslide features identification and morphology investigation using high-resolution DEM derivatives." Natural Hazards 96, no. 1: 311-330.

Original paper
Published: 07 May 2018 in Landslides
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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.

ACS Style

Kamila Pawluszek; Andrzej Borkowski; Paolo Tarolli. Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution. Landslides 2018, 15, 1851 -1865.

AMA Style

Kamila 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 Style

Kamila Pawluszek; Andrzej Borkowski; Paolo Tarolli. 2018. "Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution." Landslides 15, no. 9: 1851-1865.

Journal article
Published: 31 May 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

K. 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.

Conference paper
Published: 20 May 2017 in Advancing Culture of Living with Landslides
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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.

ACS Style

Kamila Pawłuszek; Andrzej Borkowski. Automatic Landslides Mapping in the Principal Component Domain. Advancing Culture of Living with Landslides 2017, 421 -428.

AMA Style

Kamila Pawłuszek, Andrzej Borkowski. Automatic Landslides Mapping in the Principal Component Domain. Advancing Culture of Living with Landslides. 2017; ():421-428.

Chicago/Turabian Style

Kamila Pawłuszek; Andrzej Borkowski. 2017. "Automatic Landslides Mapping in the Principal Component Domain." Advancing Culture of Living with Landslides , no. : 421-428.

Original paper
Published: 27 December 2016 in Natural Hazards
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Choosing appropriate landslide-controlling factors (LCFs) in landslide susceptibility mapping (LSM) is a challenging task and depends on the nature of terrain and expert knowledge and experience. Nowadays, it is very common to use digital elevation model (DEM) and DEM-derivatives, as a representation of the topographic conditions. The objective of this study is to explore topography in depth and simultaneously reduce redundant information within DEM-derivatives using principal component analysis. Moreover, this study investigates the impact of DEM-derived factors on LSM. Therefore, three various strategies were tested. The first strategy included a set of LCFs created from the four initial principal components, which were provided from DEM-derived factors. The second strategy included a set of parameters which contained additional lithological and environmental factors. The third strategy utilises the analytical hierarchy process (AHP) to assign weights to each LCF. The LSM was performed based on landslide susceptibility index. Obtained results show that 60% of existing landslides fell into high and very high susceptibility zones using first and second strategies. It proves that topographic factors play a significant role in LSM. Adding additional lithological and environmental factors to the set of LCFs did not improve the results significantly, unless the AHP was used in the third strategy. It improved results significantly; up to 70%. Results from second and third strategies highlight utility of AHP in LSM. Presented studies were performed on the area very prone to landslide occurrence in the region of Rożnów Lake, Poland.

ACS Style

Kamila Pawluszek; Andrzej Borkowski. Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland. Natural Hazards 2016, 86, 919 -952.

AMA Style

Kamila Pawluszek, Andrzej Borkowski. Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland. Natural Hazards. 2016; 86 (2):919-952.

Chicago/Turabian Style

Kamila Pawluszek; Andrzej Borkowski. 2016. "Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland." Natural Hazards 86, no. 2: 919-952.

Journal article
Published: 22 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Since the availability of high-resolution Airborne Laser Scanning (ALS) data, substantial progress in geomorphological research, especially in landslide analysis, has been carried out. First and second order derivatives of Digital Terrain Model (DTM) have become a popular and powerful tool in landslide inventory mapping. Nevertheless, an automatic landslide mapping based on sophisticated classifiers including Support Vector Machine (SVM), Artificial Neural Network or Random Forests is often computationally time consuming. The objective of this research is to deeply explore topographic information provided by ALS data and overcome computational time limitation. For this reason, an extended set of topographic features and the Principal Component Analysis (PCA) were used to reduce redundant information. The proposed novel approach was tested on a susceptible area affected by more than 50 landslides located on Rożnów Lake in Carpathian Mountains, Poland. The initial seven PCA components with 90% of the total variability in the original topographic attributes were used for SVM classification. Comparing results with landslide inventory map, the average user’s accuracy (UA), producer’s accuracy (PA), and overall accuracy (OA) were calculated for two models according to the classification results. Thereby, for the PCA-feature-reduced model UA, PA, and OA were found to be 72%, 76%, and 72%, respectively. Similarly, UA, PA, and OA in the non-reduced original topographic model, was 74%, 77% and 74%, respectively. Using the initial seven PCA components instead of the twenty original topographic attributes does not significantly change identification accuracy but reduce computational time.

ACS Style

Kamila Pawłuszek; Andrzej Borkowski. LANDSLIDES IDENTIFICATION USING AIRBORNE LASER SCANNING DATA DERIVED TOPOGRAPHIC TERRAIN ATTRIBUTES AND SUPPORT VECTOR MACHINE CLASSIFICATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B8, 145 -149.

AMA Style

Kamila Pawłuszek, Andrzej Borkowski. LANDSLIDES IDENTIFICATION USING AIRBORNE LASER SCANNING DATA DERIVED TOPOGRAPHIC TERRAIN ATTRIBUTES AND SUPPORT VECTOR MACHINE CLASSIFICATION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B8 ():145-149.

Chicago/Turabian Style

Kamila Pawłuszek; Andrzej Borkowski. 2016. "LANDSLIDES IDENTIFICATION USING AIRBORNE LASER SCANNING DATA DERIVED TOPOGRAPHIC TERRAIN ATTRIBUTES AND SUPPORT VECTOR MACHINE CLASSIFICATION." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8, no. : 145-149.