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.

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.

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.

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.

Abstract. 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.