Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

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

Multi-temporal landslide activity investigation by spaceborne SAR interferometry: The case study of the Polish Carpathians

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

On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

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

The Evaluation of Spectral Vegetation Indexes and Redundancy Reduction on the Accuracy of Crop Type Detection

Knowledge about crop type distribution is valuable information for effective management of agricultural productivity, food security estimation, and natural resources protection. Algorithms for automatic crop type detection have great potential to positively influence these aspects as well as speed up the process of crop type mapping in larger areas. In the presented study, we used 14 Sentinel-2 images to calculate 12 widely used spectral vegetation indices. Further, to evaluate the effect of reduced dimensionality on the accuracy of crop type mapping, we utilized principal component analysis (PCA). For this purpose, random forest (RF)-supervised classifications were tested for each index separately, as well as for the combinations of various indices and the four initial PCA components. Additionally, for each RF classification feature importance was assessed, which enabled identification of the most relevant period of the year for the differentiation of crop types. We used 34.6% of the ground truth field data to train the classifier and calculate various accuracy measures such as the overall accuracy (OA) or Kappa index. The study showed a high effectiveness of the Modified Chlorophyll Absorption in Reflectance Index (MCARI) (OA = 86%, Kappa = 0.81), Normalized Difference Index 45 (NDI45) (OA = 85%, Kappa = 0.81), and Weighted Difference Vegetation Index (WDVI) (OA = 85%, Kappa = 0.80) in crop type mapping. However, utilization of all of them together did not increase the classification accuracy (OA = 78%, Kappa = 0.72). Additionally, the application of the initial three components of PCA allowed us to achieve an OA of 78% and Kappa of 0.72, which was unfortunately lower than the single-index classification (e.g., based on only NDVI45). This shows that dimensionality reductions did not increase the classification accuracy. Moreover, feature importance from RF indicated that images captured from June and July are the most relevant for differentiating crop types. This shows that this period of the year is crucial to effectively differentiate crop types and should be undeniably used in crop type mapping

Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

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

Evaluation of Synthetic Aperture Radar Interferometric Techniques for Monitoring of Fast Deformation Caused by Underground Mining Exploitation

[EN] EPOS-PL+ is the Polish national realization of the European Plate Observing System (EPOS) project that aims to build a multidisciplinary infrastructure. It allows integration of a variety of geoscience expertise and techniques to better understand the geohazard related to the underground mining of coal in the Upper Silesian Coal Basin (USCB) in Poland. The study case in this project is the Marcel Mine, located within USCB, where the detected subsidence for the analyzed period of four months reaches 91 cm. Various interferometric processing techniques demonstrated some advantages and also some limitations in the context of mining deformation measurement, including accuracy, spatial resolution, detectable deformation rate, atmospheric delay, and  ability to detect the maximal deformation gradients. This is especially important from a mining perspective. Therefore, we investigated three different interferometric processing techniques to monitor fast mining deformation in the Marcel hard coal mine area. More specifically, we used conventional DInSAR, Small Baseline Subsets (SBAS), and Persistent Scattered Interferometry (PSInSAR). The result confirmed that none of these methods can be considered as the best. The DInSAR approach allows capturing the maximal deformation gradient, which was not possible with the PSInSAR and SBAS approaches. On the contrary, PSInSAR and SBAS allow us to provide less noisy and reliable results in the area of safety pillars.Pawluszek-Filipiak, K.; Ilieva, M.; Wielgocka, N.; Stasch, K. (2023). Evaluation of Synthetic Aperture Radar Interferometric Techniques for Monitoring of Fast Deformation Caused by Underground Mining Exploitation. Editorial Universitat Politècnica de València. 335-341. https://doi.org/10.4995/JISDM2022.2022.1386333534