TG
Thomas Glade
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1
Accurate detection of retrogressive thaw slumps (RTSs) remains a significant challenge due to their complex morphological features and subtle spectral contrasts with surrounding landscapes. This study presents a robust deep learning framework to improve RTS identification by integrating multi-source remote sensing data. Focusing on the permafrost-dominated central Qinghai-Tibet Plateau, we conducted three pioneering investigations: (i) a detailed RTS inventory integrated with topographic, environmental, spectral, and thermal analyses to uncover spatio-temporal distribution patterns; (ii) a comprehensive evaluation of twelve leading-edge semantic segmentation models for RTS identification; and (iii) the formulation of an innovative FusionSA-SegFormer model, which employs dual-level (pixel-level and feature-level) heterogeneous feature fusion of optical, spectral, thermal, and topographic remote sensing datasets. Our results highlight prominent RTS clustering within the 4700–4800 m elevation range, on moderate slopes (3–7°), in mid - slope to valley settings, and in proximity to water bodies. Temporally, the active evolution of RTSs from 2019 to 2024 was characterized by sustained degradation patterns, manifested as a continuous decline in vegetation indices and a concurrent rise in land surface temperatures. Comparative model assessments identified SA-SegFormer as the most effective baseline architecture. Building upon this, the proposed FusionSA-SegFormer demonstrated significant improvements, showing 8.8% IoU and 10.9% recall enhancements on the validation set, and achieving a superior F1-score of 0.843 (Precision: 0.838, Recall: 0.899) on the test set. Crucially, independent spatial and temporal transferability evaluations confirmed the framework's robust generalization capacity across unseen regions and varying years, maintaining consistent identification performance and high overall accuracies (>0.90). Furthermore, feature importance analysis emphasized the critical influence of spectral bands, particularly the blue band, alongside notable contributions from thermal indices. This work establishes a new benchmark for mapping permafrost disturbances and provides a valuable tool for monitoring thermokarst dynamics in warming climates.
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Accurate detection of retrogressive thaw slumps (RTSs) remains a significant challenge due to their complex morphological features and subtle spectral contrasts with surrounding landscapes. This study presents a robust deep learning framework to improve RTS identification by integrating multi-source remote sensing data. Focusing on the permafrost-dominated central Qinghai-Tibet Plateau, we conducted three pioneering investigations: (i) a detailed RTS inventory integrated with topographic, environmental, spectral, and thermal analyses to uncover spatio-temporal distribution patterns; (ii) a comprehensive evaluation of twelve leading-edge semantic segmentation models for RTS identification; and (iii) the formulation of an innovative FusionSA-SegFormer model, which employs dual-level (pixel-level and feature-level) heterogeneous feature fusion of optical, spectral, thermal, and topographic remote sensing datasets. Our results highlight prominent RTS clustering within the 4700–4800 m elevation range, on moderate slopes (3–7°), in mid - slope to valley settings, and in proximity to water bodies. Temporally, the active evolution of RTSs from 2019 to 2024 was characterized by sustained degradation patterns, manifested as a continuous decline in vegetation indices and a concurrent rise in land surface temperatures. Comparative model assessments identified SA-SegFormer as the most effective baseline architecture. Building upon this, the proposed FusionSA-SegFormer demonstrated significant improvements, showing 8.8% IoU and 10.9% recall enhancements on the validation set, and achieving a superior F1-score of 0.843 (Precision: 0.838, Recall: 0.899) on the test set. Crucially, independent spatial and temporal transferability evaluations confirmed the framework's robust generalization capacity across unseen regions and varying years, maintaining consistent identification performance and high overall accuracies (>0.90). Furthermore, feature importance analysis emphasized the critical influence of spectral bands, particularly the blue band, alongside notable contributions from thermal indices. This work establishes a new benchmark for mapping permafrost disturbances and provides a valuable tool for monitoring thermokarst dynamics in warming climates.
Journal article
(2019)
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Elmar M. Schmaltz, L. P.H. Van Beek, Thom A. Bogaard, Sabine Kraushaar, Stefan Steger, Thomas Glade
Despite the importance of land cover on landscape hydrology and slope stability, the representation of land cover dynamics in physically based models and their associated ecohydrological effects on slope stability is rather scarce. In this study, we assess the impact of different levels of complexity in land cover parameterisation on the explanatory power of a dynamic and process-based spatial slope stability model. Firstly, we present available and collected data sets and account for the stepwise parameterisation of the model. Secondly, we present approaches to simulate land cover: 1) a grassland landscape without forest coverage; 2) spatially static forest conditions, in which we assume limited knowledge about forest composition; 3) more detailed information of forested areas based on the computation of leaf area development and the implementation of vegetation-related processes; 4) similar to the third approach but with the additional consideration of the spatial expansion and vertical growth of vegetation. Lastly, the model is calibrated based on meteorological data sets and groundwater measurements. The model results are quantitatively validated for two landslide-triggering events that occurred in Western Austria. Predictive performances are estimated using the Area Under the receiver operating characteristic Curve (AUC). Our findings indicate that the performance of the slope stability model was strongly determined by model complexity and land cover parameterisation. The implementation of leaf area development and land cover dynamics further yield an acceptable predictive performance (AUC ~0.71-0.75) and a better conservativeness of the predicted unstable areas (FoC ~0.71). The consideration of dynamic land cover expansion provided better performances than the solely consideration of leaf area development. The results of this study highlight that an increase of effort in the land cover parameterisation of a dynamic slope stability model can increase the explanatory power of the model.
...
Despite the importance of land cover on landscape hydrology and slope stability, the representation of land cover dynamics in physically based models and their associated ecohydrological effects on slope stability is rather scarce. In this study, we assess the impact of different levels of complexity in land cover parameterisation on the explanatory power of a dynamic and process-based spatial slope stability model. Firstly, we present available and collected data sets and account for the stepwise parameterisation of the model. Secondly, we present approaches to simulate land cover: 1) a grassland landscape without forest coverage; 2) spatially static forest conditions, in which we assume limited knowledge about forest composition; 3) more detailed information of forested areas based on the computation of leaf area development and the implementation of vegetation-related processes; 4) similar to the third approach but with the additional consideration of the spatial expansion and vertical growth of vegetation. Lastly, the model is calibrated based on meteorological data sets and groundwater measurements. The model results are quantitatively validated for two landslide-triggering events that occurred in Western Austria. Predictive performances are estimated using the Area Under the receiver operating characteristic Curve (AUC). Our findings indicate that the performance of the slope stability model was strongly determined by model complexity and land cover parameterisation. The implementation of leaf area development and land cover dynamics further yield an acceptable predictive performance (AUC ~0.71-0.75) and a better conservativeness of the predicted unstable areas (FoC ~0.71). The consideration of dynamic land cover expansion provided better performances than the solely consideration of leaf area development. The results of this study highlight that an increase of effort in the land cover parameterisation of a dynamic slope stability model can increase the explanatory power of the model.
Conference paper
(2017)
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Elmar Schmaltz, Rens V. Van Beek, Thom Bogaard, Stefan Steger, Thomas Glade
Spatially distributed physically based slope stability models are commonly used to assess landslide susceptibility of hillslope environments. Several of these models are able to account for vegetation related effects, such as evapotranspiration, interception and root cohesion, when assessing slope stability. However, particularly spatial information on the subsurface biomass or root systems is usually not represented as detailed as hydropedo- logical and geomechanical parameters. Since roots are known to influence slope stability due to hydrological and mechanical effects, we consider a detailed spatial representation as important to elaborate slope stability by means of physically based models. STARWARS/PROBSTAB, developed by Van Beek (2002), is a spatially distributed and dynamic slope stability model that couples a hydrological (STARWARS) with a geomechanical component (PROBSTAB). The infinite slope-based model is able to integrate a variety of vegetation related parameters, such as evaporation, interception capacity and root cohesion. In this study, we test two different approaches to integrate root cohesion forces into STARWARS/PROBSTAB. Within the first approach, the spatial distribution of root cohesion is directly related to the spatial distribution of land use areas classified as forest. Thus, each pixel within the forest class is defined by a distinct species related root cohesion value where the potential maximum rooting depth is only dependent on the respective species. The second method represents a novel approach that approximates the rooting area based on the location of single tree stems. Maximum rooting distance from the stem, maximum depth and shape of the root system relate to both tree species and external influences such as relief or soil properties. The geometrical cone-shaped approximation of the root system is expected to represent more accurately the area where root cohesion forces are apparent. Possibilities, challenges and limitations of approximating species-related root systems in infinite slope models are discussed.
...
Spatially distributed physically based slope stability models are commonly used to assess landslide susceptibility of hillslope environments. Several of these models are able to account for vegetation related effects, such as evapotranspiration, interception and root cohesion, when assessing slope stability. However, particularly spatial information on the subsurface biomass or root systems is usually not represented as detailed as hydropedo- logical and geomechanical parameters. Since roots are known to influence slope stability due to hydrological and mechanical effects, we consider a detailed spatial representation as important to elaborate slope stability by means of physically based models. STARWARS/PROBSTAB, developed by Van Beek (2002), is a spatially distributed and dynamic slope stability model that couples a hydrological (STARWARS) with a geomechanical component (PROBSTAB). The infinite slope-based model is able to integrate a variety of vegetation related parameters, such as evaporation, interception capacity and root cohesion. In this study, we test two different approaches to integrate root cohesion forces into STARWARS/PROBSTAB. Within the first approach, the spatial distribution of root cohesion is directly related to the spatial distribution of land use areas classified as forest. Thus, each pixel within the forest class is defined by a distinct species related root cohesion value where the potential maximum rooting depth is only dependent on the respective species. The second method represents a novel approach that approximates the rooting area based on the location of single tree stems. Maximum rooting distance from the stem, maximum depth and shape of the root system relate to both tree species and external influences such as relief or soil properties. The geometrical cone-shaped approximation of the root system is expected to represent more accurately the area where root cohesion forces are apparent. Possibilities, challenges and limitations of approximating species-related root systems in infinite slope models are discussed.
Conference paper
(2016)
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Elmar Schmaltz, Stefan Steger, Eainer Bell, Thomas Glade, R van Beek, Thom Bogaard, D. Wang, Markus Hollaus, N Pfeifer
The causes of landslides are manifold and highly influenced by multiple interacting natural and anthropogenic factors. In particular human induced land cover changes, such as deforestation and afforestation are known to strongly influence slope stability. Thus, we investigate the understanding of differences between forested and non-forested conditions of an area is crucial in order to develop sustainable preventive countermeasures. One possibility to evaluate the influence of biomass changes on landslide activity is to apply physically based slope stability models where the dynamic influence of spatially and temporally variable vegetation areas on soil strength and hydrology is explicitly included. Some of these models also require detailed information on biomass related parameters (e.g. wood and crown volume, weight, Leaf Area Index) as well as surface and subsurface conditions. Newly developed algorithms allow deriving biomass parameters from highly resolved multi-temporal 3D Airborne Laser Scanning (ALS). This allows an improved parameterization of hydro-mechanical slope stability models since it accounts for the spatiotemporal variability in vegetation conditions. The BioSLIDE project aims to combine vegetation related parameters derived from ALS data with physically based slope stability modelling to allow a better understanding of geomorphic interdependencies at regional scale. The objective of this paper is to evaluate possibilities and potential limitations of an inclusion of ALS-derived biomass information within dynamic physically based hydro-mechanical slope stability modelling. Hereto both synthetic and real case study data will be used. This interdisciplinary approach is expected to improve spatio-temporal scenarios of anthropogenic effects and environmental changes on landslide activity.
...
The causes of landslides are manifold and highly influenced by multiple interacting natural and anthropogenic factors. In particular human induced land cover changes, such as deforestation and afforestation are known to strongly influence slope stability. Thus, we investigate the understanding of differences between forested and non-forested conditions of an area is crucial in order to develop sustainable preventive countermeasures. One possibility to evaluate the influence of biomass changes on landslide activity is to apply physically based slope stability models where the dynamic influence of spatially and temporally variable vegetation areas on soil strength and hydrology is explicitly included. Some of these models also require detailed information on biomass related parameters (e.g. wood and crown volume, weight, Leaf Area Index) as well as surface and subsurface conditions. Newly developed algorithms allow deriving biomass parameters from highly resolved multi-temporal 3D Airborne Laser Scanning (ALS). This allows an improved parameterization of hydro-mechanical slope stability models since it accounts for the spatiotemporal variability in vegetation conditions. The BioSLIDE project aims to combine vegetation related parameters derived from ALS data with physically based slope stability modelling to allow a better understanding of geomorphic interdependencies at regional scale. The objective of this paper is to evaluate possibilities and potential limitations of an inclusion of ALS-derived biomass information within dynamic physically based hydro-mechanical slope stability modelling. Hereto both synthetic and real case study data will be used. This interdisciplinary approach is expected to improve spatio-temporal scenarios of anthropogenic effects and environmental changes on landslide activity.