CLASSIFICATION OF TRANSLATIONAL LANDSLIDE ACTIVITY USING VEGETATION ANOMALIES INDICATOR (VAI) IN KUNDASANG, SABAH

This paper introduced a novel method of landslide activity mapping using vegetation anomalies indicators (VAIs) obtained from high resolution remotely sensed data. The study area was located in a tectonically active area of Kundasang, Sabah, Malaysia. High resolution remotely sensed data were used to assist manual landslide inventory process and production on VAIs. The inventory process identified 33, 139, and 31 of active, dormant, and relict landslides, respectively. Landslide inventory map were randomly divided into two groups for training (70%) and validation (30%) datasets. Overall, 7 group of VAIs were derived including (i) tree height irregularities; (ii) tree canopy gap; (iii) density of different layer of vegetation; (iv) vegetation type distribution; (v) vegetation indices (VIs); (vi) root strength index (RSI); and (vii) distribution of water-loving trees. The VAIs were used as the feature layer input of the classification process with landslide activity as the target results. The landslide activity of the study area was classified using support vector machine (SVM) approach. SVM parameter optimization was applied by using Grid Search (GS) and Genetic Algorithm (GA) techniques. The results showed that the overall accuracy of the validation dataset is between 61.4–86%, and kappa is between 0.335–0.769 for deep-seated translational landslide. SVM RBF-GS with 0.5m spatial resolution produced highest overall accuracy and kappa values. Also, the overall accuracy of the validation dataset for shallow translational is between 49.8–71.3%, and kappa is between 0.243–0.563 where SVM RBF-GS with 0.5m resolution recorded the best result. In conclusion, this study provides a novel framework in utilizing high resolution remote sensing to support labour intensive process of landslide inventory. The nature-based vegetation anomalies indicators have been proved to be reliable for landslide activity identification in Malaysia.

Remote Sensing of Landslide-Generated Sediment Plumes, Peace River, British Columbia

Quantifying the contribution of sediment delivered to rivers by landslides is needed to assess a river’s sediment load in regions prone to mass wasting. Monitoring such events, however, remains difficult. This study utilised six years of remotely sensed imagery (PlanetScope and RapidEye, Imagery courtesy of Planet Labs, Inc., San Francisco, CA, USA), topographic surveys, and field observation to examine a hydro-geologically controlled, retrogressive landslide near a tributary to the Peace River, British Columbia. The slide has been active since 2014, delivering large amounts of sediment to the Peace River, visible in a persistent plume. Here, we quantify the landslide’s sediment contribution to the Peace River, assess the hydro-meteorological drivers of plume variability, and test whether plume activity can be directly linked to landslide activity for monitoring purposes. Our results show that the landslide on average delivered 165,000 tonnes of sediment per year, a seven-fold increase of the tributary’s regular load and near half of the Peace River’s load at this location. Due to continuous erosion of landslide material, sediment supply is steady and fuelled by repeated failures. Using thresholding, the identification of ‘high’ plume activity was possible, which positively correlated with the water level in a nearby reservoir, a proxy for the state of groundwater in this region. We reason that ‘high’ plume activity is linked to increased groundwater pressure because landslide activity is groundwater-controlled and failures fuel sediment delivery to the Peace River. Using readily available imagery, it is thus possible to monitor the activity of this recurrent landslide when field data are difficult to obtain.

Increase of landslide activity after a low magnitude earthquake inferred by DInSAR interferometry

On August 16th, 2018, an Mw 5.1 earthquake struck the Molise region (central Italy), inducing 84 earthquake-triggered landslides that involved soil covers of clayey materials and flysch on gently-dip slopes predominantly. To quantify the spatio-temporal landslide activity in the months immediately after the earthquake, a Differential SAR Interferometry (DInSAR) analysis was carried out in a time span comprising two years before the earthquake and one followed, recognising both first-time and reactivated landslides. The results showed a clear increase in landslide activity following the low magnitude earthquake occurrence with respect to the one recorded in the same months of the previous years. Several coherent landslides (earth slides and earth flows) were observed following the seasonally recurrent rainfall events. Such an increase was observed for both reactivations and first-time landslides, showing a decrease of inactivity period as well as activity over wider periods. Furthermore, spatial density distribution of the landslides was investigated in the post-seismic time along transepts perpendicular and parallel to the direction of the tectonic element responsible for the seismic event, respectively. An asymmetrical distribution was deduced parallel to the fault strike with the higher number of landslides located inside the compressional sector according to a strike-slip faulting mechanism.