Abstract
Abstract. 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.
Highlights
Landslides are one of the frequent natural disasters in the world, causing substantial economic losses and casualties (Dehnavi et al, 2015, Gaidzik et al, 2017, Tiranti and Cremonini, 2019)
All the vegetation anomalies indicators (VAIs) maps and landslide inventory maps obtained from the manual interpretation were used in the fifth stage that aimed at classifying the landslide activities based on different landslide types using Support Vector machine approach
Accurate landslide activity map is necessary to ensure a complete and good quality of landslide inventory process. This increases the reliability of landslide susceptibility, hazard, and risk mapping based on the geospatial approach
Summary
Landslides are one of the frequent natural disasters in the world, causing substantial economic losses and casualties (Dehnavi et al, 2015, Gaidzik et al, 2017, Tiranti and Cremonini, 2019). Landslides impact on many aspects (socioeconomic, ecological, etc.) of the affected inhabitants This phenomenon is mainly caused by the urbanisation process, deforestation, and uncontrolled development (Scaioni et al, 2014, Schuster, 1996). Landslide inventories are the initial and fundamental steps for susceptibility, hazard, and risk assessments which consider the rule that the past is the key to the future (Highland and Bobrowsky, 2008). In this case, future landslides will probably happen under similar conditions (Van Westen et al, 2008). Landslide-prone areas with a different state of activity need to be assessed for mitigation purposes
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