# Special Issue on \"Mapping and Monitoring of Geohazards\"
## 2 Mapping and Monitoring of Geohazards
### Landslide Hazard  
Landslide hazards received the most attention, with eight papers focussing on this topic. The first paper by [PERSON] et al. [7] aims to semi-automatically map the evolution of the Butangbunasi landslide (Taiwan) using satellite EO data and investigate the potential correlation between changes in the landslide area and heavy rainfall.  
The second paper, by [PERSON] et al. [8], provides a landslide hazard assessment along the Yarlung Tsangpo River basin (Tibet Autonomous Region, China) using unmanned aerial vehicle (UAV) imagery. This allowed for the identification of rockfall source areas threatening the local highway, which were lacking in detailed historical data. The hazard assessment results will provide detailed information for any geohazards prevention work.  
Thirdly, the paper authored by [PERSON] et al. [9] proposes a semiautomatic Geographic Information System (GIS) approach, within which satellite radar interferometry (InSAR) data and geomorphological field surveys are integrated in a qualitative vulnerability matrix in order to identify different levels of damage susceptibility along a provincial road located in the Campania region (Italy).  
The fourth paper, by [PERSON] et al. [10], uses an object-based geons aggregation model for landslide susceptibility mapping (LSM) for the whole of Austria, which is a key step in the risk assessment of landslides. This study also demonstrates that geons allow for meaningful units to be obtained that are more beneficial than pixels for planning purposes.  
In similarity to [10], the paper by [PERSON] et al. [11] also focusses on LSM, but in the Three Gorges Reservoir area (Chongqing municipality, China). It employs an information gain ratio (IGR) model to select landslide causal factors, before subsequently exploring the performance of machine learning models, including support vector machines (SVM), for LSM.  
The sixth paper on landslides, authored by [PERSON] et al. [12], uses an association rule mining algorithm, Apriori, to uncover the cause-effect relationships in landslides hazard prediction. An optimised algorithm, known as IApirioriMR, is proposed and applied for the early warning study of landslide events in the Three Parallel Rivers area (Yunnan province, China).  
The seventh paper, authored by [PERSON] et al. [13], is concerned with LSM for the Shicheng County (China) based on the particle-swarm-optimized multilayer perceptron (PSO-MLP) model. The authors acknowledge that LSM is necessary for the local government to carry out landslide hazard prevention and land use planning, and further suggest that the PSO-MLP is capable of outperforming other LSM approaches.  
Lastly, the paper by [PERSON] et al. [14] focusses on landslide displacement forecasting in order to reduce the hazard associated with the Dashuitian landslide (Chongqing municipality, China). The authors present a new dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory (SLSTM) network, which achieves a higher forecast accuracy than two other advanced forecast models (long short-term memory (LSTM) network and empirical mode decomposition (EMD)-LSTM network).