A landslide susceptibility mapping is essential for landslide hazard mitigation to reduce the associated risk. This paper aims to present the results of the landslide susceptibility modeling in the Citarik sub-watershed using three bivariate statistical-based methods, i.e., frequency ratio (FR), information value (IV), and weight of evidence (WoE). The main objective of this study is to evaluate the significance of the threshold of the area under curve (AUC) value in parameter selection. In this study, 118 landslide pixels were compiled from Google Earth images, unmanned aircraft vehicle (UAV) aerial photos taken just after the landslide, official landslide reports, and field observation. Thirteen landslide causative factors were prepared in Geographic Information System (GIS) environment, derived from various satellite images and maps. The landslide data were divided into two groups, 70% of data as training data and the rest as test data. Two scenarios that involve a different number of parameters were compared to explain the threshold of the AUC value in parameter selection and model accuracy. The result of this study shows that the AUC value threshold of 0.6 for parameter selection cannot be applied in all cases, and the performance of both two scenarios was excellent in assessing landslide susceptibility in this study area. Those three landslide susceptibility zonation maps of the best scenario showed that the sub-watershed's northern, northeastern, south-eastern, and southern parts were under high to very high susceptibility to landslides, including the Cimanggung area where a recent deadly double landslide occurred.

Object identification using remote sensing data has a problem when the spatial resolution is not in accordance with the object. The fusion approach is one of methods to solve the problem, to improve the object recognition and to increase the objects information by combining data from multiple sensors. The application of fusion image can be used to estimate the environmental component that is needed to monitor in multiple views, such as evapotranspiration estimation, 3D ground-based characterisation, smart city application, urban environments, terrestrial mapping, and water vegetation. Based on fusion application method, the visible object in land area has been easily recognized using the method. The variety of object information in land area has increased the variation of environmental component estimation. The difficulties in recognizing the invisible object like Submarine Groundwater Discharge (SGD), especially in tropical area, might be decreased by the fusion method. The less variation of the object in the sea surface temperature is a challenge to be solved.

On September 28, 2018, an Mw 7.5 Palu earthquake triggered massive landslides upstream, followed by 24 debris flood events that spread to 15 villages in Sigi from September 2018 to December 2021. Debris flow and flash floods on alluvial fans inundated lowland communities, causing severe property destruction and structural damage to bridges and roadways and resulting in an estimated 900 damaged houses. Understanding their historical occurrence is essential to sustainable fan development and minimizing their threat to infrastructure and human life due to their severe geohazard potential. Poi and Bangga Villages were affected by the disastrous debris flood in Sigi Regency, Central Sulawesi. This study aimed to create a landslide inventory map, a back-analysis model, and a damage and loss assessment (DaLAs) to evaluate the potential hazard and environmental impacts of debris flow on Sigi’s alluvial fans. The result of landslide mapping showed more than 400 mapped landslides within Bangga Village in various sizes and a massive landslide within Poi Village were digitized. Then, the back-analysis model overpredicted flow direction due to vegetation, infrastructure, and road information not covered by the digital elevation model (DEM). Finally, DaLAs shows the losses caused by damaged buildings were estimated at around 65.7 and 7.4 billion rupiah in Bangga and Poi Villages, respectively.

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