Tsunami Risk and Vulnerability

The research focuses on providing reliable spatial information in support of tsunami risk and vulnerability assessment within the framework of the German-Indonesian Tsunami Early Warning System (GITEWS) project. It contributes to three major components of the project: (1) the provision of spatial information on surface roughness as an important parameter for tsunami inundation modeling and hazard assessment; (2) the modeling of population distribution, which is an essential factor in tsunami vulnerability assessment and local disaster management activities; and (3) the settlement detection and classification from remote sensing radar imagery to support the population distribution research. Regarding the surface roughness determination, research analyses on surface roughness classes and their coefficients have been conducted. This included the development of remote sensing classification techniques to derive surface roughness classes, and integration of the thus derived spatial information on surface roughness conditions to tsunami inundation modeling. This research determined 12 classes of surface roughness and their respective coefficients based on analyses of published values. The developed method for surface roughness classification of remote sensing data considered density and neighborhood conditions, and resulted in more than 90% accuracy. The classification method consists of two steps: main land use classification and density and neighborhood analysis. First, the main land uses were defined and a classification was performed applying decision tree modeling. Texture parameters played an important role in increasing the classification accuracy. The density and neighborhood analysis further substantiated the classification result towards identifying surface roughness classes. Different classes such as residential areas and trees were combined to new surface roughness classes, as “residential areas with trees”. The density and neighborhood analysis led to an appropriate representation of real surface roughness conditions. This was used as an important input for tsunami inundation modeling. By using Tohoku University’s Analysis Model for Investigation Near-field Tsunami Number 3 (TUNAMI N3), the spatially distributed surface roughness information was integrated in tsunami inundation modeling and compared to the modeling results applying a uniform surface roughness condition. An uncertainty analysis of tsunami inundation modeling based on the variation of surface roughness coefficients in the Cilacap study area was also undertaken. It was demonstrated that the inundation modeling results applying uniform and spatially distributed surface roughness resulted in high differences of inundation lengths, especially in areas far from the coastline. This result showed the important role of surface roughness conditions in resisting tsunami flow, which must be considered in tsunami inundation modeling. With respect to the second research focus, the population distribution, a concept of population distribution modeling was developed. Within the modeling process, weighting factor determination, multi-scale disaggregation and a comparative study to other methods were conducted. The basis of the developed method was a combination of census and land use data, which led to an improved spatial resolution and accuracy of the population distribution. Socio-economic data were used to derive weighting factors to distributing people to land use classes. Moreover, in case of missing input data, an approach was developed that allows for the determination of generalized weighting factors. The approach to use specific weightings, where possible and generalized ones, where necessary, led to a flexible methodology with respect to the achievable accuracy and availability of data. A comparative study was performed by comparing this new model with previously developed population distribution models. The newly developed model showed a higher accuracy. The detailed population distribution information was a valuable input for the vulnerability assessment being the main data source for human exposure assessment and an important contribution to evacuation time modeling. In support of the population distribution research, settlement classification using TerraSAR-X imagery was conducted. A current classification method of speckle divergence analysis on SAR imagery was further developed and improved by including the neighborhood concept. The settlement classification provided highly accurate results in dense urban areas, whereas the method needs to be further developed and improved for rural settlement areas. Finally, it has been shown how the results of this research can be applied. These applications cover the integration of surface roughness conditions into the tsunami inundation modeling and hazard mapping. The contributions to tsunami vulnerability assessment and evacuation planning were shown. Additionally, the results were integrated into the decision support system of the Tsunami Early Warning Center in Jakarta

LAND USE/COVER CHANGE ON POTENTIAL LOSS OF SUMATRAN TIGERS IN KERINCI SEBLAT NATIONAL PARK BASED ON REMOTE SENSING DATA

The Sumatran tiger is an animal whose life is threatened due to land use changes and human activities. This study described the correlations between land cover/use change and the potential loss of Sumatran tigers in Kerinci Seblat National Park (KSNP) based on remote sensing data. Remote sensing technology was used due to the good historical data, and it can be used for land cover change analysis. The results of the land change analysis can be used to the analysis of the changes in the suitability level of the Sumatran tiger habitat. The analysis of land change in 2000 and 2020 with the random forest classification method and changes in the level of suitability of the Sumatran Tiger habitat has been carried out. The results of the analysis of land cover/use changes showed a very significant reduction in the area of primary forest, namely 282.58 km2, while the increase in the area of plantations and secondary forests was 186.52 km2 and 101.68 km2. This change affects the suitability level of the Sumatran tiger habitat from a very suitable level decreased from 164.42 km2 to suitable and not suitable. The declining suitability level class indicated the potential loss of Sumatran tigers in the Kerinci Seblat National Park. The increasing of plantation and settlement areas will increase the activity of humans. The conflict of human activity with Sumatran tigers’ life will impact the loss of Sumatran Tigers in KSN

SPATIO-TEMPORAL ANOMALIES IN SURFACE BRIGHTNESS TEMPERATURE PRECEDING VOLCANO ERUPTIONS DETECTED BY THE LANDSAT-8 THERMAL INFRARED SENSOR (CASE STUDY: KARANGETANG VOLCANO)

Indonesia's geological as part of the “ring of fire” includes the consequence that community life could be affected by volcanic activity. The catastrophic incidence of volcanic eruptions in the last ten years has had a disastrous impact on human life. To overcome this problem, it is necessary to conduct research on the strengthening of the early warning system for volcanic eruptions utilising remote sensing technology.  This study analyses spatial and temporal anomalies of surface brightness temperature in the peak area of Karangetang volcano during the 2018-2019 eruption. Karangetang volcano is an active volcano located in North Sulawesi, with a magmatic eruption type that releases lava flow. We analyse the anomalies in the brightness temperature from channel-10 of the Landsat-8 TIRS (Thermal Infrared Scanner) time series during the period in question. The results of the research demonstrate that in the case of Karangetang Volcano the eruptions of 2018-2019 indicate increases in the surface brightness temperature of the crater region. As this volcano has many craters, the method is also very useful to establish in which crater the center of the eruption occurred

SPECTRAL CHARACTERISTICS OF FLASH FLOOD AREAS FROM MEDIUM SPATIAL OPTICAL IMAGERY

This study aims to investigate surface reflectance changes over flash flood areas in Nusa Tenggara Timur, Indonesia. Fifteen sample points from Sentinel-2 satellite imagery were used to analyse the differences in reflectance of areas before and after flash flood events. The method used involved analysis of the significant differences in the dreflectance values of each Sentinel-2 channel. The analysis results show that channels 6, 7, and 8A displayed significant differences compared to the others with regard to reflectance before and after flooding, for both settlements and shrubs. The results could be used for further research in building a reflectance index for the rapid detection of affected areas, with a focus on these channels

OBSERVING THE INUNDATED AREA USING LANDSAT-8 MULTITEMPORAL IMAGES AND DETERMINATION OF FLOOD-PRONE AREA IN BANDUNG BASIN

Flood is the most frequent hydro-meteorological disaster in Indonesia. Flood disasters in the Bandung basin result from increasing population density, especially in the Citarum riverbank area, accompanied by land use changes in upstream of the Citarum catchment area which has disrupted the river’s function. One of the basic issues that need to be investigated is which areas of the Bandung basin are prone to flooding. This study offers an effective and efficient method of mapping flood-prone areas based on flood events that have occurred in the past through the use of historical remote sensing image data. In this research, Landsat-8 imagery was used to observe the inundated area in the Bandung basin in the past (2014–2018) using an improved algorithm, the modified normalized water index (MNDWI). The results of the study show that MNDWI is the appropriate parameter to be used to detect flooded areas in the Bandung basin area that have heterogeneous land surface conditions. The flood-prone area was determined based on flood events for 2014 to 2018, identified as inundated areas in the images. The estimation of the flood-prone area in the Bandung basin is 11,886.87 ha. Most of the flood-prone areas are in the subdistricts of Rancaekek, Bojongsoang, Solokan Jeruk, Ciparay, Cileunyi, Bale Endah and Cikancung. This area geographically or naturally is a water habitat area. Therefore, if the area will be used for residential, this will have consequences that flood will always be a threat to the area.

The dynamics of shoreline change analysis based on the integration of remote sensing and geographic information system (GIS) techniques in Pekalongan coastal area, Central Java, Indonesia

Coastal areas are found in the dynamic zone at the interface between the three major natural systems of the Earth's surface. The phenomenon of shoreline change is one of the most frequent problems encountered in the coastal environment and is caused by natural processes that result in dynamic changes in the coastal area. Coastal area change can affect the vulnerability of the coastal environment and its properties, such as shoreline stabilization, flood control, sediment retention, natural protection and others. The method of integrating remote sensing data with geographic information system (GIS) techniques has been widely used to monitor and analyze the dynamics of shoreline change in coastal areas. The purpose of this study is to map and analyze the dynamics of shoreline change from 1978 to 2017 in the study area. An approach combining spectral value index and visual interpretation of Landsat images was used and proposed to indicate the separation of land and water bodies, for shoreline extraction. The normalized difference water index (NDWI) can be used as a spectral value index approach for differentiating land and water bodies. Furthermore, the analysis of shoreline changes was performed using the digital shoreline analysis system (DSAS). Based on calculations made using DSAS, it can be seen that the pattern of coastline change tends to be dominated by offshore erosion. The results of this study may also be important as input data for coastal hazard assessment as part of the effort to overcome the problem of flood tides

Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 Imagery in the Barito Watershed, South Kalimantan Province, Indonesia

Flood disasters occur frequently in Indonesia and can cause property damage and even death. This research aimed to provide rapid flood mapping based on remote sensing data by using a cloud platform. In this study, the Google Earth Engine cloud platform was used to quickly detect major floods in the Barito watershed in South Kalimantan province, Indonesia. The data used in this study were Sentinel-1 images before and after the flood event, and surface reflectance of Sentinel-2 images available on the Google Earth Engine platform. Flooding is detected using the threshold method. In this study, we determined the threshold using the Otsu method and statistical sampling thresholds (SST). Four SST scenarios were used in this study, combining the mean and standard deviation of the difference backscatter of Sentinel-1 images. The results of this study showed that the second SST scenario could classify floods with the highest accuracy of 73.2%. The inundation area determined by this method was 4,504.33 km2. The first, third and fourth SST scenarios and the Otsu method could reduce the flood load with an overall accuracy of 48.37%, 43.79%, 55.5% and 68.63%, respectively. The SST scenario is considered to be a reasonably good method for rapid flood detection using Sentinel-1 satellite imagery. This rapid detection method can be applied to other areas to detect flooding. This information can be quickly produced to help stakeholders determine appropriate flood management strategies

DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2

This paper describes the detection of the surface water area in Cirata dam,  upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir

The use of multi-sensor satellite imagery to analyze flood events and land cover changes using change detection and machine learning techniques in the Barito watershed

Indonesia is one of the countries in the world that is frequently affected by floods. Flood disasters can have various negative impacts and therefore need to be analyzed to determine prevention and mitigation measures. This study examined land cover change, flood detection, and flood distribution using multitemporal Sentinel-1 and Landsat-8 satellite imagery in the Barito watershed. A combination of change detection and the application of the Otsu algorithm was used to detect floodplains from Sentinel-1 imagery. Land use/land cover (LULC) changes are detected using a combination of change detection and machine learning in the form of a random forest algorithm. The overlay technique was used to analyze the distribution of floodplains. In this study, the floodplain in the study area was mapped to 109,623 ha. The change detection method detects a decrease in the areas of primary forest, secondary forest, fields, rice fields, shrubs and ponds, respectively, by 13,020 ha, 116,235 ha, 259 ha, 146,696 ha, 47,308 ha, and 9,601 ha. Settlements, bare land, plantations and water bodies increase by 14,879 ha, 64,830 ha, 218,916 ha, and 34,768 ha, respectively. Flooding was mainly found in the classes of rice fields, water bodies and primary forests