ASHFALL DISPERSAL MAPPING OF THE 2020 TAAL VOLCANO ERUPTION USING DIWATA-2 IMAGERY FOR DISASTER ASSESSMENT

Natural disasters incur many fatalities and economic losses for vulnerable and developing countries such as the Philippines. It is crucial that during calamities, on-ground surveillance is supplemented by low-cost and time-efficient methods such as satellite remote sensing. Diwata-2 is a Philippine microsatellite specifically equipped for disaster assessment. In this study, the capabilities of this satellite in ashfall detection were explored by closely examining the case of the Taal volcano eruption on January 12, 2020. Satellite images covering parts of CALABARZON and Metropolitan Manila before and after the phreatomagmatic eruption were compared. The presence and extent of heavy ash over the study area were identified after the image classification using the Support Vector Machine (SVM) algorithm. A decrease in vegetation cover and built-up areas was also observed. Upon validation, an overall accuracy of 91.4562 and Kappa coefficient of 0.8833 were achieved for the post-eruption ashfall extent map, exhibiting the potential of Diwata-2 imagery in monitoring volcanic eruptions and similar phenomena.

SUPPLEMENTING SATELLITE IMAGERY WITH SOCIAL MEDIA DATA FOR REMOTE RECONNAISSANCE: A CASE STUDY OF THE 2020 TAAL VOLCANO ERUPTION

Social sensing and satellite imagery are named as the top emerging data sources for disaster management. There is a wealth of data, both in quantity and quality that can be extracted from social media platforms such as Twitter, given that the content published by users is generally in real-time and includes a geotag or toponym. To reduce costs, risks, and time, performing reconnaissance using remote sources of information is highly suggested. This study explores how social media data can be used to supplement satellite imagery in post-disaster remote reconnaissance using the January 2020 Taal Volcano Eruption in the Philippines. Tweets about the volcanic eruption were scraped, and ashfall-affected locations mentioned in tweet content were extracted using Named Entity Recognition (NER). To visualize the progression of the tweeted locations, dot density maps and hotspot maps were generated. Additionally, a potential ashfall extent map was generated from processed DIWATA-2 satellite imagery using Support Vector Machine (SVM) classification. An intersection of both dot density map and ashfall extent map was performed for comparative analysis of both data. Validation was carried out by matching the ashfall-affected locations with ground reports from local government offices and news reports. The use of social media data complements satellite image classification in the detection of disaster damage for a quick and cost-efficient remote reconnaissance. This information can be utilized by rescue teams for faster emergency response and relief operations during and after a disaster.

Student’s Level of Understanding on Mobile Learning based Volcano Eruption

This study aims to analyze student's level of understanding on mobile learning based volcano eruption. This research is quantitative descriptive. The sample were 200 students who lives in the area that affected by the eruption of Merapi Volcano (Magelang Regency, Sleman Regency, and Yogyakarta City), recruited using a simple random sampling. The instruments were online survey questionnaire of Student's Level of Understanding on Mobile Learning based Volcano Eruption. The distribution of the data is normal, reliable and homogen based the analysis used IBM SPSS Statistics 22 software. The results of this study indicate that students who lives in Merapi Volcano prone area have a good understanding about mobile learning based volcano eruption. Level of student's understanding on mobile learning meet a good criteria with percentage 72,80%, level of student's understanding on volcano eruption meet a good criteria with percentage 73,40%, and student's understanding on disaster mitigation meet a good criteria with higher percentage 76,40%.

Disaster Risk Analysis of Merapi Volcano Eruption in Cangkringan District Sleman Regency

Abstrak Cangkringan is located in the Merapi Volcano Disaster Prone Areas which has the potential to be affected by eruption. The eruption of Merapi Volcano is a consequence that must be faced by the local resident, so that the need for disaster risk analysis in the region through research is a must. This disaster risk analysis research aims to (1) Analyze the risk level of Merapi Volcano eruption in Cangkringan. (2) Analyze the risk distribution of Merapi Volcano eruption in Cangkringan.This research is a descriptive research with a quantitative approach conducted in Cangkringan District, Sleman Regency, Special Region of Yogyakarta. The population in this study is the entire village in Cangkringan. The entire area is the subject of this research. The variables of this reseach are hazard, vulnerability and capacity. This study used primary data and secondary data. Data collection techniques used are observation, interviews, and documentation. Data analysis techniques used are scoring, overlay and descriptive.The results of this study indicate: (1) The level of risk of Merapi Volcano Eruption in Cangkringan is divided into four levels which are high, medium, low and very low. The area of Cangkringan has a high level of risk covering an area of 19,00% of the total area, the medium-risk level is 38,38% of the total area, the low-risk level is 16,61% of the total area, the very low-risk level is 20,23% of the total area of Cangkringan District. The higher the level of disaster risk, the greater the potential loss due to the eruption of Merapi Volcano. (2) The distribution of disaster risk of Merapi Volcano Eruption in Cangkringan is in the entire village. The distribution of high-risk level is in part of Umbulharjo Village, part of Glagaharjo Village and part of Argomulyo Village. The distribution of medium-risk level is in part of Umbulharjo Village, part of Kepuharjo Village and part of Glagaharjo Village. The distribution of low-risk level is in part of Kepuharjo Village, part of Wukirsari Village and part of Argomulyo Village. The distribution of very low-risk level is in part of Wukirsari Village and part of Argomulyo Village.