One of the most devastating disasters in Indonesia was the Mount Merapi eruption in 2010. After the eruption there still exists the secondary hazard of volcanic mudflow, which has caused damage and casualties. Volcanic mudflow is a mixture of pyroclastic material and rainwater, meaning that in the rainy season the area along rivers becomes a high volcanic mudflow hazard, including the area along Putih River in Magelang Regency, Central Java Province. The development of Spatial Data Infrastructure (SDI) plays an important role in disaster management, especially in disaster mitigation efforts. Building an SDI which shares information on spatial conditions in the area along the Putih River could save many lives and reduce the risk from volcanic mudflow. This research was conducted employing interview surveys, field surveys and secondary data collection at government institutions. The results of the analysis have provided a geoportal prototype as an information gateway for the mitigation of volcanic mudflow along the Putih River and the reduction of disaster risk both for the government and community.

Coronavirus Disease (COVID-19) pandemic is currently being a concern in all parts of the world, including Indonesia. Yogyakarta Special Region, especially Sleman Regency, is a red zone, which is an area that has a very high transmission rate of Covid-19. Padukuhan Mancasan Kleben, is one of the hamlets located near the government center of Sleman Regency where community activity and mobility are quite high. There are many business buildings located along the main road. The purpose of this research is to analyze the vulnerability to transmission of Coronavirus Disease (COVID-19) based on building function using Analytical Hierarchy Process (AHP) and Spatial Multi Criteria Evaluation (SMCE) methods. Types of buildings as houses and store are identified using Unmanned Aerial Vehicle (UAV) image. Types of buildings used as physical variables in the analysis. Based on the result, from total of 363 buildings, there are 35 buildings that have a high level of vulnerability and 328 buildings with low vulnerability. A low level of vulnerability is found in buildings that function as shophouse. Meanwhile, the low level of vulnerability is found in buildings used as houses and public facilities. This is because during the pandemic, several public facilities in Mancasan Kleben are not yet operational. Mitigation efforts that need to be implemented are increasing awareness of ourselves and the surrounding environment. The implementation of healthy living habits by implementing CITA MAS JAJAR, avoiding crowds and not traveling if it is not too important, can help prevent the transmission of Coronavirus Disease (COVID-19)

Bali is an island situated among the Indonesian archipelago with huge potential to host mangrove forests. Using remote sensing technology advances, satellite images, such as Landsat images, might be employed to analyse mangrove forest distribution and density. This paper presents an analysis of mangrove distribution in Badung Regency and Denpasar City, Bali, as a basis for the management and conservation of mangrove ecosystems. This study used Landsat 8 OLI images and a vegetation index to analyse the mangrove forest distribution and density in this area. It started by identifying mangrove forests using the RGB 564 band and continued to distinguish between mangrove and non-mangrove objects using unsupervised classification, before analysing mangrove density using the NDVI formula. The results show that the mangrove forest area in 2020 was 1,269.20 ha, with an accuracy rate of 83%. Mangroves were found on the deepest or most curved coastline of the Benoa Bay area, on enclosed waters. This distribution follows the river network in the lower reach, which has thick deposits and is uninfluenced by large currents and waves. Based on the vegetation index analysis results, the mangrove forest area observed mainly had a moderate density, with a total area of 510.85 ha (40%), followed by high density (413.15 ha/ 33%) and low density (340.51 ha/ 27%).

This research was intended to determined community capacity and strategies to enhanced resilience amid the devastating Covid-19 pandemic in urban areas.Community capacity was measured using quantitative and qualitative assessment methods. The former included a questionnaire survey of every member of the Covid-19 task force at the neighborhood level (census), while the latter collected qualitative data through field surveys and in-depth interviews. Community capacity served as the dependent variable, and the independent variables were threefold: preparedness capacity, adaptive capacity, and mitigation capacity. The collected data were analyzed quantitatively through statistical calculations (validity, reliability, and linear regression tests), then the descriptive analysis of the qualitative data complemented the results. Both validity and reliability tests yielded r-count>r-table for each variable (reliability= 0.427>0.339), meaning that the data were valid and reliable. Further, the analysis produced three community capacity levels: 44% high, 29% medium, and 27% low. Based on the highest percentage, it could be inferred that the community had very good capacity, it was showed that recilience was quite high. The linear regression test revealed interdependent variables with Sig.<0.05, rejecting the null hypothesis (Ho). With a level of influence of 48%, mitigation capacity was found to had the most significant influence (R2) among the research variables. Practicing health protocols, increasing media for information dissemination, and strengthening the community’s socioeconomic state were among the recommended strategies to increased capacity. ABSTRAKTujuan penelitian ini adalah untuk mengetahui kapasitas masyarakat serta strategi peningkatan kapasitas ketahanan masyarakat dalam menghadapi bencana pandemi Covid-19 di wilayah perkotaan.Metode pendekatan untuk mengetahui kapasitas masyarakat dilakukan secara kuantitatif dan kualitatif. Pendekatan kuantitiatif dengan pendekatan kuesioner. Pengambilan data dilakukan secara sensus yaitu populasi anggota satgas Covid-19 tingkat RT, dengan teknik pengambilan data menggunakan kuesioner. Sedangkan data kualitatif diperoleh dengan survei lapangan dan wawancara mendalam. Variabel dependen berupa kapsitas masyarakat sedangkan variabel independen mencakup kapasitas kesiapan, kapasitas bertahan hidup (adaptasi) dan kapasitas mitigasi. Kemudian data dianalisis secara kuantitaitf melalui perhitungan statistik (uji validitas, uji reliabilitas, dan uji regresi linier) dan didukung secara kualitiatif melalui analisis deskriptif.Berdasarkan uji statistik variabel penelitian menunjukkan nilai r hitung pada setiap variabel pertanyaan kuesioner > r tabel sehingga data valid, sedangkan reliabilitas data menunjukkan r hitung > r tabel yaitu 0,427 > 0,339 sehingga data reliabel. Analisis kapasitas masyarakat menunjukkan 44% kelas tinggi, 29% kelas sedang, dan 27% kelas rendah, sehingga kapasitas masyarakat sudah sangat baik dengan ketahanan yang cukup tinggi. Berdasarkan hasil perhitungan dengan pendekatan linier, variabel saling berpengaruh dengan nilai Sig. < 0,05 (Ho ditolak), sedangkan R Square pengaruh paling besar adalah variabel mitigasi dengan tingkat pengaruh 48%.  Strategi peningkatan kapasitas ketahanan masyarakat dapat dilakukan dengan penerapan protokol kesehatan, peningkatan media informasi, dan penguatan sosial ekonomi masyarakat

Availability of Spatial Data Infrastructure (SDI) has an important role in disaster management. The purpose of this research is to analyze the readiness of the Spatial Data Infrastructure (IDS) as an effort to mitigate the lava flood in Kali Putih, Magelang Regency, Central Java. The research method was carried out by interview, survey and secondary data collection and SWOT analysis. The results of the SWOT analysis, on the Strength-Opportunity matrix, conclude that the optimization of Spatial Data Infrastructure (IDS) and the Indonesian National Standard (SNI) in the field of Geospatial Information (IG). The Strenght-Threath analysis concludes the need to use quality spatial data for government agencies. Opportunity - Weakness analysis concludes that there is a need for Web GIS development and the need to improve the quality of GI and the quality of human resources in the field of GI. Threat-Weakness analysis resulted in a conclusion, namely the need to refer to the one map and one data policy as well as the ID and SDI field policies.