The land surface in the Padang City is thought to be experiencing a continuous relative subsidence due to natural processes and man-made activities. Factors that affect land subsidence include earthquakes, sea level rise, infrastructure development, sediment transport, and excessive use of groundwater sources. The purpose of this research is to map the rate of land subsidence which is processed from the Sentinel 1-A radar, satellite imagery using the Differential Synthetic Aperture Radar (DInSAR) method. The data used are two pairs of Sentinel-1A level 1 Single Looking Complex (SLC) imagery which were acquired in 2018 and 2019. Image processing is carried out by filtering and multilooking techniques on Synthetic Aperture Radar (SAR) images. The following process changes the phase unwrapping to the ground level phase using phase displacement. Land subsidence in 2018–2019 from DInSAR processing reached -10.5 cm / year. The largest land subsidence occurred in North Padang with an average of -7.64 cm/year. Land subsidence in the Padang City, which is located near the estuary, is due to the nature of the alluvial sediment material. The use of Sentinel 1 SAR remote sensing data can provide important information in the context of mitigating land subsidence in the Padang City. Therefore, we need the right policies to handle future land subsidence cases. Land subsidence mapping is one of the factors that determine the vulnerability of coastal areas to disasters
The relatively constant availability of land in a region causes competition in its utilization with various landuse-change consequences. One of which is being the important triggering factors of landslide hazard, particularly in vulnerable regions, such as those working in Banjarnegara Regency, Central Java. This research was aimed at: (1) to identify landuse changepattern in 2001-2015 periods, and (2) to evaluate spatial utilization or existing land use consistencytowards spatial pattern allocated in RTRW of Banjarnegara Regency and its relation to landslide events and their locations. Methods used consisted of Landsat images spatial-analysis andspatial utilization consistency-analysis.
This study aims to create a system model that implements the concept of Geovisualization on shoreline changes in Padang city. This implementation is to make it easier to identify shoreline changes. The method used to detect changes is by interpreting satellite imagery with the Modified Normalized Difference Water Index (MNDWI) approach and the Digital Shoreline Analysis System (DSAS). The imagery used is Landsat 7 and Landsat 8 from 2000 to 2020. The model is designed with a Software Development Life Cycle (SDLC) approach. The results obtained are in the form of twenty shorelines per year as well as the amount of abrasion and accretion values from the interpretation. These results are visualized on an online-based map system that allows users to explore, synthesize, present and analyze the interpretation data. In conclusion, the Geovisualization system model is able to make serial data imagery presented dynamically to facilitate identification of shoreline changes.
Permasalahan banjir pada daerah sekitar aliran sungai Batang Tapan hampir setiap tahun terjadi terutama pada saat puncak musim hujan. Nagari Binjai Tapan merupakan wilayah langganan banjir di Kecamatan Ranah Ampek Hulu Tapan. Pemerintah nagari berperan besar untuk memberikan informasi kepada masyarakat terutama tentang informasi kerentanan terhadap bahaya banjir. Pemanfaatan teknologi informasi merupakan sebuah kebutuhan bagi aparatur pemerintahan untuk memudahkan dan mempercepat penyebaran informasi bagi masyarakat yang lebih luas. Pelatihan Pemetaan bencana akan dilakukan menggunakan teknologi geospasial yang juga sudah digunakan oleh sebagian besar masyarakat seperti GPS Mobile Phone. Seluruh informasi akan disimpan dalam sistem informasi geografi (SIG). Metode dan pendekatan dalam pelatihan ini adalah Pengenalan alat, penggunaannya dan evaluasi. Hasil kegiatan dapat diperoleh data bahwa sebagian besar perangkat nagari belum pernah menggunakan aplikasi pemetaan, baik yang sudah tersedia pada perangkat smartphone maupun yang bisa diakses secara gratis di internet. Hasil pelatihan menambah pengetahuan dan kemampuan perangkat nagari dalam menggunakan aplikasi pemetaan dalam pembuatan peta dan informasi bencana banjir di Nagari Binjai Tapan.
The high population development of Padang City is faced with limited land for areas and its infrastructure, resulting in a dense and slum-prone environmental condition of the City. The Central Government, together with the Padang City Government, held a program aimed at rehabilitating slum areas with the KOTAKU (Kota Tanpa Kumuh) program. This policy needs to be evaluated with the identity of the distribution and typology of slum areas to see changes in the slum area spatially. This study aims to identify the distribution of slum areas and describe slum areas’ typology using spatial autocorrelation in the City of Padang. Based on the survey results, slum areas were identified in 45 (forty-five) well-known subdistrict in 11 districts with a total area of 129.16 hectares of slum areas. There are 8,282 households in slum identified areas in the City of Padang. The spatial typology of slum areas resulted in a p-value of 0.061, a z-score of 1.867, and a moran index of 0.095 with a clustered pattern. The influence of spatial dependence is presented by the distribution of Moran and sub-district in the City of Padang’s urban fringe area. This condition is supported by a result of the concentration of activities at the core of the Padang City sub-district, causing it to not develop in the urban fringes area.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.