scholarly journals RAINFALL THRESHOLDS FOR LANDSLIDE IN GARUT REGENCY, WEST JAVA USING HIMAWARI-8 DATA

2021 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Jalu Tejo Nugroho ◽  
Nanik Suryo Haryani ◽  
Fajar Yulianto ◽  
Mohammad Ardha
Keyword(s):  
Remote Sensing ◽  
Natural Disasters ◽  
Temporal Resolution ◽  
Threshold Value ◽  
Disaster Mitigation ◽  
Intensity Maximum ◽  
Rainfall Thresholds ◽  
Area Distribution ◽  
Landslide Event ◽  
Cloud Top Temperature

Landslide was one of natural disasters that affected by the weather. The intensity of landslide in Indonesia tended to increase from year to year with a larger area distribution. Remote sensing was a method that can be used to support disaster mitigation and response activities including landslide because this technology allows monitoring and analysis both spatially and temporally. One of the remote sensing satellites that can be used for monitoring landslide was Himawari-8. This weather satellite was launched in 2014 and had a temporal resolution of 10 minutes making it effective for meteorological, environmental and disaster observations. This research has used Himawari-8 rainfall data which extracted from cloud top temperature to determine the intensity of rainfall that causes landslide in Garut Regency. The daily accumulation of rainfall for five days before the landslide event up to five days after the landslide event has been investigated statistically to analyze the conditions of rainfall that trigger landslides. Rainfall thresholds for landslide was determined by the intensity maximum of daily accumulation. It was found that the intensity of rainfall that has potential to cause landslides based on the threshold value is as follows: Malangbong District 60.3 mm/day, Banjarwangi District 32.3 mm/day, Pasirwangi District 36.9 mm/day, Cisewu District 35.1 mm/day and Talegong District 52.8 mm/day. Landslide in four districts have corresponded with the day where the intensity of rainfall was maximum. Meanwhile for Talegong District, the landslide was occurred a day after its maximum.Keywords: rainfall, Himawari-8, landslide, remote sensing, thresholdLongsor merupakan salah satu bencana alam yang dipengaruhi oleh cuaca. Intensitas longsor di Indonesia cenderung meningkat dari tahun ke tahun dengan sebaran wilayah yang lebih luas. Penginderaan jauh merupakan metode yang dapat digunakan untuk mendukung kegiatan mitigasi dan tanggap bencana termasuk longsor karena teknologi ini memungkinkan pemantauan dan analisis baik secara spasial maupun temporal. Salah satu satelit penginderaan jauh yang dapat digunakan untuk pemantauan longsor adalah Himawari-8. Satelit cuaca ini diluncurkan pada tahun 2014 dan memiliki resolusi temporal 10 menit sehingga efektif untuk pengamatan meteorologi, lingkungan dan bencana. Penelitian ini menggunakan data curah hujan Himawari-8 yang diekstrak dari suhu puncak awan untuk mengetahui intensitas curah hujan penyebab longsor di Kabupaten Garut. Akumulasi curah hujan harian selama lima hari sebelum kejadian longsor sampai dengan lima hari setelah kejadian longsor diteliti secara statistik untuk menganalisis kondisi curah hujan yang memicu terjadinya longsor. Ambang batas curah hujan untuk longsor ditentukan oleh intensitas maksimum akumulasi harian. Diketahui bahwa intensitas curah hujan yang berpotensi menimbulkan longsor berdasarkan nilai ambang batas adalah sebagai berikut: Kecamatan Malangbong 60,3 mm / hari, Kecamatan Banjarwangi 32,3 mm / hari, Kecamatan Pasirwangi 36,9 mm / hari, Kecamatan Cisewu 35,1 mm / hari dan Kecamatan Talegong 52,8 mm / hari. Tanah longsor di empat kecamatan telah sesuai dengan hari dimana intensitas curah hujan maksimal. Sedangkan untuk Kecamatan Talegong, longsor terjadi sehari setelah maksimumnya.Kata kunci: curah hujan, Himawari-8, longsor, penginderaan jauh, ambang batas 

Based on IDRISI Remote Sensing Images Land-Use Types of Supervised Classification Techniques

2013 ◽  
Vol 415 ◽  
pp. 305-308
Author(s):  
Kun Zhang ◽  
Hai Feng Wang ◽  
Zhuang Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Classification Techniques ◽  
Analysis And Evaluation ◽  
Remote Sensing Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Remote Sensing Image Classification ◽  
Land Use Types ◽  
Area Distribution

With remote sensing technology and computer technology, remote sensing classification technology has been rapid progress. In the traditional classification of remote sensing technology, based on the combination of today's technology in the field of remote sensing image classification, some new developments and applications for land cover classification techniques to make more comprehensive elaboration. Using the minimum distance classifier extracts of the study area land use types. Ultimately extracted land use study area distribution image and make its analysis and evaluation.

scholarly journals Remote Sensing Is Changing Our View of the Coast: Insights from 40 Years of Monitoring at Narrabeen-Collaroy, Australia

2018 ◽  
Vol 10 (11) ◽  
pp. 1744 ◽  
Author(s):  
Kristen Splinter ◽  
Mitchell Harley ◽  
Ian Turner
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Temporal Resolution ◽  
Monitoring Program ◽  
Google Earth ◽  
Airborne Lidar ◽  
Data Collection Program ◽  
Insight Into ◽  
Collection Program

Narrabeen-Collaroy Beach, located on the Northern Beaches of Sydney along the Pacific coast of southeast Australia, is one of the longest continuously monitored beaches in the world. This paper provides an overview of the evolution and international scientific impact of this long-term beach monitoring program, from its humble beginnings over 40 years ago using the rod and tape measure Emery field survey method; to today, where the application of remote sensing data collection including drones, satellites and crowd-sourced smartphone images, are now core aspects of this continuing and much expanded monitoring effort. Commenced in 1976, surveying at this beach for the first 30 years focused on in-situ methods, whereby the growing database of monthly beach profile surveys informed the coastal science community about fundamental processes such as beach state evolution and the role of cross-shore and alongshore sediment transport in embayment morphodynamics. In the mid-2000s, continuous (hourly) video-based monitoring was the first application of routine remote sensing at the site, providing much greater spatial and temporal resolution over the traditional monthly surveys. This implementation of video as the first of a now rapidly expanding range of remote sensing tools and techniques also facilitated much wider access by the international research community to the continuing data collection program at Narrabeen-Collaroy. In the past decade the video-based data streams have formed the basis of deeper understanding into storm to multi-year response of the shoreline to changing wave conditions and also contributed to progress in the understanding of estuary entrance dynamics. More recently, ‘opportunistic’ remote sensing platforms such as surf cameras and smartphones have also been used for image-based shoreline data collection. Commencing in 2011, a significant new focus for the Narrabeen-Collaroy monitoring program shifted to include airborne lidar (and later Unmanned Aerial Vehicles (UAVs)), in an enhanced effort to quantify the morphological impacts of individual storm events, understand key drivers of erosion, and the placing of these observations within their broader regional context. A fixed continuous scanning lidar installed in 2014 again improved the spatial and temporal resolution of the remote-sensed data collection, providing new insight into swash dynamics and the often-overlooked processes of post-storm beach recovery. The use of satellite data that is now readily available to all coastal researchers via Google Earth Engine continues to expand the routine data collection program and provide key insight into multi-decadal shoreline variability. As new and expanding remote sensing technologies continue to emerge, a key lesson from the long-term monitoring at Narrabeen-Collaroy is the importance of a regular re-evaluation of what data is most needed to progress the science.

scholarly journals Questionable Reform: The Adoption of the Double-Entry Bookkeeping and Accrual Basis Accounting System in Korea

2018 ◽  
Vol 33 (1) ◽  
pp. 57-80
Author(s):  
Im Tobin ◽  
Lee Hyunkuk ◽  
Lim Dongwan
Keyword(s):  
Physical Properties ◽  
Natural Disasters ◽  
State Level ◽  
Disaster Mitigation ◽  
Mitigation Measures ◽  
School Buildings ◽  
Accounting System ◽  
Seismic Evaluation ◽  
Political Factors ◽  
Double Entry

This study examines the factors that influence human vulnerability to natural disasters by focusing on the seismic evaluation of school buildings in Korea. Since natural disasters such as an earthquake often do not take people’s lives directly, but rather indirectly through the destruction of physical structures, seismic reinforcement of school buildings may reduce the vulnerability of their occupants by strengthening structures to withstand such disasters. Disaster mitigation measures are implemented within a state; however, little is known about how they are distributed when the physical properties of structures are taken into account. This paper analyzes a panel data based on the structural properties of school buildings in eight different provinces between 2011 and 2015 using a logistic regression model. The results show that factors identified in cross-country studies, such as economic capacity and political factors, still have influence on earthquake preparedness at the state level, even when the physical properties of structures or technical factors are considered.

scholarly journals Application of Smoothed Particle Hydrodynamics (SPH) for the Simulation of Flow-like Landslides on 3D Terrains

2022 ◽  
Author(s):  
Binghui Cui ◽  
Liaojun Zhang
Keyword(s):  
Risk Assessment ◽  
Smoothed Particle Hydrodynamics ◽  
Disaster Mitigation ◽  
Sph Method ◽  
Landslide Event ◽  
Particle Hydrodynamics ◽  
Smoothed Particle ◽  
Simulation Results ◽  
Mitigation Design ◽  
The Impact

Abstract Flow-type landslide is one type of landslide that generally exhibits characteristics of high flow velocities, long jump distances, and poor predictability. Simulation of it facilitates propagation analysis and provides solutions for risk assessment and mitigation design. The smoothed particle hydrodynamics (SPH) method has been successfully applied to the simulation of two-dimensional (2D) and three-dimensional (3D) flow-like landslides. However, the influence of boundary resistance on the whole process of landslide failure is rarely discussed. In this study, a boundary algorithm considering the friction is proposed, and integrated into the boundary condition of the SPH method, and its accuracy is verified. Moreover, the Navier-Stokes equation combined with the non-Newtonian fluid rheology model was utilized to solve the dynamic behavior of the flow-like landslide. To verify its performance, the Shuicheng landslide event, which occurred in Guizhou, China, was taken as a case study. In the 2D simulation, a sensitivity analysis was conducted, and the results showed that the shearing strength parameters have more influence on the computation accuracy in comparison with the coefficient of viscosity. Afterwards, the dynamic characteristics of the landslide, such as the velocity and the impact area, were analyzed in the 3D simulation. The simulation results are in good agreement with the field investigations. The simulation results demonstrate that the SPH method performs well in reproducing the landslide process, and facilitates the analysis of landslide characteristics as well as the affected areas, which provides a scientific basis for conducting the risk assessment and disaster mitigation design.

Technological advances to improve the quantification of volcanic emissions

2021 ◽  
Author(s):  
Christopher Fuchs ◽  
Jonas Kuhn ◽  
Nicole Bobrowski ◽  
Ulrich Platt
Keyword(s):  
Remote Sensing ◽  
Temporal Resolution ◽  
Volcanic Activity ◽  
Imaging Techniques ◽  
Correlation Spectroscopy ◽  
Trace Gas ◽  
Optical Absorption Spectroscopy ◽  
Integration Time ◽  
Volcanic Plume ◽  
Flux Measurements

<p>Variations in volcanic trace gas composition and fluxes are a valuable indicator for changes in magmatic systems and therefore allow monitoring of the volcanic activity. An established method to measure trace gas emissions is to use remote sensing techniques like, for example, Differential Optical Absorption Spectroscopy (DOAS) and more recently SO<sub>2</sub>-cameras, that can quantify volcanic sulphur dioxide (SO<sub>2</sub>) emissions during quiescent degassing and eruptive phases, making it possible to correlate fluxes with volcanic activity. </p><p>We present flux measurements of volcanic SO<sub>2</sub> emissions based on the novel remote sensing technique of Imaging Fabry-Pérot Interferometer Correlation Spectroscopy (IFPICS) in the UV spectral range. The basic principle of IFPICS lies in the application of an Fabry-Pérot Interferometer (FPI) as wavelength selective element. The FPIs periodic transmission profile is matched to the periodic spectral absorption features of SO<sub>2</sub>, resulting in high spectral information for its detection. This technique yields a higher trace gas selectivity and sensitivity than imaging approaches based on interference filters, e.g. SO<sub>2</sub>-cameras and an increased spatio-temporal resolution over spectroscopic imaging techniques, e.g. imaging DOAS. Hence, IFPICS shows reduced cross sensitivities to broadband absorption (e.g. to ozone, aerosols), which allows the application to weaker volcanic SO<sub>2</sub> emitters and increases the range of possible atmospheric conditions. It further raises the possibility to apply IFPICS to other trace gas species like, for example, bromine monoxide, that still can be characterized with a high spatial and temporal resolution (< 1 HZ).</p><p>In October 2020, we acquired SO<sub>2</sub> column density distribution images of Mt Etna volcanic plume with a detection limit of 2x10<sup>17</sup> molec cm<sup>-2</sup>, 1 s integration time, 400x400 pixel spatial, and 0.3 Hz temporal resolution.  We compare the SO<sub>2</sub> fluxes retrieved by IFPICS with simultaneous flux measurements using the mutli-axis DOAS technique.</p>

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

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