Use and Contestations of Earth Observation Technologies in Disaster Risk Reduction and Management

2021 ◽  
pp. 53-65
Author(s):  
Godwell Nhamo ◽  
David Chikodzi
Keyword(s):  
Risk Reduction ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Earth Observation

scholarly journals Earth Observation Actionable Information Supporting Disaster Risk Reduction Efforts in a Sustainable Development Framework

2018 ◽  
Author(s):  
Alberto Lorenzo-Alonso ◽  
Marino Palacios ◽  
Ángel Utanda
Keyword(s):  
Sustainable Development ◽  
Risk Reduction ◽  
Disaster Risk Reduction ◽  
European Space Agency ◽  
Disaster Risk ◽  
Earth Observation ◽  
Ancillary Data ◽  
Development Framework ◽  
Space Agency ◽  
Key Factor

Disaster Risk Reduction (DRR) is a high priority on the agenda of main stakeholders involved in sustainable development and Earth Observation (EO) can provide useful, timely and economical information in this context. This short communication outlines the European Space Agency’s (ESA) specific initiative to promote the use and uptake of satellite data in the global development community: ‘Earth Observation for Sustainable Development (EO4SD)’. One activity area under EO4SD is devoted to Disaster Risk Reduction: EO4SD DRR. Within this project, a team of European companies and institutions are tasked to develop EO services for supporting the implementation of DRR in International Financial Institutions’ (IFI) projects. Integration of satellite-borne data and ancillary data to generate insight and actionable information is thereby considered a key factor for improved decision making. To understand and fully account for the essential user requirements (IFI & Client States), engagement with technical leaders is crucial. Fit-for-purpose use of data and comprehensive capacity building eventually ensure scalability and long-term transferability. Future perspectives of EO4SD and DRR regarding mainstreaming are also highlighted.

Supporting disaster risk reduction with satellite Earth Observation: Landslide hazard assessment for the Chin road corridor, Myanmar 

2021 ◽  
Author(s):  
Jan Kolomazník ◽  
Ivana Hlavacova ◽  
Matthias Schloegl
Keyword(s):  
Risk Reduction ◽  
Landslide Susceptibility ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Earth Observation ◽  
Landslide Hazard ◽  
Slope Instability ◽  
Landslide Inventory ◽  
Maximum Slope ◽  
The Road

<p>EO4SD (Earth Observation for Sustainable Development) initiative of the European Space Agency aims at facilitating the uptake and integration of satellite information products and services into development activities of international financial institutions and their partners in targeted countries. Its disaster risk reduction (DRR) cluster plays a crucial role when it comes to impacts of natural hazards on societies.</p><p>We present a recent service established within the EO4SD-DRR cluster, which aimed at providing evidence-based support to the design of reconstruction works on the road corridor in mountainous and landslide prone terrain between towns of Kalay and Hakha in Chin state, Myanmar. The whole service is constituted by an ensemble of analytical products and comprises four major components: (1) establishment of a landslide inventory, (2) derivation of landslide susceptibility, (3) slope instability analysis, and (4) overall landslide exposure assessment.</p><p>First, a landslide inventory of historic landslide events was derived from optical satellite imagery. Second, by linking the landslide inventory with geomorphological features derived from a digital elevation model as well as geological and land cover data, a comprehensive landslide susceptibility map was derived. This was accomplished by employing robust machine learning ensemble methods, inherently tackling the problem of class imbalance, and yielding not only the estimated susceptibility, but also its corresponding uncertainty. Third, a slope instability assessment was obtained via multi-temporal InSAR. Interferometric analysis provided estimates of terrain displacement velocities from Sentinel-1 data from ascending and descending trajectories and by leveraging both persistent scatterer and the small baselines methods. As the atmospheric phase screen could not be reliably estimated  the area of interest had to be split into several sub-areas processed independently. Due to large amount of points with non-linear displacements and varying noise levels, InSAR measurement points were filtered using both coherence threshold and features representing length of reliable period derived by segmentation of displacement time series. Displacement velocities were converted from satellite line-of-sight to direction of maximum slope gradient and point attributes were supplemented with metadata indicating detected points’ reliability based on combination of coherence and directional sensitivity. Finally, exposure of road segments to landslide hazard represented by susceptibility and estimated slope instabilities was quantified and presented in dedicated web application to allow intuitive identification of hazard hot-spots.</p><p>Despite several methodological challenges products demonstrate robustness and utility of Earth Observation technology to address landslide hazard screening and to support targeting and protecting investments into landslide mitigation measures along the road corridor.</p>

scholarly journals Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time

2018 ◽  
Vol 10 (9) ◽  
pp. 1378 ◽  
Author(s):  
Daniele Ehrlich ◽  
Michele Melchiorri ◽  
Aneta Florczyk ◽  
Martino Pesaresi ◽  
Thomas Kemper ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Population Density ◽  
Risk Reduction ◽  
Natural Hazards ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Earth Observation ◽  
Human Settlement ◽  
Global Population ◽  
Over Time

Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction.

scholarly journals WCDRR and the CEOS activities on disaters

2015 ◽  
Vol XL-7/W3 ◽  
pp. 845-850 ◽  
Author(s):  
I. Petiteville ◽  
C. Ishida ◽  
J. Danzeglocke ◽  
A. Eddy ◽  
F. Gaetani ◽  
...  
Keyword(s):  
Risk Reduction ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Earth Observation ◽  
Lessons Learned ◽  
Rapid Urbanization ◽  
Weather Events ◽  
Major Disaster ◽  
Earth Observation Satellites ◽  
High Level

Agencies from CEOS (Committee on Earth Observation Satellites) have traditionally focused their efforts on the response phase. Rapid urbanization and increased severity of weather events has led to growing economic and human losses from disasters, requiring international organisations to act now in all disaster risk management (DRM) phases, especially through improved disaster risk reduction policies and programmes. As part of this effort, CEOS agencies have initiated a series of actions aimed at fostering the use of Earth observation (EO) data to support disaster risk reduction and at raising the awareness of policy and decision-makers and major stakeholders of the benefits of using satellite EO in all phases of DRM. <br><br> CEOS is developing a long-term vision for sustainable application of satellite EO to all phases of DRM. CEOS is collaborating with regional representatives of the DRM user community, on a multi-hazard project involving three thematic pilots (floods, seismic hazards and volcanoes) and a Recovery Observatory that supports resilient recovery from one major disaster. These pilot activities are meant as trail blazers that demonstrate the potential offered by satellite EO for comprehensive DRM. <br><br> In the framework of the 2015 3<sup>rd</sup> World Conference on Disaster Risk Reduction (WCDRR), the CEOS space agencies intend to partner with major stakeholders, including UN organizations, the Group on Earth Observations (GEO), international relief agencies, leading development banks, and leading regional DRM organisations, to define and implement a 15-year plan of actions (2015- 2030) that responds to high-level Post-2015 Framework for Disaster Risk Reduction priorities. This plan of action will take into account lessons learned from the CEOS pilot activities.

scholarly journals Earth Observation Actionable Information Supporting Disaster Risk Reduction Efforts in a Sustainable Development Framework

2018 ◽  
Vol 11 (1) ◽  
pp. 49 ◽  
Author(s):  
Alberto Lorenzo-Alonso ◽  
Ángel Utanda ◽  
María Aulló-Maestro ◽  
Marino Palacios
Keyword(s):  
Sustainable Development ◽  
Risk Reduction ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Earth Observation ◽  
Ancillary Data ◽  
Use Of Data ◽  
Development Framework ◽  
Key Factor ◽  
Fit For Purpose

Disaster risk reduction (DRR) is a high priority on the agenda of main stakeholders involved in sustainable development, and earth observation (EO) can provide useful, timely, and economical information in this context. This short communication outlines the European Space Agency’s (ESA) specific initiative to promote the use and uptake of satellite data in the global development community: Earth Observation for Sustainable Development (EO4SD). One activity area under EO4SD is devoted to disaster risk reduction (EO4SD DRR). Within this project, a team of European companies and institutions are tasked to develop EO services for supporting the implementation of DRR in International Financial Institutions’ (IFI) projects. Integration of satellite-borne data and ancillary data to generate insight and actionable information is thereby considered a key factor for improved decision-making. To understand and fully account for the essential user requirements (IFI and client states), engagement with technical leaders is crucial. Fit-for-purpose use of data and comprehensive capacity building eventually ensure scalability and long-term transferability. Future perspectives of EO4SD and DRR regarding mainstreaming are also highlighted.

Analisis Longsor Cililin Dan Model Pengupayaan Kestabilan Lerengnya

2019 ◽  
Vol 16 (3) ◽  
Author(s):  
Wisyanto
Keyword(s):  
Land Cover ◽  
Risk Reduction ◽  
Disaster Risk Reduction ◽  
Disaster Risk ◽  
Landslide Risk ◽  
West Java ◽  
Water Condition ◽  
Reduction Program ◽  
Landslide Risk Reduction

Landslides have occurred in various places in Indonesia. Likewise with West Java, there were many regions that has experienced repeated landslides. Having many experience of occurrences of landslides, we should have had a good landslide risk reduction program. Indeed, the incidence of landslides depends on many variables. Due to that condition, it may that a region would have different variable with another region. So it is impossible to generalize the implementation of a mitigation technology for all areas prone to landslides. Research of the Cililin's landslide is to anticipate the next disasters that may happen in around the area of 2013 Cililin Landslide. Through observation lithological conditions, water condition, land cover and landscape, as well as consideration of wide dimension of the building footing, the distance of building to the slopes and so forth, it has been determined some efforts of disaster risk reduction in the area around the landslide against the occurrence of potential landslide in the future.Bencana tanah longsor telah terjadi di berbagai tempat di Indonesia. Demikian halnya dengan Jawa Barat, tidak sedikit daerahnya telah berulang kali mengalami longsor. Seharusnya dengan telah banyaknya kejadian longsor, kita mampu mengupayakan program penurunan risiko longsor secara baik. Memang kejadian longsor bergantung pada banyak variabel, dimana dari satu daerah dengan daerah yang lain akan sangat memungkinkan mempunyai variabel yang berbeda, sehingga tidak mungkin kita membuat generalisasi penerapan suatu teknologi mitigasinya untuk semua daerah rawan longsor. Penelitian longsor di Cililin dilakukan untuk mengantisipasi terjadinya bencana di sekitar daerah Longsor Cililin 2013 yang lalu. Melalui pengamatan kondisi litologi, keairan, tutupan lahan dan bentang alam yang ada, serta pertimbangan akan dimensi luas pijakan bangunan, jarak batas bangunan dengan lereng dan lain sebagainya, telah ditentukan beberapa upaya penurunan risiko bencana di daerah sekitar longsor terhadap potensi kejadian longsor dimasa mendatang.Keywords: Landslide, risk reduction, footing of building, Cililin

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