scholarly journals DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE

2016 ◽  
Vol XLI-B8 ◽  
pp. 1385-1388 ◽  
Author(s):  
H. Miyazaki ◽  
M. Nagai ◽  
R. Shibasaki
Keyword(s):  
Public Health ◽  
Time Series ◽  
Risk Management ◽  
Satellite Data ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
Training Data ◽  
Series Data ◽  
Landsat Data ◽  
Human Settlement

Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

scholarly journals DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE

2016 ◽  
Vol XLI-B8 ◽  
pp. 1385-1388
Author(s):  
H. Miyazaki ◽  
M. Nagai ◽  
R. Shibasaki
Keyword(s):  
Public Health ◽  
Time Series ◽  
Risk Management ◽  
Satellite Data ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
Training Data ◽  
Series Data ◽  
Landsat Data ◽  
Human Settlement

Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

scholarly journals Time-Series Model-Adjusted Percentile Features: Improved Percentile Features for Land-Cover Classification Based on Landsat Data

2020 ◽  
Vol 12 (18) ◽  
pp. 3091
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Jiangning Yang
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Time Series Model ◽  
Training Data ◽  
Series Data ◽  
Landsat Data ◽  
Data Sampling ◽  
Continuous Change

Percentile features derived from Landsat time-series data are widely adopted in land-cover classification. However, the temporal distribution of Landsat valid observations is highly uneven across different pixels due to the gaps resulting from clouds, cloud shadows, snow, and the scan line corrector (SLC)-off problem. In addition, when applying percentile features, land-cover change in time-series data is usually not considered. In this paper, an improved percentile called the time-series model (TSM)-adjusted percentile is proposed for land-cover classification based on Landsat data. The Landsat data were first modeled using three different time-series models, and the land-cover changes were continuously monitored using the continuous change detection (CCD) algorithm. The TSM-adjusted percentiles for stable pixels were then derived from the synthetic time-series data without gaps. Finally, the TSM-adjusted percentiles were used for generating supervised random forest classifications. The proposed methods were implemented on Landsat time-series data of three study areas. The classification results were compared with those obtained using the original percentiles derived from the original time-series data with gaps. The results show that the land-cover classifications obtained using the proposed TSM-adjusted percentiles have significantly higher overall accuracies than those obtained using the original percentiles. The proposed method was more effective for forest types with obvious phenological characteristics and with fewer valid observations. In addition, it was also robust to the training data sampling strategy. Overall, the methods proposed in this work can provide accurate characterization of land cover and improve the overall classification accuracy based on such metrics. The findings are promising for percentile-based land cover classification using Landsat time series data, especially in the areas with frequent cloud coverage.

Challenges and Strategies for Climate Change Adaptation Among Pacific Island Nations

2012 ◽  
Vol 6 (4) ◽  
pp. 415-423
Author(s):  
Mollie J. Mahany ◽  
Mark E. Keim
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Risk Management ◽  
Climate Change Adaptation ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
Small Island ◽  
Pacific Island ◽  
Small Island Developing States ◽  
The Pacific

ABSTRACTFew regions of the world are at higher risk for environmental disasters than the Pacific Island countries and territories. During 2004 and 2005, the top public health leadership from 19 of 22 Pacific Island countries and territories convened 2 health summits with the goal of developing the world's first comprehensive regional strategy for sustainable disaster risk management as applied to public health emergencies. These summits followed on the objectives of the 1994 Barbados Plan of Action for the Sustainable Development of Small Island Developing States and those of the subsequent Yokohama Strategy and Plan of Action for a Safer World. The outputs of the 2004 and 2005 Pacific Health Summits for Sustainable Disaster Risk Management provide a detailed description of challenges and accomplishments of the Pacific Island health ministries, establish a Pacific plan of action based upon the principles of disaster risk management, and provide a locally derived, evidence-based approach for many climate change adaptation measures related to extreme weather events in the Pacific region. The declaration and outputs from these summits are offered here as a guide for developmental and humanitarian assistance in the region (and for other small-island developing states) and as a means for reducing the risk of adverse health effects resulting from climate change.(Disaster Med Public Health Preparedness. 2012;6:415-423)

scholarly journals From Science to Policy and Practice: A Critical Assessment of Knowledge Management Before, During, and After Environmental Public Health Disasters

2019 ◽  
Vol 34 (s1) ◽  
pp. s73-s74
Author(s):  
Mélissa Généreux ◽  
Marc Lafontaine ◽  
Angela Eykelbosh
Keyword(s):  
Public Health ◽  
Risk Management ◽  
Knowledge Management ◽  
Participant Observation ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
World Health ◽  
Policy And Practice ◽  
Environmental Public Health ◽  
Wide Range

Introduction:Canada, like many countries, increasingly faces environmental public health (EPH) disasters. Such disasters often require both short- and long-term responses, necessitate evacuation and relocation, cause major environmental impacts, and generate the need for specific knowledge and expertise (chemistry, epidemiology, risk assessment, mental health, etc.).Aim:Given the importance of evidence-based, risk-informed decision making, we aimed to critically assess the integration of EPH expertise and research into each phase of disaster risk management in several Canadian and other jurisdictions.Methods:In-depth interviews were conducted with 23 leaders in disaster risk management from Canada, United States, United Kingdom, and Australia, and were complemented by other methods (i.e. participant observation, information gathered from participation in scientific events, and document review). Three criteria were explored: governance, knowledge creation and translation, and related needs and barriers. An interview guide was developed based on a standardized toolkit from the World Health Organization. Data were analyzed through a four-step content analysis.Results:Six cross-cutting themes emerged from the analysis. These themes are identified as critical factors in successful disaster knowledge management: 1) blending the best of traditional and modern approaches, 2) fostering community engagement; 3) cultivating relationships, 4) investing in preparedness and recovery, 5) putting knowledge into practice, and 6) ensuring sufficient human and financial resources. A wide range of promising knowledge-to-action strategies was also identified, including mentorship programs, communities of practice, advisory groups, systematized learning, and comprehensive repositories of tools and resources.Discussion:There is no single roadmap to incorporate EPH knowledge and expertise into disaster risk management. Our findings suggest that beyond structures and plans, it is necessary to cultivate relationships and share responsibility for ensuring the safety, health, and wellbeing of affected communities while respecting the local culture, capacity, and autonomy. Any such considerations should be incorporated into disaster risk management planning.

Defining Disaster-Related Health Risk: A Primer for Prevention

2018 ◽  
Vol 33 (3) ◽  
pp. 308-316 ◽  
Author(s):  
Mark Keim
Keyword(s):  
Public Health ◽  
Risk Management ◽  
Health Risk ◽  
Disaster Risk ◽  
International Standards ◽  
Disaster Risk Management ◽  
Health Determinants ◽  
Publication Type ◽  
Ongoing Research ◽  
Public Health Decision

AbstractEffective disaster risk management requires not only management of the immediate problem (disaster-related injuries and disease), but also of the patient’s risk factors and of the underlying health determinants. This requires an accurate and well-validated process for assessment of the determinants of disease.Ideally, disaster risk management is based on a prioritization process. Once hazards have been identified, they are assessed in terms of the probability and impact in terms of losses. The hazards associated with the greatest probability and impact loss are prioritized. In addition to prioritization, risk assessment also offers a process for ongoing research involving the interaction of health determinants, risk, and protective factors that may contribute to future adverse health outcomes.Recently, assessments of health risk have become an integral part of local, state, and national emergency preparedness programs. One of the strengths of these assessments is the convening of multi-sectoral input for public health decision making and plans. However, this diversity of input also creates challenges in development of a common nomenclature for assessing and communicating the characteristics of this risk. Definitions remain ambiguous for many of the key indicators of disaster risk, especially those applied to health risk.This report is intended as a primer for defining disaster-related health risk. This framework is discussed within a nomenclature that is consistent with international standards for risk management and public health prevention.KeimM. Defining disaster-related health risk: a primer for prevention. Prehosp Disaster Med. 2018;33(3):308-316.

scholarly journals Assessing Capacity and Implementation Status of the Disaster Risk Management Strategy for Health and Community Disaster Resilience in Malawi

2021 ◽  
Author(s):  
Ozius Dewa ◽  
Donald Makoka ◽  
Olalekan A. Ayo-Yusuf
Keyword(s):  
Public Health ◽  
Risk Management ◽  
Human Resource ◽  
Health Sector ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
Disaster Resilience ◽  
Human Resource Capacity ◽  
Implementation Status ◽  
Resource Capacity

AbstractFloods are among the most frequently occurring natural hazards in Malawi, often with public health implications. This mixed methods study assessed the capacity for and implementation status of the disaster risk management (DRM) strategy for the health sector in Malawi, using flooding in the Nsanje District as a case. Data were collected using desk review and a workshop methodology involving key officials from government ministries, national and international development partners, and the academia. The results show that Malawi had recently strengthened its DRM institutional frameworks, with a pronounced policy shift from reactive to proactive management of disasters. Health sector personnel and structures were key contributors in the design and implementation of DRM activities at all levels. Development partners played a significant role in strengthening DRM coordination and implementation capacity. Lack of funding and the limited availability, and often fragmented nature, of vulnerability and risk assessment data were identified as key challenges. Limited human resource capacity and inadequate planning processes at district level impeded full implementation of DRM policies. These findings call for community-level interventions for improved coordination, planning, and human resource capacity to strengthen community disaster resilience and improve public health. The approach used in this study can serve as a model framework for other districts in Malawi, as well as in other low- and middle-income countries in the context of Sendai Framework implementation.

Barriers to Climate Disaster risk Management for Public Health: Lessons from a Pilot Survey of National Public Health Representatives

2021 ◽  
pp. 1-4
Author(s):  
Hannah Marcus ◽  
Liz Hanna
Keyword(s):  
Public Health ◽  
Risk Management ◽  
Emergency Preparedness ◽  
Large Scale ◽  
Online Survey ◽  
National Level ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
World Federation ◽  
National Public Health

Abstract Objectives: This study sought to examine current national disaster risk management capacities, and identify governance barriers to strengthening national preparedness for responding to public health emergencies, associated with the anticipated climate-driven intensification of natural disaster cycles. Methods: A mixed-methods online survey, assessing broader governance constraints to climate change adaptation (CCA) for public health, was distributed to representatives of national public health associations, and societies of 82 member countries under the World Federation of Public Health Associations. Specific questions relevant to disaster risk management capacities and barriers were analyzed as part of a narrowed focus on the CCA subdomain of emergency preparedness. Results: Existence of some technology, infrastructure, and/ or human resources, necessary to develop early warning and other surveillance systems for climate-related health risks was reported by 9 out of 11 responding countries. However, 7 reported persistent limitations and/ or regional discrepancies. Most significant identified barriers to strengthening emergency preparedness at the national level included governance coordination challenges, and, in the case of many developing countries, technical, medical, and human resource shortages. Conclusions: The development of new frameworks for intersectoral governance and large-scale resource mobilization will prove crucial to ongoing efforts to strengthen national climate-health resiliency and prepare for disaster-associated health threats.

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