Natural hazards and their related impacts can have powerful implications for humanity, particularly communities with deep reliance on natural resources. The development of effective early warning systems(EWS) can cont...Natural hazards and their related impacts can have powerful implications for humanity, particularly communities with deep reliance on natural resources. The development of effective early warning systems(EWS) can contribute to reducing natural hazard impacts on communities by improving risk reduction strategies and activities.However, current shortcomings in the conception and applications of EWS undermine risk reduction at the grassroots level. This article explores various pathways to involve local communities in EWS from top-down to more participatory approaches. Based on a literature review and three case studies that outline various levels of participation in EWS in Kenya, Hawai'i, and Sri Lanka, the article suggests a need to review the way EWS are designed and applied, promoting a shift from the traditional expert-driven approach to one that is embedded at the grassroots level and driven by the vulnerable communities. Such a community-centric approach also raises multiple challenges linked to a necessary shift of conception of EWS and highlights the need for more research on pathways for sustainable community engagement.展开更多
Accurate,consistent,and high-resolution Land Use&Cover(LUC)information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers,pressures,state,and impacts on land systems.Never...Accurate,consistent,and high-resolution Land Use&Cover(LUC)information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers,pressures,state,and impacts on land systems.Nevertheless,the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change.Hereafter,we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data,combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region.Findings suggest that time-first approach is a valuable alternative to space-first approaches,accounting for the intra-annual variability of classes,hence improving classification performances.Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest,providing more accurate predictions with lower uncertainty.The produced land cover map has a high accuracy,an improved spatial resolution,while at the same time preserving the statistical significance(i.e.class proportion)of the official national dataset.This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability.However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.展开更多
文摘Natural hazards and their related impacts can have powerful implications for humanity, particularly communities with deep reliance on natural resources. The development of effective early warning systems(EWS) can contribute to reducing natural hazard impacts on communities by improving risk reduction strategies and activities.However, current shortcomings in the conception and applications of EWS undermine risk reduction at the grassroots level. This article explores various pathways to involve local communities in EWS from top-down to more participatory approaches. Based on a literature review and three case studies that outline various levels of participation in EWS in Kenya, Hawai'i, and Sri Lanka, the article suggests a need to review the way EWS are designed and applied, promoting a shift from the traditional expert-driven approach to one that is embedded at the grassroots level and driven by the vulnerable communities. Such a community-centric approach also raises multiple challenges linked to a necessary shift of conception of EWS and highlights the need for more research on pathways for sustainable community engagement.
基金funded by the Data Science Impulse grant of the University of Geneva.
文摘Accurate,consistent,and high-resolution Land Use&Cover(LUC)information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers,pressures,state,and impacts on land systems.Nevertheless,the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change.Hereafter,we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data,combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region.Findings suggest that time-first approach is a valuable alternative to space-first approaches,accounting for the intra-annual variability of classes,hence improving classification performances.Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest,providing more accurate predictions with lower uncertainty.The produced land cover map has a high accuracy,an improved spatial resolution,while at the same time preserving the statistical significance(i.e.class proportion)of the official national dataset.This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability.However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.
