GIS and Remote Sensing
GIS and Remote Sensing in Disaster Risk Reduction (DRR)
The Disaster Risk Reduction Program at Florida International University (FIU) with support from the U.S. Agency for International Development Office of Foreign Disaster Assistance (USAID) and the Geographic Information Systems and Remote Sensing Research Center at FIU, have developed an online training in disaster risk assessment modeling.
The training encompasses three modules: 1) Digital Elevation Models and hydrological surface runoff modeling in a GIS environment; 2) Spatio-temporal precipitation estimates derived from remotely sensed data; and 3) Land cover / land use mapping using remote sensing. The purpose of the modules is to establish data processing procedures to elevate data and extract spatially explicit information from raw data that can be applied in disaster risk modeling, including to the Central American Probabilistic Risk Assessment (CAPRA), a modeling platform promoted by the World Bank, with support of CEPREDENAC, the Inter-American Development Bank (IDB), AND THE United Nations International Strategy for Disaster Reduction (UN-ISDR).
The training needs were established in coordination with the World Bank and the ERN Consortium, developer of the CAPRA Platform. The modules provide alternative approaches and also guide the user in the decision-making process on choice of methods and potential data sources. For this purpose, each module will include essential questions that need to be answered to evaluate suitability of alternative routes that can be pursued to derive the necessary information. A major identified challenge in hazard modeling is the concept of scales in relation to model appropriateness and respective data needs at each scale of interest.
The online modules are distributed and accessible through a web portal. Participants will be given access to the portal to download training module materials and to upload completed assignments. The modules are to be taken consecutively and successfully completed three months after the start date.
The course is oriented to students with: (1) knowledge on mid-level statistics, (2) formal experience in geographic information systems and/or remote sensing modeling and, 3) some work experience in education or application of GIS and/or remote sensing modeling.