Sentinels-4-African-DRR
Copernicus User Uptake in Africa via technical support in the field of Disaster Management and Disaster Risk Reduction
Training Module Handbook
Foreword
Acknowledgement
This Training Module Handbook is developed in the framework of the project “Copernicus User Uptake in Africa via technical support in the field of Disaster Management and Disaster Risk Reduction” (Short forms: Sentinels-4-African-DRR; S4ADRR). The project is funded through the Framework Partnership Agreement on Copernicus User Uptake (FPCUP Action 2019-2-48) and performed by the German Space Agency at DLR, with the support of the Center for Remote Sensing of Land Surfaces (ZFL) of the University of Bonn, Germany.
The project team collaborates closely with the United Nations Platform for Space-Based Information for Disaster Management and Emergency Response (UN-SPIDER), a program of the United Nations Office for Outer Space Affairs (UNOOSA). Further, there is close collaboration with the teams responsible for the Global Flood Awareness System (GloFAS) and the Global Drought Observatory (GDO) of the Copernicus Emergency Management Service (EMS).
Scope
The S4ADRR project aims for improving the accessibility and usability of Copernicus for different African user groups with a thematic focus on disaster management and disaster risk reduction. This is approached by developing targeted training and information materials based on exchange and feedback-cycles with the user network and additionally supported through the organization of training events, either virtually or linked to relevant events, e.g., events organized by UN-SPIDER.
Points of Contact
DLR:
Jens Danzeglocke: jens.danzeglocke@dlr.de
ZFL:
Adrian Strauch: adrian.strauch@uni-bonn.de
Jonas Schreier: jonas.schreier@uni-bonn.de
Introduction
Introduction to the S4ADRR Training Module Handbook
The training materials and guidelines developed in the S4ADRR project are organized in a modular structure. A basic module providing a general overview of the portfolio of the Copernicus program and available to tutorials, overviews and other materials focused on finding, accessing, using, and learning to use services, products, and data on a more general level.
Subsequent modules are focused on thematic fields of application, specifically flood and drought. Here, the focus is on providing more detailed knowledge about available services and data with specific, thematic application examples in mind. Further, advanced methods for analyzing freely available data are addressed in these modules.
In this first version, basic materials are available for all three modules. In future iterations, based on user’s feedback, additional materials like for example video tutorials, use cases, and additional tutorials and guidelines, will be developed and made available. Based on multiple stages and feedback cycles, we aim to better understand user needs and adapt the materials accordingly. The modular approach is visualized in Figure 1.
General Module
Using Remote Sensing, Earth Observation and GIS in the Context of Natural Disasters
Thematic introduction to remote sensing and Copernicus in the context of natural disasters
An introduction that covers the basics of remote sensing for natural disasters as well as the Copernicus portfolio. Recommended reading before going into other modules. Click here to access this document
Video: Downloading Sentinel data from the Copernicus Browser
A short video guide on the basics of how to acquire a Sentinel scene from the Copernicus Browser. Click here to access this document
Impact Assessment of Natural Hazards
This guide aims to explain how to assess natural hazards using GIS. Click here to access this document
Flood Module
Using Remote Sensing, Earth Observation and GIS in the Context of Floods
Flooding is a natural and regular reality in most rivers where a pulse of overflowing water caused by natural phenomena such as heavy rainfall, peak seasonal rains, or snow melt overwhelms the river channel causing an overflow. Extreme flooding affects water management, conservation efforts, and hydro morphological altercations of the ecosystem services and human life. The mitigation of the effects of floods requires regular information on the location, extent, time, and depth of the floods [1].
The causative factors of flooding pose a challenge in the acquisition of primary data about flooding events. In most cases, flooding is caused by heavy rainfall which in combination with the infrastructure development dynamics and the impact of the floods on the existing infrastructure make it difficult to access flood areas. On the other hand, secondary data may be inappropriate for use in this case due to the unique properties associated with every flood event.
Earth observation (EO) data from space is very useful in various stages of the flood management process since it provides near real-time information without direct contact with the flood area. The recent technological advances in Earth observation data acquisition and processing have enhanced the acquisition of relatively high data at regular intervals. In this context, Earth observation provides primary data of the flood area that can be utilized for flood assessment and monitoring.
Several satellites, carrying different types of sensors on board, have been launched into space and are constantly acquiring images of the earth’s surface. In principle, there are two main types of sensors; namely Optical and Radar. Optical sensors measure reflected solar light and thus can only function during daytime and cannot penetrate through cloud cover, on the other hand, radar sensors emit a microwave signal and measure the backscatter from objects on the Earth’s surface [2].
Radar sensors have a particular advantage over optical sensors since they are active sensors and do not rely on solar illumination and therefore penetrate through clouds and acquire images any time of the day. Moreover, radar sensors are sensitive to the texture of the target object for example roughness and wetness, and therefore can be utilized in applications such as marine pollution, soil moisture, and forest biomass mapping. In the case of flooding, the most appropriate type of sensor is the radar sensor since it can acquire images in cloudy scenes, which is mostly the case during flooding events. However, the processing and analysis of radar data is challenging and requires particular software and expertise [3]. Regarding floods, we provide the following step-by-step guides and tutorials.
Thematic introduction to floods
An extensive introduction to floods as a natural hazard within the context of earth observation. Click here to access this document
Using GloFAS for flood monitoring: Website overview
In this document, the Copernicus Global Flood Awareness System (GloFAS) and its interface will be explained. Click here to access this document
Using radar data to detect flooded areas
Here, we will explain how to use Copernicus Sentinel-1 Radar data and ESA SNAP to produce your own flood data. Click here to access this document
Using optical data to detect flooded areas
Optical data can also be viable for flood detection. This segment will explain how to work with multispectral imagery for floods. Click here to access this document
Video: Introduction to GloFAS
Basics of the Global Flood Awareness System (GloFAS) and its interface in video form. Click here to access this document
Video: Introduction to the GFM Tools
With the Global Flood Monitoring (GFM), GloFAS recently added a powerful and comprehensive module into its GloFAS portfolio, which will be showcased here. Click here to access this document
Video: Accessing Data from GloFAS
This video covers the extensive “data access” section of GloFAS. Click here to access this document
Accessing Data from GloFAS
This document covers the extensive “data access” section of GloFAS in written form. Click here to access this document
Drought Module
Using Remote Sensing, Earth Observation and GIS in the Context of Droughts
Droughts are slow-onset disasters that can have severe impacts in many different ways. In general, drought can be categorized into four types: Meteorological, hydrological, agricultural as well as socio-economic drought. The types are distinguished by the duration of the drought as well as its institutional and social impacts (see Figure 2). A prolonged meteorological drought can eventually lead to hydrological drought as well as agricultural drought, which in turn can cause socio-economic disruption depending on a country’s resilience and vulnerability. Actively monitoring drought conditions can improve preparedness and reduce potential impacts [4].
Although remote sensing and earth observation (EO) data cannot detect drought itself, proxies of drought, such as vegetation health, can be used to monitor agricultural drought conditions. In this guide, we will present Copernicus’s Global Drought Observatory (GDO) which provides ready-made analysis data on current droughts with global coverage. We will also discuss datasets that can be used for further, drought-related analyses. We will focus on the Copernicus Global Drought Observatory, which offers on-demand, state-of-the-art drought data online and free of charge.
Thematic introduction to droughts
An extensive introduction to droughts as a natural hazard within the context of earth observation. Click here to access this document
Video overview: The GDO interface
In this video, we provide an introduction to GDO and a hands-on overview of its functions. Click here to access this document
The GDO platform – An overview
In written form, we introduce the GDO platform and its interface, covering the basics. Click here to access this document
GDO Interface – Additional functions
More advanced features of the GDO interface are explained in this module, focusing on the drought impact report. Contains similar content to the video, in written form. Click here to access this document
Video showcase: Using GDO data in QGIS
A Brief use case example with GDO data in a GIS environment, including an example of how to link GDO data with other datasets. Click here to access this document
Use case: Drought statistics over administrative districts (using the example of Kenya 2022)
This recording shows, step-by-step, how to combine GDO data with Copernicus land cover data as well as vector data of administrative boundaries.
The aim is to create a map which shows the fraction of drought-affected cropland per municipal district. Click here to access this document
Fire Module
Using Remote Sensing, Earth Observation and GIS in the Context of Wildfires
Fires (or Wildfires) are considered one of the most destructive natural disasters that can cause adverse impacts to nature and society, as well as economy and built environments [5]. Generally, fires are ignited by natural or human activities. Natural activities include lightning and volcanic eruptions. This module aims to give you an introduction to the observation of fire disasters from space.
Thematic introduction to wildfires
An extensive introduction to fire as a natural hazard within the context of earth observation Click here to access this document
Introduction to the GWIS
A tutorial on Copernicus’s Global Wildfire Information Service (GWIS). Click here to access this document
References
[1] F. Carreño Conde and M. De Mata Muñoz, “Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study,” Water, vol. 11, no. 12, Art. no. 12, Dec. 2019, doi: 10.3390/w11122454.
[2] “Multi‐temporal synthetic aperture radar flood mapping using change detection - Clement - 2018 - Journal of Flood Risk Management - Wiley Online Library.” https://onlinelibrary.wiley.com/doi/full/10.1111/jfr3.12303 (accessed Aug. 30, 2022).
[3] T. Perrou, A. Garioud, and I. Parcharidis, “Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin,” Front. Earth Sci., vol. 12, no. 3, pp. 506–520, Sep. 2018, doi: 10.1007/s11707-018-0711-2.
[4] V. Graw et al., “Timing is Everything - Drought Classification for Risk Assessment,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Jul. 2018, pp. 8267–8270. doi: 10.1109/IGARSS.2018.8517584.
[5] CRED, “EM-DAT | The international disasters database. Centre for Research on the Epidemiology of Disasters,” 2019. https://www.emdat.be/ (accessed Sep. 29, 2019).