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Table of Contents

GEODATE

Volume 35, Issue 3, 2022

GEODATE

Volume 35, Issue 2 2022

Remote Sensing and Disaster Management

Dr Karen Joyce, Senior Lecturer, James Cook University

Introduction

Did you know that the Australian Government officially declared 45 disasters during 2021? New South Wales was the most frequently affected state (15 events), and the most commonly occurring disaster was a flood or storm (38 events). We also experienced three cyclones, a bushfire, an earthquake, and two tornadoes. All that is in addition to the Covid-19 pandemic! It seems that Mother Nature has been rather busy disrupting things over the past year.

We frequently learn about many of these disasters through various media portals at the height of their most intense impact – while they are unfolding. That is why you may have heard the terms ‘emergency response’ and ‘disaster management’ used somewhat interchangeably. However, that is not entirely correct. The response is a component of the more comprehensive disaster management cycle.

To effectively manage disasters and lessen their impact on lives and livelihoods, it is critical to look beyond the response phase of the disaster management cycle. The complete cycle details four phases – reduction (prevention), readiness (preparedness), response, and recovery. The way in which we address each phase separately – as well as in an interconnected manner – will have a bearing on our success in managing the disaster and its impacts as a whole. 

Remote sensing has a key role to play in informing disaster management decisions through each phase of the cycle and ultimately in affecting the success of the actions we take. Below we will explore the benefits of remote sensing and how it can be incorporated into the disaster management cycle.

How can remote sensing help?

Remote sensing allows us to capture information about something without being in physical contact with it. This ability to be remote from our feature, target, or phenomenon of interest is a superpower in disaster management! It enables us to use satellites, drones, and other aircraft to acquire the information we need without putting people or equipment in harm’s way. It is also one of the most efficient means of covering large areas promptly and consistently. 

Given the unique benefits of remote sensing, the role of these data in disaster management is many and varied. In the reduction (prevention) phase of the disaster management cycle, remote sensing contributes data towards understanding the environment in a pre-disaster state. This includes providing a data source for generating land cover/land use maps, identifying the location of potentially hazardous features (e.g. fault lines, river channels) to avoid when planning and developing infrastructure, and modelling the benefits of any disaster mitigation interventions. During the readiness phase, there is an acceptance that an event is imminent, so the focus of remote sensing data shifts towards forecasting models and using imagery for education and outreach. As we move to the response phase, rapid data acquisition becomes mission critical, and the priority of remote sensing becomes to provide a location, magnitude, and extent of damage; priority areas for rescue missions; and modelling to determine or predict if and where impacts are changing over time. Finally, the focus will shift to long-term monitoring, debris removal, vegetation regeneration, and reconstruction in the cycle’s recovery phase. 

Case study 1: Flooding

Humans have a natural propensity to live close to water, despite the potential hazard that it poses. In fact, the number of people around the world living on floodplains is increasing. In combination with climate change increasing the probability of high rainfall events in many places, we can only expect flooding-related disasters and their impacts to grow in the future. As such, putting measures in place to reduce the flooding risk and ensure communities are ready are critical. 

Remote sensing provides a valuable data source for communities experiencing and at risk of flooding. Many local councils use highly detailed digital elevation models (DEMs) derived from remotely sensed data such as LiDAR of their local areas to assess risk areas and plan interventions. DEMs help delineates catchment boundaries and model likely paths of water flow, highlighting areas of low elevation where water is likely to pool. This can inform engineering and town planning interventions to either divert water flows or determine certain areas are unsuitable for particular land uses (e.g. housing in high-risk areas)

Figure 1: Sentinel-2 satellite imagery over the flood devastated New South Wales town of Lismore. Red colouring depicts healthy vegetation, while the extensive flood waters are seen in shades of green radiating out from the central river system on the 1st and 31st of March.

Both recent and archived satellite imagery is also helpful in identifying current and past flooding events. The sequence of Sentinel satellite images in Figure 1 from northern New South Wales demonstrate how remote sensing can help identify the extent of significant flooding events. However, this figure also shows a limitation of this type of satellite imagery – clouds. This impacts our ability to accurately determine the flood extent, and flood waters will likely recede before the next satellite overpass. This is seen in the image from 21st March, where Lismore residents had a slight reprieve from rain and floodwaters and moved into recovery before the second event passed through later in the month. 

Unfortunately, in severe rain events, capturing satellite data that is not obscured by clouds can be difficult. Alternatives are to use airborne or satellite systems equipped with RADAR sensors that can ‘see through’ the clouds or to deploy a drone capability. Drone-based remote sensing is now commonly used within emergency services and is particularly beneficial in the response phase of the disaster management cycle. This is because drones are relatively quick and easy to deploy with flexibility and capture exceptionally highly detailed data (Figure 2).

Figure 2: Queensland State Emergency Services use a range of drone technologies to capture drone imagery as part of their disaster response capability. The images were captured during the Townsville floods in 2019 and provided courtesy of Queensland Fire and Emergency Services.

Case study 2: Cyclones

According to the Bureau of Meteorology (BoM), nine and 11 tropical cyclones develop annually in the Australian region. On average, four of these cross the coast and may present a hazard to local communities. Much of what the BoM understands regarding tracking and monitoring these cyclones over time is derived from remote sensing.

Broad-scale remote sensing satellite observations are the foundation of tropical cyclone warning systems. Predictive models based on these data give us advanced warning of the likely severity of any given event and the expected location where it may impact. As a cyclone approaches, the readiness phase of the disaster management cycle is enacted, and remote sensing is used to communicate the cyclone progression to the public. Images such as that in Figure 3, where the impending cyclone looks particularly large and well formed, can be used to inform communities and drive evacuations where necessary. 

Figure 3: The massive extent of category 4 Tropical Cyclone Yasi as viewed on the afternoon of 2nd February 2011 shortly before landfall by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS).

Although the image in Figure 3 is impressive, unfortunately the satellite that captured it only revisits the same location once per day. This means that if we want information in a timely manner with greater frequency, we need to rely on another system. Higher altitude systems that are specifically designed to capture and transmit data multiple times every hour are called geostationary satellites. These satellites monitor weather systems at continental scales with high frequency. However, what they offer in timeliness, they lack in detail, and it is impossible to track specific disaster impacts with these broad-scale satellites.

Case study 3: Volcanic eruptions

From monitoring ongoing volcanic unrest to capturing data over highly explosive eruptions, remotely sensed data has a huge role in volcanic disaster management. Catastrophic eruptions can bury entire towns in lava or ash (Figure 4), while the atmospheric plumes (Figure 5) wreak havoc with the aviation industry, potentially ground flights worldwide.

Figure 4: Volcanic ash blankets Tongatapu in Tonga following the eruption of nearby island Hunga Tong-Hunga Ha’apai, captured by Sentinel-2. The island is seen covered in lush green vegetation prior to the eruption and it takes some time for the ash to dissipate throughout the recovery period.
Figure 5: Rapid development of the volcanic smoke, gas, and ash plume in ten-minute intervals following the Tongan eruption, captured by the United States Geostationary Operational Environmental Satellite 17 (GEOS-17).

We often see the surface expressions of eruptions as lahars, lava flows, new land formations, atmospheric gas and ash ejections, and the occasional secondary disaster by triggering a tsunami. However, remote sensing is also used to monitor the ‘belly rumblings’ of active volcanoes. Minor deformations and associated earthquakes are mapped and monitored using interferometric synthetic aperture RaDAR or InSAR. This technique can measure changes in the surface shape of a volcano on a centimetre scale. Scientists try to use this information to predict when a volcano will likely erupt, informing both the reduction and readiness phases of the disaster management cycle.

Summary

From high-altitude satellites to small off-the-shelf drone systems, there are many ways in which a wide variety of remote sensing data types are incorporated into the disaster management cycle. These data can be captured over large areas on time to help inform decision-making about disasters through reduction, readiness, response, and recovery. By taking advantage of all data sources at hand in an integrated manner throughout the disaster management cycle, we hopefully lessen the impact, extent, and duration of events on the community.

Student activities

  1. Define the terms natural hazard and natural disaster.

  2. Suggest reasons why New South Wales experienced the most disastrous events in 2021.

  3.  
    1. Outline the four phases of the disaster management cycle.
    2. Explain how each phase of the disaster management cycle is important in reducing the impact of natural disasters on lives and livelihoods.

  4. Evaluate the importance of remote sensing in disaster management.

  5. Choose an area of the world that has experienced a significant natural disaster.

    1. Create a satellite data time series animation of the location before, during and after the event by following the instructions in the video https://youtu.be/c0WFAsOgHgQ
    2. Describe changes that have occurred in the ecosystem as a result of the natural disaster.
    3. Identify evidence that the ecosystem has, or is, recovering from the impacts of the natural disaster and state how long the recovery process has taken.

  6. Study the declared disaster listings for Australia (https://www.disasterassist.gov.au/find-a-disaster/australian-disasters). Identify any patterns emerging over the years regarding the types of disasters we have experienced in Australia, their location or frequency.

  7. Access the website http://satview.bom.gov.au/ showing recent continental scale satellite data compiled by the Bureau of Meteorology.

    1. Explore the different layers of recent continental scale satellite data and the notes available to the viewer. 
    2. Using the notes, identify the cloud patterns on the imagery and any specific weather events observable on the day you view the satellite data.

  8. For a natural disaster of your choice, create your emergency preparation plan using the online template from the Red Cross or their preparedness app, both available via https://www.redcross.org.au/prepare/.

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