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<title>Google Earth Engine for OSINT - 1&nbsp; Remote Sensing</title>
<title>Remote Sensing for OSINT - Remote Sensing</title>
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@@ -85,7 +85,7 @@ gtag('config', 'G-RK9ZLZQ6GL', { 'anonymize_ip': true});
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<h1 class="quarto-secondary-nav-title"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Remote Sensing</span></h1>
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<h2 id="toc-title">Table of contents</h2>
<ul>
<li><a href="#active-and-passive-sensors" id="toc-active-and-passive-sensors" class="nav-link active" data-scroll-target="#active-and-passive-sensors"><span class="toc-section-number">1.1</span> Active and Passive Sensors</a></li>
<li><a href="#resolution" id="toc-resolution" class="nav-link" data-scroll-target="#resolution"><span class="toc-section-number">1.2</span> Resolution</a>
<li><a href="#active-and-passive-sensors" id="toc-active-and-passive-sensors" class="nav-link active" data-scroll-target="#active-and-passive-sensors">Active and Passive Sensors</a></li>
<li><a href="#resolution" id="toc-resolution" class="nav-link" data-scroll-target="#resolution">Resolution</a>
<ul class="collapse">
<li><a href="#spatial-resolution" id="toc-spatial-resolution" class="nav-link" data-scroll-target="#spatial-resolution"><span class="toc-section-number">1.2.1</span> Spatial Resolution</a></li>
<li><a href="#spectral-resolution" id="toc-spectral-resolution" class="nav-link" data-scroll-target="#spectral-resolution"><span class="toc-section-number">1.2.2</span> Spectral Resolution</a></li>
<li><a href="#temporal-resolution" id="toc-temporal-resolution" class="nav-link" data-scroll-target="#temporal-resolution"><span class="toc-section-number">1.2.3</span> Temporal Resolution</a></li>
<li><a href="#spatial-resolution" id="toc-spatial-resolution" class="nav-link" data-scroll-target="#spatial-resolution">Spatial Resolution</a></li>
<li><a href="#spectral-resolution" id="toc-spectral-resolution" class="nav-link" data-scroll-target="#spectral-resolution">Spectral Resolution</a></li>
<li><a href="#temporal-resolution" id="toc-temporal-resolution" class="nav-link" data-scroll-target="#temporal-resolution">Temporal Resolution</a></li>
</ul></li>
<li><a href="#orbits" id="toc-orbits" class="nav-link" data-scroll-target="#orbits"><span class="toc-section-number">1.3</span> Orbits</a></li>
<li><a href="#summary" id="toc-summary" class="nav-link" data-scroll-target="#summary">Summary</a></li>
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<h1 class="title d-none d-lg-block"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">Remote Sensing</span></h1>
<h1 class="title d-none d-lg-block">Remote Sensing</h1>
</div>
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</header>
<p>Before learning how to load, process, and analyze satellite imagery in Google Earth Engine, it will be helpful to know a few basic principles of remote sensing. This section provides a brief overview of some important concepts and terminology that will be used throughout the course, including active and passive sensors; spatial, spectral, and temporal resolution; and orbits.</p>
<section id="active-and-passive-sensors" class="level2" data-number="1.1">
<h2 data-number="1.1" class="anchored" data-anchor-id="active-and-passive-sensors"><span class="header-section-number">1.1</span> Active and Passive Sensors</h2>
<section id="active-and-passive-sensors" class="level2">
<h2 class="anchored" data-anchor-id="active-and-passive-sensors">Active and Passive Sensors</h2>
<p><a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/remote-sensing">Remote sensing</a> is the science of obtaining information about an object or phenomenon without making physical contact with the object. Remote sensing can be done with various types of electromagnetic radiation such as visible, infrared, or microwave. The electromagnetic radiation is either emitted or reflected from the object being sensed. The reflected radiation is then collected by a sensor and processed to obtain information about the object.</p>
<p><img src="./images/diagram.png" class="img-fluid"></p>
<p>While most satellite imagery is optical, meaning it captures sunlight reflected by the earth’s surface, Synthetic Aperture Radar (SAR) satellites such as Sentinel-1 work by emitting pulses of radio waves and measuring how much of the signal is reflected back. This is similar to the way a bat uses sonar to “see” in the dark: by emitting calls and listening to echoes.</p>
</section>
<section id="resolution" class="level2" data-number="1.2">
<h2 data-number="1.2" class="anchored" data-anchor-id="resolution"><span class="header-section-number">1.2</span> Resolution</h2>
<p>Resolution is one of the most important attributes of satellite imagery.</p>
<p>here are three types of resolution: spatial, spectral, and temporal.</p>
<section id="spatial-resolution" class="level3" data-number="1.2.1">
<h3 data-number="1.2.1" class="anchored" data-anchor-id="spatial-resolution"><span class="header-section-number">1.2.1</span> Spatial Resolution</h3>
<section id="resolution" class="level2">
<h2 class="anchored" data-anchor-id="resolution">Resolution</h2>
<p>Resolution is one of the most important attributes of satellite imagery. There are three types of resolution: spatial, spectral, and temporal. Let’s look at each of these.</p>
<section id="spatial-resolution" class="level3">
<h3 class="anchored" data-anchor-id="spatial-resolution">Spatial Resolution</h3>
<p>Spatial resolution governs how “sharp” an image looks. The Google Maps satellite basemap, for example, is really sharp Most of the optical imagery that is freely available has relatively low spatial resolution (it looks more grainy than, for example, the Google satellite basemap),</p>
<p><img src="./images/Landsat.png" class="img-fluid"> <img src="./images/Sentinel2.png" class="img-fluid"> <img src="./images/Maxar.png" class="img-fluid"></p>
</section>
<section id="spectral-resolution" class="level3" data-number="1.2.2">
<h3 data-number="1.2.2" class="anchored" data-anchor-id="spectral-resolution"><span class="header-section-number">1.2.2</span> Spectral Resolution</h3>
<p>What open source imagery lacks in spatial resolution it often makes up for with <em>spectral</em> resolution. Really sharp imagery from MAXAR, for example, collects</p>
<p>Different materials reflect light differently. An apple absorbs shorter wavelengths (e.g.&nbsp;blue and green), and reflects longer wavelengths (red). Our eyes use that information– the color– to distinguish between different objects. But our eyes can only see a relatively small sliver of the electromagnetic spectrum covering blue, yellow, and red; we can’t see UV or infrared wavelengths, for example, though the extent to which different materials reflect or absorb these wavelengths is just as useful for distinguishing between them. For example, Astroturf (fake plastic grass) and real grass will both look green to us, espeically from a satellite image. But living plants absorb radiation from the sun in a part of the light spectrum that we can’t see. There’s a spectral index called the Normalized Difference Vegetation Index (NDVI) which exploits this fact to isolate vegetation in multispectral satellite imagery. So if we look at <a href="https://en.wikipedia.org/wiki/Gillette_Stadium">Gilette Stadium</a> near Boston, we can tell that the three training fields south of the stadium are real grass (they generate high NDVI values, showing up red), while the pitch in the stadium itself is astroturf (generating low NDVI values, showing up blue).</p>
<section id="spectral-resolution" class="level3">
<h3 class="anchored" data-anchor-id="spectral-resolution">Spectral Resolution</h3>
<p>What open access imagery lacks in spatial resolution it often makes up for with <em>spectral</em> resolution. Really sharp imagery from MAXAR, for example, mostly collects light in the visible light spectrum, which is what our eyes can see. But there are other parts of the electromagnetic spectrum that we can’t see, but which can be very useful for distinguishing between different materials. Many satellites that have a lower spatial resolution than MAXAR, such as Landsat and Sentinel-2, collect data in a wider range of the electromagnetic spectrum.</p>
<p>Different materials reflect light differently. An apple absorbs shorter wavelengths (e.g.&nbsp;blue and green), and reflects longer wavelengths (red). Our eyes use that information– the color– to distinguish between different objects. Below is a plot of the spectral profiles of different materials:</p>
<iframe title="Spectral Profiles of Different Materials" aria-label="Interactive line chart" id="datawrapper-chart-b1kcX" src="https://datawrapper.dwcdn.net/b1kcX/3/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important; border: none;" height="400">
</iframe>
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<p>The visible portion of the spectrum is highlighted on the left, ranging from 400nm (violet) to 700nm (red). Our eyes (and satellite imagery in the visible light spectrum) can only see this portion of the light spectrum; we can’t see UV or infrared wavelengths, for example, though the extent to which different materials reflect or absorb these wavelengths is just as useful for distinguishing between them. The European Space Agency’s Sentinel-2 satellite collects spectral information well beyond the visible light spectrum, enabling this sort of analysis. It chops the electromagnetic spectrum up into “bands”, and measures how strongly wavelengths in each of those bands is reflected:</p>
<p><img src="images/S2_bands.png" class="img-fluid"></p>
<p>To illustrate why this is important, consider Astroturf (fake plastic grass). Astroturf and real grass will both look green to us, espeically from a satellite image. But living plants strongly reflect radiation from the sun in a part of the light spectrum that we can’t see (near-infrared). There’s a spectral index called the Normalized Difference Vegetation Index (NDVI) which exploits this fact to isolate vegetation in multispectral satellite imagery. So if we look at <a href="https://en.wikipedia.org/wiki/Gillette_Stadium">Gilette Stadium</a> near Boston, we can tell that the three training fields south of the stadium are real grass (they generate high NDVI values, showing up red), while the pitch in the stadium itself is astroturf (generating low NDVI values, showing up blue).</p>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img src="images/NDVI.jpg" class="img-fluid figure-img"></p>
<p></p><figcaption class="figure-caption">VHR image of Gilette Stadium with Sentinel-2 derived NDVI overlay</figcaption><p></p>
</figure>
</div>
<p>In other words, even though these fields are all green and indistinguishable to the human eye, their <em>spectral profiles</em> beyond the visible light spectrum differ, and we can use this information to distinguish between them. Below is a plot of the spectral profiles of different materials, including oil.</p>
<iframe title="Spectral Profiles of Different Materials" aria-label="Interactive line chart" id="datawrapper-chart-b1kcX" src="https://datawrapper.dwcdn.net/b1kcX/3/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important; border: none;" height="400">
</iframe>
<script type="text/javascript">!function(){"use strict";window.addEventListener("message",(function(e){if(void 0!==e.data["datawrapper-height"]){var t=document.querySelectorAll("iframe");for(var a in e.data["datawrapper-height"])for(var r=0;r<t.length;r++){if(t[r].contentWindow===e.source)t[r].style.height=e.data["datawrapper-height"][a]+"px"}}}))}();
</script>
<p>The European Space Agency’s Sentinel-2 satellite collects spectral information well beyond the visible light spectrum, enabling this sort of analysis. It chops the electromagnetic spectrum up into “bands”, and measures how strongly wavelengths in each of those bands is reflected:</p>
<p><img src="images/S2_bands.png" class="img-fluid"></p>
<p>We’ll be using this satellite to distinguish between oil and other materials, similar to the way we were able to distinguish between real and fake grass at Gilette Stadium. First, we’ll have to do a bit of pre-processing on the Sentinel-2 imagery after which we’ll train a machine learning model to identify oil.</p>
<p>In other words, even though these fields are all green and indistinguishable to the human eye, their <em>spectral profiles</em> beyond the visible light spectrum differ, and we can use this information to distinguish between them.</p>
<p>Astroturf is a trivial example. But suppose we were interested in identifying makeshift oil refineries in Northern Syria that constitute a key source of rents for whichever group controls them. As demonstrated in the <a href="./refineries.html">‘Refinery Identification’</a> case study, we can train an algorithm to identify the spectral signatures of oil, and use that to automatically detect them in satellite imagery.</p>
</section>
<section id="temporal-resolution" class="level3" data-number="1.2.3">
<h3 data-number="1.2.3" class="anchored" data-anchor-id="temporal-resolution"><span class="header-section-number">1.2.3</span> Temporal Resolution</h3>
<p>Finally, the frequency with which we There is often a tradeoff between spatial and temporal resolution.</p>
<section id="temporal-resolution" class="level3">
<h3 class="anchored" data-anchor-id="temporal-resolution">Temporal Resolution</h3>
<p>Finally, the frequency with which we can access new imagery is an important consideration. This is called the <strong>temporal resolution</strong>.</p>
<p>The Google Maps basemap is very high resolution, available globally, and is freely available. But it has no <em>temporal</em> dimension: it’s a snapshot from one particular point in time. If the thing we’re interested in involves <em>changes</em> over time, this basemap will be of limited use.</p>
<p>The <strong>“revisit rate”</strong> is the amount of time it takes for the satellite to pass over the same location twice. The revisit rate is inversely proportional to the satellite’s altitude: the higher the satellite is, the more frequently it can pass over the same location. This generally means that there’s a tradeoff between spatial resolution and temporal resolution: the higher the spatial resolution, the lower the revisit rate. However, some satellite constellations such as Planet’s SkySat are able to achieve both high spatial and temporal resolution by launching lots of small satellites into orbit at once. Below is a comparison of revisit rates for various satellites:</p>
<ul>
<li><a href="https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/revisit-and-coverage">Sentinel 1</a>: 3 days (6 days as of 23/12/21, since Sentinel-1B was decomisioned)</li>
<li><a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-2">Sentinel 2</a>: 5 days</li>
<li><a href="https://landsat.gsfc.nasa.gov/satellites/landsat-9/#:~:text=Landsat%209%20replaces%20Landsat%207,for%20Landsat%208%20%2B%20Landsat%207.">Landsat 8-9</a>: 8 days</li>
<li><a href="https://www.planet.com/pulse/12x-rapid-revisit-announcement/">Planet SkySat</a>: 2-3 hours</li>
</ul>
<p>The <strong>“revisit rate”</strong> is the amount of time it takes for the satellite to pass over the same location twice. For example, the Sentinel-2 constellation’s two satellites can achieve a revisit rate of 5 days, as shown in this cool video from the European Space Agency:</p>
<div class="quarto-video"><video id="video_shortcode_videojs_video1" class="video-js vjs-default-skin vjs-fluid" controls="" preload="auto" data-setup="{}" title=""><source src="https://dlmultimedia.esa.int/download/public/videos/2016/08/004/1608_004_AR_EN.mp4"></video></div>
<p>Some satellite constellations are able to achieve much higher revisit rates. Sentinel-2 has a revisit rate of 5 days, but SkySat capable of imaging the same point on earth around 12 times per day! How is that possible? Well, as the video above demonstrated, the Sentinel-2 constellation is composed of two satellites that share the same orbit, 180 degrees apart. In contrast, the SkySat constellation comprises 21 satellites, each with its own orbital path:</p>
<div class="quarto-video"><video id="video_shortcode_videojs_video2" class="video-js vjs-default-skin vjs-fluid" controls="" preload="auto" data-setup="{}" title=""><source src="https://assets.planet.com/products/hi-res/Planet_Block_3_HD_1080p.mp4"></video></div>
<p>This allows SkySat to achieve a revisit rate of 2-3 <em>hours</em>. The catch, however, is that you need to pay for it (and it <a href="https://apollomapping.com/blog/an-update-on-skysat-tasking-pricing-and-video-capabilities">ain’t cheap</a>). Below is a comparison of revisit rates for various other optical satellites:</p>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img src="images/revisit_chart.png" class="img-fluid figure-img"></p>
<p></p><figcaption class="figure-caption">A chart of revisit times for different satellites from <a href="https://link.springer.com/article/10.1007/s10712-021-09637-5">Sutlieff et. al.(2021)</a></figcaption><p></p>
</figure>
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</section>
</section>
<section id="orbits" class="level2" data-number="1.3">
<h2 data-number="1.3" class="anchored" data-anchor-id="orbits"><span class="header-section-number">1.3</span> Orbits</h2>
<p>The Landsat satellites are in a sun-synchronous orbit, meaning they pass over the same spot on Earth at the same time every day. The Sentinel satellites are in a polar orbit, meaning they pass over the same spot on Earth twice a day, once in the morning and once in the afternoon. NASA have created a great <a href="https://svs.gsfc.nasa.gov/4745">visualisation</a> showing the orbits of the Landsat and Sentinel-2 satellites:</p>
<div class="quarto-video"><video id="video_shortcode_videojs_video1" class="video-js vjs-default-skin vjs-fluid" controls="" preload="auto" data-setup="{}" title=""><source src="https://svs.gsfc.nasa.gov/vis/a000000/a004700/a004745/landsat_w_sentinel_ls8ls9sAsB_fade_1080p60.mp4"></video></div>
<p>The Sentinel satellites are in a lower orbit than Landsat, meaning they are closer to the Earth and have a higher resolution.</p>
<section id="summary" class="level2">
<h2 class="anchored" data-anchor-id="summary">Summary</h2>
<p>You should hopefully have a better understanding of what satellite imagery is, and how it can be used to answer questions about the world. In the <a href="./ch2.html">next section</a>, we’ll look at the various types of satellite imagery stored in the Google Earth Engine catalogue.</p>
</section>
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