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docs/F1.html

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 <section id="exploring-images" class="level1" data-number="5">
 <h1 data-number="5"><span class="header-section-number">5</span> Exploring Images</h1>
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 <section id="author-1" class="level2 unlisted unnumbered">
 <h2 class="unlisted unnumbered anchored" data-anchor-id="author-1">Author</h2>
 <p>Jeff Howarth</p>
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 <li>Sign up for an Earth Engine account, open the Code Editor, and save&nbsp;your script (Chap. F1.0).</li>
 </ul>
 </section>
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 <section id="accessing-an-image" class="level2" data-number="5.1">
 <h2 data-number="5.1" class="anchored" data-anchor-id="accessing-an-image"><span class="header-section-number">5.1</span> Accessing&nbsp;an Image</h2>
 <p>If you have not already done so, be sure to add the book’s code repository to the Code Editor by entering&nbsp;<a href="https://www.google.com/url?q=https://code.earthengine.google.com/?accept_repo%3Dprojects/gee-edu/book&amp;sa=D&amp;source=editors&amp;ust=1670414092189999&amp;usg=AOvVaw1jWHeBmeq93I_lo_9useCA"></a><a href="https://www.google.com/url?q=https://code.earthengine.google.com/?accept_repo%3Dprojects/gee-edu/book&amp;sa=D&amp;source=editors&amp;ust=1670414092190705&amp;usg=AOvVaw3Z7cK8r6eOSYUceNjA8oUg">https://code.earthengine.google.com/?accept_repo=projects/gee-edu/book</a>&nbsp;into your browser. The book’s scripts will then be available in the script manager panel. If you have trouble finding the repo, you can visit <a href="https://www.google.com/url?q=https://docs.google.com/presentation/d/1Kt6wGNoesYm__Cu3k3bnlbbyPN6m9SF4hQHK-pIDHfc/edit%23slide%3Did.g18a7b4b055d_0_624&amp;sa=D&amp;source=editors&amp;ust=1670414092191415&amp;usg=AOvVaw2eETuRpR5worezkj7citx6">this link</a>&nbsp;for help.</p>
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 <section id="survey-of-raster-datasets" class="level1" data-number="6">
 <h1 data-number="6"><span class="header-section-number">6</span> Survey&nbsp;of Raster Datasets</h1>
 <p>The previous chapter introduced you to images, one of the core building blocks of remotely sensed imagery in Earth Engine. In this chapter, we will expand on this concept of images by introducing image collections. Image collections in Earth Engine organize many different images into one larger data storage structure. Image collections include information about the location, date collected, and other properties of each image, allowing you to sift through the ImageCollection&nbsp;for the exact image characteristics needed for your analysis.</p>
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 <section id="authors" class="level2 unlisted unnumbered">
 <h2 class="unlisted unnumbered anchored" data-anchor-id="authors">Authors</h2>
 <p>Andréa Puzzi Nicolau, Karen Dyson, David Saah, Nicholas Clinton</p>
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 <li>Use the Inspector tab to assess pixel values&nbsp;(Chap. F1.1).</li>
 </ul>
 </section>
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 <section id="image-collections-an-organized-set-of-images" class="level2" data-number="6.1">
 <h2 data-number="6.1" class="anchored" data-anchor-id="image-collections-an-organized-set-of-images"><span class="header-section-number">6.1</span> Image Collections: An Organized Set of Images</h2>
 <p>There are many different types of image collections&nbsp;available in Earth Engine. These include collections of individual satellite images, pre-made composites that combine multiple images into one blended image, classified LULC maps, weather data, and other non-optical data sets. Each one of these is useful for different types of analyses. For example, one recent study examined the drivers of wildfires in Australia (Sulova and Jokar&nbsp;2021). The research team used the European Center for Medium-Range Weather Forecast Reanalysis (ERA5) dataset produced by the European Center for Medium-Range Weather Forecasts (ECMWF) and&nbsp;is freely available in Earth Engine. We will look at this dataset later in the chapter.</p>
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 </section>
 <section id="the-remote-sensing-vocabulary" class="level1" data-number="7">
 <h1 data-number="7"><span class="header-section-number">7</span> The Remote&nbsp;Sensing Vocabulary</h1>
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 <section id="authors-1" class="level2 unlisted unnumbered">
 <h2 class="unlisted unnumbered anchored" data-anchor-id="authors-1">Authors</h2>
 <p>Karen Dyson, Andréa Puzzi Nicolau, David Saah, Nicholas Clinton</p>
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 <li>Navigate among Earth Engine result tabs (Chap. F1.0).</li>
 <li>Visualize images with a variety of false-color band combinations (Chap. F1.1).</li>
 </ul>
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 <section id="introduction-1" class="level2 unlisted unnumbered">
 <h2 class="unlisted unnumbered anchored" data-anchor-id="introduction-1">Introduction</h2>
 <p>Images and image collections form the basis of many remote sensing analyses in Earth Engine. There are many different types of satellite imagery available to use in these analyses, but not every dataset is appropriate for every analysis. To choose the most appropriate dataset for your analysis, you should consider multiple factors. Among these are the resolution of the dataset—including the spatial, temporal, and spectral resolutions—as well as how the dataset was created and its quality.</p>