We need to know how to perform some calculations on image bands before we can calculate The layer showing the pixels with both loss and gain is missing. We're almost able to make the image shown at the start of the tutorial.
Opacity of the layer can be affected by the slider next to the layer name. Each layer can be turned off or on using the checkbox next to it, and the Layers button in the upper right of the map reveals these EachĪddLayer() call adds a layer to the map. Observe that there are three addLayer() calls. Forest loss (red), year 2000 cover (green) and gain (blue).
The result should look something like Figure 11. Map.addLayer(gainImage.updateMask(gainImage), Map.addLayer(lossImage.updateMask(lossImage), Loss (green) and gain (blue) which are binary (, 'Forest Cover') Treecover2000 band is expressed as a percent and has values much higher than The reason the image looks red is that the first three bands are Recall that when a multi-band image is added to a map, the first three bands of the imageĪre chosen as red, green, and blue, respectively, and stretched according to the data Study detected forest in the year 2000, red is estimated forest loss over the study period,īlue is forest gain during that period, magenta is areas where forest has been both lost and You'll have an image that looks something like Figure 2, where green represents where the ( Learn moreĪbout default image visualizations in Earth Engine). (2013) forest change data.ĭon't worry, you'll make it look better soon.
Night changes cover code#
Code Editor (JavaScript) var gfc2014 = ee.Image('UMD/hansen/global_forest_change_2015') Ĭlick on the Run button at the top of the Code Editor and you should see
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