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2022 Spezielle Themen der Fernerkundung/hansen_02b_CalculatingLossAndGainArea.js

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+//Now that you're more familiar with the bands in the Hansen et al. dataset, 
+//we can use concepts learned so far to compute statistics about forest gain 
+//and loss in a region of interest. For this we'll need to use vector data 
+//(points, lines, and polygons). A vector dataset is represented as a 
+//FeatureCollection in Earth Engine. (Learn more about feature collections 
+//and how to import vector data.)
+
+//In this section, we'll compare the total amount of forest loss that happened 
+//within the Republic of the Congo in the year 2012 to the amount of forest loss that 
+//happened within the country's protected areas at the same time.
+
+//As you learned in the Earth Engine API tutorial, the key method for calculating 
+//statistics in an image region is reduceRegion(). (Learn more about reducing image 
+//regions.) For example, suppose we want to calculate the number of pixels estimated 
+//to represent forest loss during the study period. For that purpose, consider the 
+//following code.
+
+
+//The example uses the ee.Reducer.sum() reducer to sum the values of the 
+//pixels in lossImage within the Republic of the Congo feature. Because lossImage consists 
+//of pixels that have a value of 1 or 0 (for loss or not loss, respectively), 
+//the sum of these values is equivalent to the number of pixels of loss in the 
+//region.
+
+
+// Load country features from Large Scale International Boundary (LSIB) dataset.
+var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
+
+// Subset the Republic of the Canada feature from countries.
+var germany = countries.filter(ee.Filter.eq('country_na', 'Germany'));
+
+// Get the forest loss image.
+var gfc2014 = ee.Image("UMD/hansen/global_forest_change_2020_v1_8");
+var lossImage = gfc2014.select(['loss']);
+
+// Sum the values of forest loss pixels in Canada.
+var stats = lossImage.reduceRegion({
+  reducer: ee.Reducer.sum(),
+  geometry: germany,
+  scale: 30,
+  maxPixels: 2e9
+});
+print(stats);
+
+
+//By expanding the object printed to the console, observe that the 
+//result is 4897933 (in the original example!) pixels of forest lost. You can 
+//clean up the printout in the console a bit by labeling the output 
+//and getting the result of interest from the dictionary returned 
+//by reduceRegion():
+
+print('pixels representing loss: ', stats.get('loss'));
+
+
+//You're almost ready to answer the question of how much area was lost in 
+//the Republic of the Congo. 
+//The remaining part is to convert pixels into actual area. This conversion 
+//is important because we don't necessarily know the size of the pixels input 
+//to reduceRegion(). To help compute areas, Earth Engine has the ee.Image.pixelArea() 
+//method which generates an image in which the value of each pixel is the pixel's 
+//area in square meters. Multiplying the loss image with this area image and then 
+//summing over the result gives us a measure of area:
+// Load country features from Large Scale International Boundary (LSIB) dataset.
+var countries = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017');
+
+// Subset Republic of the Congo feature from countries.
+var germany = countries.filter(ee.Filter.eq('country_na', 'Germany'));
+
+// Get the forest loss image.
+//var gfc2014 = ee.Image('UMD/hansen/global_forest_change_2015'); //we did already open that one
+var lossImage = gfc2014.select(['loss']);
+var areaImage = lossImage.multiply(ee.Image.pixelArea()).divide(1000000);
+
+
+// Sum the values of forest loss pixels in Canada.
+var stats = areaImage.reduceRegion({
+  reducer: ee.Reducer.sum(),
+  geometry: germany,
+  scale: 30,
+  maxPixels: 2e9
+});
+
+print('pixels representing loss: ', stats.get('loss'), 'square km');
+
+
+//You are now ready to answer the question at the start of this section - how 
+//much forest area was lost in the Republic of the Congo in 2012, and how much of that 
+//was in protected areas? Have a look in the next script.