[Community Tutorials] Studying Land Surface Temperature in Uganda

tutorials/ph-ug-temp/index.md

@@ -0,0 +1,102 @@
+---
+title: Studying Land Surface Temperature for Public Health Research in Uganda
+description: This tutorial shows how to describe, visualize, and export Earth Engine exposure data for public health research.
+author: hiyer09
+tags: temperature, descriptive analysis, Africa, remote sensing
+date_published: 2019-09-17
+---
+
+[Open In Code Editor](https://code.earthengine.google.com/71e14cc3b383200c83007f89f82d1076)
+
+Learning objectives:
+* Introduce remote sensing data.
+* Learn how to obtain and process raster climate data in Google Earth Engine.
+
+Remote sensing (satellite-derived) data are a rich source of information about climate variables, including land cover, vegetation, rainfall, and temperature. Satellite data are available going back to the 1980s in many cases, and so they provide a means for doing historical analyses of changing geographies that can be linked with epidemiologic data for analysis.
+In this exercise, we will learn how to acquire remote sensing data from Google Earth Engine. We will learn how to perform time series analysis to study changes in climate variables over our chosen geography and time period. We will also apply some common geospatial procedures (zonal, focal statistics). Finally, we will learn how to export our Earth Engine raster files to Google Drive. 
+
+
+## Writing a simple Javascript program to extract and process data
+
+We will analyze Land Surface Temperature (LST) data collected using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. This satellite captures images of the Earth’s surface in 1-2 day intervals, with spatial resolution of 1000m. Data are available from March 5, 2000 to the present. In addition to temperature, the MODIS satellite captures data on other climate and Earth-based variables (e.g. vegetation indices).
+Using the Earth Engine code editor, users can manipulate these data over a shorter time period. The tool provides LST long-term and short-term time series for user-defined regions and time periods. This tool will restrict data from March 5, 2000 to the present. 
+
+1. Pull in a shape file of Uganda. 
+
+    In these lines of code, we are creating a new variable called region as before. However, now we are pulling in a “FeatureCollection” object, and filtering by ‘Country’ to select ‘Uganda.’ FeatureCollections are groups of features (spatial data and attributes).  “Filter” is the command to extract a specific set of feature data from a feature collection. We then map it.
+
+  ```
+  var ug = ee.FeatureCollection('ft:1tdSwUL7MVpOauSgRzqVTOwdfy17KDbw-1d9omPw') 
+    .filter(ee.Filter.eq('Country', 'Uganda'));
+
+  Map.addLayer(ug)
+  ```
+2. Read in land surface data.
+
+    Now, we need to read in land surface temperature data. 'ImageCollection' objects store collections of images. Using the Earth Engine Data Catalog, we can find different image collections. The 'ImageCollection' we are reading in here contains the LST data, but we can read in a different 'ImageCollection' for other types of data (e.g. vegetation index, or rain fall, light at night).
+Our code applies filters to restrict images for Uganda, within January to December 2015.
+
+```
+
+var modis = ee.ImageCollection('MODIS/MOD11A2') // read in image collection
+
+var mod11a2 = modis.
+  filterBounds(ug). // just pull for Uganda
+  filterDate('2015-01-01', '2015-12-31'); // take 1 year (2015)
+
+var modLSTday = mod11a2.select('LST_Day_1km'); // pull just 1km day LST
+
+```
+3. Create a function to convert temperature units.
+
+    We then need to convert temperature from Kelvin to Celsius. We can do this by writing a function to do the conversion, and then by mapping over the data.
+
+```
+var modLSTc = modLSTday.map(function(img) {
+  return img.multiply(0.02).subtract(273.15).copyProperties(img,['system:time_start','system:time_end']); 
+}); 
+
+```
+4. Describe land surface temperature data using time series.
+
+    It can be helpful to describe your data using a time series graph. We can plot the mean land surface temperature over the year using the following code:
+
+```
+// Charts Long-term Time Series
+var TS1 = ui.Chart.image.series(modLSTc, ug,
+ ee.Reducer.mean(), 1000, 'system:time_start').setOptions({
+   title: 'LST Long-Term Time Series',
+   vAxis: {title: 'LST Celsius'},
+ });
+ print(TS1);
+
+```
+
+5. Visualize processed data on a map.
+
+    Now, we may want to visualize our data. We can take the mean LST in celsius, and clip to Uganda. This code will produce a map of the mean temperatures for us to view in the map window.
+
+```
+
+var clippedLSTc = modLSTc.mean().clip(ug);
+// Add clipped image layer to map
+Map.addLayer(clippedLSTc, {'min': 0, 'max': 40, 'palette':"0000ff,32cd32,ffff00,ff8c00,ff0000 "});
+
+```
+6. Export data for further analysis.
+
+    Finally, we need to export our raster image file to perform further analysis (e.g. link to participant or clinic data). We can use the export command to download the processed image data to our Google Drive folder.
+
+```
+
+Export.image.toDrive({
+        image: clippedLSTc,
+        description: 'LST_Celsius_ug',
+        region: ug,
+        scale: 1000,
+        crs: 'EPSG:4326',
+        maxPixels: 1e10,
+      });
+
+```
+With that, we have successfully described, processed, and exported land surface temperature data for 2015 in Uganda.