Zach Levitt, '20.5
Jeff Howarth, Associate Professor of Geography
Middlebury College, Vermont, USA
These module functions are provided here as a reference for the main tutorial, which outlines a workflow for calculating topographic variables in Google Earth Enginel. This overview is organized in the same way as the module script, reflecting the major tasks that are involved in performing this type of analysis.
Use this line to reference these functions in another script:
var tt = require('users/zlevitt/chis:topoTools.js');Each function and section is linked below.
Input: path (string)
Output: image (image)
//Load DEM from path
exports.loadImageAsset = function(path){
return ee.Image(path)
}Input: image (image), crs (string), scale (number), optional: mask (image)
Output: image (image)
//Reduces resolution of image
exports.processElevationData = function(image,crs,scale,mask) {
var output = image
.reduceResolution({
reducer: ee.Reducer.mean(),
maxPixels: 1024
})
// Request the data at the scale and projection of the MODIS image.
.reproject({
crs: crs,
scale: scale
});
if (mask !== null){
output = output.updateMask(mask);
}
return output;
};Input: dem (image)
Output: Slope in degrees (image)
//Calculate slope in degrees from elevation data
exports.calculateSlopeDegrees = function(dem){
return ee.Terrain.slope(dem)
}Input: dem (image)
Output: Heat load index (image)
//Calculate heat load index, based on Theobald et al (2015)
exports.calculateTheobaldHLI = function(dem) {
var aspectRadians = ee.Terrain.aspect(dem).multiply(ee.Number(0.01745329252))
var slopeRadians = ee.Terrain.slope(dem).multiply(ee.Number(0.01745329252))
var foldedAspect = aspectRadians.subtract(ee.Number(4.3196899)).abs().multiply(ee.Number(-1)).add(ee.Number(3.141593)).abs()
var theobaldHLI = (slopeRadians.cos().multiply(ee.Number(1.582)).multiply(ee.Number(0.82886954044))).subtract(foldedAspect.cos().multiply(ee.Number(1.5)).multiply(slopeRadians.sin().multiply(ee.Number(0.55944194062)))).subtract(slopeRadians.sin().multiply(ee.Number(0.262)).multiply(ee.Number(0.55944194062))).add(foldedAspect.sin().multiply(ee.Number(0.607)).multiply(slopeRadians.sin())).add(ee.Number(-1.467));
return theobaldHLI.exp().rename('hli')
};Input: dem (image), kernelRadius (number)
Output: Mean image based on radius (image)
// Calculate a neighborhood mean using a pre-defined kernel radius
exports.calculateNeighborhoodMean = function(dem, kernelRadius) {
return dem.reduceNeighborhood({
reducer: ee.Reducer.mean(),
kernel: ee.Kernel.square(kernelRadius,'pixels',false),
optimization: 'boxcar',
});
}Input: dem (image), kernelRadius (number)
Output: Standard deviation image based on radius (image)
//Calculate a neighborhood standard deviation using a pre-defined kernel radius
exports.calculateNeighborhoodStdDev = function(dem, kernelRadius) {
return dem.reduceNeighborhood({
reducer: ee.Reducer.stdDev(),
kernel: ee.Kernel.square(kernelRadius,'pixels',false),
optimization: 'window',
});
}Input: image (image), meanImage (image), stdDevImage (image)
Output: Standardized topographic position index
//Calculate standardized topographic position index, based on Theobald et al (2015)
exports.calculateStandardizedTPI = function(image, meanImage, stdDevImage) {
return (image.subtract(meanImage)).divide(stdDevImage)
};Input: image (image), meanImage (number)
Output: Topographic position index (image)
//Calculate topographic position index from a pre-defined mean image
exports.calculateTPI = function(image, meanImage) {
return image.subtract(meanImage).rename('tpi')
}Input: image1 (image), image2 (number), image3 (number)
Output: Mean topographic position index (image)
//Calculate mean topographic position index, based on Theobald et al (2015)
exports.calculateMeanTPI = function(image1,image2,image3){
return (image1.add(image2).add(image3)).divide(ee.Number(3)).rename('meanTPI')
}Input: dem (image), slopeDegrees (image), theobaldHLI (image), meanTPI (image),tpi_270m (image)
Output: Landforms (image)
exports.calculateLandforms = function(dem,slopeDegrees,theobaldHLI,meanTPI,tpi_270m){
var slopeReclass = ee.Image(1)
.where(slopeDegrees.gt(50), 5000)
.where(slopeDegrees.gt(2).and(slopeDegrees.lte(50)), 1000)
.where(slopeDegrees.lte(2), 2000)
.selfMask();
var theobaldHLIReclass = ee.Image(1)
.where(theobaldHLI.lte(0.448), 100)
.where(theobaldHLI.gt(0.448).and(theobaldHLI.lte(0.767)), 200)
.where(theobaldHLI.gt(0.767), 300)
.selfMask();
var meanTPIReclass = ee.Image(1)
.where(meanTPI.lte(-1.2), 10)
.where(meanTPI.gt(-1.2).and(meanTPI.lte(-0.75)), 20)
.where(meanTPI.gt(-0.75).and(meanTPI.lte(0)), 30)
.where(meanTPI.gt(0),40)
.selfMask();
var tpi_270mReclass = ee.Image(1)
.where(tpi_270m.lte(-5), 1)
.where(tpi_270m.gt(-5).and(tpi_270m.lte(0)), 2)
.where(tpi_270m.gt(0).and(tpi_270m.lte(30)), 3)
.where(tpi_270m.gt(30).and(tpi_270m.lte(300)), 4)
.where(tpi_270m.gt(300),5)
.selfMask();
var reclassCombined = slopeReclass.add(theobaldHLIReclass).add(meanTPIReclass).add(tpi_270mReclass).updateMask(dem);
return reclassCombined
.where(reclassCombined.eq(2344)
.or(reclassCombined.eq(1344)),11)
.where(reclassCombined.eq(1244),12)
.where(reclassCombined.eq(1144),13)
.where(reclassCombined.eq(1145)
.or(reclassCombined.eq(1245))
.or(reclassCombined.eq(1345))
.or(reclassCombined.eq(2345)),14)
.where(reclassCombined.gte(5000).and(reclassCombined.lte(6000)),15)
.where(reclassCombined.eq(1341)
.or(reclassCombined.eq(1342))
.or(reclassCombined.eq(1343)),21)
.where(reclassCombined.eq(1241)
.or(reclassCombined.eq(1242))
.or(reclassCombined.eq(1243)),22)
.where(reclassCombined.eq(1141)
.or(reclassCombined.eq(1142))
.or(reclassCombined.eq(1143)),23)
.where(reclassCombined.eq(2341)
.or(reclassCombined.eq(2342))
.or(reclassCombined.eq(2343)),24)
.where(reclassCombined.eq(1331)
.or(reclassCombined.eq(1332))
.or(reclassCombined.eq(1333))
.or(reclassCombined.eq(1334))
.or(reclassCombined.eq(1335)),31)
.where(reclassCombined.eq(1231)
.or(reclassCombined.eq(1232))
.or(reclassCombined.eq(1233))
.or(reclassCombined.eq(1234))
.or(reclassCombined.eq(1235)),32)
.where(reclassCombined.eq(1131)
.or(reclassCombined.eq(1132))
.or(reclassCombined.eq(1133))
.or(reclassCombined.eq(1134))
.or(reclassCombined.eq(1135)),33)
.where(reclassCombined.eq(2332)
.or(reclassCombined.eq(2333))
.or(reclassCombined.eq(2334))
.or(reclassCombined.eq(2335))
.or(reclassCombined.eq(2331)),34)
.where(reclassCombined.eq(1112)
.or(reclassCombined.eq(1113))
.or(reclassCombined.eq(1121))
.or(reclassCombined.eq(1122))
.or(reclassCombined.eq(1123))
.or(reclassCombined.eq(1124))
.or(reclassCombined.eq(1212))
.or(reclassCombined.eq(1213))
.or(reclassCombined.eq(1221))
.or(reclassCombined.eq(1222))
.or(reclassCombined.eq(1223))
.or(reclassCombined.eq(1224))
.or(reclassCombined.eq(1312))
.or(reclassCombined.eq(1313))
.or(reclassCombined.eq(1321))
.or(reclassCombined.eq(1322))
.or(reclassCombined.eq(1323))
.or(reclassCombined.eq(1324))
.or(reclassCombined.eq(2312))
.or(reclassCombined.eq(2313))
.or(reclassCombined.eq(2321))
.or(reclassCombined.eq(2322))
.or(reclassCombined.eq(2323))
.or(reclassCombined.eq(2324)),41)
.where(reclassCombined.eq(1211)
.or(reclassCombined.eq(1311))
.or(reclassCombined.eq(2311))
.or(reclassCombined.eq(1111)),42)
.updateMask(reclassCombined.select('constant').gt(999))
}Input: landforms (image)
Output: Remapped landforms (image)
//Reclassify landforms layer to fit with visualization parameters.
exports.remapLandforms = function(landforms){
return landforms.remap([11,12,13,14,15,21,22,23,24,31,32,33,34,41,42],[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14])
}Input: landforms (image)
Output: Remapped simplified landforms (image)
//Reclassify landforms layer to fit with simplified visualization parameters.
exports.remapLandformsSimple = function(landforms){
return landforms.remap([11,12,13,14,15,21,22,23,24,31,32,33,34,41,42],[0,0,0,0,0,1,1,1,1,2,2,2,2,3,3])
}Input: landforms (image),geometry (geometry), scale (number), dictionary (dictionary)
Output: Pie chart showing percent of geometry by landform class (chart)
exports.outputChart = function(landforms,geometry,scale,dictionary){
//Calculate areal stats for landforms (full classes)
var stats = ee.Image.pixelArea().addBands(landforms).reduceRegion({
reducer: ee.Reducer.sum().group({
groupField: 1
}),
geometry: geometry,
scale: scale,
});
//reformat stats
var statsFormatted = ee.List(stats.get('groups'))
.map(function(el) {
var d = ee.Dictionary(el);
return [dictionary.get(d.get('group')), ee.Number(d.get('sum')).multiply(0.00000038610215855)];
});
//flatten dictionary
var statsDictionary = ee.Dictionary(statsFormatted.flatten());
return ui.Chart.array.values({
array: statsDictionary.values(),
axis: 0,
xLabels: statsDictionary.keys()
}).setChartType('PieChart')
}Input: title (string), palette (list)
Output: Chart style object (object)
exports.makeChartStyle = function(title, palette){
return {
title: title,
pieHole: 0.33,
padding: '12px',
colors: palette,
chartArea: {
left: 16,
height: '75%',
width: '100%'
},
titleTextStyle: {
color: '#000000',
fontSize: 13,
padding:'5px',
},
legend: {
textStyle: {
alignment: 'end',
color: 'black',
fontName: 'Helvetica',
fontSize: 18
},
position: 'left'
}
}
}