update for v0.1.1-alpha release

README.md

@@ -14,21 +14,16 @@ Murray et al. (in submission) Losses and gains of Earth's tidal wetlands.
 
 #### Usage
 The code developed to map tidal wetlands globally in Earth Engine is designed to be deployed separately in Earth Engine in following order:
-* Develop covariate layers from Landsat Archive Imagery `javascript/covariate_reduce.js`
-* Sample covariate layers using a training library annotated by ecosystem type `javascript/covariate_sample.js`
-* Train a Random Forest model for predicting the distribution of tidal wetlands (Stage 1) `javascript/tidal_wetland_extent.js`
-* Tidal wetland extent post-processing `javascript/extent_post_processor_mmu.js`
-* Identify change flag areas between t1 and t7 `javascript/change_flag.js` and process `javascript/change_post_processor.js`
-* Classify the change flat into loss, gain or stable (Stage 2) `javascript/tidal_wetland_change_classifier.js`
-* Identify the type of ecosystem lost or gained over the study period (Stage 3) `javascript/tidal_wetland_type.js`
-
-The result is an image with six bands:
-* "loss"
-* "lossYear"
-* "lossType"
-* "gain"
-* "gainYear"
-* "gainType"
+1. Develop covariate layers from Landsat Archive Imagery `javascript/covariate_reduce.js`
+2. Sample covariate layers using a training library annotated by ecosystem type `javascript/covariate_sample.js`
+3. Train a Random Forest model for predicting the distribution of tidal wetlands (Stage 1) `javascript/tidal_wetland_extent.js`
+4. Tidal wetland extent post-processing `javascript/extent_post_processor_mmu.js`
+5. Identify change flag areas between t1 and t7 `javascript/change_flag.js` 
+6. Post process change `javascript/change_post_processor.js`
+7. Classify the change flat into loss, gain or stable (Stage 2) `javascript/tidal_wetland_change_classifier.js`
+8. Identify the type of ecosystem lost or gained over the study period (Stage 3) `javascript/tidal_wetland_type.js`
+
+The result is an image with six bands: loss, lossYear, lossType, gain, gainYear, gainType.
 
 ![img](figures/wetland_change.jpg)
 

javascript/change_flag.js

@@ -39,7 +39,7 @@ var makeLossGain = function(y1, y2, year){
   var loss = y1.select([1])
     .updateMask(y2.select([1]).unmask().lt(1))
     .updateMask(gic2001.select([1])) // can only be lost if occurred in 2001
-    .updateMask(gic2019.select([1]).unmask().lt(1)) // can only be lost if no occurrence in 2019
+    .updateMask(gic2019.select([1]).unmask().lt(1)) // can only be lost if no occurrences in 2019
     .rename(['loss']);
   var lossYear = loss.remap([1],[year])
     .rename(['lossYear']).int()

javascript/covariate_reduce.js

@@ -4,7 +4,7 @@
 
 var simpleCoastLine = ee.FeatureCollection('projects/UQ_intertidal/dataMasks/simpleNaturalEarthCoastline_v1').first().geometry();
 var site = ee.Geometry.Polygon([-180, 60, 0, 60, 180, 60, 180, -60, 10, -60, -180, -60], null, false);
-var dataMask = ee.Image('projects/UQ_intertidal/dataMasks/topyBathyEcoMask_300m_v2_0_3'); 
+var dataMask = ee.Image('projects/UQ_intertidal/dataMasks/topyBathyEcoMask_300m_v2_0_3');  // sets mapping area
 var startDate = '2017-01-01';
 var endDate = '2019-12-31';
 var bandSelect = ['green', 'swir1', 'swir2', 'nir', 'red', 'blue'];
@@ -133,8 +133,7 @@ var covariates = {
 
 var covariateName = 'evi'; // export separately ([awe, ndw, mnd, ndv, evi, nir_1090, gre_1090, swi_1090]))
 
-var assetName = 'projects/UQ_intertidal/covariate_layers/L3_'
-  .concat(covariateName)
+var assetName = 'foo' // set file path
   .concat('_')
   .concat(startDate.slice(0,4))
   .concat(endDate.slice(0,4))

javascript/covariate_sample.js

@@ -3,9 +3,9 @@
  */
 
 var trainingSet = ee.FeatureCollection('foo'); // path to training set
-var startDate = '2014-01-01'; // Reference period
-var endDate = '2016-12-31'; // Reference period
-var covariatePath = 'foo', // Covariates path
+var startDate = '2014-01-01'; // reference period for sampling
+var endDate = '2016-12-31'; // reference period for sampling
+var covariatePath = 'foo', // path to covariates
 var yearString = startDate.slice(0,4)
   .concat(endDate.slice(0,4));
 
@@ -47,7 +47,7 @@ var predictorSet = trainingSet.map(sampleCovariates);
 
 // export
 
-var assetName = 'foo'; // path and asset name
+var assetName = 'foo'; // path and asset name of predictorSet
 
 var vars = {
   startDate:startDate,

javascript/tidal_wetland_change_classifier.js

@@ -2,7 +2,7 @@
  * Classify change flags as loss, gain or stable.
  */
 
-var changeFlagImage = ee.Image('foo'); // path to change flag image
+var changeFlagImage = ee.Image('foo'); // path to changeFlag_pp2
 var site = ee.Geometry.Polygon([-180, 60, 0, 60, 180, 60, 180, -60, 10, -60, -180, -60], null, false);  
 var gic2001 = ee.Image('foo'), // import post-processed tw_export (pp)
     gic2004 = ee.Image('foo'),
@@ -11,11 +11,19 @@ var gic2001 = ee.Image('foo'), // import post-processed tw_export (pp)
     gic2013 = ee.Image('foo'),
     gic2016 = ee.Image('foo'),
     gic2019 = ee.Image('foo');
-var covariatePath = 'foo', // Covariates path
+var covariatePath = 'foo', // path to covariates folder
+var trainingSet = 'foo' // path to loss/gain/stable training data
 
-// Single image from change Flag
-var changeFlag = changeFlagImage.select(['loss']).unmask().add(changeFlagImage.select(['gain']).unmask()).gte(1).selfMask();
 
+// Single image from change Flag
+var changeFlag = changeFlagImage
+  .select(['loss'])
+  .unmask()
+  .add(changeFlagImage
+    .select(['gain'])
+    .unmask())
+  .gte(1)
+  .selfMask();
 
 var covariateLoader = function(covariateCode, yearString){
   var assetPath = covariatePath 
@@ -48,7 +56,7 @@ var s1_probCollection = ee.ImageCollection([
     gic2019.select([0]).addBands(ee.Image(7).int())
     ]);
 
-var linearFit = s1_probCollection.reduce(ee.Reducer.linearFit()); // the trend line plus would help with gain, minus with loss
+var linearFit = s1_probCollection.reduce(ee.Reducer.linearFit());
 
 var fitTrend = linearFit.select('scale'); // trend
 
@@ -64,13 +72,12 @@ var covariateComposite = covariateLoader('awe', '20172019').subtract(covariateLo
 
 var bands = covariateComposite.bandNames();
 
-var trainingSet = 'foo' // path to loss/gain/stable training data
 
 var predictorSet = trainingSet.map(samplePredictors)
-  .distinct('.geo') // remove spatial duplicates
-  .distinct('awe_0010') // remove other duplicates
+  .distinct('.geo') 
+  .distinct('awe_0010') 
   .filter(ee.Filter.neq('awe_min', null))
-  .randomColumn('random',1) //add random number //v200 to v202 were seed 1. Now trying seed 2 to see what happens to mangroves in Norfolk. 
+  .randomColumn('random',1)
   .sort('random'); 
 
 var classifier = ee.Classifier.smileRandomForest({

javascript/tidal_wetland_extent.js

@@ -11,9 +11,9 @@ var globOptions = {
   endDate: '2019-12-31',
   outScale: 30, 
   probabilityThreshold: 50,
-  covariatePath: 'foo', // Covariates path
+  covariatePath: 'foo', // path to covariate folder
   landsatCollection: 'C01/T1_SR',
-  trainingDataID:'foo',  // Training data path
+  trainingDataID:'foo',  // path to predictorSet
   dateGenerated: ee.Date(Date.now()),
   classLabelList: ['Land', 'Water', 'Tidal Flat', 'Mangrove', 'Saltmarsh', 'Seagrass', 'Aquaculture'],
   classValueList: [0,1,2,3,5,6,7],