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main.js
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main.js
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/***** End of imports. If edited, may not auto-convert in the playground. *****/
/**
* Final Project: main module
* Joseph R. Tricarico
* Rutgers School of Environmental and Biological Sciences
* 573:462 Advanced Geomatics
* Dr. Richard G. Lathrop
* May 10, 2021
*
* @fileoverview New Jersey Shoreline Change Analysis
*
****************************** DATA ATTRIBUTIONS ******************************
*
* Contains modified Copernicus Sentinel data [2015-] processed by Sentinel Hub
*
* Global 1 Arc-Minute Elevation data from NOAA NCEI
*
* JRC Global Surface Water Mapping Layers, v 1.2
* Jean-Francois Pekel, Andrew Cottam, Noel Gorelick, Alan S. Belward, High-
* resolution mapping of global surface water and its long-term changes.
* Nature 540, 418-422 (2016). (https://www.nature.com/articles/nature20584)
*
* National Agriculture Imagery Program, USDA Farm Production and
* Conservation Business Center, Geospatial Enterprise Operations
*
*******************************************************************************
**/
/** IMPORT REQUIRED CODE from other file(s) */
var logging = require('users/jrt/gee:final/logging.js');
var settings = require('users/jrt/gee:final/settings.js');
var util = require('users/jrt/gee:final/util.js');
var vis = require('users/jrt/gee:final/vis.js');
// set logging level
if(settings.DEBUG) logging.level = 'DEBUG';
/** Set GEOMETRIES */
settings.AOI = studyArea; // barnegatBayRectangle|studyArea
settings.BUFFER_MASK_PATH = 'users/jrt/final/bufferMask'; //...bufferMask|...noaaMask
settings.BASE_SHORELINE = noaaMedResShoreline; // noaaMedResShoreline|noaaCompositeShoreline
settings.TRAINING_REGION = studyArea; // barnegatBayRectangle|studyArea
/** Set NAMESPACE(s) */
var data = {}; // this allows us to dynamically reference data collections
var otsu = {}; // hold variables to calculate NAIP-based NDWI threshold
var manNDWI = {}; // hold vars to calculate change w/ manual NDWI threshold
var manNIR = {}; // hold vars to calculate change w/ manual NIR threshold
/** Local functions extend util module */
/**
* Add a pair of layers to the map for YEAR_1 and YEAR_2 in settings dictionary
* @param {string} objPrefix - prefix of layer stored in global data dictionary
* @param {object} [vp] - [Image visualization parameters](https://git.io/JObzs)
* @param {string} [name] - The name of the layer. Defaults to "Layer N".
* @param {Boolean} [shown] - A flag indicating whether the layer should be on by default.
* @returns {void}
*/
util.addTwoLayers = function(objPrefix, vp, name, shown){
vp = (typeof vp !== 'undefined') ? vp : {};
// TODO: pull name from
name = (typeof name !== 'undefined') ? name : 'Layer (Unnamed)';
shown = (typeof shown !== 'undefined') ? shown : true;
var i, iStr, newName, eeObj;
for(i = 1; i < 3; i++){
iStr = i.toString();
newName = settings['YEAR_'+ iStr] + ' ' + name;
eeObj = data[objPrefix + iStr];
Map.addLayer(eeObj, vp, newName, shown);
}
};
// Make reusable combined reducer (adapted from Murray et al., 2018)
// https://developers.google.com/earth-engine/guides/reducers_intro#combining-reducers
var superDuperReducer = ee.Reducer.median()
// .combine(ee.Reducer.percentile(
// [0, 10, 25, 75, 90, 100],
// // https://en.wikipedia.org/wiki/Quantile#Specialized_quantiles
// ['min', 'decile1', 'quartile1', 'quartile3', 'decile9', 'max']), '', true)
.combine(ee.Reducer.stdDev(), '', true) // args = reducer, no prefix, use same inputs
// .combine(ee.Reducer.intervalMean(0, 10).setOutputs(['intMean0to10']), '', true)
// .combine(ee.Reducer.intervalMean(10, 25).setOutputs(['intMean10to25']), '', true)
// .combine(ee.Reducer.intervalMean(25, 50).setOutputs(['intMean25to50']), '', true)
// .combine(ee.Reducer.intervalMean(50, 75).setOutputs(['intMean50to75']), '', true)
// .combine(ee.Reducer.intervalMean(75, 90).setOutputs(['intMean75to90']), '', true)
// .combine(ee.Reducer.intervalMean(90, 100).setOutputs(['intMean90to100']), '', true)
.combine(ee.Reducer.intervalMean(10, 90).setOutputs(['intMean10to90']), '', true)
.combine(ee.Reducer.intervalMean(25, 75).setOutputs(['iqrMean']), '', true)
;
/******************************************************************************
*************************** GENERAL INPUT PROCESSING **************************
*******************************************************************************/
/***************************
* SHORELINE BUFFER / MASK *
***************************/
/** MAKE VECTOR FEATURES */
/**
* Returns input buffered by settings.BUFFER_M
* @param {ee.Feature} f - feature
* @returns {ee.Feature}
*/
var bufferShoreline = function(f){
return f.simplify(settings.MAX_ERROR)
.buffer(settings.BUFFER_M, settings.MAX_ERROR);
};
// turn shoreline collection into a collection with one feature
// different resolutions available at https://shoreline.noaa.gov/
var baseShoreline = settings.BASE_SHORELINE;
baseShoreline = baseShoreline
.filterBounds(settings.AOI) // narrow feature collection to features intersecting study area
// uncomment line below for composite shoreline only!
// .filterMetadata('F_CODE', 'greater_than', 0) // don't count water boundaries - composite ONLY!
.union(settings.MAX_ERROR); // returns a collection containing a single feature
// logging.debug('baseShoreline', baseShoreline);
// map over collection (even though it's one feature) and buffer feature
data.bufferedShoreline = baseShoreline.map(bufferShoreline); // FeatureClass
/** GET OR CREATE RASTER MASK */
var makeBufferMask = function(){
// make the buffer mask if we don't already have it
data.bufferMask = ee.Image(1).byte().clipToCollection(data.bufferedShoreline);
Export.image.toAsset({ // export mask for future use
assetId: 'final/bufferMask',
image: data.bufferMask,
description: 'bufferMaskExport' + settings.EXPORT_BUFFER_SCALE_M + 'm',
maxPixels: settings.EXPORT_MAX_PIXELS,
region: settings.AOI, // TODO: maybe don't need this?
scale: settings.EXPORT_BUFFER_SCALE_M,
});
};
try {
// usually getInfo is bad but in this case we want to wait and see if this exists+
// and it is not resource intensive. Alternative we could use evaluate() but that
// would involve restructuring every piece of code that depends on this image --
// but that probably would have been the better way to do it.
ee.Image(settings.BUFFER_MASK_PATH).getInfo();
data.bufferMask = ee.Image(settings.BUFFER_MASK_PATH);
logging.debug('found buffer mask:', settings.BUFFER_MASK_PATH);
} catch (error) {
// this will break the script. Export mask & run again.
logging.debug('Error getting bufferMask:', error);
makeBufferMask(); // sets data.bufferMask and makes export task
}
logging.debug("data.bufferMask", data.bufferMask);
// TODO: turn mask into shapes with transparent fill for visualization
// data.bufferMaskOutline = ee.Image().byte().paint({
// featureCollection: data.bufferedShoreline,
// color: 1,
// width: 2,
// });
/************
* NOAA ESI *
************/
/**
* Below code doesn't work because ESI is a string (There is 5A, 5B, etc)
* TODO: enumerate unique ESI values, then assign sequential palette in viz sec
* to indicate less sensitive -> more sensitive and add color legend for ESI vals
*/
// data.esiOutlines = ee.Image().byte().paint({
// featureCollection: esi,
// color: 'ESI',
// });
/******************************************************************************
******************************* S2 DATA ANALYSIS ******************************
*******************************************************************************/
/***********************
* S2 INPUT PROCESSING *
***********************/
// declare filter used for multiple datasets
var spaceTimeFilter = ee.Filter.and(ee.Filter.bounds(settings.AOI),
util.getEntireRangeFilter());
// SENTINEL-2 INPUT PROCESSING //
// Generate a pair of cloud-masked S2 layers
// https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY
// data.s2CloudFilter = ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20); // NOT USED
// filter by space and time - TODO: maybe the latter is unnecessary?
s2 = s2.filter(spaceTimeFilter).select(
["B.+", 'QA60'], // all bands starting with B + 60m QA (bitmask) band
[ 'coastal', 'blue', 'green', 'red', 'vre5', 'vre6', 'vre7', 'nir', 'nnir',
'waterVapor', 'cirrus', 'swir1', 'swir2', 'QA60']);
s2Clouds = s2Clouds.filter(spaceTimeFilter);
var s2Join = ee.Join.saveFirst('cloud_mask').apply({
primary: s2,
secondary: s2Clouds,
condition: ee.Filter.equals({leftField: 'system:index', rightField: 'system:index'})
});
// cast Join result as ImageCollection
// https://developers.google.com/earth-engine/tutorials/tutorial_js_02#casting
s2Join = ee.ImageCollection(s2Join);
// mask out pixels not in the shoreline buffer
// TODO: figure out if this is better or worse than doing .clipToCollection(settings.AOI)
s2Join = s2Join.map(function(i){return i.updateMask(data.bufferMask);}); // TODO: see if needed
var s2CloudMasked = s2Join.map(util.maskS2Clouds);
// stir in some covariates
data.s21 = s2CloudMasked.filter(util.getPeriodRangeFilter(1));
data.s22 = s2CloudMasked.filter(util.getPeriodRangeFilter(2));
/************************************************
* S2: CALCULATE COVARIATES AND MAKE COMPOSITES *
************************************************/
/**
* Returns dictionary of Images, with each image containing related covariates
* @param {ee.Image} img with standardized band names (see util.indexFunctions)
* @returns {Object} Object containing ee.Image objects
*/
var getCovariates = function(img){
var covariates = { // dictionary of ee.Images
// awei: img.map(util.indexFunctions.applyAWEI)
// .reduce(superDuperReducer, settings.PARALLEL_SCALE),
// ndwi: img.map(util.indexFunctions.applyNDWI)
// .reduce(superDuperReducer, settings.PARALLEL_SCALE),
// mndwi: img.map(util.indexFunctions.applyMNDWI)
// .reduce(superDuperReducer, settings.PARALLEL_SCALE),
swi: img.map(util.indexFunctions.applySWI)
.reduce(superDuperReducer, settings.PARALLEL_SCALE),
nir: img.select(['nir'])
.reduce(ee.Reducer.intervalMean(10, 90).setOutputs(['intMean10to90'])),
// elevation: etopo1.select(['bedrock']).resample('bicubic'),
};
return covariates;
};
/**
* Returns composite image using all covariates in getCovariates()
* @param {ee.Image} img
* @returns {ee.Image}
*/
var getCovariateComposite = function(img){
var composite, covariates = getCovariates(img);
for(var key in covariates){
try { // add band to existing composite image
composite = composite.addBands(covariates[key]);
} catch(error) { // composite is not defined
composite = covariates[key];
}
}
return composite;
};
// Option 1/2: more bands, more resource-intensive
data.s2composite1 = getCovariateComposite(data.s21); // composite for first year
data.s2composite2 = getCovariateComposite(data.s22); // composite for second year
// // Option 2/2: fewer bands, less resource-intensive, for dev purposes
// data.s2composite1 = getCovariates(data.s21).swi; // composite for first year
// data.s2composite2 = getCovariates(data.s22).swi; // composite for second year
util.makeImgExpTasks(data.s2composite1, 'Year1Composite');
util.makeImgExpTasks(data.s2composite2, 'Year2Composite');
/****************************
* S2: SUP. CLASSIFICATION *
***************************/
var sup = {}; // namespace
var doSupervisedClassification = function() {
sup.sample = data.bufferMask.sample({ // just to help us pick places to draw
// region: data.bufferedShoreline,
numPixels: 400,
// dropNulls: false,
geometries: true,
scale: 10,
tileScale:4,
});
util.makeImgExpTasks(sup.sample, 'supSample');
logging.debug('sup.sample', sup.sample);
// S2 composite bands used for prediction
sup.bands = ['swi_median', 'swi_stdDev', 'swi_iqrMean', 'swi_intMean10to90',
'nir_intMean10to90',];
sup.collection = ee.FeatureCollection([wet, dry]);
sup.training = data.s2composite1 // TODO: find out if this is the best way to do this or if it's even needed
.sampleRegions({
collection: sup.collection,
properties: ['class'], // 0 or 1
scale: 10,
});