OsaWaterII_1987Training.js

// Script: OsaWaterII_1987Training
// Authors: Tanner Johnson, Taufiq Rashid, John Langstaff
// Date: August 7, 2018 
// Project: Osa Peninsula Water Resources II, Summer 2018 
// Node: Athens - Georgia
// Contacts: tannerjohnson56@gmail.com, taufiq.rashid06@gmail.com, jtlang24@gmail.com

// Description: This code classifies land use on the Osa peninsula based on Landsat 5 imagery using training 
//              data from 1987. The code creates a cloud free, greenest pixel composite to use for classification. 
//              In addition to standard Landsat 5 bands, it classifies based on NDVI, EVI, NDWI, NDMI, NDBI, slope and 
//              Tasseled-Cap indices (brightness, greenness, and wetness).

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Assets 
var PalmPlantation = /* color: #ff0000 */ee.FeatureCollection(
        [ee.Feature(
            ee.Geometry.Point([-83.04282188415527, 8.616749754964427]),
            {
              "landcover": 1,
              "system:index": "0"
            }),
        ee.Feature(
            ee.Geometry.Point([-83.03964614868164, 8.618277265812559]),
            {
              "landcover": 1,
              "system:index": "1"
            }),
        ee.Feature(
            ee.Geometry.Polygon(
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                  [-83.4836483001709, 8.923230003689142],
                  [-83.48321914672852, 8.924162713228801]]]),
            {
              "landcover": 1,
              "system:index": "2"
            }),
        ee.Feature(
            ee.Geometry.Polygon(
                [[[-83.48605155944824, 8.926282498769824],
                  [-83.48673820495605, 8.92560416873676],
                  [-83.48605155944824, 8.925010628923195],
                  [-83.48527908325195, 8.925773751363266]]]),
            {
              "landcover": 1,
              "system:index": "3"
            }),
        ee.Feature(
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                  [-83.04222106933594, 8.621586851474916],
                  [-83.04089069366455, 8.621586851474916],
                  [-83.04114818572998, 8.622774900797003]]]),
            {
              "landcover": 1,
              "system:index": "4"
            })]),
    Mangrove = /* color: #22f7aa */ee.FeatureCollection(
        [ee.Feature(
            ee.Geometry.Polygon(
                [[[-83.58621597290039, 8.873623385876924],
                  [-83.58621597290039, 8.872393734813654],
                  [-83.58501434326172, 8.872393734813654],
                  [-83.58497142791748, 8.87387779592734]]]),
            {
              "landcover": 2,
              "system:index": "0"
            }),
        ee.Feature(
            ee.Geometry.Polygon(
                [[[-83.5561752319336, 8.871672902964693],
                  [-83.5561752319336, 8.870146430847264],
                  [-83.55420112609863, 8.871248883568772]]]),
            {
              "landcover": 2,
              "system:index": "1"
            }),
        ee.Feature(
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                  [-83.55694770812988, 8.888887676557626],
                  [-83.55840682983398, 8.891431663201862]]]),
            {
              "landcover": 2,
              "system:index": "2"
            }),
        ee.Feature(
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                  [-83.58145236968994, 8.827042178810473]]]),
            {
              "landcover": 2,
              "system:index": "3"
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        ee.Feature(
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                  [-83.57119560241699, 8.866839052821218]]]),
            {
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        ee.Feature(
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            {
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        ee.Feature(
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        ee.Feature(
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                  [-83.47223281860352, 8.698759843782224]]]),
            {
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        ee.Feature(
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                  [-83.46064567565918, 8.68493011568448]]]),
            {
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        ee.Feature(
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                  [-83.12908172607422, 8.538923421610123]]]),
            {
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        ee.Feature(
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                  [-83.12264442443848, 8.53824438681118],
                  [-83.12281608581543, 8.540493684954656]]]),
            {
              "landcover": 2,
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        ee.Feature(
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                  [-83.1381368637085, 8.554243822900803],
                  [-83.13843727111816, 8.555856460895582]]]),
            {
              "landcover": 2,
              "system:index": "11"
            }),
        ee.Feature(
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                  [-83.58029365539551, 8.865142949971874]]]),
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        ee.Feature(
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                  [-83.46970081329346, 8.694729760987531]]]),
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        ee.Feature(
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                  [-83.32056999206543, 8.724127157894364]]]),
            {
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    Water = /* color: #003399 */ee.FeatureCollection(
        [ee.Feature(
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                  [-83.56999397277832, 8.799497767764725],
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                  [-83.56922149658203, 8.80297538936056]]]),
            {
              "landcover": 3,
              "system:index": "0"
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        ee.Feature(
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                  [-83.55720520019531, 8.804332501116992]]]),
            {
              "landcover": 3,
              "system:index": "1"
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        ee.Feature(
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                  [-83.57763290405273, 8.84648530208452]]]),
            {
              "landcover": 3,
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        ee.Feature(
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                  [-83.56887817382812, 8.891177265332562]]]),
            {
              "landcover": 3,
              "system:index": "3"
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        ee.Feature(
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                  [-83.14659118652344, 8.59774021517892]]]),
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        ee.Feature(
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        ee.Feature(
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                  [-83.1250262260437, 8.599798213656523]]]),
            {
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        ee.Feature(
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                  [-83.60527038574219, 8.800939712392442]]]),
            {
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    Grassland_Pasture = /* color: #f56e3f */ee.FeatureCollection(
        [ee.Feature(
            ee.Geometry.Polygon(
                [[[-83.35313200950623, 8.558158701988756],
                  [-83.35312128067017, 8.557638842313473],
                  [-83.3521020412445, 8.557638842313473],
                  [-83.35213422775269, 8.558381498775157]]]),
            {
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        ee.Feature(
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                  [-83.28237533569336, 8.468424690725357]]]),
            {
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        ee.Feature(
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    ExposedSoil_Urban = /* color: #ffd665 */ee.FeatureCollection(
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            {
              "landcover": 5,
              "system:index": "12"
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        ee.Feature(
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            {
              "landcover": 5,
              "system:index": "13"
            }),
        ee.Feature(
            ee.Geometry.Point([-83.48502159118652, 8.619465325538455]),
            {
              "landcover": 5,
              "system:index": "14"
            }),
        ee.Feature(
            ee.Geometry.Point([-83.48184585571289, 8.62285976131994]),
            {
              "landcover": 5,
              "system:index": "15"
            }),
        ee.Feature(
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            {
              "landcover": 5,
              "system:index": "16"
            }),
        ee.Feature(
            ee.Geometry.Point([-83.49197387695312, 8.613015813580633]),
            {
              "landcover": 5,
              "system:index": "17"
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        ee.Feature(
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                  [-83.30323219299316, 8.516854172680883],
                  [-83.30301761627197, 8.517915223420777]]]),
            {
              "landcover": 5,
              "system:index": "18"
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        ee.Feature(
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                  [-83.30490589141846, 8.52279601891428],
                  [-83.3047342300415, 8.52377217053733]]]),
            {
              "landcover": 5,
              "system:index": "19"
            }),
        ee.Feature(
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                  [-83.45849990844727, 8.96291044499761],
                  [-83.45905780792236, 8.962995227631904]]]),
            {
              "landcover": 5,
              "system:index": "20"
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        ee.Feature(
            ee.Geometry.Polygon(
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                  [-83.45412254333496, 8.961384354196738],
                  [-83.45330715179443, 8.961384354196738],
                  [-83.45330715179443, 8.961808268951115]]]),
            {
              "landcover": 5,
              "system:index": "21"
            }),
        ee.Feature(
            ee.Geometry.Polygon(
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                  [-83.45558166503906, 8.95795062646004],
                  [-83.45571041107178, 8.95828976150754],
                  [-83.45631122589111, 8.95828976150754]]]),
            {
              "landcover": 5,
              "system:index": "22"
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    PrimaryForest = /* color: #f6210c */ee.FeatureCollection(
        [ee.Feature(
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              "landcover": 6,
              "system:index": "0"
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        ee.Feature(
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                  [-83.5230016708374, 8.56657184249421]]]),
            {
              "landcover": 6,
              "system:index": "1"
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        ee.Feature(
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                  [-83.59925150871277, 8.622912799137072]]]),
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              "landcover": 6,
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        ee.Feature(
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              "landcover": 6,
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        ee.Feature(
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            {
              "landcover": 6,
              "system:index": "4"
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        ee.Feature(
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        ee.Feature(
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                  [-83.43321204185486, 8.747870335723707]]]),
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        ee.Feature(
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                  [-83.55823516845703, 8.586367980911534]]]),
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                  [-83.56029510498047, 8.456496773764028]]]),
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    NegraForra = /* color: #7cd0f5 */ee.FeatureCollection(
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                  [-83.52059841156006, 8.864252492840906]]]),
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        ee.Feature(
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        ee.Feature(
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                  [-83.52538347244263, 8.877291113711442],
                  [-83.52544784545898, 8.878181539214921]]]),
            {
              "landcover": 8,
              "system:index": "10"
            })]),
    geometry = /* color: #d63000 */ee.FeatureCollection([]);



/*The structure for this code was adapted from the US Virgin Islands Ecological Forecasting Summer 2017 code*/ 
//This code contains 1987 Training Data
 
/////////////////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////// Dictionary ////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////


/*These elements are specific to the sensor and are different for Landsat 5 imagery.*/
var falseColor = {bands: ['B4', 'B3', 'B2']};
var trueColor = {bands: ['B3','B2','B1'],min: 0, max: 0.2, gamma: 1.3};


var collection = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR'); 
var bandNames = ee.List(['B1','B2','B3','B4','B5','B7','elevation','slope',]);
var sensor_band_dict =ee.Dictionary({
    L5 : ee.List([0,1,2,3,4,5,'elevation', 'slope']),
});

/*Peninsula boundaries. Change this to match your Osa Peninsula Asset in Google Earth Engine.
To use this code for a separate study area, import a shapefile as a GEE Asset, and insert it here*/

var studyArea = ee.FeatureCollection('projects/ee-developgeo/assets/2018Sum_GA_OsaPeninsulaWater_StudyArea');
Map.centerObject(studyArea);


/*possible years for study with Landsat 5*/
var YEARS = {'1987': 1987, '1988': 1988, '1998': 1998,'1999': 1999,'2000': 2000,'2001':2001
};

/*add training points. We used the classes Palm, Mangrove, Wetland, Water, Grassland, Urban, Primary Forest, and Secondary Forest 
These points were created based on ground truth data provided by Osa Conservation for 2018 and historical imagery. 
To use this code with another study area, create new training points and insert them here.*/

var newfc1= PalmPlantation //1
.merge(Mangrove)           //2
.merge(Water)              //3
.merge(Grassland_Pasture)  //4
.merge(ExposedSoil_Urban)  //5
.merge(PrimaryForest)      //6 
.merge(SecondaryForest)    //7
.merge(NegraForra);        //8

//DEM layers
var DEM1 = ee.Image('JAXA/ALOS/AW3D30_V1_1').select('AVE').clip(studyArea);

/////////////////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////// User Interface/////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////
//*****The following UI code was adapted from the Chile Water Resources Team 2017 Summer Code****

// Create UI Panels 
var panel = ui.Panel({style: {width:'300px'}});
ui.root.insert(0,panel);

//intro
var intro = ui.Label('USVI Classification Tool', 
{fontWeight: 'bold', fontSize: '24px', margin: '10px 5px'}
);

var subtitle = ui.Label('This script analyzes landsat 5 imagery',
  {margin: '0 0 0 12px',fontSize: '12px',color: 'gray'});
panel.add(intro).add(subtitle);

//select year
var selectYear = ui.Select({
  items: Object.keys(YEARS),
});
selectYear.setPlaceholder('Select year of study...');
panel.add(ui.Label('1. Select year')).add(selectYear); 


/// Create Land Use Map
var mapbutton = ui.Label('2. Create Land Use Map');
panel.add(mapbutton);
panel.add(ui.Button("Create Map",landMap));
var additional_directions = ui.Label
  ('Classified Imagery may take up to a couple minutes to display. Click tasks to export classified image to drive. Click layers to flip from composite to classified image.', 
  {margin: '0 0 0 12px',fontSize: '12px',color: 'gray'});
panel.add(additional_directions);

var outputPanel = ui.Panel();
print(outputPanel);

/////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////// Define terms based on user input //////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////
function landMap(){
 var yearNum = (ee.Number.parse(selectYear.getValue()));
 var startDate = ee.Date.fromYMD(yearNum.subtract(2),1,1);
 var endDate =ee.Date.fromYMD(yearNum,12,31);
 var newfc = newfc1; 
 var DEM = DEM1; 
 var elevation = DEM.select('AVE').rename('elevation');
 var slope = ee.Terrain.slope(DEM);

/////////////////////////////////////////////////////////////////////////////////////////////////
/////////////////// Create cloud free composite //////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////

//functions to add bands NDVI, EVI, NDWI, NDBI, NDMI, Tasseled-Cap indices, and mask using pixel_qa band
function bandFloat(img) {
  return img.toFloat().divide(10000);
}
function ndvi_calc(img){
  return img.normalizedDifference(['B4', 'B3']).select([0],['NDVI']); 
}
function evi_calc(img){
  return img.expression(                                           
    '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', {
      'NIR': img.select('B4'),
      'RED': img.select('B3'),
      'BLUE': img.select('B1')
  }).select([0],['EVI']);
}
function ndwi_calc(img){
  return img.normalizedDifference(['B2', 'B4']).select([0],['NDWI']);
}
function ndbi_calc(img){
  return img.normalizedDifference(['B5', 'B4']).select([0],['NDBI']);
}
function ndmi_calc(img){
  return img.normalizedDifference(['B4','B5']).select([0],['NDMI']);
}
function tcb_calc(img){
  return img.expression(
    '(0.2043 * BLUE) + (0.4158 * GREEN) + (0.5524 * RED) + (0.5741 * NIR) + (0.3124 * SWIR1) + (0.2303 * SWIR2)', {
      'SWIR2': img.select('B7'),
      'SWIR1': img.select('B5'),
      'NIR': img.select('B4'),
      'RED': img.select('B3'),
      'GREEN': img.select('B2'),
      'BLUE': img.select('B1')
    }).select([0],['TCB']);
}
function tcg_calc(img){
  return img.expression(
    '(-0.1603 * BLUE) + (0.2819 * GREEN) + (-0.4934 * RED) + (0.7940 * NIR) + (-0.0002 * SWIR1) + (-0.1446 * SWIR2)', {
      'SWIR2': img.select('B7'),
      'SWIR1': img.select('B5'),
      'NIR': img.select('B4'),
      'RED': img.select('B3'),
      'GREEN': img.select('B2'),
      'BLUE': img.select('B1')
    }).select([0],['TCG']);
}
function tcw_calc(img){
  return img.expression(
    '(0.0315 * BLUE) + (0.2021 * GREEN) + (0.3102 * RED) + (0.1594 * NIR) + (-0.6806 * SWIR1) + (-0.6109 * SWIR2)', {
      'SWIR2': img.select('B7'),
      'SWIR1': img.select('B5'),
      'NIR': img.select('B4'),
      'RED': img.select('B3'),
      'GREEN': img.select('B2'),
      'BLUE': img.select('B1')
    }).select([0],['TCW']);
}

function addIndices(in_image){
    return in_image.addBands([ndvi_calc(in_image), evi_calc(in_image), ndwi_calc(in_image), 
    ndbi_calc(in_image), ndmi_calc(in_image), tcb_calc(in_image), tcg_calc(in_image), tcw_calc(in_image)]);
}
function addDEM(in_image){
    return in_image.addBands([elevation, slope]);
}

//Function to mask clouds using cfmask
function mask_sr(image_sr) {
  var mask_band = image_sr.select('pixel_qa').lt(69);
  return image_sr.mask(mask_band);
}

//applies functions for gathering, cloud masking, and cloud shadow masking Landsat imagery
function LEDAPScfmaskImages(startDate,endDate){
  return ee.ImageCollection(collection)
        .filterDate(startDate,endDate)
        .filterBounds(studyArea)
        .map(addDEM)
        .map(mask_sr)
        .select(sensor_band_dict.get('L5'),bandNames)
        .map(bandFloat)
        .map(addIndices); 
}
/*function to calculate maximum value composite based on NDVI percentile
parameters: landsat image collection, studyarea, percentile value*/
function maxvalcompNDVI(collection,studyArea,percc){
  
  var ndvi_loc=collection.select('NDVI').toArray().clip(studyArea)
  .arrayLength(0).multiply(percc/100).floor().int();
  
  var ndvi_loc2=collection.select('NDVI').toArray().clip(studyArea)
  .arrayLength(0).multiply(percc/100).add(1).floor().int();
  
  var bandNames=ee.Image(collection.first()).bandNames();
  var nb=ee.Image(collection.first()).bandNames().length().subtract(1);
  var out_list = ee.List.sequence(0,nb).map(function(i) {
  return collection.select([bandNames.get(i)])
  .toArray().arraySort().clip(studyArea)
  .arraySlice(0,ndvi_loc,ndvi_loc2, 1)
  .arrayFlatten([['0'],['0']]).rename([bandNames.get(i)]);});
  
  var first=out_list.slice(0,1).get(0);
  function combine(img, prev){return ee.Image(prev).addBands(img)}
  return ee.Image(out_list.slice(1,nb.add(1)).iterate(combine,first));
  
}//credits: Richard Massey, PhD student,  Northern Arizona University

//Get all images and cloud and shadow mask them
var allImages = LEDAPScfmaskImages(startDate,endDate);

//number of bands per season image
var nbf=ee.Image(allImages.first()).bandNames().length();

//reduce collections to maximum value composite based on NDVI percentile
var pctl=35; //range: 0-99
var compImage = maxvalcompNDVI(allImages,studyArea,pctl);
print('Output Image:',compImage);


var yearName = ee.String(ee.Number.parse(selectYear.getValue()))
Map.addLayer(compImage.clip(studyArea), trueColor, 'GP Comp '+ yearName.getInfo());


/////////////////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////// Classification ////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////////////////////
// **** the following is adapted from Everglades Fall 2016 Ecological Forescasting team code ******


//Create a dictionary explaining the class meanings  
var classes = [
  {'landcover':1,'description':'PalmPlantation'},
  {'landcover':2,'description':'Mangrove'},
  {'landcover':3,'description':'Water'},
  {'landcover':4,'description':'Grassland_Pasture'},
  {'landcover':5,'description':'ExposedSoil_Urban'},
  {'landcover':6,'description':'Primary Forest'},
  {'landcover':7,'description':'SecondaryForest'},
  {'landcover':8,'description':'NegraForra'}
];
print('Class Descriptions', classes);

//use these bands for classification
var cBands=['B1','B2', 'B3','B4','B5','B7','slope', 'NDVI', 'EVI', 'NDWI', 'NDBI', 'NDMI', 'TCB', 'TCG', 'TCW'];


//add a random number column to geometry imports, use seed (1, 2, and 3)
var newfc2 = ee.FeatureCollection(newfc).randomColumn('random', 2);

 
//define your classification samples to incl. newfc2 and the properties to be considered
var samples = compImage.select(cBands).sampleRegions({
     collection: newfc2,   
     properties: ['landcover', 'random'], 
    scale: 30 
});


//split training points (80% and 20%)  
var training = samples.filterMetadata('random', 'less_than', 0.8);
var testing = samples.filterMetadata('random', 'not_less_than', 0.8);

//only use the 80% for classification
var classifier = ee.Classifier.smileRandomForest(200).train({
      features: training, 
     classProperty: 'landcover'
});

//apply classifier
var classified = compImage.classify(classifier);

//to validate, compare 20% testing points to the classification product in errorMatrix
var validation = testing.classify(classifier);                                     
var errorMatrix = validation.errorMatrix('landcover', 'classification');           
print('Error Matrix:', errorMatrix);
print('Overall Accuracy:', errorMatrix.accuracy());
print('Kappa Coefficient: ', errorMatrix.kappa());


//CSS color codes to display map
var palette = ['f5f907', // PalmPlantation(1) - yellow
              '22f7aa', // Mangrove(2) - blue/green
              '003399' , // Water(3) - dark blue
              'f56e3f' , // Grassland_Pasture(4) - red
              'eecb66', // ExposedSoil_Urban(5) - tan
              '004d00', // PrimaryForest(6) - dark green
              '00a600', // SecondaryForest(7) - light green
              '7cd0f5', // Wetland(8) - light blue
];
 
Map.addLayer(classified, 
    {min: 1, max: 8, palette: palette}, 'Classification ' + yearName.getInfo());


////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////EXPORT CODE ///////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////

//Export the classified image to the Drive (to finish the process, select run in 'Tasks')

Export.image.toDrive({
  image: classified,
  //assetId: 'classi',
  folder: 'exports',
  description: 'CLASSIFIED_IMAGE_1997', //change for each photo
  scale: 30,
  region: studyArea.geometry().bounds()
});

//Export the composite image to the Drive (to finish the process, select run in 'Tasks')
Export.image.toDrive({
  image: compImage,
  folder: 'exports',
  description: 'LS_Composite', //change for each photo
  scale: 30,
  region: studyArea.geometry().bounds()
});

}


////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////Classification Legend //////////////////////////////////
////////////////////////////////////////////////////////////////////////////////////////////


// set position of panel
var legend = ui.Panel({
  style: {
    position: 'bottom-right',
    padding: '8px 15px'
  }
});
 
// Create legend title
var legendTitle = ui.Label({
  value: 'Classification Legend',
  style: {
    fontWeight: 'bold',
    fontSize: '18px',
    margin: '0 0 4px 0',
    padding: '0'
    }
});
 
// Add the title to the panel
legend.add(legendTitle);
 
// Creates and styles 1 row of the legend.
var makeRow = function(color, name) {
 
      // Create the label that is actually the colored box.
      var colorBox = ui.Label({
        style: {
          backgroundColor: '#' + color,
          // Use padding to give the box height and width.
          padding: '8px',
          margin: '0 0 4px 0'
        }
      });
 
      // Create the label filled with the description text.
      var description = ui.Label({
        value: name,
        style: {margin: '0 0 4px 6px'}
      });
 
      // return the panel
      return ui.Panel({
        widgets: [colorBox, description],
        layout: ui.Panel.Layout.Flow('horizontal')
      });
};
 
 
//CSS color codes to display map
var paletteKey = {
              'f5f907': 'Palm Plantation',
              '22f7aa': 'Mangrove',
              '003399': 'Water',
              'f56e3f': 'Grassland, Pasture',
              'eecb66': 'Exposed Soil, Urban',
              '004d00': 'Primary Forest',
              '00a600': 'Secondary Forest',
              '7cd0f5': 'Wetland'
};

legend.add(makeRow('f5f907', 'Palm Plantation'));
legend.add(makeRow('22f7aa', 'Mangrove'));
legend.add(makeRow('003399', 'Water'));
legend.add(makeRow('f56e3f', 'Grassland, Pasture'));
legend.add(makeRow('eecb66', 'Exposed Soil, Urban'));
legend.add(makeRow('004d00', 'Primary Forest'));
legend.add(makeRow('00a600', 'Secondary Forest'));
legend.add(makeRow('7cd0f5', 'Wetland'));
 
 
// add legend to map (alternatively you can also print the legend to the console)
Map.add(legend);