GEE_2nd_predictors

// Javascript code for computing the following data in Google Earth Engine: 
// Enhanced Vegetation Index (EVI), Sentinel-1 Normalized Difference Index, Sentinel-2 and -1 texture metrics, SRTM topographic data

// Parameters for filtering of Sentinel-2 image collection
var explo = 'SCH'
var year = '2017'
var START_DATE = ee.Date(year+'-03-01');
var END_DATE = ee.Date(year+'-10-01');
var region = Plots.filter(ee.Filter.eq('Explrtr', explo)).geometry().bounds();
Map.addLayer(region)
Map.addLayer(Plots)

// Cloud mask section same as in Colab script, needed to run here again to process the predictors
//Cloud mask parameters

var CLOUD_FILTER = 60;
var CLD_PRB_THRESH = 50;
var NIR_DRK_THRESH = 0.15;
var CLD_PRJ_DIST = 2;
var BUFFER = 100;

function maskEdges(s2_img) {
  return s2_img.updateMask(
      s2_img.select('B8A').mask().updateMask(s2_img.select('B9').mask()));
}

function get_s2_sr_cld_col(aoi, start_date, end_date) {
  var s2_sr_col = (ee.ImageCollection('COPERNICUS/S2_SR')
        .filterBounds(aoi)
        .filterDate(start_date, end_date)
        .filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', CLOUD_FILTER)))
        .map(maskEdges)
  var s2_cloudless_col = (ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')
        .filterBounds(aoi)
        .filterDate(start_date, end_date))
  return ee.ImageCollection(ee.Join.saveFirst('s2cloudless').apply({
        'primary': s2_sr_col,
        'secondary': s2_cloudless_col,
        'condition': ee.Filter.equals({
            'leftField': 'system:index',
            'rightField': 'system:index'
        })
    }))
}

var s2_sr_cld_col = get_s2_sr_cld_col(region, START_DATE, END_DATE)

function add_cloud_bands(img) {
  var  cld_prb = ee.Image(img.get('s2cloudless')).select('probability')
  var  is_cloud = cld_prb.gt(CLD_PRB_THRESH).rename('clouds')
  return img.addBands(ee.Image([cld_prb, is_cloud]))
}

function add_shadow_bands(img) {
  var not_water = img.select('SCL').neq(6)
  var SR_BAND_SCALE = 1e4
  var dark_pixels = img.select('B8').lt(NIR_DRK_THRESH*SR_BAND_SCALE).multiply(not_water).rename('dark_pixels')
  var shadow_azimuth = ee.Number(90).subtract(ee.Number(img.get('MEAN_SOLAR_AZIMUTH_ANGLE')));
  var cld_proj = (img.select('clouds').directionalDistanceTransform(shadow_azimuth, CLD_PRJ_DIST*10)
        .reproject({'crs': img.select(0).projection(), 'scale': 100})
        .select('distance')
        .mask()
        .rename('cloud_transform'))
  var shadows = cld_proj.multiply(dark_pixels).rename('shadows')
  return img.addBands(ee.Image([dark_pixels, cld_proj, shadows]))
}

function add_cld_shdw_mask(img) {
  var img_cloud = add_cloud_bands(img)
  var img_cloud_shadow = add_shadow_bands(img_cloud)
  var is_cld_shdw = img_cloud_shadow.select('clouds').add(img_cloud_shadow.select('shadows')).gt(0)
  var is_cld_shdw = (is_cld_shdw.focal_min(2).focal_max(BUFFER*2/20)
        .reproject({'crs': img.select([0]).projection(), 'scale': 20})
        .rename('cloudmask'))
  return img_cloud_shadow.addBands(is_cld_shdw)
}

function apply_cld_shdw_mask(img) {
  var  not_cld_shdw = img.select('cloudmask').not()
  return img.select('B.*').updateMask(not_cld_shdw)
}

print(s2_sr_cld_col)

var s2CloudMasked = (s2_sr_cld_col.map(add_cld_shdw_mask)
                                  .map(apply_cld_shdw_mask))

var S2_testimage = s2CloudMasked.median()
var rgbVis = {min: 0, max: 3000, bands: ['B4', 'B3', 'B2']};

var radius = 6
var square = ee.Kernel.square(radius)
var image_16int = S2_testimage.toUint16()
var entropy_image = image_16int.entropy(square)
var entropy_clip = entropy_image.clip(geometry)
//print(entropy_clip)
//Map.addLayer(entropy_clip)


/*Export.image.toDrive({
  image: entropy_clip.select(['B8']).float(),
  description: 'S2_B8_entr',
  fileNamePrefix: 'S2_B8_entr', 
  folder: 'bands',
  region: geometry,
  scale: 10,
  crs: 'EPSG:32632',
  maxPixels: 6000000000,

  })*/

/////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////Calculation of EVI and EVI texture////////////////////////
///////////////////////////////////////////////////////////////////////////////////////////////////

var EVI_S2 = image_16int.expression(
  "2.5 * (B08 - B04) / ((B08 + 6.0 * B04 - 7.5 * B02) + 10000)",{
    "B08" : image_16int.select("B8"),
    "B04" : image_16int.select("B4"),
    "B02" : image_16int.select("B2")
    
  });

// Further process the EVI (clipping, rescaling what is neccessary for further texture computation)

var EVI = EVI_S2.clip(region)
var EVI_rescale = EVI.multiply(100)
var EVI_glcm = EVI_rescale.toUint16()
//Map.addLayer(EVI_glcm, {min: 0, max: 100},"EVI")

// Calculation of Grey-Level-Co-Occurrence-Matrix for Haralick texture features based on EVI
var glcm = EVI_glcm.glcmTexture().clip(region)
print("glcm:", glcm)
// glcm will give all Haralick texture features
// csv. file with all featuers is extracted via Zonal Statistics and only entropy, dissimilarity, contrast and homogeneity features are manually
// added to "model_parameters.csv" 

var ZonalStats = glcm.reduceRegions({
    collection: Plots.filter(ee.Filter.eq('Explrtr', explo)).select(['EP']), 
    reducer: ee.Reducer.median(), 
    scale: 10
  })

Export.table.toDrive({
    collection: ZonalStats,
    description: 'glcm',
    fileNamePrefix: 'glcm', 
    folder: 'SEBAS',
    fileFormat: 'CSV'
})

// Map.addLayer(glcm)

/////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////Calculation of Sentinel-1 texture/////////////////////////
///////////////////////////////////////////////////////////////////////////////////////////////////

// Sentinel-1 image collection for Normalized Difference Index of VH and VV backscatter parameters and texture metrics

var collection_VH = ee.ImageCollection("COPERNICUS/S1_GRD").filter(ee.Filter.eq('instrumentMode', 'IW'))
                                                           .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
                                                           .filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
                                                           .filterBounds(region).filterDate('2017-12-01', '2018-02-28')
                                                           .select(['VH'])

var S1_VH = collection_VH.median()
var S1_VH_expl = S1_VH.clip(region)

Export.image.toDrive({
  image: S1_VH_expl.float(),
  description: 'S1_VH_sch',
  fileNamePrefix: 'S1_VH_sch', 
  folder: 'bands',
  region: geometry,
  scale: 10,
  crs: 'EPSG:32632',
  maxPixels: 6000000000,

  })
  
var collection_VV = ee.ImageCollection("COPERNICUS/S1_GRD").filter(ee.Filter.eq('instrumentMode', 'IW'))
                                                           .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
                                                           .filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
                                                           .filterBounds(region).filterDate('2017-12-01', '2018-02-28')
                                                           .select(['VV'])

var S1_VV = collection_VV.median()
var S1_VV_expl = S1_VV.clip(geometry)


Export.image.toDrive({
  image: S1_VV_expl.float(),
  description: 'S1_VV_sch',
  fileNamePrefix: 'S1_VV_sch', 
  folder: 'bands',
  region: geometry,
  scale: 10,
  crs: 'EPSG:32632',
  maxPixels: 6000000000,

  })
  
// Computing Normalized Difference of Sentinel-1 VV and VH

var S1_ndi = S1_VH_expl.subtract(S1_VV_expl).divide(S1_VH_expl.add(S1_VV_expl))
print(S1_ndi)
//Map.addLayer(S1_ndi)
var S1_rescale = S1_ndi.multiply(100)

// Calculation of Grey-Level-Co-Occurrence-Matrix for Haralick texture features based on Sentinel-1 Normalized Difference 

var S1_glcm = S1_rescale.toUint16()
Map.addLayer(S1_glcm, {min: 0, max: 100},"NDI")

var glcm = S1_glcm.glcmTexture().clip(geometry)
print("glcm:", glcm)
//Map.addLayer(glcm)

// glcm will give all Haralick texture features
// csv. file with all featuers is extracted via Zonal Statistics and only entropy, dissimilarity, contrast and homogeneity features are manually
// added to "model_parameters.csv" 

var ZonalStats = glcm.reduceRegions({
    collection: Plots.filter(ee.Filter.eq('Explrtr', explo)).select(['EP']), 
    reducer: ee.Reducer.median(), 
    scale: 10
  })

Export.table.toDrive({
    collection: ZonalStats,
    description: 'glcm',
    fileNamePrefix: 'glcm', 
    folder: 'SEBAS',
    fileFormat: 'CSV'
})

// Export image data of predictor "Sentinel-1 entropy" for modelling DBH_sd on raster data 
// Select entropy from glcm and export it to drive

var S1_entropy = glcm.select("VH_ent")
print (S1_entropy)

Export.image.toDrive({
  image: S1_entropy,
  description: 'S1_entropy_sch',
  fileNamePrefix: 'S1_entropy_sch', 
  folder: 'bands',
  region: geometry,
  scale: 10,
  crs: 'EPSG:32632',
  maxPixels: 6000000000,

  })

// Download topographic data for Figure 10 (thesis)

var dataset = ee.Image('CGIAR/SRTM90_V4');
var elevation = dataset.select('elevation');
var slope = ee.Terrain.slope(elevation);
var aspect = ee.Terrain.aspect(elevation);
var slope_alb = slope.clip(region)
var aspect_alb = aspect.clip(region)

//Map.addLayer(slope_alb, {min: 0, max: 60}, 'slope');

Export.image.toDrive({
  image: slope_alb,
  description: 'slope_alb',
  fileNamePrefix: 'slope_alb', 
  folder: 'bands',
  region: geometry,
  scale: 90,
  crs: 'EPSG:32632',
  maxPixels: 6000000000,

  })