Vjylku

I'm trying to get the mean value of pixels from an NDVI raster, that intersect the geometry of a FeatureCollection, that is a multipart polygon which represents my sample area, but I can't find the way.

Here's the code:

 // Load geometry

var limites_BSHC = ee.FeatureCollection ('users/charlieswall/tesis/BSHC_LIMITES');

var poligonos = ee.FeatureCollection('users/charlieswall/tesis/POLIGONO_BSCH_DATOS_COMPLETO');

var p_inicio = ee.FeatureCollection('users/charlieswall/tesis/puntos_fecha_BSHC');





// load and filter the collection

var Landsat8_1 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');

var Landsat8_1_Filtrada =   Landsat8_1.filterBounds(limites_BSHC)

.filterDate('2015-05-4', '2015-08-31')

.filterMetadata('CLOUD_COVER','less_than', 10);





//filter by bounds

var Landsat8_2 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');

var Landsat8_2_Filtrada =   Landsat8_1.filterBounds(limites_BSHC)

.filterDate('2015-08-20', '2015-10-01')

.filterMetadata('CLOUD_COVER','less_than', 10);



//////getting imagery in to variables

//junio

var imagen1 = ee.Image ('LANDSAT/LC08/C01/T1_TOA/LC08_226075_20150807');

var imagen2 = ee.Image ('LANDSAT/LC08/C01/T1_TOA/LC08_226076_20150807');



//septiembre

var imagen3 = ee.Image ('LANDSAT/LC08/C01/T1_TOA/LC08_226075_20150924');

var imagen4 = ee.Image ('LANDSAT/LC08/C01/T1_TOA/LC08_226076_20150924');



//Create mosaics per period    

var mosaic_junio = ee.ImageCollection ([ imagen1, imagen2]).mosaic().clip(limites_BSHC);    

var mosaic_septiembre = ee.ImageCollection([imagen3,imagen4]).mosaic().clip(limites_BSHC);



//Compute NDVI per mosaic    

// Compute the Normalized Difference Vegetation Index (NDVI).

var nir_junio = mosaic_junio.select('B5');

var red_junio = mosaic_junio.select('B4');

var ndvi_junio = nir_junio.subtract(red_junio).divide(nir_junio.add(red_junio )).rename('NDVI_jun');



var nir_septiembre = mosaic_septiembre.select('B5');

var red_septiembre = mosaic_septiembre.select('B4');

var ndvi_septiembre = nir_septiembre.subtract(red_septiembre).divide(nir_septiembre.add(red_septiembre )).rename('NDVI');



print ( ndvi_septiembre);



//Extracting pixel values from sample poligons  

var prueba = ndvi_septiembre.gt(poligonos);

//mask pixels

var septiembre = mosaic_septiembre.updateMask(prueba). addBands(ndvi_septiembre);



var ndvif = function(image){

  //add the NDVI band to the image

  var ndvi = ndvi_septiembre.rename('NDVI');

  //isolate the NDVI band

  var quality = ndvi.select('NDVI');

  //get pixels above the POLYGON

  var ndvi01 = quality.gt(poligonos);

  //create a mask from high likelihood pixels

  var ndvimask = image.mask().and(ndvi01);

  //mask those pixels from the image

  return image.updateMask(ndvimask).addBands(ndvi);

};

var collection= ndvi_septiembre.map(ndvif);

It returns an error that says: ndvi_septiembre.map is not a function

I had to try some others ways, and finally got the values by .reduceRegions script, then I wanted to know, how engine computes the pixels by feature so, it's important to have in mind that the scale affects that 'how' depending if the centroid of each pixel is in the area of that feature or not, to be computed. In this way, I used the sampleRegions script in order to get the centroids by feature, to then be able to calculate the mean manually and it works well. Here's the code:

// Load vector files

var limites_BSHC = ee.FeatureCollection ('users/charlieswall/tesis/BSHC_LIMITES');

var poligonos = ee.FeatureCollection('users/charlieswall/tesis/POLIGONO_BSCH_DATOS_COMPLETO');

var p_inicio = ee.FeatureCollection('users/charlieswall/tesis/puntos_fecha_BSHC');



//Call and filter imagery first period

var Landsat8_1 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');

var Landsat8_1_Filtrada =   Landsat8_1.filterBounds(limites_BSHC)

.filterDate('2015-05-4', '2015-08-31')

.filterMetadata('CLOUD_COVER','less_than', 10);





//Call and filter imagery, second period

var Landsat8_2 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');

var Landsat8_2_Filtrada =   Landsat8_1.filterBounds(limites_BSHC)

.filterDate('2015-08-20', '2015-10-01')

.filterMetadata('CLOUD_COVER','less_than', 10);





//Print properties

print (Landsat8_1_Filtrada,'junio', Landsat8_2_Filtrada, 'septiembre'); 



//Save imagery into variables

//June

var imagen1 = ee.Image ('LANDSAT/LC08/C01/T1_TOA/LC08_226075_20150807');

var imagen2 = ee.Image ('LANDSAT/LC08/C01/T1_TOA/LC08_226076_20150807');



//September

var imagen3 = ee.Image ('LANDSAT/LC08/C01/T1_TOA/LC08_226075_20150924');

var imagen4 = ee.Image ('LANDSAT/LC08/C01/T1_TOA/LC08_226076_20150924');



//Create mosaics



var mosaic_agosto = ee.ImageCollection ([ imagen1, imagen2]).mosaic().clip(limites_BSHC);



var mosaic_septiembre = ee.ImageCollection([imagen3,imagen4]).mosaic().clip(limites_BSHC);





//Compute ndvi values per mosaic



var ndvi_ago = mosaic_agosto.normalizedDifference(['B5','B4']).rename('NDVI_AGO');

var ndvi_sep = mosaic_septiembre.normalizedDifference(['B5','B4']);



//Get ndvi values per polygon and centroid points



var muestreo = ndvi_ago.sampleRegions({

  collection:poligonos,

  properties:['BIOMASA__T','CODIGO_DE:','fecha'],

  scale:30,

  geometries:true

  });



//Get ndvi mean values

var reduciendo = ndvi_ago.reduceRegions({

  collection: poligonos,

  reducer: ee.Reducer.mean(),

  scale: 30,

});





Map.addLayer (reduciendo);

Map.addLayer (ndvi_ago);

Map.centerObject (limites_BSHC);





  print(ndvi_ago);

  print (reduciendo);

  print(muestreo);