Return to Question

added 176 characters in body

Source Link

edited Jan 26, 2021 at 6:27

// Import the Landsat 8 SR image collecton.

Map.addLayer(study_area, {color: 'Black'}, "study_area");

function maskL8sr(image) { // Bits 3 and 5 are cloud shadow and cloud, respectively. var cloudShadowBitMask = (1 << 3); var cloudsBitMask = (1 << 5); // Get the pixel QA band. var qa = image.select('pixel_qa'); // Both flags should be set to zero, indicating clear conditions. var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0) .and(qa.bitwiseAnd(cloudsBitMask).eq(0)); return image.updateMask(mask); }

var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') .filterBounds(study_area) .map(maskL8sr) .select('B5' , 'B4'); print(dataset);

var startDate = ee.Date('2013-05-01'); // set analysis start time var endDate = ee.Date('2013-9-30'); // set analysis end time

// 'Reduce' stack of images by using the median value for each pixel and clip to study area var output_bands = dataset.reduce(ee.Reducer.median()).clip(study_area); print(output_bands) // var index = output_bands.normalizedDifference(['B5_median', 'B4_median']); // NDVI calculated using NIR and red bands print(index); // DISPLAY metadata

// DISPLAY output band Map.addLayer(index, {}, 'NDVI');

print(ui.Chart.image.series({ imageCollection: index, region: study_area, reducer: ee.Reducer.median(), scale: 30 }).setOptions({title: 'Cloud-masked NDVI over time'}));

// Import the Landsat 8 SR image collecton.

Map.addLayer(study_area, {color: 'Black'}, "study_area");

function maskL8sr(image) {
  // Bits 3 and 5 are cloud shadow and cloud, respectively.
  var cloudShadowBitMask = (1 << 3);
  var cloudsBitMask = (1 << 5);
  // Get the pixel QA band.
  var qa = image.select('pixel_qa');
  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
                 .and(qa.bitwiseAnd(cloudsBitMask).eq(0));
  return image.updateMask(mask);
}

var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
                  .filterBounds(study_area)
                  .map(maskL8sr)
                  .select('B5' , 'B4');
print(dataset);

var startDate = ee.Date('2013-05-01'); // set analysis start time
var endDate = ee.Date('2013-9-30'); // set analysis end time


// 'Reduce' stack of images by using the median value for each pixel and clip to study area
var output_bands = dataset.reduce(ee.Reducer.median()).clip(study_area);
print(output_bands)
// 
var index = output_bands.normalizedDifference(['B5_median', 'B4_median']); // NDVI calculated using NIR and red bands
print(index); // DISPLAY metadata

// DISPLAY output band
Map.addLayer(index, {}, 'NDVI');



print(ui.Chart.image.series({
  imageCollection: index,
  region: study_area,
  reducer: ee.Reducer.median(),
  scale: 30
}).setOptions({title: 'Cloud-masked NDVI over time'}));

// Import the Landsat 8 SR image collecton.

Map.addLayer(study_area, {color: 'Black'}, "study_area");

var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') .filterBounds(study_area) .map(maskL8sr) .select('B5' , 'B4'); print(dataset);

var startDate = ee.Date('2013-05-01'); // set analysis start time var endDate = ee.Date('2013-9-30'); // set analysis end time

// DISPLAY output band Map.addLayer(index, {}, 'NDVI');

print(ui.Chart.image.series({ imageCollection: index, region: study_area, reducer: ee.Reducer.median(), scale: 30 }).setOptions({title: 'Cloud-masked NDVI over time'}));

// Import the Landsat 8 SR image collecton.

Map.addLayer(study_area, {color: 'Black'}, "study_area");

function maskL8sr(image) {
  // Bits 3 and 5 are cloud shadow and cloud, respectively.
  var cloudShadowBitMask = (1 << 3);
  var cloudsBitMask = (1 << 5);
  // Get the pixel QA band.
  var qa = image.select('pixel_qa');
  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
                 .and(qa.bitwiseAnd(cloudsBitMask).eq(0));
  return image.updateMask(mask);
}

var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
                  .filterBounds(study_area)
                  .map(maskL8sr)
                  .select('B5' , 'B4');
print(dataset);

var startDate = ee.Date('2013-05-01'); // set analysis start time
var endDate = ee.Date('2013-9-30'); // set analysis end time


// 'Reduce' stack of images by using the median value for each pixel and clip to study area
var output_bands = dataset.reduce(ee.Reducer.median()).clip(study_area);
print(output_bands)
// 
var index = output_bands.normalizedDifference(['B5_median', 'B4_median']); // NDVI calculated using NIR and red bands
print(index); // DISPLAY metadata

// DISPLAY output band
Map.addLayer(index, {}, 'NDVI');



print(ui.Chart.image.series({
  imageCollection: index,
  region: study_area,
  reducer: ee.Reducer.median(),
  scale: 30
}).setOptions({title: 'Cloud-masked NDVI over time'}));

Source Link

asked Jan 26, 2021 at 4:41

WhitmanCD

Error generating chart: User memory limit exceeded, when changing dataset from T1_TOA to T1_SR

So, I got the code to work for T1_TOA band, but then when I switched it over to T1_SR band it gave me this error: User memory limit exceeded. So, I poked around the forum and found the code to limit my date frames, but I still get the error. Not quite sure what I'm missing.

// Import the Landsat 8 SR image collecton.

Map.addLayer(study_area, {color: 'Black'}, "study_area");

var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') .filterBounds(study_area) .map(maskL8sr) .select('B5' , 'B4'); print(dataset);

var startDate = ee.Date('2013-05-01'); // set analysis start time var endDate = ee.Date('2013-9-30'); // set analysis end time

// DISPLAY output band Map.addLayer(index, {}, 'NDVI');

print(ui.Chart.image.series({ imageCollection: index, region: study_area, reducer: ee.Reducer.median(), scale: 30 }).setOptions({title: 'Cloud-masked NDVI over time'}));

ndvi memory