How to classify NDVI using Google Earth Engine?

How to classify NDVI using Google Earth Engine?

I want to classify my NDVI raster using training data in Google Earth Engine. For that i am using the following code. But it is returning some error like ndvi is not a usable band.

How is classification done in Google Earth Engine?

The general workflow for classification is: Collect training data. Assemble features which have a property that stores the known class label and properties storing numeric values for the predictors. Instantiate a classifier. Set its parameters if necessary. Train the classifier using the training data. Classify an image or feature collection.

How is training data stored in Google Earth Engine?

The training data is a FeatureCollection with a property storing the class label and properties storing predictor variables. Class labels should be consecutive, integers starting from 0. If necessary, use remap () to convert class values to consecutive integers. The predictors should be numeric.

How are polygons used to train Google Earth Engine?

If the training data are polygons representing homogeneous regions, every pixel in each polygon is a training point. You can use polygons to train as illustrated in the following example: // Make a cloud-free Landsat 8 TOA composite (from raw imagery).

How to extract the NDVI of an image?

You will need to define a list of years you are interested in, and map over that list to get a mean NDVI image for every year. Then use reduceRegion on every image to obtain the mean NDVI value in the geometry. Export the resulting image collection as a table. Thanks for contributing an answer to Geographic Information Systems Stack Exchange!

Can you export NDVI layers to Google Earth?

I tried with both EVI and NDVI. Looks like the problem is something related to the projection. It’s weird when the shape/size is preserved for Europe, but not for my study area in Australia. The web version of Google Earth has a limited export ability. Google Earth Pro is better but it still won’t fill your need.

Why is NDVI higher over clouds than water?

Because NDVI is higher over clouds than water, water areas get cloudy pixels, while vegetated areas all appear green because NDVI is highest when the vegetation in the pixel is photosynthetically active. Figure 9. Landsat 8 greenest pixel composite. Now you’ve seen several ways to composite and mosaic images in Earth Engine.

How to calculate the NDVI of a pixel?

NDVI uses this to create a single value roughly reflecting the photosynthetic activity occurring at a pixel. The calculation is (NIR – red) / (NIR + red). This results in a number between 1 and -1, where pixels with high photosynthetic activity have a high NDVI. This is one way to compute NDVI in Earth Engine:

How is the vegetation index in Earth Engine?

Earth Engine is free to use for research, education, and nonprofit use. To access this dataset in Earth Engine, please sign up for Earth Engine then return to this page. The Normalized Difference Vegetation Index is generated from the Near-IR and Red bands of each scene as (NIR – Red) / (NIR + Red), and ranges in value from -1.0 to 1.0.

Which is the latest version of Gimms NDVI?

The latest version of the GIMMS NDVI dataset is named NDVI3g (third generation GIMMS NDVI from AVHRR sensors). The NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI) contains gridded daily NDVI derived from the NOAA AVHRR Surface Reflectance product.