Annual Time Series NDVI using MODIS Data in Google Earth Engine (Sample – Nigeria)

What is NDVI?

The Normalized Difference Vegetation Index (NDVI) is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High-Resolution Radiometer (NOAA-AVHRR) derived NDVI.

The NDVI is calculated from these individual measurements as follows:

NDVI= (NIR-Red) \ (NIR+Red)

In this tutorial, we will look at a simple method to calculate Annual NDVI Time Series for Ethiopia (sample area). You can edit the code and calculate NDVI for any country. In addition, we will learn to export the data to our drive so that we can download and use it other processing tool such as ArcGIS.

About Data

MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m

For this work, the MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m was used. The NASA LP DAAC at the USGS EROS Center archives this product and can be downloaded from this website. This product provides a Vegetation Index (VI) value at a per pixel basis.

This product is generated from the MODIS/MCD43A4 surface reflectance composites.

Here is the code:

var countries = ee.FeatureCollection("ft:1tdSwUL7MVpOauSgRzqVTOwdfy17KDbw-1d9omPw")
var country_name = ['Ethiopia'] // Change the country name here. 
var region = countries.filter(ee.Filter.inList('Country', country_name));
Map.centerObject(region,7);  //Zoom to Study area

// Choose country using GEE Feature Collection
var countries = ee.FeatureCollection("ft:1tdSwUL7MVpOauSgRzqVTOwdfy17KDbw-1d9omPw")
var country_name = ['Ethiopia']
var region = countries.filter(ee.Filter.inList('Country', country_name));
Map.centerObject(region,7);  //Zoom to Study area

// collect data and filter by dates

var modisNDVI = ee.ImageCollection('MODIS/MCD43A4_NDVI');

//Image collection for NDVI for all years, one value per month
var collection05 = ee.ImageCollection(modisNDVI.filterDate('2015-01-01', '2016-12-31'));
var collection01 = ee.ImageCollection(modisNDVI.filterDate('2016-01-01', '2016-12-31'));

// clip to specified region
var clipped05 = collection05.mean().clip(region)
var clipped01 = collection01.mean().clip(region)

//charts//
// Long-Term Time ersies
var TS5 = ui.Chart.image.seriesByRegion(collection05, region, ee.Reducer.mean(), 'NDVI', 500, 'system:time_start').setOptions({
   title: 'NDVI Long-Term Time Series',
      vAxis:{title:'NDVI'},
});
print(TS5);

//Short-Term Time Series
var TS1 = ui.Chart.image.seriesByRegion(collection05, region, ee.Reducer.mean(), 'NDVI', 500, 'system:time_start').setOptions({
      title: 'Short-Term Time Series',
      vAxis:{title:'NDVI'},
});
print(TS1);

//Add to map
Map.addLayer(clipped01, {min: 0.0, max:1, palette: ['FFFFFF', 'CC9900', '33CC00','009900','006600','000000']}, 'Annual MODIS NDVI 2015');
Map.addLayer(clipped05, {min: 0.0, max:1, palette: ['FFFFFF', 'CC9900', '33CC00','009900','006600','000000']}, 'Annual MODIS NDVI 2016');

The short-term and long-term time series charts are listed below: