Locating Potential Land Grabs with Remote Sensing

Learning objectives of this topic

Take a close look at certain tenure regimes and ownership patterns
Make use of NDVI time series
Do a temporal comparison and calculate mean values of your dataset
Plot the time series and visualize your dataset
Link irregularities to changes in landownership

The code used in this example can be found below the videos a well as by following this link:

https://code.earthengine.google.com/3a052ebb812865be63c4a62c35dc8930

Up next:

Draw conclusions and estimations on certain tenure regimes or ownership patterns
See an example of how to locate potential land grabs in Subsahara-Africa

Up next:

How to use the NDVI time series
Monitor changes in irrigation
Conclude possible tenure change

Code used in the video

var Decrease = /* color: #ffc82d */ee.Geometry.Point([31.033888403992595, -17.394174311133924]),
    Increase = /* color: #00ffff */ee.Geometry.Point([31.007109229187908, -17.44453938264605]),
    VisParamNDVI = {"opacity":1,"bands":["NDVI"],"min":879.2,"max":6696.104347826087,"palette":["a31a14","cc7116","f9e6e6","8ee829","3a9d28"]},
    VisParamChange = {"opacity":1,"bands":["NDVI"],"min":-1531.7630434782604,"max":2844.0456521739134,"palette":["a31a14","cc7116","f9e6e6","8ee829","3a9d28"]};

// Select Zimbabwe as an roi to reduce datasets 
var Zim = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017').filterMetadata('country_co', 'equals', 'ZI')
Map.addLayer(Zim)



// Call MODIS Vegetation indices as the basis for the showcase 
var NDVI = ee.ImageCollection('MODIS/MOD13Q1').select('NDVI')
 print('Modis Collection:', NDVI );

 
// filter the collection to make use of two years at start and end 
var ndvi2001 = NDVI
  .filterDate('2001-01-01', '2002-12-31');
print('Ndvi 2001:', ndvi2001 );
  
var ndvi2010 = NDVI
  .filterDate('2010-01-01', '2011-12-31');
  

// We want to create an NDVI mean for the first  and the second timestep and clip it to Zambia 
var mean2001 = ndvi2001.mean().clip(Zim)
print('Mean 2001', mean2001)
var mean2010 = ndvi2010.mean().clip(Zim)


// Set visualization parameters


Map.addLayer(mean2001, VisParamNDVI)
Map.addLayer(mean2010, VisParamNDVI)

var difference = mean2010.subtract(mean2001)
Map.addLayer(difference, VisParamChange)


var plotIrrigation = ui.Chart.image.seriesByRegion(NDVI, Increase, ee.Reducer.mean(), 'NDVI', 500)
.setOptions({ title: 'NDVI Time Series INCREASE',  hAxis: {title: 'Date'}, 
vAxis: {title: 'NDVI'}
});
print(plotIrrigation);


var plotRainfed = ui.Chart.image.seriesByRegion(NDVI, Decrease, ee.Reducer.mean(), 'NDVI', 500)
.setOptions({ title: 'NDVI Time Series DECREASE',  hAxis: {title: 'Date'}, 
vAxis: {title: 'NDVI'}
});
print(plotRainfed);

Up next:

Start your analysis in GEE
Get detailed information about the code-workflow
Calculate a temporal comparison
Learn how to compare production, NDVI – values or productivity in the beginning to the one in the end of the time series
Learn how to create subsets and work with them
Calculate the means of your two datasets

Code used in the video

var Decrease = /* color: #ffc82d */ee.Geometry.Point([31.033888403992595, -17.394174311133924]),
    Increase = /* color: #00ffff */ee.Geometry.Point([31.007109229187908, -17.44453938264605]),
    VisParamNDVI = {"opacity":1,"bands":["NDVI"],"min":879.2,"max":6696.104347826087,"palette":["a31a14","cc7116","f9e6e6","8ee829","3a9d28"]},
    VisParamChange = {"opacity":1,"bands":["NDVI"],"min":-1531.7630434782604,"max":2844.0456521739134,"palette":["a31a14","cc7116","f9e6e6","8ee829","3a9d28"]};

// Select Zimbabwe as an roi to reduce datasets 
var Zim = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017').filterMetadata('country_co', 'equals', 'ZI')
Map.addLayer(Zim)



// Call MODIS Vegetation indices as the basis for the showcase 
var NDVI = ee.ImageCollection('MODIS/MOD13Q1').select('NDVI')
 print('Modis Collection:', NDVI );

 
// filter the collection to make use of two years at start and end 
var ndvi2001 = NDVI
  .filterDate('2001-01-01', '2002-12-31');
print('Ndvi 2001:', ndvi2001 );
  
var ndvi2010 = NDVI
  .filterDate('2010-01-01', '2011-12-31');
  

// We want to create an NDVI mean for the first  and the second timestep and clip it to Zambia 
var mean2001 = ndvi2001.mean().clip(Zim)
print('Mean 2001', mean2001)
var mean2010 = ndvi2010.mean().clip(Zim)


// Set visualization parameters


Map.addLayer(mean2001, VisParamNDVI)
Map.addLayer(mean2010, VisParamNDVI)

var difference = mean2010.subtract(mean2001)
Map.addLayer(difference, VisParamChange)


var plotIrrigation = ui.Chart.image.seriesByRegion(NDVI, Increase, ee.Reducer.mean(), 'NDVI', 500)
.setOptions({ title: 'NDVI Time Series INCREASE',  hAxis: {title: 'Date'}, 
vAxis: {title: 'NDVI'}
});
print(plotIrrigation);


var plotRainfed = ui.Chart.image.seriesByRegion(NDVI, Decrease, ee.Reducer.mean(), 'NDVI', 500)
.setOptions({ title: 'NDVI Time Series DECREASE',  hAxis: {title: 'Date'}, 
vAxis: {title: 'NDVI'}
});
print(plotRainfed);

Up next:

Learn how to map the differences of the means
Plot your whole Dataset and analyze it
Spot irregularities by monitoring the NDVI and the agriculture ptoduction
Link them to changes in land ownership

Code used in the video

var Decrease = /* color: #ffc82d */ee.Geometry.Point([31.033888403992595, -17.394174311133924]),
    Increase = /* color: #00ffff */ee.Geometry.Point([31.007109229187908, -17.44453938264605]),
    VisParamNDVI = {"opacity":1,"bands":["NDVI"],"min":879.2,"max":6696.104347826087,"palette":["a31a14","cc7116","f9e6e6","8ee829","3a9d28"]},
    VisParamChange = {"opacity":1,"bands":["NDVI"],"min":-1531.7630434782604,"max":2844.0456521739134,"palette":["a31a14","cc7116","f9e6e6","8ee829","3a9d28"]};

// Select Zimbabwe as an roi to reduce datasets 
var Zim = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017').filterMetadata('country_co', 'equals', 'ZI')
Map.addLayer(Zim)



// Call MODIS Vegetation indices as the basis for the showcase 
var NDVI = ee.ImageCollection('MODIS/MOD13Q1').select('NDVI')
 print('Modis Collection:', NDVI );

 
// filter the collection to make use of two years at start and end 
var ndvi2001 = NDVI
  .filterDate('2001-01-01', '2002-12-31');
print('Ndvi 2001:', ndvi2001 );
  
var ndvi2010 = NDVI
  .filterDate('2010-01-01', '2011-12-31');
  

// We want to create an NDVI mean for the first  and the second timestep and clip it to Zambia 
var mean2001 = ndvi2001.mean().clip(Zim)
print('Mean 2001', mean2001)
var mean2010 = ndvi2010.mean().clip(Zim)


// Set visualization parameters


Map.addLayer(mean2001, VisParamNDVI)
Map.addLayer(mean2010, VisParamNDVI)

var difference = mean2010.subtract(mean2001)
Map.addLayer(difference, VisParamChange)


var plotIrrigation = ui.Chart.image.seriesByRegion(NDVI, Increase, ee.Reducer.mean(), 'NDVI', 500)
.setOptions({ title: 'NDVI Time Series INCREASE',  hAxis: {title: 'Date'}, 
vAxis: {title: 'NDVI'}
});
print(plotIrrigation);


var plotRainfed = ui.Chart.image.seriesByRegion(NDVI, Decrease, ee.Reducer.mean(), 'NDVI', 500)
.setOptions({ title: 'NDVI Time Series DECREASE',  hAxis: {title: 'Date'}, 
vAxis: {title: 'NDVI'}
});
print(plotRainfed);