Assessing Land Tenure Regimes

Learning objectives of this use case

The relationship between land tenure and land governace
Four main sources which provide data to monitor land tenure
- Landmark
- World database of protected areas (protected planet)
- Global forrest watch
- Global deforrestation dataset

The Google Earth Engine Code used here can be found below the videos as well as by following this link:

https://code.earthengine.google.com/8fceb83eb5092e7b01450f4754969f9c

Up next: Introduction to first Use Case

Unterstand the realtionship between land tenure and land governance
Find out how indigenous people and conservation success correlate.
Get to know the different data sources you’ll make use of.

Up next: Hands on exercise

Get a brief introduction to the Google Earth Engine
Learn how to apply different bands
Do some basic coding and print your first results

Code used in the video

var indig_cons = ee.FeatureCollection("users/konradhentze/indig_conservation_area"),
    cat1a_cons = ee.FeatureCollection("users/konradhentze/prot_area_cat1a");

///////////////////////// COMPARING DEFORESTATION \\\\\\\\\\\\\\\\\\\\\\\\\

Map.addLayer(indig_cons, {}, 'Indigenous conservation area')
Map.addLayer(cat1a_cons, {}, 'Conservation area IUCN cat 1a')

//load the Hansen dataset as an image

var HansenChange = ee.Image('UMD/hansen/global_forest_change_2019_v1_7')
print('Hansen forest cover change dataset', HansenChange)

//we SELECT with .select([]) to extract uniques variables from the dataset above
var lossImage = HansenChange.select(['loss']); //HINT: Caps in your variables ease reading
var gainImage = HansenChange.select(['gain']); //HINT: [] is always something within something. Mostly a band
var treeCover = HansenChange.select(['treecover2000']);

print('Loss image Hansen dataset', lossImage)

// use .and to COMBINE the two bands above
// the condition means 1 where a AND b (meaning the very same pixel shows loss in one and gain in the other dataset)
var gainAndLoss = gainImage.and(lossImage);
//print the result and see: We could have renamed the band it does not make sense 
print('Forest gain and loss from Hansen dataset', gainAndLoss)

// Show the loss and gain image and the other images from the previous section 
Map.addLayer(HansenChange.mask(HansenChange), {
  bands: ['treecover2000'],
  palette: ['000000', '00FF00'],
  max: 100
}, 'forest cover masked');

Map.addLayer(HansenChange.mask(HansenChange), {
  bands: ['loss'],
  palette: ['FF0000'],
  max: 100
}, 'forest loss masked');

Map.addLayer(HansenChange.mask(HansenChange), {
  bands: ['gain'],
  palette: ['0000FF'],
  max: 100
}, 'forest gain masked');

Map.addLayer(gainAndLoss.updateMask(gainAndLoss),
    {palette: 'FF00FF'}, 'Gain and Loss');
    



///////////////////////// CALCULATION AND STATISTICS WITH REDUCERS \\\\\\\\\\\\\\\\\\\\\\\\\
//By using a reducer (calculating means, stats, etc.) we can actually create spatial statistics based on 
//information from other features or layer. 


// call the conservation areas from the assets.
print(indig_cons)
print(cat1a_cons)


// Sum the values of forest loss pixels in the protected areas.

// to do this, we first have to use the mutliply function to get the square meters instead of the number of pixels
var areaImage = lossImage.multiply(ee.Image.pixelArea());

var SumLossIndig = areaImage.reduceRegion({
  reducer: ee.Reducer.sum(),
  geometry: indig_cons,
  scale: 30,
});

var SumLossCat1a = areaImage.reduceRegion({
  reducer: ee.Reducer.sum(),
  geometry: cat1a_cons,
  scale: 30,
});

print(SumLossIndig);
print('Forestry loss in indigenous area: ', SumLossIndig.get('loss'), 'square meters');

print(SumLossCat1a);
print('Forestry loss in protected area: ', SumLossCat1a.get('loss'), 'square meters');



Map.addLayer(indig_cons, {}, 'Indigenous conservation area')
Map.addLayer(cat1a_cons, {}, 'Conservation area IUCN cat 1a')

Up next: Combine and visualize your data

Learn how to finally combine tenure data and data on deforrestation
Learn how to calculate the sum of forrest loss in different areasand
Finally visualize your data

Code used in the video

var indig_cons = ee.FeatureCollection("users/konradhentze/indig_conservation_area"),
    cat1a_cons = ee.FeatureCollection("users/konradhentze/prot_area_cat1a");

///////////////////////// COMPARING DEFORESTATION \\\\\\\\\\\\\\\\\\\\\\\\\

Map.addLayer(indig_cons, {}, 'Indigenous conservation area')
Map.addLayer(cat1a_cons, {}, 'Conservation area IUCN cat 1a')

//load the Hansen dataset as an image

var HansenChange = ee.Image('UMD/hansen/global_forest_change_2019_v1_7')
print('Hansen forest cover change dataset', HansenChange)

//we SELECT with .select([]) to extract uniques variables from the dataset above
var lossImage = HansenChange.select(['loss']); //HINT: Caps in your variables ease reading
var gainImage = HansenChange.select(['gain']); //HINT: [] is always something within something. Mostly a band
var treeCover = HansenChange.select(['treecover2000']);

print('Loss image Hansen dataset', lossImage)

// use .and to COMBINE the two bands above
// the condition means 1 where a AND b (meaning the very same pixel shows loss in one and gain in the other dataset)
var gainAndLoss = gainImage.and(lossImage);
//print the result and see: We could have renamed the band it does not make sense 
print('Forest gain and loss from Hansen dataset', gainAndLoss)

// Show the loss and gain image and the other images from the previous section 
Map.addLayer(HansenChange.mask(HansenChange), {
  bands: ['treecover2000'],
  palette: ['000000', '00FF00'],
  max: 100
}, 'forest cover masked');

Map.addLayer(HansenChange.mask(HansenChange), {
  bands: ['loss'],
  palette: ['FF0000'],
  max: 100
}, 'forest loss masked');

Map.addLayer(HansenChange.mask(HansenChange), {
  bands: ['gain'],
  palette: ['0000FF'],
  max: 100
}, 'forest gain masked');

Map.addLayer(gainAndLoss.updateMask(gainAndLoss),
    {palette: 'FF00FF'}, 'Gain and Loss');
    



///////////////////////// CALCULATION AND STATISTICS WITH REDUCERS \\\\\\\\\\\\\\\\\\\\\\\\\
//By using a reducer (calculating means, stats, etc.) we can actually create spatial statistics based on 
//information from other features or layer. 


// call the conservation areas from the assets.
print(indig_cons)
print(cat1a_cons)


// Sum the values of forest loss pixels in the protected areas.

// to do this, we first have to use the mutliply function to get the square meters instead of the number of pixels
var areaImage = lossImage.multiply(ee.Image.pixelArea());

var SumLossIndig = areaImage.reduceRegion({
  reducer: ee.Reducer.sum(),
  geometry: indig_cons,
  scale: 30,
});

var SumLossCat1a = areaImage.reduceRegion({
  reducer: ee.Reducer.sum(),
  geometry: cat1a_cons,
  scale: 30,
});

print(SumLossIndig);
print('Forestry loss in indigenous area: ', SumLossIndig.get('loss'), 'square meters');

print(SumLossCat1a);
print('Forestry loss in protected area: ', SumLossCat1a.get('loss'), 'square meters');



Map.addLayer(indig_cons, {}, 'Indigenous conservation area')
Map.addLayer(cat1a_cons, {}, 'Conservation area IUCN cat 1a')