ETH Global Sentinel-2 10m Canopy Height (2020)

[pinkychow1010]

Contact Details

pinkychow1010@gmail.com

Provide Dataset Link

https://gee-community-catalog.org/projects/canopy/?h=canopy+height

Describe the example

Forest vulnerability mapping in Brazil

// This script combines the canopy height data with road assessibility to address vulnerable tall forest in Brazil

// load study area
var admin1 = ee.FeatureCollection("projects/sat-io/open-datasets/geoboundaries/HPSCGS-ADM1");
var geometry = admin1.filter(ee.Filter.eq('shapeGroup', 'BRA'));

// Center the map on the geometry and set the base map options
Map.centerObject(geometry);
Map.setOptions("Hybrid");

// create a tall forest mask for trees higher than 10 m
var canopy_height = ee.Image("users/nlang/ETH_GlobalCanopyHeight_2020_10m_v1");
var forest_mask = canopy_height.gt(10);

var ch_vis = {
  min: 0,
  max: 30,
  palette: ['white','green']
};

// visualize canopy height for reference
Map.addLayer(canopy_height.clip(geometry), ch_vis, "Canopy Height", false);

// Load and preprocess the land-based travel speed image
var landBasedTravelSpeed = ee.Image('Oxford/MAP/friction_surface_2019')
  .select('friction')
  .clip(geometry);

var travelSpeedVisParams = {
  min: 0.005,
  max: 0.02,
  palette: ['#FF0000', '#FFFF00', '#00FF00', '#0000FF'] // Red to Blue gradient
};


// Add the land-based travel speed layer
Map.addLayer(landBasedTravelSpeed, travelSpeedVisParams, 'Land-based travel speed', false);

// Load the forest loss data
var loss = ee.Image('UMD/hansen/global_forest_change_2023_v1_11').select('lossyear');

var treeLossVisParams = {
  min: 0,
  max: 23,
  palette: ['white', 'red']
};

// Add layers for tree loss before and after 2019 for comparison on vulnerability mapping
Map.addLayer(loss.clip(geometry).updateMask(loss.lt(20)), treeLossVisParams, 'Tree Loss before 2019', false, 0.5);
Map.addLayer(loss.clip(geometry).updateMask(loss.gt(18)), treeLossVisParams, 'Tree Loss after 2019 onwards', false, 0.9);

// Define urban areas as the source for cost surface calculation
var urbanAreas = ee.ImageCollection('projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL')
  .sort('system:time_start', false)
  .first()
  .gt(10)
  .clip(geometry);

// Compute the cumulative cost surface from urban areas
var costSurface = landBasedTravelSpeed.cumulativeCost({
  source: urbanAreas,
  maxDistance: 500000, // Maximum distance in meters
  geodeticDistance: true
}).clip(geometry);

// Define visualization parameters for the cost surface layer
var costSurfaceVisParams = {
  min: 3,
  max: 8,
  palette: [
    'black',      // Very high cost - Black
    '#FF0000',    // High cost - Red
    '#FFA500',    // Medium cost - Orange
    '#FFD700',    // Low-Medium cost - Yellow
    '#ADFF2F',    // Low cost - Green-Yellow
    '#00FF00'     // Very Low cost - Green
  ]
};

// Add the cost surface layer to the map, masked by forest cover
Map.addLayer(
  costSurface.log().updateMask(forest_mask),
  costSurfaceVisParams,
  'Cost Model'
);

Code of Conduct

• I agree to follow this project's Code of Conduct

[samapriya]

Hi @pinkychow1010
Thank you so much for these tutorials but as I cannot use them with the data corruption I am going ahead and closing these issues. As and when these get fixed I am more than happy to include them in the tutorials and thank you so much for contributing