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PROBLEMS

Troubleshooting errors

This activity aims to help you practice troubleshooting your scripts in Earth Engine. Each script below contains several errors. Your task is to fix the errors and articulate a rule of thumb for handing each type of error in your workflows.

Please do the following:

  1. Make a “troubleshooting” folder in your Earth Engine repository.
  2. Start a new script for each of the workflows below.
  3. Fix the errors and document how you fixed the errors in this worksheet.

Please note, you will receive a copy of this worksheet in lab.

At the end of lab, please fill out this form where you will submit links to your scripts. Please ask an instructor if you need help getting links to your scripts.

Thank you.

practice 01

/*    
// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><

    AUTHOR:   
    DATE:     Oct 14, 2024
    TITLE:    Troubleshooting Practice 1

// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><
*/



// -------------------------------------------------------------
//  Access image from this cloud address:
//  "projects/ee-patterns/assets/p01/chipmanHill_2023_35cm_DEMHF"
// -------------------------------------------------------------

var image = ee.ImageCollection("projects/ee-patterns/assets/p01/chipmanHill_2023_35cm_DEMHF");

print("FIRST IMAGE", image);

// -------------------------------------------------------------
//  Customize Map. 
// -------------------------------------------------------------

// Set map center and zoom level.

Map.centerObject(image, 15);

//  Set basemap style to hybrid.

Map.setOptions('HYBRID');

// -------------------------------------------------------------
//  Display image as a map layer;
//  Stretch layer display values over image data range.
// -------------------------------------------------------------

// Print min and max values of image. 

print("Min & max value of image", geo.iCart.iMinMax(image));

// Chart histogram of actual data values.

print("Image histogram", geo.iCart.iHistogram(image));

// Define viz dictionary. 

var single_viz = 
    {
        min: [86],        
        max: [246],        
    }
;

// Add map layer. 

Map.addLayer(image,single_viz,"Digital Elevation Model (meters)");


var i_min_max = geo.iCart.iMinMax(image);

print("Min & max value of image", i_min_max);

Map.addLayer(image, {min: 96, max: 247}, "Image displayed by min and max", false);

// -------------------------------------------------------------
//  Calculate the PERCENT slope of each location in image. 
// -------------------------------------------------------------

var image_slope = ee.Terrain.slope(image);

var image_slope_percent = image_slope.divide(180).multiply(Math.PI).tan().multiply(100);

// -------------------------------------------------------------
//  Display slope image as a map layer;
//  Stretch layer display values over image data range.
// -------------------------------------------------------------

print("slope min max", geo.iCart.iMinMax(image_slope_percent));

var output_histogram = geo.iCart.iHistogram(image_slope_percent);

print("slope histogram", output_histogram);

Map.addLayer(image_slope_percent, {min:0, max: 87}, "Image slope - steeper is darker");

// -------------------------------------------------------------
//  Calculate the hillshade of each location in image. 
// -------------------------------------------------------------

var image_hs = ee.Terrain.hillshade(image.multiply(2), 45, 315);

print("hs min max", geo.iCart.iMinMax(image_hs));

var output_histogram = geo.iCart.iHistogram(image_hs);

print("hs histogram", output_histogram);

Map.addLayer(image_hs, {}, "Image hillshade");

// -------------------------------------------------------------
//  Calculate the deviation from mean elevation;
//  Use 10 as the distance argument.
//  Try to display the layer with this palette: ['blue', 'white', 'red']
// -------------------------------------------------------------

var image_dme = geo.iTerrain.devFromMeanElev(image, 10);

print("dme min max", geo.iCart.iMinMax(image_dme, 3));  

var output_histogram = geo.iCart.iHistogram(image_dme);

print("dme histogram", output_histogram);

Map.addLayer(image_dme, {min: -1, max:1, palette: ['blue', 'white', 'red']}, "Image DME");

practice 02

 /*    
// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><

    AUTHOR:   
    DATE:     Oct 14, 2024
    TITLE:    Troubleshooting Practice 02

// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><
*/


var geo = require("users/jhowarth/eePatterns:modules/geo.js");

print("geo methods dictionary", geo.help);    // Prints dictionary of all tools in module.
print("geo palettes", geo.iPalettes);         // Prints dictionary of all palettes in module. 

// -------------------------------------------------------------------------------
//  Gather image from "projects/ee-patterns/assets/p02/NOAA-CoNED-SOCAL".
//  Print a histogram - do you see land vs ocean floor in the histogram?
// -------------------------------------------------------------------------------

// var bathy = ee.Image("projects/sat-io/open-datasets/gebco/gebco_grid/gebco_2023_n900_s00_w-1800_e-900");

var image = ee.Image("projects/ee-patterns/assets/p02/NOAA-CoNED-SOCAL");

print("Image", image);

// Chart histogram of data values.

print("Image histogram", geo.iCart.iHistogram(image, 30));

// -------------------------------------------------------------------------------
//  Area of interest for practice problem.
// -------------------------------------------------------------------------------

var aoi = image;


// Center map on aoi at zoom level 9 and set base layer to hybrid.

Map.centerObject(aoi, 9);
Map.setOptions('hybrid');


// -------------------------------------------------------------------------------
//  Apply a vertical exageration (z-factor) of 2 to elevation data for hillshade. 
//  Make a hillshade from this image and display the hillshade image as layer on Map.
// -------------------------------------------------------------------------------

var image_ve = image.multiply(2);

var image_hs = ee.Terrain.hillshade(imagee_ve, 315, 35);

Map.addLayer(image_hs, {min:255, max:0}, "Hillshade");

// -------------------------------------------------------------------------------
//  Separate the original elevation image (without vertical exageration) into two different images:

//  (1) land elevations
//  (2) ocean floor elevations (bathymetry)

//  Each image should only store land or bathymetry elevation values, respectively.
//  All other values should be masked.
// -------------------------------------------------------------------------------

var image_land = image.selfMask(image.gt(0));

print("image land", image_land);

var image_bathy = image.updateMask(image.lte(0)) ;

// -------------------------------------------------------------------------------
//  Reclassify the bathymetry image at equal 100 meter intervals. 
//  Display the reclassified image as layer on Map with 0.5 opacity.
//  To improve contrast, set max of viz dictionary such that all locations less than -2000 m (or class 20) are the darkest blue in palette.
//  Use this palette: geo.iPalettes.iBathy.eleven
// -------------------------------------------------------------------------------

// Equal intervals

var image_bathy_reclass = geo.iReclass.equalInterval(aoi, -100);

// Print min and max values of image. 

print("Min & max value of bathy reclass", geo.iCart.iMinMax(image_bathy_reclass, 30, aoi));

// Chart histogram of actual data values.

print("Bathy histogram", geo.iCart.iHistogram(image_bathy_reclass, 30, aoi));

// // Define viz dictionary. 

var viz_bathy = 
    {
        max: [20],        
        min: [0], 
        palette: geo.iPalettes.iBathy.eleven
    }
;

// Add map layer. 

Map.addLayer(image_bathy_reclass,viz_bathy,"Bathymetry", 1, 0.5);

// -------------------------------------------------------------------------------
//  Define the Pleistocene shoreline as 130 meters lower than today's shoreline.
//  Make a boolean image that shows all land above ocean in Pleistocene.
//  Display boolean image as layer on Map with all non-land masked.
// -------------------------------------------------------------------------------

var image_pleistocene = image_bathy.add(130);

var image_pleistocene_boolean = image_pleistocene.gt(0);

var image_pleistocene_boolean_simple = image_bathy.gte(-130).updateMask();

Map.addLayer(image_pleistocene_boolean.selfMask(), {min:0, max:1, palette: "#ffffff"}, "Pleistocene Land", 1, 0.5);

// -------------------------------------------------------------------------------
//  Reclassify the land image at equal 100 meter intervals. 
//  Display the reclassified image as layer on Map with 0.5 opacity.
//  Use this palette: geo.iPalettes.iHypso.bartholomew
// -------------------------------------------------------------------------------

var image_land_reclass = geo.iReclass.equalInterval(image_land, 100);

print("image land reclass", image_land_reclass);

// Print min and max values of image. 

print("Min & max value of land_image_reclass", geo.iCart.iMinMax(image_land_reclass, 30, aoi));

var viz_land = 

    {
        min: [0],        
        max: [30], 
        palette: geo.iPalettes.iHypso.bartholome
    }
;

Map.addLayer(image_land_reclass,viz_land,"Image land", 1, 0.5);

practice 03

/*    
// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><

    AUTHOR:   
    DATE:     October 14, 2024
    TITLE:    Troubleshooting Practice 03

// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><
*/


var geo = require("users/jhowarth/eePatterns:modules/geo.js");

print("geo methods dictionary", geo.help);    // Prints dictionary of all tools in module.  
print("geo palettes", geo.iPalettes);         // Prints dictionary of all palettes in module. 


// -------------------------------------------------------------
//  DATA SOURCES 
// ------------------------------------------------------------- 

var fc_address_county = "TIGER/2018/Counties";
var fc_address_town = "projects/conservation-atlas/assets/cadastre/Boundary_TWNBNDS_poly";
var fc_address_parcel = "projects/vt-conservation/assets/state/FS_VCGI_OPENDATA_Cadastral_VTPARCELS_poly_standardized_parcels_SP_v1";
var fc_address_soils = "projects/conservation-atlas/assets/soils/VT_Data_NRCS_Soil_Survey_Units";
var ic_address_dem = "USGS/3DEP/1m"; 

var palette_soils = geo.iPalettes.iSoils.parent_materials_addison_county;
var class_labels_soils = geo.iPalettes.iSoils.parent_materials_addison_county_labels;

print("SOILS Palette and Class labels", palette_soils, class_labels_soils);

// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  1. GATHER HUMAN GEOGRAPHY
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  1.1 Make Addison County layer
// -------------------------------------------------------------

var fc_county = ee.FeatureCollection(fc_address_county)
  .filter(ee.Filter.eq("NAME", "ADDISON"))
;

print(
  "COUNTY first row",
  fc_county.first()
);


Map.addLayer(fc_county, {color: 'yellow'}, "1.1: Addison County", false);

var result_1p1 = fc_county; 

// -------------------------------------------------------------
//  1.2 Make study town (Middlebury) layer
// -------------------------------------------------------------

var town_name = "Middlebury";

var fc_town = ee.FeatureCollection(fc_address_town)
  .filter(ee.Filter.eq("TOWNNAME",  town_name.toUpperCase()))
;

Map.addLayer(fc_town, {color: 'green'}, "1.2: Study town: ".concat(town_name), false);

var result_1p2 = fc_town; 

// -------------------------------------------------------------
//  1.3 Center map on study town
// -------------------------------------------------------------

Map.centerObject(fc_town, 13);
Map.setOptions('hybrid');

// -------------------------------------------------------------
//  1.4 Make parcels in study town layer
// -------------------------------------------------------------

var fc_parcels = ee.FeatureCollection(fc_address_parcel);

print(
  "PARCEL first row",
  fc_parcels.first()
);


// Filter by attribute  

var fc_filtered_parcels = fc_parcels.filter(
  ee.Filter.eq('TOWN', town_name.toUpperCase()))
;

// Check filtered result.  

print(
  "Parcels Townname",
  fc_parcels.size(),
  fc_filtered_parcels.size()
);

// Add filtered data to map.  

Map.addLayer(fc_filtered_parcels, {color: 'cyan'}, "1.4: Parcels in Study Town", false);

var result_1p4 = fc_filtered_parcels;

// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  2. GATHER SOILS DATA AND FILTER FOR ADDISON COUNTY 
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  2.1 Gather soils data
// -------------------------------------------------------------

// Gather fc data from address.

var fc_soils = ee.FeatureCollection(fc_address_soils);

// Inspect the table.  

// print(
//   "SOILS",
//   "number of rows",
//   fc_soils.size(),
//   "names of columns",
//   fc_soils.first().propertyNames(),
//   "first row of table",
//   fc_soils.first(),
//   "unique values for target column",
//   fc_soils.aggregate_array('PARENT').distinct().sort()
// );

// Display as map layer. 

Map.addLayer(fc_soils, {color: 'white'}, "2.1: Vermont Soils", false);

var result_2p1 = fc_soils;

// -------------------------------------------------------------
//  2.2 Filter for soils that overlap Addison County
// -------------------------------------------------------------

// Filter feature collection by bounds of another vector dataset. 

var fc_filter_bounds_soils = fc_soils.filter(
    ee.Filter.bounds(fc_county)
);

// Check filtered result.  

// print(
//   "FC SIZE BEFORE VS AFTER FILTER",
//   fc_soils.size(),
//   fc_filter_bounds_soils.size()
// );

// Add filtered data to map.

Map.addLayer(fc_filter_bounds_soils, {color: 'magenta'}, "2.2 Soils that overlap Addison County", false);  

var result_2p2 = fc_filter_bounds_soils; 

// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  3. MAKE PARENT MATERIAL IMAGE FOR STUDY TOWN. 
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  3.1 Make nominal image of soil parent material. 
// -------------------------------------------------------------

var image_nominal_soils = geo.fcConvert.toNumericImage(fc_filter_bounds_soils, "PARENT")
;

// -------------------------------------------------------------
//  3.2 Mask by Addison County.
// -------------------------------------------------------------

var image_boolean_county = geo.fcConvert.toBooleanImage(fc_county);

var image_with_mask_soils = image_nominal_soils.updateMask(fc_county);

// -------------------------------------------------------------
//  3.3 Display masked soils image as layer. 
// -------------------------------------------------------------

var viz_soils = {

  min: 0,
  max: 10,
  palette: palette_soils

};

Map.addLayer(image_with_mask_soils, viz_soils, "3.3 Parent Materials of Addison County");

var result_3p3 = image_with_mask_soils;

// -------------------------------------------------------------
//  3.4 Add legend to botom-left of Map. 
// -------------------------------------------------------------

var legend_nominal_soils = geo.iCart.legendNominal(
  "PARENT MATERIAL", 
  viz_soils, 
  class_labels_soils, 
  "bottom-left"
  )
;

// Add legend to Map.  

Map.add(legend_nominal_soils);


// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  4. GATHER DEM 
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  4.1 Construct image collection. 
// -------------------------------------------------------------

var ic_dem = ee.ImageCollection(ic_address_dem);

// print(
//   "IC DEM", 
//   ic_dem.size(),
//   ic_dem.first() 
// );

// -------------------------------------------------------------
//  4.2 Filter for images that overlap Addison County.
// -------------------------------------------------------------

var ic_filtered_dem_county = ic_dem.filter(ee.Filter.bounds(image_boolean_county));

// print("IC DEM Addison", ic_filtered_dem_county.first(), ic_filtered_dem_county.size());

// -------------------------------------------------------------
//  4.3 Mosaic filtered image collection to image. 
// -------------------------------------------------------------

var ic_dem_mosaic = geo.icFlatten.mosaicToImage(ic_filtered_dem_county);

// print("Mosaic", ic_dem_mosaic);

// -------------------------------------------------------------
//  4.4 Mask mosaic image by Addison County. 
// -------------------------------------------------------------

var image_with_mask_dem = ic_dem_mosaic.updateMask(image_boolean_county);

// -------------------------------------------------------------
//  4.5 Make hillshade from masked image and display as layer with 0.5 opacity.
// -------------------------------------------------------------

var hs = ee.Terrain.hillshade(image_with_mask_dem, 315, 35);

Map.addLayer(hs, {min:0, max: 255}, "4.5 Hillshade", true, 0.5);

var result_4p5 = hs;

// -------------------------------------------------------------
//  4.6 Make hillshade from masked image and display as layer with 0.5 opacity.
// -------------------------------------------------------------

var slope = ee.Terrain.slope(image_with_mask_dem);

Map.addLayer(slope, {min:45, max:0}, "4.6 Slope", false, 0.5);

var result_4p6 = slope;

// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  5. PAINT STROKES
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  5.1 Make strokes for parcels ("white" and 0.5 weight) and add as layer.
// -------------------------------------------------------------

var strokes_parcels = geo.fcCart.paintStrokes(fc_filtered_parcels, "white", 0.5);

Map.addLayer(strokes_parcels, {}, "5.1 Parcel Outlines");

// -------------------------------------------------------------
//  5.2 Make strokes for town ("white" and 4 weight) and add as layer.
// -------------------------------------------------------------

var strokes_town = geo.fcCart.paintStrokes(fc_town, "white", 4);

Map.addLayer(strokes_town, {}, "5.2 Town Outlines");

practice 4

/*     
// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><

    AUTHOR:   
    DATE:   10/2/2024       
    TITLE:  Troubleshooting Practice 4   

// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><
*/

var geo = require("users/jhowarth/eePatterns:modules/geo.js");

print("geo methods dictionary", geo.help);    // Prints dictionary of all tools in module.  
print("geo palettes", geo.iPalettes);         // Prints dictionary of all palettes in module. 

// Feature collections

var vt_town_address = "projects/conservation-atlas/assets/cadastre/Boundary_TWNBNDS_poly";
var rare_address = "projects/conservation-atlas/assets/rarity/Significant_Natural_Communities";
var large_blocks_middlebury = "projects/ee-patterns/assets/vt-conservation/t04_habitat_blocks";

// Images

var valley_bottom_address = "projects/vt-conservation/assets/champlain_basin/LakeChamplainBasin";
var imp_address = "projects/vt-conservation/assets/landcover/STATEWIDE_2022_50cm_LANDCOVER_Impervious";


// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  1. Define study region.
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  1.1 Define study town (Middlebury)
// -------------------------------------------------------------

var town = ee.FeatureCollection(vt_town_address);

var study_town = town
  .filter(ee.Filter.eq("TOWNNAME", "MIDDLEBURY"));

Map.addLayer(study_town, {color: "black"}, "1.1 Study Town", false);

// print(study_town, study_town.first().getNumber("SHAPE_area").divide(4046.86));

var result_1p1 = study_town;

// -------------------------------------------------------------
//  1.2 Convert study town into boolean (to use as mask later)
// -------------------------------------------------------------

var town_mask = geo.fcConvert.toBooleanImage(study_town);

Map.addLayer(town_mask, {min:0, max:1}, "1.2 town mask", false);

// -------------------------------------------------------------
//  1.3 Define aoi as study town and towns that overlap (share boundary) with study town.
// -------------------------------------------------------------

var aoi = town.filterBounds(study_town);

Map.addLayer(aoi, {color: "white"}, "1.3 Area of interest (aoi)", false);


var result_1p3 = aoi;

// -------------------------------------------------------------
//  1.4 Convert aoi into a boolean (to use as mask later).
// -------------------------------------------------------------

var aoi_mask = geo.fcConvert.toBooleanImage(aoi);

Map.addLayer(aoi_mask, {min:0, max:1}, "1.4 aoi mask", false);

// -------------------------------------------------------------
//  1.5 Center map on study town and set basemap to hybrid. 
// -------------------------------------------------------------

Map.centerObject(study_town, 12);
Map.setOptions('hybrid');

// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  2. Make blocks inclusive of rare communities.  
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  2.1 Gather habitat blocks cloud asset that overlap aoi. 
// -------------------------------------------------------------

var blocks_asset = ee.FeatureCollection(large_blocks_middlebury)
  .filterBounds(aoi)
;

Map.addLayer(blocks_asset, {color: 'GreenYellow'}, "2.1 Blocks overlap aoi", false);

var result_2p1 = blocks_asset;

// -------------------------------------------------------------
//  2.2 Convert 2.1 to a boolean image.
// -------------------------------------------------------------

var image_boolean_blocks = geo.fcConvert.toBooleanImage(blocks_asset)
  .updateMask(aoi_mask)
;

// var classes_areas = geo.iZonal.areaClasses(image_boolean_blocks.unmask(), 5, study_town, "max");

// print(
//     "Area (square meters) blocks",
//     classes_areas
// );

// var class_percent_of_region = geo.iZonal.classPercentRegion(classes_areas);

// print(
//   "Area (percent of region)",
//   class_percent_of_region
//   )
// ;

print("image_boolean_blocks", image_boolean_blocks);

Map.addLayer(image_boolean_blocks, {min:0, max:1}, "2.2 Blocks boolean", false);

// -------------------------------------------------------------
//  2.3 Gather rare natural communities that overlap aoi.
// -------------------------------------------------------------

var rare_nc = ee.FeatureCollection(rare_address)
  .filterBounds(aoi)
;

Map.addLayer(rare_nc, {color:'cyan'}, "2.3 Rare Natural Communities overlap aoi", false);

var result_2p3 = rare_nc;

// -------------------------------------------------------------
//  2.4 Make boolean image of rare natural communities that overlap aoi.
// -------------------------------------------------------------

var image_boolean_rare = geo.fcConvert.toBooleanImage(rare_nc)
  .updateMask(aoi_mask)
;

Map.addLayer(image_boolean_rare, {min:0, max:1}, "2.4 Image rare", false);

// -------------------------------------------------------------
//  2.5 Find locations that are either blocks or rare natural communities.
// -------------------------------------------------------------

var blocks_or_rare = image_boolean_blocks.or(image_boolean_rare);

Map.addLayer(blocks_or_rare, {color: "Yellow"}, "2.5 Blocks or Rare", false);

var classes_areas = geo.iZonal.areaClasses(blocks_or_rare, 5, study_town, "max");

// print(
//     "Area (square meters) + rarity",
//     classes_areas
// );

// var class_percent_of_region = geo.iZonal.classPercentRegion(classes_areas);

// print(
//   "Area (percent of region)",
//   class_percent_of_region
//   )
// ;

// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  3. Classify habitat blocks versus habitat connectors.  
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  3.1 Gather valley bottom, make it boolean (anything not 0 is true), and mask for aoi.
// -------------------------------------------------------------

var valley_bottoms = ee.Image(valley_bottom_address)
  // .neq(0)
  .updateMask(aoi_mask)
;

var output_min_max = geo.iCart.iMinMax(valley_bottoms, 10, study_town);

print("Min & max value of valley bottom image", output_min_max);

Map.addLayer(valley_bottoms, {min:0, max:1}, "3.1 Valley bottoms", false, 1);

// -------------------------------------------------------------
//  3.2 Erase all impervious land from valley bottoms.
// -------------------------------------------------------------

var imp = ee.Image(imp_address)
  .unmask()
  .updateMask(aoi_mask)
;

var valley_bottoms_not_impervious = valley_bottoms.multiply(imp.eq(0));

// print(valley_bottoms_not_impervious);

var output_min_max = geo.iCart.iMinMax(valley_bottoms_not_impervious, 10, study_town);

print("Min & max value of image", output_min_max);


Map.addLayer(valley_bottoms_not_impervious, {min:0, max:1}, "3.2 Valley bottoms without Impervious", false);

// -------------------------------------------------------------
//  3.3 Distinguish valley bottoms without impervious that do not intersect habitat blocks as a new habitat class.
//  Your goal here is to make a single layer where the value 1 represents habitat blocks and 2 represents corridors. 
// -------------------------------------------------------------

//  Note to TAs: there are a lot of different ways to solve this step, so do not feel like you need to force
//  students to use my solution. There are alternatives. 

// One way

var habitat_classes = blocks_or_rare
  .eq(0)
  .multiply(valley_bottoms_not_impervious)
  .multiply(2)
  .add(blocks_or_rare)
  ;

// Another way

// var habitat_classes = blocks_or_rare
//   .eq(0)
//   .multiply(valley_bottoms_not_impervious)
//   .remap(
//     [0,1],
//     [0,2])
//   .add(blocks_or_rare)
//   ;


print("habitat classes", habitat_classes);

Map.addLayer(habitat_classes.selfMask(), {min:0, max:2, palette: ["black", "#9370DB", "#DBD570"]}, "3.3b Habitat blocks and connectors", false);

// -------------------------------------------------------------
//  3.4 Export image to asset and display as map layer.  
// -------------------------------------------------------------

//  I started getting some "tile" errors here that tells me EE is getting sweating the task. 
//  So that is why I exported the result from 3.3 as an asset and continued working with it. 

// geo.iExport.toCloudAsset(
//   habitat_classes, 
//   "p04_habitat_blocks_and_connectors",
//   aoi, 
//   "mode"
// );

var image_asset = ee.Image("projects/ee-patterns/assets/vt-conservation/p04_habitat_blocks_and_connectors");


var viz_habitat = {
  min:1,
  max:2, 
  palette: ["#9370DB", "#DBD570"]
};

Map.addLayer(image_asset.selfMask(), viz_habitat, "3.3 Habitat blocks and connectors", true);

// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-
//  4. A little cartography 
// -+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-

// -------------------------------------------------------------
//  4.1 Add a legend.  
// -------------------------------------------------------------

// Make legend from image with nominal data.

var legend_nominal = geo.iCart.legendNominal(
  "Act 171 Habitat Elements", 
  viz_habitat, 
  ["Blocks", "Connectors"], 
  "position-on-map"
  )
;

// Add legend to Map.  

Map.add(legend_nominal);

// -------------------------------------------------------------
//  4.2 Add outlines of study town.  
// -------------------------------------------------------------

var strokes = geo.fcCart.paintStrokes(study_town, "white", 4);

Map.addLayer(strokes, {}, "Study town outlines");

// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
//  PRACTICE CHECKS
// ><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

// Please calculate percent area based on your result from 3.4.
// When calculating area, please use 5 for scale and study_town (Middlebury) for extent.
// The last two checks in Canvas will ask you to report area for habitat blocks and habitat connectors separately. 

var habitat_classes_areas = geo.iZonal.areaClasses(habitat_classes, 5, study_town, "max");
var class_percent_of_region = geo.iZonal.classPercentRegion(habitat_classes_areas);

print(
  "Check 3.4: Area (percent of region) habitat",
  class_percent_of_region
  )
;