Learn to crosswalk LCMS datasets to different levels

  • Currently, all LCMS deliverables are delivered at the highest level (largest number of classes)

  • This notebook facilitates crosswalking of LCMS deliverables to different levels

  • Lower levels provide greater accuracy, while higher levels provide greater thematic detail

  • Use this notebook to find the level that suits your data needs and tolerance for map error

Copyright 2025 Ian Housman

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

github github

#Boiler plate
#Import modules

try:
    from  geeViz.geeView import *
except:
    !python -m pip install geeViz
    from  geeViz.geeView import *

import geeViz.examples.lcmsLevelLookup as ll
import pandas as pd
import numpy as np
import glob
from IPython.display import Markdown
print('Done')

Done

First, we’ll take a look at the various levels for LCMS data

  • This is a standard way of crosswalking LCMS data to an appropriate level of thematic detail for your needs

  • You can also crosswalk LCMS data in many other ways by combining different sets of Change, Land Cover, and Land Use classes in various manners

# Bring in the lookup dictionary and convert it to a Pandas dataframe for easy viewing
products = list(ll.all_lookup.keys())

color_lookup = {}
def color_cells(val):
    if val in color_lookup:
        color = color_lookup[val]
        return f'background-color: #{color};color:#1b1716;border-top: 1px solid #1b1716;text-shadow:1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;'
    else:
        return ''
for product in products:
    product_title = product.replace('_',' ')
    product_lookup = ll.all_lookup[product]
    available_levels = ll.product_levels[product]
    
    highest_level = max(available_levels)
  
    
    highest_level = [n for n in product_lookup.keys() if len(n.split("-")) == highest_level]
    table = [highest_level]

    for level in available_levels[1:]:
        table.append(['-'.join(l.split('-')[:level]) for l in highest_level])
    
    table = np.transpose(table)
    

    color_lookup = {}
    out_table = [[product_lookup[v][2] for v in r] for r in table]
    for r in table:
        for v in r:
            color_lookup[product_lookup[v][2]] = product_lookup[v][1]
    
    df = pd.DataFrame(out_table,index= None)
    blankIndex=[''] * len(df)
    
    df.columns = [f'Level {l}' for l in available_levels]
    df =df[df.columns[::-1]]
    # Apply the styling to the DataFrame
    df.index +=1
    df = df.style.applymap(color_cells)

    
    display(Markdown(f'<h1>{product_title} Levels</h1>'))
    display(df)

C:\Users\ihousman\AppData\Local\Temp\ipykernel_28608\240870574.py:41: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead.
  df = df.style.applymap(color_cells)

Land Cover Levels

  Level 1 Level 2 Level 3 Level 4
1 Vegetated Tree Vegetated Tree Tree
2 Vegetated Tree Vegetated Tree Tall Shrub & Tree Mix (SEAK Only)
3 Vegetated Tree Vegetated Tree Shrub & Tree Mix
4 Vegetated Tree Vegetated Tree Grass/Forb/Herb & Tree Mix
5 Vegetated Tree Vegetated Tree Barren & Tree Mix
6 Vegetated Non-Tree Vegetated Shrub Tall Shrub (SEAK Only)
7 Vegetated Non-Tree Vegetated Shrub Shrub
8 Vegetated Non-Tree Vegetated Shrub Grass/Forb/Herb & Shrub Mix
9 Vegetated Non-Tree Vegetated Shrub Barren & Shrub Mix
10 Vegetated Non-Tree Vegetated Grass/Forb/Herb Grass/Forb/Herb
11 Vegetated Non-Tree Vegetated Grass/Forb/Herb Barren & Grass/Forb/Herb Mix
12 Non-Vegetated Non-Vegetated Barren or Impervious Barren or Impervious
13 Non-Vegetated Non-Vegetated Snow or Ice Snow or Ice
14 Non-Vegetated Non-Vegetated Water Water
15 Non-Processing Area Mask Non-Processing Area Mask Non-Processing Area Mask Non-Processing Area Mask

Change Levels

  Level 1 Level 2 Level 3
1 Stable Stable Stable
2 Stable Gain Gain
3 Loss Loss Slow Loss
4 Loss Loss Fast Loss
5 Non-Processing Area Mask Non-Processing Area Mask Non-Processing Area Mask

Land Use Levels

  Level 1 Level 2 Level 3
1 Anthropogenic Agriculture Agriculture
2 Anthropogenic Developed Developed
3 Non-Anthropogenic Forest Forest
4 Non-Anthropogenic Other Non-Forest Wetland
5 Non-Anthropogenic Other Other
6 Non-Anthropogenic Rangeland or Pasture Rangeland or Pasture
7 Non-Processing Area Mask Non-Processing Area Mask Non-Processing Area Mask

Learn how to crosswalk and symbolize LCMS products at a specific level

  • You need to crosswalk (remap) values and provide the relevant symbology to render the maps properly

  • The code below will show different products and levels and their respective crosswalk (remap) class numbers and symbology properties

for product in ll.product_levels.keys():
    product_title = product.replace('_',' ')
    for level in ll.product_levels[product]:
        remap_dict = ll.getLevelNRemap(level, bandName=product)
        print('Product:',product_title, 'Level:',level, remap_dict)

Crosswalk and visualize all LCMS products and levels

  • This will apply the crosswalk (remap) and update the symbology for all products and levels

  • A map viewer will then open to visualize the resulting layers

Map.clearMap()
lcms = ee.ImageCollection("USFS/GTAC/LCMS/v2023-9")


for product in ll.product_levels.keys():
    product_title = product.replace('_',' ')
    lc = lcms.select([product])
    isFirst = True
    reducer = ee.Reducer.mode() if product != 'Change' else ee.Reducer.max()
    levels = ll.product_levels[product]
    
    for level in levels:
        remap_dict = ll.getLevelNRemap(level, bandName=product)
        lcT = lc.map(lambda img: img.remap(remap_dict["remap_from"], remap_dict["remap_to"]).rename([product]).set(remap_dict["viz_dict"])) # Crosswalk and set symbology
        Map.addLayer(lcT, {"autoViz": True, "canAreaChart": True, "includeClassValues": False,"reducer":reducer}, f"{product_title} Level {level}", isFirst) # Visualize output
        isFirst = False

Map.setCenter(-111.83856, 40.73678, 11)
Map.turnOnAutoAreaCharting()
Map.view()

The v2024-10 release introduces a new set of classes for Change

  • This will illustrate how the new classes and the different levels relate

# New 2024.10 release levels
# Bring in the lookup dictionary and convert it to a Pandas dataframe for easy viewing
# Function to apply color based on lookup

html = ''
products = ['Change','Land_Cover','Land_Use']
for product in products:
    product_title = product.replace('_',' ')
    product_lookup = ll.all_lookup_2024_10[product]
    available_levels = ll.product_levels_2024_10[product]
    
    highest_level = max(available_levels)
  
    
    highest_level = [n for n in product_lookup.keys() if len(n.split("-")) == highest_level]
   
    table = [highest_level]

    for level in available_levels[1:]:
        table.append(['-'.join(l.split('-')[:level]) for l in highest_level])
  
    table = np.transpose(table)
    
    color_lookup = {}
    out_table = [[product_lookup[v][2] for v in r] for r in table]
    for r in table:
        for v in r:
            color_lookup[product_lookup[v][2]] = product_lookup[v][1]
    
    df = pd.DataFrame(out_table,index= None)
    blankIndex=[''] * len(df)
    
    df.columns = [f'Level {l}' for l in available_levels]
    df =df[df.columns[::-1]]
   
    # Apply the styling to the DataFrame
    df.index +=1
    df = df.style.applymap(color_cells)
    display(Markdown(f'<h1>{product_title} Levels</h1>'))
    display(df)
    html += f"""<h3>{product_title}</h3>\n{df.to_html()}"""
print(html)

C:\Users\ihousman\AppData\Local\Temp\ipykernel_28608\4255807673.py:38: FutureWarning: Styler.applymap has been deprecated. Use Styler.map instead.
  df = df.style.applymap(color_cells)

Change Levels

  Level 1 Level 2 Level 3
1 Disturbance Wind Wind
2 Disturbance Wind Hurricane
3 Disturbance Other Loss Snow or Ice Transition
4 Disturbance Desiccation Desiccation
5 Disturbance Inundation Inundation
6 Disturbance Fire Prescribed Fire
7 Disturbance Fire Wildfire
8 Disturbance Mechanical Land Transformation Mechanical Land Transformation
9 Disturbance Tree Removal Tree Removal
10 Disturbance Insect, Disease, or Drought Stress Defoliation
11 Disturbance Insect, Disease, or Drought Stress Southern Pine Beetle
12 Disturbance Insect, Disease, or Drought Stress Insect, Disease, or Drought Stress
13 Disturbance Other Loss Other Loss
14 Vegetation Successional Growth Vegetation Successional Growth Vegetation Successional Growth
15 Stable Stable Stable
16 Non-Processing Area Mask Non-Processing Area Mask Non-Processing Area Mask

Land Cover Levels

  Level 1 Level 2 Level 3 Level 4
1 Vegetated Tree Vegetated Tree Tree
2 Vegetated Tree Vegetated Tree Tall Shrub & Tree Mix (AK Only)
3 Vegetated Tree Vegetated Tree Shrub & Tree Mix
4 Vegetated Tree Vegetated Tree Grass/Forb/Herb & Tree Mix
5 Vegetated Tree Vegetated Tree Barren & Tree Mix
6 Vegetated Non-Tree Vegetated Shrub Tall Shrub (AK Only)
7 Vegetated Non-Tree Vegetated Shrub Shrub
8 Vegetated Non-Tree Vegetated Shrub Grass/Forb/Herb & Shrub Mix
9 Vegetated Non-Tree Vegetated Shrub Barren & Shrub Mix
10 Vegetated Non-Tree Vegetated Grass/Forb/Herb Grass/Forb/Herb
11 Vegetated Non-Tree Vegetated Grass/Forb/Herb Barren & Grass/Forb/Herb Mix
12 Non-Vegetated Non-Vegetated Barren or Impervious Barren or Impervious
13 Non-Vegetated Non-Vegetated Snow or Ice Snow or Ice
14 Non-Vegetated Non-Vegetated Water Water
15 Non-Processing Area Mask Non-Processing Area Mask Non-Processing Area Mask Non-Processing Area Mask

Land Use Levels

  Level 1 Level 2
1 Anthropogenic Agriculture
2 Anthropogenic Developed
3 Non-Anthropogenic Forest
4 Non-Anthropogenic Other
5 Non-Anthropogenic Rangeland or Pasture
6 Non-Processing Area Mask Non-Processing Area Mask 
<h3>Change</h3>
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<table id="T_3ee46">
  <thead>
    <tr>
      <th class="blank level0" >&nbsp;</th>
      <th id="T_3ee46_level0_col0" class="col_heading level0 col0" >Level 1</th>
      <th id="T_3ee46_level0_col1" class="col_heading level0 col1" >Level 2</th>
      <th id="T_3ee46_level0_col2" class="col_heading level0 col2" >Level 3</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th id="T_3ee46_level0_row0" class="row_heading level0 row0" >1</th>
      <td id="T_3ee46_row0_col0" class="data row0 col0" >Disturbance</td>
      <td id="T_3ee46_row0_col1" class="data row0 col1" >Wind</td>
      <td id="T_3ee46_row0_col2" class="data row0 col2" >Wind</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row1" class="row_heading level0 row1" >2</th>
      <td id="T_3ee46_row1_col0" class="data row1 col0" >Disturbance</td>
      <td id="T_3ee46_row1_col1" class="data row1 col1" >Wind</td>
      <td id="T_3ee46_row1_col2" class="data row1 col2" >Hurricane</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row2" class="row_heading level0 row2" >3</th>
      <td id="T_3ee46_row2_col0" class="data row2 col0" >Disturbance</td>
      <td id="T_3ee46_row2_col1" class="data row2 col1" >Other Loss</td>
      <td id="T_3ee46_row2_col2" class="data row2 col2" >Snow or Ice Transition</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row3" class="row_heading level0 row3" >4</th>
      <td id="T_3ee46_row3_col0" class="data row3 col0" >Disturbance</td>
      <td id="T_3ee46_row3_col1" class="data row3 col1" >Desiccation</td>
      <td id="T_3ee46_row3_col2" class="data row3 col2" >Desiccation</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row4" class="row_heading level0 row4" >5</th>
      <td id="T_3ee46_row4_col0" class="data row4 col0" >Disturbance</td>
      <td id="T_3ee46_row4_col1" class="data row4 col1" >Inundation</td>
      <td id="T_3ee46_row4_col2" class="data row4 col2" >Inundation</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row5" class="row_heading level0 row5" >6</th>
      <td id="T_3ee46_row5_col0" class="data row5 col0" >Disturbance</td>
      <td id="T_3ee46_row5_col1" class="data row5 col1" >Fire</td>
      <td id="T_3ee46_row5_col2" class="data row5 col2" >Prescribed Fire</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row6" class="row_heading level0 row6" >7</th>
      <td id="T_3ee46_row6_col0" class="data row6 col0" >Disturbance</td>
      <td id="T_3ee46_row6_col1" class="data row6 col1" >Fire</td>
      <td id="T_3ee46_row6_col2" class="data row6 col2" >Wildfire</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row7" class="row_heading level0 row7" >8</th>
      <td id="T_3ee46_row7_col0" class="data row7 col0" >Disturbance</td>
      <td id="T_3ee46_row7_col1" class="data row7 col1" >Mechanical Land Transformation</td>
      <td id="T_3ee46_row7_col2" class="data row7 col2" >Mechanical Land Transformation</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row8" class="row_heading level0 row8" >9</th>
      <td id="T_3ee46_row8_col0" class="data row8 col0" >Disturbance</td>
      <td id="T_3ee46_row8_col1" class="data row8 col1" >Tree Removal</td>
      <td id="T_3ee46_row8_col2" class="data row8 col2" >Tree Removal</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row9" class="row_heading level0 row9" >10</th>
      <td id="T_3ee46_row9_col0" class="data row9 col0" >Disturbance</td>
      <td id="T_3ee46_row9_col1" class="data row9 col1" >Insect, Disease, or Drought Stress</td>
      <td id="T_3ee46_row9_col2" class="data row9 col2" >Defoliation</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row10" class="row_heading level0 row10" >11</th>
      <td id="T_3ee46_row10_col0" class="data row10 col0" >Disturbance</td>
      <td id="T_3ee46_row10_col1" class="data row10 col1" >Insect, Disease, or Drought Stress</td>
      <td id="T_3ee46_row10_col2" class="data row10 col2" >Southern Pine Beetle</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row11" class="row_heading level0 row11" >12</th>
      <td id="T_3ee46_row11_col0" class="data row11 col0" >Disturbance</td>
      <td id="T_3ee46_row11_col1" class="data row11 col1" >Insect, Disease, or Drought Stress</td>
      <td id="T_3ee46_row11_col2" class="data row11 col2" >Insect, Disease, or Drought Stress</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row12" class="row_heading level0 row12" >13</th>
      <td id="T_3ee46_row12_col0" class="data row12 col0" >Disturbance</td>
      <td id="T_3ee46_row12_col1" class="data row12 col1" >Other Loss</td>
      <td id="T_3ee46_row12_col2" class="data row12 col2" >Other Loss</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row13" class="row_heading level0 row13" >14</th>
      <td id="T_3ee46_row13_col0" class="data row13 col0" >Vegetation Successional Growth</td>
      <td id="T_3ee46_row13_col1" class="data row13 col1" >Vegetation Successional Growth</td>
      <td id="T_3ee46_row13_col2" class="data row13 col2" >Vegetation Successional Growth</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row14" class="row_heading level0 row14" >15</th>
      <td id="T_3ee46_row14_col0" class="data row14 col0" >Stable</td>
      <td id="T_3ee46_row14_col1" class="data row14 col1" >Stable</td>
      <td id="T_3ee46_row14_col2" class="data row14 col2" >Stable</td>
    </tr>
    <tr>
      <th id="T_3ee46_level0_row15" class="row_heading level0 row15" >16</th>
      <td id="T_3ee46_row15_col0" class="data row15 col0" >Non-Processing Area Mask</td>
      <td id="T_3ee46_row15_col1" class="data row15 col1" >Non-Processing Area Mask</td>
      <td id="T_3ee46_row15_col2" class="data row15 col2" >Non-Processing Area Mask</td>
    </tr>
  </tbody>
</table>
<h3>Land Cover</h3>
<style type="text/css">
#T_fed98_row0_col0, #T_fed98_row1_col0, #T_fed98_row2_col0, #T_fed98_row2_col3, #T_fed98_row3_col0, #T_fed98_row4_col0, #T_fed98_row5_col0, #T_fed98_row6_col0, #T_fed98_row7_col0, #T_fed98_row8_col0, #T_fed98_row9_col0, #T_fed98_row10_col0 {
  background-color: #61BB46;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row0_col1, #T_fed98_row0_col2, #T_fed98_row0_col3, #T_fed98_row1_col1, #T_fed98_row1_col2, #T_fed98_row2_col1, #T_fed98_row2_col2, #T_fed98_row3_col1, #T_fed98_row3_col2, #T_fed98_row4_col1, #T_fed98_row4_col2 {
  background-color: #004E2B;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row1_col3 {
  background-color: #009344;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row3_col3 {
  background-color: #ACBB67;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row4_col3 {
  background-color: #8B8560;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row5_col1, #T_fed98_row6_col1, #T_fed98_row7_col1, #T_fed98_row8_col1, #T_fed98_row9_col1, #T_fed98_row10_col1 {
  background-color: #8DA463;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row5_col2, #T_fed98_row6_col2, #T_fed98_row6_col3, #T_fed98_row7_col2, #T_fed98_row8_col2 {
  background-color: #F89A1C;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row5_col3 {
  background-color: #CAFD4B;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row7_col3 {
  background-color: #8FA55F;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row8_col3 {
  background-color: #BEBB8E;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row9_col2, #T_fed98_row9_col3, #T_fed98_row10_col2 {
  background-color: #E5E98A;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row10_col3 {
  background-color: #DDB925;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row11_col0, #T_fed98_row11_col1, #T_fed98_row12_col0, #T_fed98_row12_col1, #T_fed98_row13_col0, #T_fed98_row13_col1 {
  background-color: #58646E;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row11_col2, #T_fed98_row11_col3 {
  background-color: #893F54;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row12_col2, #T_fed98_row12_col3 {
  background-color: #E4F5FD;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row13_col2, #T_fed98_row13_col3 {
  background-color: #00B6F0;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_fed98_row14_col0, #T_fed98_row14_col1, #T_fed98_row14_col2, #T_fed98_row14_col3 {
  background-color: #1B1716;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
</style>
<table id="T_fed98">
  <thead>
    <tr>
      <th class="blank level0" >&nbsp;</th>
      <th id="T_fed98_level0_col0" class="col_heading level0 col0" >Level 1</th>
      <th id="T_fed98_level0_col1" class="col_heading level0 col1" >Level 2</th>
      <th id="T_fed98_level0_col2" class="col_heading level0 col2" >Level 3</th>
      <th id="T_fed98_level0_col3" class="col_heading level0 col3" >Level 4</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th id="T_fed98_level0_row0" class="row_heading level0 row0" >1</th>
      <td id="T_fed98_row0_col0" class="data row0 col0" >Vegetated</td>
      <td id="T_fed98_row0_col1" class="data row0 col1" >Tree Vegetated</td>
      <td id="T_fed98_row0_col2" class="data row0 col2" >Tree</td>
      <td id="T_fed98_row0_col3" class="data row0 col3" >Tree</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row1" class="row_heading level0 row1" >2</th>
      <td id="T_fed98_row1_col0" class="data row1 col0" >Vegetated</td>
      <td id="T_fed98_row1_col1" class="data row1 col1" >Tree Vegetated</td>
      <td id="T_fed98_row1_col2" class="data row1 col2" >Tree</td>
      <td id="T_fed98_row1_col3" class="data row1 col3" >Tall Shrub & Tree Mix (AK Only)</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row2" class="row_heading level0 row2" >3</th>
      <td id="T_fed98_row2_col0" class="data row2 col0" >Vegetated</td>
      <td id="T_fed98_row2_col1" class="data row2 col1" >Tree Vegetated</td>
      <td id="T_fed98_row2_col2" class="data row2 col2" >Tree</td>
      <td id="T_fed98_row2_col3" class="data row2 col3" >Shrub & Tree Mix</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row3" class="row_heading level0 row3" >4</th>
      <td id="T_fed98_row3_col0" class="data row3 col0" >Vegetated</td>
      <td id="T_fed98_row3_col1" class="data row3 col1" >Tree Vegetated</td>
      <td id="T_fed98_row3_col2" class="data row3 col2" >Tree</td>
      <td id="T_fed98_row3_col3" class="data row3 col3" >Grass/Forb/Herb & Tree Mix</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row4" class="row_heading level0 row4" >5</th>
      <td id="T_fed98_row4_col0" class="data row4 col0" >Vegetated</td>
      <td id="T_fed98_row4_col1" class="data row4 col1" >Tree Vegetated</td>
      <td id="T_fed98_row4_col2" class="data row4 col2" >Tree</td>
      <td id="T_fed98_row4_col3" class="data row4 col3" >Barren & Tree Mix</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row5" class="row_heading level0 row5" >6</th>
      <td id="T_fed98_row5_col0" class="data row5 col0" >Vegetated</td>
      <td id="T_fed98_row5_col1" class="data row5 col1" >Non-Tree Vegetated</td>
      <td id="T_fed98_row5_col2" class="data row5 col2" >Shrub</td>
      <td id="T_fed98_row5_col3" class="data row5 col3" >Tall Shrub (AK Only)</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row6" class="row_heading level0 row6" >7</th>
      <td id="T_fed98_row6_col0" class="data row6 col0" >Vegetated</td>
      <td id="T_fed98_row6_col1" class="data row6 col1" >Non-Tree Vegetated</td>
      <td id="T_fed98_row6_col2" class="data row6 col2" >Shrub</td>
      <td id="T_fed98_row6_col3" class="data row6 col3" >Shrub</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row7" class="row_heading level0 row7" >8</th>
      <td id="T_fed98_row7_col0" class="data row7 col0" >Vegetated</td>
      <td id="T_fed98_row7_col1" class="data row7 col1" >Non-Tree Vegetated</td>
      <td id="T_fed98_row7_col2" class="data row7 col2" >Shrub</td>
      <td id="T_fed98_row7_col3" class="data row7 col3" >Grass/Forb/Herb & Shrub Mix</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row8" class="row_heading level0 row8" >9</th>
      <td id="T_fed98_row8_col0" class="data row8 col0" >Vegetated</td>
      <td id="T_fed98_row8_col1" class="data row8 col1" >Non-Tree Vegetated</td>
      <td id="T_fed98_row8_col2" class="data row8 col2" >Shrub</td>
      <td id="T_fed98_row8_col3" class="data row8 col3" >Barren & Shrub Mix</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row9" class="row_heading level0 row9" >10</th>
      <td id="T_fed98_row9_col0" class="data row9 col0" >Vegetated</td>
      <td id="T_fed98_row9_col1" class="data row9 col1" >Non-Tree Vegetated</td>
      <td id="T_fed98_row9_col2" class="data row9 col2" >Grass/Forb/Herb</td>
      <td id="T_fed98_row9_col3" class="data row9 col3" >Grass/Forb/Herb</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row10" class="row_heading level0 row10" >11</th>
      <td id="T_fed98_row10_col0" class="data row10 col0" >Vegetated</td>
      <td id="T_fed98_row10_col1" class="data row10 col1" >Non-Tree Vegetated</td>
      <td id="T_fed98_row10_col2" class="data row10 col2" >Grass/Forb/Herb</td>
      <td id="T_fed98_row10_col3" class="data row10 col3" >Barren & Grass/Forb/Herb Mix</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row11" class="row_heading level0 row11" >12</th>
      <td id="T_fed98_row11_col0" class="data row11 col0" >Non-Vegetated</td>
      <td id="T_fed98_row11_col1" class="data row11 col1" >Non-Vegetated</td>
      <td id="T_fed98_row11_col2" class="data row11 col2" >Barren or Impervious</td>
      <td id="T_fed98_row11_col3" class="data row11 col3" >Barren or Impervious</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row12" class="row_heading level0 row12" >13</th>
      <td id="T_fed98_row12_col0" class="data row12 col0" >Non-Vegetated</td>
      <td id="T_fed98_row12_col1" class="data row12 col1" >Non-Vegetated</td>
      <td id="T_fed98_row12_col2" class="data row12 col2" >Snow or Ice</td>
      <td id="T_fed98_row12_col3" class="data row12 col3" >Snow or Ice</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row13" class="row_heading level0 row13" >14</th>
      <td id="T_fed98_row13_col0" class="data row13 col0" >Non-Vegetated</td>
      <td id="T_fed98_row13_col1" class="data row13 col1" >Non-Vegetated</td>
      <td id="T_fed98_row13_col2" class="data row13 col2" >Water</td>
      <td id="T_fed98_row13_col3" class="data row13 col3" >Water</td>
    </tr>
    <tr>
      <th id="T_fed98_level0_row14" class="row_heading level0 row14" >15</th>
      <td id="T_fed98_row14_col0" class="data row14 col0" >Non-Processing Area Mask</td>
      <td id="T_fed98_row14_col1" class="data row14 col1" >Non-Processing Area Mask</td>
      <td id="T_fed98_row14_col2" class="data row14 col2" >Non-Processing Area Mask</td>
      <td id="T_fed98_row14_col3" class="data row14 col3" >Non-Processing Area Mask</td>
    </tr>
  </tbody>
</table>
<h3>Land Use</h3>
<style type="text/css">
#T_ce339_row0_col0, #T_ce339_row1_col0 {
  background-color: #FF9EAB;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_ce339_row0_col1 {
  background-color: #FBFF97;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_ce339_row1_col1 {
  background-color: #E6558B;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_ce339_row2_col0, #T_ce339_row2_col1, #T_ce339_row3_col0, #T_ce339_row4_col0 {
  background-color: #004E2B;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_ce339_row3_col1 {
  background-color: #9DBAC5;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_ce339_row4_col1 {
  background-color: #A6976A;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
#T_ce339_row5_col0, #T_ce339_row5_col1 {
  background-color: #1B1716;
  color: #1b1716;
  border-top: 1px solid #1b1716;
  text-shadow: 1px 1px 0 #bfb7b0,-1px 1px 0 #bfb7b0,-1px -1px 0 #bfb7b0,1px -1px 0 #bfb7b0;
}
</style>
<table id="T_ce339">
  <thead>
    <tr>
      <th class="blank level0" >&nbsp;</th>
      <th id="T_ce339_level0_col0" class="col_heading level0 col0" >Level 1</th>
      <th id="T_ce339_level0_col1" class="col_heading level0 col1" >Level 2</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th id="T_ce339_level0_row0" class="row_heading level0 row0" >1</th>
      <td id="T_ce339_row0_col0" class="data row0 col0" >Anthropogenic</td>
      <td id="T_ce339_row0_col1" class="data row0 col1" >Agriculture</td>
    </tr>
    <tr>
      <th id="T_ce339_level0_row1" class="row_heading level0 row1" >2</th>
      <td id="T_ce339_row1_col0" class="data row1 col0" >Anthropogenic</td>
      <td id="T_ce339_row1_col1" class="data row1 col1" >Developed</td>
    </tr>
    <tr>
      <th id="T_ce339_level0_row2" class="row_heading level0 row2" >3</th>
      <td id="T_ce339_row2_col0" class="data row2 col0" >Non-Anthropogenic</td>
      <td id="T_ce339_row2_col1" class="data row2 col1" >Forest</td>
    </tr>
    <tr>
      <th id="T_ce339_level0_row3" class="row_heading level0 row3" >4</th>
      <td id="T_ce339_row3_col0" class="data row3 col0" >Non-Anthropogenic</td>
      <td id="T_ce339_row3_col1" class="data row3 col1" >Other</td>
    </tr>
    <tr>
      <th id="T_ce339_level0_row4" class="row_heading level0 row4" >5</th>
      <td id="T_ce339_row4_col0" class="data row4 col0" >Non-Anthropogenic</td>
      <td id="T_ce339_row4_col1" class="data row4 col1" >Rangeland or Pasture</td>
    </tr>
    <tr>
      <th id="T_ce339_level0_row5" class="row_heading level0 row5" >6</th>
      <td id="T_ce339_row5_col0" class="data row5 col0" >Non-Processing Area Mask</td>
      <td id="T_ce339_row5_col1" class="data row5 col1" >Non-Processing Area Mask</td>
    </tr>
  </tbody>
</table>

Now, we’ll look at how to crosswalk and symbolize the various products and levels of LCMS data

  • This will present the crosswalk classes for each prduct and each level

  • It will then present the JSON that can be used to symbolize the outputs in GEE. This can easily be adapted to be used in other environments

# Provide crosswalk and symbology for each level of each product
products = ['Change','Land_Cover','Land_Use']
out_html = ''
for product in products:
    
    product_title = product.replace('_',' ')
    out_html += f"""<h3>{product_title}</h3>\n"""
    for level in ll.product_levels_2024_10[product][1:]:
        remap_dict = ll.getLevelNRemap(level, bandName=product,lookup=ll.all_lookup_2024_10)
        
        out_html += f"""<strong>Level {level}:</strong>\
            <p>Remap From:  <code>{remap_dict['remap_from']}</code></p>\
            <p>Remap To:  <code>{remap_dict['remap_to']}</code></p>\
            <p>Visualization JSON: <code>{remap_dict['viz_dict']}</code></p><br>\n"""
    out_html += """<hr>\n"""
display(Markdown(out_html))
print(out_html)

Change

Level 2:

Remap From: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]

Remap To: [1, 1, 8, 2, 3, 4, 4, 5, 6, 7, 7, 7, 8, 9, 10, 11]

Visualization JSON: {'Change_class_names': ['Wind', 'Desiccation', 'Inundation', 'Fire', 'Mechanical Land Transformation', 'Tree Removal', 'Insect, Disease, or Drought Stress', 'Other Loss', 'Vegetation Successional Growth', 'Stable', 'Non-Processing Area Mask'], 'Change_class_palette': ['FF09F3', 'CC982E', '0ADAFF', 'D54309', 'FAFA4B', 'AFDE1C', 'F39268', 'C291D5', '00A398', '3D4551', '1B1716'], 'Change_class_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}


Level 1:

Remap From: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]

Remap To: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4]

Visualization JSON: {'Change_class_names': ['Disturbance', 'Vegetation Successional Growth', 'Stable', 'Non-Processing Area Mask'], 'Change_class_palette': ['D54309', '00A398', '3D4551', '1B1716'], 'Change_class_values': [1, 2, 3, 4]}


Land Cover

Level 3:

Remap From: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

Remap To: [1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 5, 6, 7]

Visualization JSON: {'Land_Cover_class_names': ['Tree', 'Shrub', 'Grass/Forb/Herb', 'Barren or Impervious', 'Snow or Ice', 'Water', 'Non-Processing Area Mask'], 'Land_Cover_class_palette': ['004E2B', 'F89A1C', 'E5E98A', '893F54', 'E4F5FD', '00B6F0', '1B1716'], 'Land_Cover_class_values': [1, 2, 3, 4, 5, 6, 7]}


Level 2:

Remap From: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

Remap To: [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4]

Visualization JSON: {'Land_Cover_class_names': ['Tree Vegetated', 'Non-Tree Vegetated', 'Non-Vegetated', 'Non-Processing Area Mask'], 'Land_Cover_class_palette': ['004E2B', '8DA463', '893F54', '1B1716'], 'Land_Cover_class_values': [1, 2, 3, 4]}


Level 1:

Remap From: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

Remap To: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3]

Visualization JSON: {'Land_Cover_class_names': ['Vegetated', 'Non-Vegetated', 'Non-Processing Area Mask'], 'Land_Cover_class_palette': ['61BB46', '58646E', '1B1716'], 'Land_Cover_class_values': [1, 2, 3]}


Land Use

Level 1:

Remap From: [1, 2, 3, 4, 5, 6]

Remap To: [1, 1, 2, 2, 2, 3]

Visualization JSON: {'Land_Use_class_names': ['Anthropogenic', 'Non-Anthropogenic', 'Non-Processing Area Mask'], 'Land_Use_class_palette': ['FF9EAB', '004E2B', '1B1716'], 'Land_Use_class_values': [1, 2, 3]}


<h3>Change</h3>
<strong>Level 2:</strong>            <p>Remap From:  <code>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]</code></p>            <p>Remap To:  <code>[1, 1, 8, 2, 3, 4, 4, 5, 6, 7, 7, 7, 8, 9, 10, 11]</code></p>            <p>Visualization JSON: <code>{'Change_class_names': ['Wind', 'Desiccation', 'Inundation', 'Fire', 'Mechanical Land Transformation', 'Tree Removal', 'Insect, Disease, or Drought Stress', 'Other Loss', 'Vegetation Successional Growth', 'Stable', 'Non-Processing Area Mask'], 'Change_class_palette': ['FF09F3', 'CC982E', '0ADAFF', 'D54309', 'FAFA4B', 'AFDE1C', 'F39268', 'C291D5', '00A398', '3D4551', '1B1716'], 'Change_class_values': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}</code></p><br>
<strong>Level 1:</strong>            <p>Remap From:  <code>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]</code></p>            <p>Remap To:  <code>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 4]</code></p>            <p>Visualization JSON: <code>{'Change_class_names': ['Disturbance', 'Vegetation Successional Growth', 'Stable', 'Non-Processing Area Mask'], 'Change_class_palette': ['D54309', '00A398', '3D4551', '1B1716'], 'Change_class_values': [1, 2, 3, 4]}</code></p><br>
<hr>
<h3>Land Cover</h3>
<strong>Level 3:</strong>            <p>Remap From:  <code>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</code></p>            <p>Remap To:  <code>[1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 5, 6, 7]</code></p>            <p>Visualization JSON: <code>{'Land_Cover_class_names': ['Tree', 'Shrub', 'Grass/Forb/Herb', 'Barren or Impervious', 'Snow or Ice', 'Water', 'Non-Processing Area Mask'], 'Land_Cover_class_palette': ['004E2B', 'F89A1C', 'E5E98A', '893F54', 'E4F5FD', '00B6F0', '1B1716'], 'Land_Cover_class_values': [1, 2, 3, 4, 5, 6, 7]}</code></p><br>
<strong>Level 2:</strong>            <p>Remap From:  <code>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</code></p>            <p>Remap To:  <code>[1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4]</code></p>            <p>Visualization JSON: <code>{'Land_Cover_class_names': ['Tree Vegetated', 'Non-Tree Vegetated', 'Non-Vegetated', 'Non-Processing Area Mask'], 'Land_Cover_class_palette': ['004E2B', '8DA463', '893F54', '1B1716'], 'Land_Cover_class_values': [1, 2, 3, 4]}</code></p><br>
<strong>Level 1:</strong>            <p>Remap From:  <code>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</code></p>            <p>Remap To:  <code>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3]</code></p>            <p>Visualization JSON: <code>{'Land_Cover_class_names': ['Vegetated', 'Non-Vegetated', 'Non-Processing Area Mask'], 'Land_Cover_class_palette': ['61BB46', '58646E', '1B1716'], 'Land_Cover_class_values': [1, 2, 3]}</code></p><br>
<hr>
<h3>Land Use</h3>
<strong>Level 1:</strong>            <p>Remap From:  <code>[1, 2, 3, 4, 5, 6]</code></p>            <p>Remap To:  <code>[1, 1, 2, 2, 2, 3]</code></p>            <p>Visualization JSON: <code>{'Land_Use_class_names': ['Anthropogenic', 'Non-Anthropogenic', 'Non-Processing Area Mask'], 'Land_Use_class_palette': ['FF9EAB', '004E2B', '1B1716'], 'Land_Use_class_values': [1, 2, 3]}</code></p><br>
<hr>

Next, we’ll take a look at the accuracy of LCMS at different levels

  • This method will parse our text file outputs into a more shareable html format

  • Notice that accuracy generally decreases as the number of classes increase

# Parse Accuracy outputs
acc_files = 'data/LCMS_2024-10_Accuracy_Tables/*.txt'
version = '2024-10'
out_html = ''
files = glob.glob(acc_files)
for file in files:
    fn = os.path.splitext(os.path.basename(file))[0]
    product = fn.split('_stats_')[1].split('_Level')[0].replace('_',' ')
    sa = fn.split('_')[-1]
    level = fn.split('Level_')[1].split('_')[0]
    title = f"""LCMS v{version} {sa} {product} Level {level} Accuracy """
 
    o = open(file,'r')
    lines = o.read()
    o.close()
    # print(lines)
    first_lines = lines.split('#------------------------------------------------------\n')[0]
    first_lines = first_lines.split('\n')[2:]
    first_lines = '\n'.join(first_lines)
    cm = lines.split('#------------------------------------------------------\n')[1]
   
    first_lines=first_lines.replace('\n','<br>\n')
    first_lines = f"""<p>{first_lines}</p>"""
    
    cm = cm.replace(' ',',').split('\n')
    ns = list(range(2,100))
    ns.reverse()
    for i in ns:
        cm = [l.replace(','*i,',') for l in cm]
    cm[1] = cm[1]+','*(len(cm[2].split(','))-2)
    cm[2] = ','+cm[2]
    cm = [l.replace('_',' ').split(',') for l in cm if l != '']
   
    df = pd.DataFrame(cm[1:],index=None,columns=None )
    out_html += f"""<h3>{title}</h3>{first_lines}{df.to_html(index=False,header=False)}<br>"""
    
   
display(Markdown(out_html))
print(out_html)

LCMS v2024-10 AK Change Level 1 Accuracy

Overall Accuracy: 97.03 +/- 0.07
Balanced Accuracy: 64.97 +/- 1.44
Kappa: 0.54

Users Accuracy (100%-Commission Error):
Stable: 98.42
Loss: 56.39
Gain: 55.47

Users Error:
Stable: 0.05
Loss: 2.71
Gain: 1.26

Producers Accuracy (100%-Omission Error):
Stable: 98.54
Loss: 36.81
Gain: 59.57

Producers Error:
Stable: 0.05
Loss: 2.13
Gain: 1.29

Number of Samples in each class:
Stable: 55984
Loss: 551
Gain: 1478

Observed
Stable Loss Gain Users Acc Users SE
Predicted Stable 55267.98 311.86 577.52 98.42 0.05
Loss 141.70 189.50 4.85 56.39 2.71
Gain 675.35 13.51 858.07 55.47 1.26
Producers Acc 98.54 36.81 59.57 None
Producers SE 0.05 2.13 1.29 None

LCMS v2024-10 CONUS Change Level 1 Accuracy

Overall Accuracy: 89.50 +/- 0.05
Balanced Accuracy: 67.10 +/- 0.36
Kappa: 0.50

Users Accuracy (100%-Commission Error):
Stable: 94.41
Loss: 50.61
Gain: 54.14

Users Error:
Stable: 0.04
Loss: 0.56
Gain: 0.27

Producers Accuracy (100%-Omission Error):
Stable: 93.92
Loss: 50.53
Gain: 56.86

Producers Error:
Stable: 0.04
Loss: 0.56
Gain: 0.28

Number of Samples in each class:
Stable: 310309
Loss: 8060
Gain: 31879

Observed
Stable Loss Gain Users Acc Users SE
Predicted Stable 290830.28 3505.11 13718.17 94.41 0.04
Loss 3707.68 4037.60 232.48 50.61 0.56
Gain 15131.31 448.36 18390.79 54.14 0.27
Producers Acc 93.92 50.53 56.86 None
Producers SE 0.04 0.56 0.28 None

LCMS v2024-10 AK Change Level 2 Accuracy

Overall Accuracy: 96.80 +/- 0.07
Balanced Accuracy: 23.97 +/- 6.33
Kappa: 0.41

Users Accuracy (100%-Commission Error):
Desiccation: Too few samples to assess accuracy
Fire: 82.76
Veg-Growth: 51.33
Harvest: 64.81
Insect-Disease-Drought: 6.28
Inundation: Too few samples to assess accuracy
Mechanical: Too few samples to assess accuracy
Other: 0.51
Stable: 98.48
Wind: Too few samples to assess accuracy

Users Error:
Desiccation: Too few samples to assess accuracy
Fire: 4.42
Veg-Growth: 1.47
Harvest: 15.30
Insect-Disease-Drought: 4.74
Inundation: Too few samples to assess accuracy
Mechanical: Too few samples to assess accuracy
Other: 0.41
Stable: 0.05
Wind: Too few samples to assess accuracy

Producers Accuracy (100%-Omission Error):
Desiccation: Too few samples to assess accuracy
Fire: 53.08
Veg-Growth: 55.37
Harvest: 16.90
Insect-Disease-Drought: 5.24
Inundation: Too few samples to assess accuracy
Mechanical: Too few samples to assess accuracy
Other: 0.55
Stable: 98.29
Wind: Too few samples to assess accuracy

Producers Error:
Desiccation: Too few samples to assess accuracy
Fire: 4.68
Veg-Growth: 1.52
Harvest: 6.13
Insect-Disease-Drought: 3.98
Inundation: Too few samples to assess accuracy
Mechanical: Too few samples to assess accuracy
Other: 0.44
Stable: 0.05
Wind: Too few samples to assess accuracy

Number of Samples in each class:
Desiccation: 2 (Too few samples to assess accuracy)
Fire: 147
Veg-Growth: 1477
Harvest: 101
Insect-Disease-Drought: 85
Inundation: 9 (Too few samples to assess accuracy)
Mechanical: 23 (Too few samples to assess accuracy)
Other: 185
Stable: 55984
Wind: 0

Observed
Desiccation Fire Veg-Growth Harvest Insect-Disease-Drought Inundation Mechanical Other Stable Wind Users Acc Users SE
Predicted Desiccation 0.0 0.00 0.84 0.00 0.00 0.00 0.00 1.10 36.78 0.0 0.0 0.0
Fire 0.0 60.40 4.94 0.15 0.00 0.00 0.00 0.00 7.49 0.0 82.76 4.42
Veg-Growth 0.0 2.49 593.27 4.45 2.81 0.00 0.26 0.61 551.88 0.0 51.33 1.47
Harvest 0.0 2.97 0.00 6.32 0.00 0.00 0.26 0.07 0.13 0.0 64.81 15.3
Insect-Disease-Drought 0.0 0.00 0.57 2.82 1.64 0.00 0.00 0.00 21.17 0.0 6.28 4.74
Inundation 0.0 0.00 0.84 0.00 0.00 0.00 0.00 0.00 44.00 0.0 0.0 0.0
Mechanical 0.0 0.00 0.00 0.00 0.00 0.00 0.29 0.00 0.97 0.0 23.08 37.48
Other 0.0 0.00 1.48 1.13 0.52 0.07 0.16 1.58 304.04 0.0 0.51 0.41
Stable 2.2 47.94 469.51 22.43 26.41 4.95 1.87 284.34 55522.07 0.0 98.48 0.05
Wind 0.0 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.0 0.0 0.0
Producers Acc 0.0 53.08 55.37 16.90 5.24 0.00 10.24 0.55 98.29 0.0 None
Producers SE 0.0 4.68 1.52 6.13 3.98 0.00 17.97 0.44 0.05 0.0 None

LCMS v2024-10 CONUS Change Level 2 Accuracy

Overall Accuracy: 92.48 +/- 0.05
Balanced Accuracy: 29.42 +/- 2.97
Kappa: 0.40

Users Accuracy (100%-Commission Error):
Desiccation: 17.92
Fire: 71.90
Veg-Growth: 49.87
Harvest: 85.22
Insect-Disease-Drought: 20.98
Inundation: 14.41
Mechanical: 25.44
Other: 0.92
Stable: 95.73
Wind: 2.29

Users Error:
Desiccation: 3.09
Fire: 3.70
Veg-Growth: 0.38
Harvest: 1.93
Insect-Disease-Drought: 1.27
Inundation: 3.27
Mechanical: 4.05
Other: 0.21
Stable: 0.04
Wind: 2.37

Producers Accuracy (100%-Omission Error):
Desiccation: 39.80
Fire: 36.87
Veg-Growth: 42.79
Harvest: 17.00
Insect-Disease-Drought: 17.30
Inundation: 23.39
Mechanical: 6.11
Other: 11.03
Stable: 96.58
Wind: 3.33

Producers Error:
Desiccation: 5.89
Fire: 2.84
Veg-Growth: 0.35
Harvest: 0.91
Insect-Disease-Drought: 1.07
Inundation: 5.01
Mechanical: 1.09
Other: 2.35
Stable: 0.03
Wind: 3.43

Number of Samples in each class:
Desiccation: 134
Fire: 398
Veg-Growth: 31326
Harvest: 3381
Insect-Disease-Drought: 2634
Inundation: 132
Mechanical: 777
Other: 299
Stable: 301757
Wind: 49

Observed
Desiccation Fire Veg-Growth Harvest Insect-Disease-Drought Inundation Mechanical Other Stable Wind Users Acc Users SE
Predicted Desiccation 27.53 0.00 1.27 0.00 1.09 0.00 0.55 0.00 123.22 0.00 17.92 3.09
Fire 0.00 106.21 21.51 2.41 1.12 0.00 0.17 0.03 16.27 0.00 71.9 3.7
Veg-Growth 2.43 12.31 8552.50 133.78 27.20 3.53 30.72 3.35 8382.84 1.66 49.87 0.38
Harvest 0.00 5.44 2.44 288.19 0.91 3.19 5.87 0.34 28.89 2.90 85.22 1.93
Insect-Disease-Drought 0.00 17.16 69.02 19.06 214.34 0.00 0.33 3.81 697.87 0.03 20.98 1.27
Inundation 0.00 0.00 0.18 0.00 0.00 16.67 0.55 0.00 98.27 0.00 14.41 3.27
Mechanical 0.00 0.00 0.55 6.75 0.55 0.18 29.48 0.00 78.19 0.17 25.44 4.05
Other 5.19 7.10 43.65 569.10 33.55 3.99 58.99 19.65 1385.65 5.29 0.92 0.21
Stable 34.02 139.87 11295.43 661.69 958.81 42.94 353.18 150.92 305791.03 16.41 95.73 0.04
Wind 0.00 0.00 0.34 14.18 1.25 0.77 2.44 0.00 19.99 0.91 2.29 2.37
Producers Acc 39.80 36.87 42.79 17.00 17.30 23.39 6.11 11.03 96.58 3.33 None
Producers SE 5.89 2.84 0.35 0.91 1.07 5.01 1.09 2.35 0.03 3.43 None

LCMS v2024-10 AK Land Cover Level 1 Accuracy

Overall Accuracy: 95.35 +/- 0.09
Balanced Accuracy: 95.10 +/- 0.15
Kappa: 0.88

Users Accuracy (100%-Commission Error):
VEG: 98.17
NON-VEG: 87.67

Users Error:
VEG: 0.07
NON-VEG: 0.26

Producers Accuracy (100%-Omission Error):
VEG: 95.60
NON-VEG: 94.60

Producers Error:
VEG: 0.10
NON-VEG: 0.19

Number of Samples in each class:
VEG: 48503
NON-VEG: 9510

Observed
VEG NON-VEG Users Acc Users SE
Predicted VEG 41696.31 779.23 98.17 0.07
NON-VEG 1919.01 13645.78 87.67 0.26
Producers Acc 95.60 94.60 None
Producers SE 0.10 0.19 None

LCMS v2024-10 CONUS Land Cover Level 1 Accuracy

Overall Accuracy: 95.71 +/- 0.03
Balanced Accuracy: 81.89 +/- 0.25
Kappa: 0.59

Users Accuracy (100%-Commission Error):
VEG: 98.15
NON-VEG: 57.39

Users Error:
VEG: 0.02
NON-VEG: 0.34

Producers Accuracy (100%-Omission Error):
VEG: 97.30
NON-VEG: 66.48

Producers Error:
VEG: 0.03
NON-VEG: 0.35

Number of Samples in each class:
VEG: 317873
NON-VEG: 32375

Observed
VEG NON-VEG Users Acc Users SE
Predicted VEG 322919.24 6077.77 98.15 0.02
NON-VEG 8949.47 12055.29 57.39 0.34
Producers Acc 97.30 66.48 None
Producers SE 0.03 0.35 None

LCMS v2024-10 AK Land Cover Level 2 Accuracy

Overall Accuracy: 83.79 +/- 0.15
Balanced Accuracy: 85.39 +/- 0.24
Kappa: 0.75

Users Accuracy (100%-Commission Error):
TREE_VEG: 79.43
NON-TREE_VEG: 85.38
NON-VEG: 87.67

Users Error:
TREE_VEG: 0.28
NON-TREE_VEG: 0.24
NON-VEG: 0.26

Producers Accuracy (100%-Omission Error):
TREE_VEG: 86.68
NON-TREE_VEG: 74.89
NON-VEG: 94.60

Producers Error:
TREE_VEG: 0.24
NON-TREE_VEG: 0.28
NON-VEG: 0.19

Number of Samples in each class:
TREE_VEG: 25183
NON-TREE_VEG: 23320
NON-VEG: 9510

Observed
TREE VEG NON-TREE VEG NON-VEG Users Acc Users SE
Predicted TREE VEG 17089.42 4218.15 206.29 79.43 0.28
NON-TREE VEG 2491.10 17897.63 572.94 85.38 0.24
NON-VEG 136.05 1782.95 13645.78 87.67 0.26
Producers Acc 86.68 74.89 94.60 None
Producers SE 0.24 0.28 0.19 None

LCMS v2024-10 CONUS Land Cover Level 2 Accuracy

Overall Accuracy: 87.20 +/- 0.06
Balanced Accuracy: 80.74 +/- 0.21
Kappa: 0.76

Users Accuracy (100%-Commission Error):
TREE_VEG: 90.47
NON-TREE_VEG: 88.22
NON-VEG: 57.39

Users Error:
TREE_VEG: 0.08
NON-TREE_VEG: 0.07
NON-VEG: 0.34

Producers Accuracy (100%-Omission Error):
TREE_VEG: 85.22
NON-TREE_VEG: 90.53
NON-VEG: 66.48

Producers Error:
TREE_VEG: 0.10
NON-TREE_VEG: 0.07
NON-VEG: 0.35

Number of Samples in each class:
TREE_VEG: 202848
NON-TREE_VEG: 115025
NON-VEG: 32375

Observed
TREE VEG NON-TREE VEG NON-VEG Users Acc Users SE
Predicted TREE VEG 116843.92 10762.01 1549.40 90.47 0.08
NON-TREE VEG 19003.98 176309.33 4528.37 88.22 0.07
NON-VEG 1264.05 7685.42 12055.29 57.39 0.34
Producers Acc 85.22 90.53 66.48 None
Producers SE 0.10 0.07 0.35 None

LCMS v2024-10 AK Land Cover Level 3 Accuracy

Overall Accuracy: 72.05 +/- 0.19
Balanced Accuracy: 76.38 +/- 0.43
Kappa: 0.64

Users Accuracy (100%-Commission Error):
TREES: 79.43
SHRUBS: 72.71
GRASS: 43.40
BARREN: 67.68
SNOW: 90.59
WATER: 96.83

Users Error:
TREES: 0.28
SHRUBS: 0.47
GRASS: 0.45
BARREN: 0.62
SNOW: 0.36
WATER: 0.31

Producers Accuracy (100%-Omission Error):
TREES: 86.68
SHRUBS: 42.51
GRASS: 61.02
BARREN: 81.79
SNOW: 96.68
WATER: 89.60

Producers Error:
TREES: 0.24
SHRUBS: 0.40
GRASS: 0.53
BARREN: 0.56
SNOW: 0.23
WATER: 0.52

Number of Samples in each class:
TREES: 25183
SHRUBS: 14860
GRASS: 8460
BARREN: 4587
SNOW: 2828
WATER: 2095

Observed
TREES SHRUBS GRASS BARREN SNOW WATER Users Acc Users SE
Predicted TREES 17089.42 3059.00 1159.14 145.11 0.00 61.18 79.43 0.28
SHRUBS 1282.76 6541.98 1099.74 73.43 0.00 0.00 72.71 0.47
GRASS 1208.34 5064.13 5191.78 252.73 0.00 246.78 43.4 0.45
BARREN 135.83 725.69 741.32 3871.29 188.61 57.42 67.68 0.62
SNOW 0.00 0.00 264.89 355.61 5974.48 0.00 90.59 0.36
WATER 0.22 0.00 51.06 35.03 16.85 3146.50 96.83 0.31
Producers Acc 86.68 42.51 61.02 81.79 96.68 89.60 None
Producers SE 0.24 0.40 0.53 0.56 0.23 0.52 None

LCMS v2024-10 CONUS Land Cover Level 3 Accuracy

Overall Accuracy: 79.11 +/- 0.07
Balanced Accuracy: 71.77 +/- 3.20
Kappa: 0.70

Users Accuracy (100%-Commission Error):
TREES: 90.47
SHRUBS: 74.22
GRASS: 74.10
BARREN: 40.03
SNOW: 82.65
WATER: 93.95

Users Error:
TREES: 0.08
SHRUBS: 0.17
GRASS: 0.12
BARREN: 0.41
SNOW: 7.02
WATER: 0.30

Producers Accuracy (100%-Omission Error):
TREES: 85.22
SHRUBS: 60.53
GRASS: 86.90
BARREN: 54.69
SNOW: 62.30
WATER: 81.00

Producers Error:
TREES: 0.10
SHRUBS: 0.17
GRASS: 0.10
BARREN: 0.48
SNOW: 7.80
WATER: 0.46

Number of Samples in each class:
TREES: 202848
SHRUBS: 36067
GRASS: 78958
BARREN: 21543
SNOW: 840
WATER: 9992

Observed
TREES SHRUBS GRASS BARREN SNOW WATER Users Acc Users SE
Predicted TREES 116843.92 5987.33 4774.68 921.50 0.35 627.55 90.47 0.08
SHRUBS 6578.43 48374.23 8398.92 1828.75 0.00 0.87 74.22 0.17
GRASS 12425.55 19746.46 99789.72 2040.16 0.00 658.59 74.1 0.12
BARREN 1161.78 5792.38 1645.11 5833.12 14.20 124.02 40.03 0.41
SNOW 0.00 0.00 0.00 5.05 24.04 0.00 82.65 7.02
WATER 102.28 19.28 228.65 37.54 0.00 6017.32 93.95 0.3
Producers Acc 85.22 60.53 86.90 54.69 62.30 81.00 None
Producers SE 0.10 0.17 0.10 0.48 7.80 0.46 None

LCMS v2024-10 AK Land Cover Level 4 Accuracy

Overall Accuracy: 64.45 +/- 0.20
Balanced Accuracy: 35.76 +/- 0.35
Kappa: 0.57

Users Accuracy (100%-Commission Error):
TREES: 71.86
TS-TREES: 0.00
SHRUBS-TRE: 0.88
GRASS-TREE: 1.81
BARREN-TRE: nan
TS: 57.69
SHRUBS: 25.66
GRASS-SHRU: nan
BARREN-SHR: nan
GRASS: 42.29
BARREN-GRA: 2.56
BARREN-IMP: 67.68
SNOW: 90.59
WATER: 96.83

Users Error:
TREES: 0.31
TS-TREES: 0.00
SHRUBS-TRE: 1.34
GRASS-TREE: 0.66
BARREN-TRE: nan
TS: 0.64
SHRUBS: 0.80
GRASS-SHRU: nan
BARREN-SHR: nan
GRASS: 0.45
BARREN-GRA: 2.41
BARREN-IMP: 0.62
SNOW: 0.36
WATER: 0.31

Producers Accuracy (100%-Omission Error):
TREES: 91.85
TS-TREES: 0.00
SHRUBS-TRE: 0.03
GRASS-TREE: 0.74
BARREN-TRE: 0.00
TS: 63.39
SHRUBS: 12.88
GRASS-SHRU: 0.00
BARREN-SHR: 0.00
GRASS: 63.47
BARREN-GRA: 0.20
BARREN-IMP: 81.79
SNOW: 96.68
WATER: 89.60

Producers Error:
TREES: 0.21
TS-TREES: 0.00
SHRUBS-TRE: 0.04
GRASS-TREE: 0.27
BARREN-TRE: 0.00
TS: 0.65
SHRUBS: 0.43
GRASS-SHRU: 0.00
BARREN-SHR: 0.00
GRASS: 0.54
BARREN-GRA: 0.19
BARREN-IMP: 0.56
SNOW: 0.23
WATER: 0.52

Number of Samples in each class:
TREES: 21599
TS-TREES: 472
SHRUBS-TRE: 1787
GRASS-TREE: 1121
BARREN-TRE: 204
TS: 5407
SHRUBS: 5641
GRASS-SHRU: 3541
BARREN-SHR: 271
GRASS: 7843
BARREN-GRA: 617
BARREN-IMP: 4587
SNOW: 2828
WATER: 2095

Observed
TREES TS-TREES SHRUBS-TRE GRASS-TREE BARREN-TRE TS SHRUBS GRASS-SHRU BARREN-SHR GRASS BARREN-GRA BARREN-IMP SNOW WATER Users Acc Users SE
Predicted TREES 15129.01 270.36 1080.63 537.81 54.68 1286.84 1218.91 294.46 50.72 932.56 0.87 136.69 0.00 61.18 71.86 0.31
TS-TREES 0.00 0.00 0.00 0.00 0.00 1.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0.0
SHRUBS-TRE 9.10 0.00 0.43 0.00 0.00 7.23 31.41 0.00 0.00 0.00 0.00 0.43 0.00 0.00 0.88 1.34
GRASS-TREE 0.00 0.00 0.00 7.41 0.00 0.00 107.91 29.38 30.91 222.40 3.30 7.99 0.00 0.00 1.81 0.66
BARREN-TRE 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN
TS 343.92 167.11 201.49 67.29 28.44 3469.31 1212.16 103.87 29.68 386.46 0.00 3.67 0.00 0.00 57.69 0.64
SHRUBS 191.29 49.96 128.03 100.90 4.34 466.73 765.97 405.57 88.70 704.70 8.58 69.76 0.00 0.00 25.66 0.8
GRASS-SHRU 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN
BARREN-SHR 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN NaN
GRASS 681.15 25.27 223.49 271.98 6.45 219.03 2356.03 2439.96 7.25 5041.63 149.05 252.73 0.00 246.78 42.29 0.45
BARREN-GRA 0.00 0.00 0.00 0.00 0.00 0.00 0.00 41.86 0.00 0.00 1.10 0.00 0.00 0.00 2.56 2.41
BARREN-IMP 116.05 0.00 5.64 10.69 3.44 22.91 256.67 367.86 78.25 364.84 376.48 3871.29 188.61 57.42 67.68 0.62
SNOW 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 239.51 25.38 355.61 5974.48 0.00 90.59 0.36
WATER 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 51.06 0.00 35.03 16.85 3146.50 96.83 0.31
Producers Acc 91.85 0.00 0.03 0.74 0.00 63.39 12.88 0.00 0.00 63.47 0.20 81.79 96.68 89.60 None
Producers SE 0.21 0.00 0.04 0.27 0.00 0.65 0.43 0.00 0.00 0.54 0.19 0.56 0.23 0.52 None

LCMS v2024-10 CONUS Land Cover Level 4 Accuracy

Overall Accuracy: 67.18 +/- 0.08
Balanced Accuracy: 40.41 +/- 2.27
Kappa: 0.57

Users Accuracy (100%-Commission Error):
TREES: 82.35
TS-TREES: Not Modeled
SHRUBS-TRE: 10.68
GRASS-TREE: 35.26
BARREN-TRE: 26.15
TS: Not Modeled
SHRUBS: 39.11
GRASS-SHRU: 38.32
BARREN-SHR: 34.34
GRASS: 73.74
BARREN-GRA: 1.58
BARREN-IMP: 40.03
SNOW: 82.65
WATER: 93.95

Users Error:
TREES: 0.11
TS-TREES: Not Modeled
SHRUBS-TRE: 1.04
GRASS-TREE: 0.42
BARREN-TRE: 3.65
TS: Not Modeled
SHRUBS: 0.40
GRASS-SHRU: 0.24
BARREN-SHR: 0.50
GRASS: 0.12
BARREN-GRA: 0.66
BARREN-IMP: 0.41
SNOW: 7.02
WATER: 0.30

Producers Accuracy (100%-Omission Error):
TREES: 93.59
TS-TREES: Not Modeled
SHRUBS-TRE: 1.50
GRASS-TREE: 16.97
BARREN-TRE: 1.63
TS: Not Modeled
SHRUBS: 21.32
GRASS-SHRU: 40.25
BARREN-SHR: 23.13
GRASS: 88.40
BARREN-GRA: 0.20
BARREN-IMP: 54.69
SNOW: 62.30
WATER: 81.00

Producers Error:
TREES: 0.08
TS-TREES: Not Modeled
SHRUBS-TRE: 0.15
GRASS-TREE: 0.23
BARREN-TRE: 0.26
TS: Not Modeled
SHRUBS: 0.25
GRASS-SHRU: 0.25
BARREN-SHR: 0.37
GRASS: 0.10
BARREN-GRA: 0.08
BARREN-IMP: 0.48
SNOW: 7.80
WATER: 0.46

Number of Samples in each class:
TREES: 163335
TS-TREES: Not Modeled
SHRUBS-TRE: 10900
GRASS-TREE: 26494
BARREN-TRE: 2119
TS: Not Modeled
SHRUBS: 13095
GRASS-SHRU: 15831
BARREN-SHR: 7141
GRASS: 75723
BARREN-GRA: 3235
BARREN-IMP: 21543
SNOW: 840
WATER: 9992

Observed
TREES SHRUBS-TRE GRASS-TREE BARREN-TRE SHRUBS GRASS-SHRU BARREN-SHR GRASS BARREN-GRA BARREN-IMP SNOW WATER Users Acc Users SE
Predicted TREES 94704.14 3394.83 9587.66 489.02 1399.95 1184.25 31.53 2759.18 65.99 810.22 0.35 581.85 82.35 0.11
SHRUBS-TRE 310.94 94.82 109.76 4.04 203.43 110.39 16.06 36.90 0.00 1.16 0.00 0.00 10.68 1.04
GRASS-TREE 2242.73 835.58 4624.44 403.40 1090.87 1355.59 495.61 1824.39 88.04 110.13 0.00 43.29 35.26 0.42
BARREN-TRE 0.37 0.00 4.33 37.86 34.66 21.66 43.32 0.18 0.00 0.00 0.00 2.42 26.15 3.65
SHRUBS 283.42 387.61 1152.19 138.42 5829.34 4009.42 1372.74 1453.63 36.62 241.95 0.00 0.00 39.11 0.4
GRASS-SHRU 180.41 623.53 2576.79 381.75 10259.01 15876.80 4533.47 5328.32 945.74 720.78 0.00 0.87 38.32 0.24
BARREN-SHR 17.33 134.30 430.97 271.72 1282.94 2172.26 3038.24 368.78 265.83 866.01 0.00 0.00 34.34 0.5
GRASS 2959.94 843.68 8473.98 147.96 6105.01 12325.28 971.54 99037.43 746.62 2031.15 0.00 658.59 73.74 0.12
BARREN-GRA 0.00 0.00 0.00 0.00 104.34 121.30 118.98 0.00 5.66 9.01 0.00 0.00 1.58 0.66
BARREN-IMP 403.66 17.83 293.72 446.57 1029.34 2257.78 2505.26 999.90 645.21 5833.12 14.20 124.02 40.03 0.41
SNOW 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.05 24.04 0.00 82.65 7.02
WATER 90.94 3.64 2.79 4.91 1.98 11.28 6.03 226.13 2.52 37.54 0.00 6017.32 93.95 0.3
Producers Acc 93.59 1.50 16.97 1.63 21.32 40.25 23.13 88.40 0.20 54.69 62.30 81.00 None
Producers SE 0.08 0.15 0.23 0.26 0.25 0.25 0.37 0.10 0.08 0.48 7.80 0.46 None

LCMS v2024-10 AK Land Use Level 1 Accuracy

Overall Accuracy: 99.83 +/- 0.02
Balanced Accuracy: 72.71 +/- 2.79
Kappa: 0.59

Users Accuracy (100%-Commission Error):
Anthro: 85.26
Non-Anthro: 99.85

Users Error:
Anthro: 3.84
Non-Anthro: 0.02

Producers Accuracy (100%-Omission Error):
Anthro: 45.44
Non-Anthro: 99.98

Producers Error:
Anthro: 3.94
Non-Anthro: 0.01

Number of Samples in each class:
Anthro: 2226
Non-Anthro: 55787

Observed
Anthro Non-Anthro Users Acc Users SE
Predicted Anthro 72.53 12.53 85.26 3.84
Non-Anthro 87.10 57868.17 99.85 0.02
Producers Acc 45.44 99.98 None
Producers SE 3.94 0.01 None

LCMS v2024-10 CONUS Land Use Level 1 Accuracy

Overall Accuracy: 90.70 +/- 0.05
Balanced Accuracy: 88.25 +/- 0.09
Kappa: 0.76

Users Accuracy (100%-Commission Error):
Anthro: 81.52
Non-Anthro: 94.02

Users Error:
Anthro: 0.13
Non-Anthro: 0.05

Producers Accuracy (100%-Omission Error):
Anthro: 83.13
Non-Anthro: 93.37

Producers Error:
Anthro: 0.12
Non-Anthro: 0.05

Number of Samples in each class:
Anthro: 74206
Non-Anthro: 276042

Observed
Anthro Non-Anthro Users Acc Users SE
Predicted Anthro 75736.91 17168.65 81.52 0.13
Non-Anthro 15364.43 241731.78 94.02 0.05
Producers Acc 83.13 93.37 None
Producers SE 0.12 0.05 None

LCMS v2024-10 AK Land Use Level 2 Accuracy

Overall Accuracy: 84.93 +/- 0.15
Balanced Accuracy: 73.97 +/- 3.73
Kappa: 0.77

Users Accuracy (100%-Commission Error):
Agriculture: 75.75
Developed: 91.97
Forest: 83.08
Other: 92.37
Rangeland: 81.96

Users Error:
Agriculture: 7.09
Developed: 3.83
Forest: 0.27
Other: 0.22
Rangeland: 0.25

Producers Accuracy (100%-Omission Error):
Agriculture: 75.62
Developed: 37.70
Forest: 84.70
Other: 89.00
Rangeland: 82.82

Producers Error:
Agriculture: 7.09
Developed: 4.37
Forest: 0.26
Other: 0.26
Rangeland: 0.24

Number of Samples in each class:
Agriculture: 1076
Developed: 1150
Forest: 24106
Other: 9535
Rangeland: 22146

Observed
Agriculture Developed Forest Other Rangeland Users Acc Users SE
Predicted Agriculture 27.72 0.00 6.07 0.00 2.80 75.75 7.09
Developed 0.00 46.36 1.59 2.33 0.13 91.97 3.83
Forest 0.82 35.29 16054.33 157.39 3077.25 83.08 0.27
Other 0.00 4.70 69.24 13358.38 1029.99 92.37 0.22
Rangeland 8.11 36.62 2823.23 1490.92 19807.05 81.96 0.25
Producers Acc 75.62 37.70 84.70 89.00 82.82 None
Producers SE 7.09 4.37 0.26 0.26 0.24 None

LCMS v2024-10 CONUS Land Use Level 2 Accuracy

Overall Accuracy: 83.93 +/- 0.06
Balanced Accuracy: 73.95 +/- 0.27
Kappa: 0.77

Users Accuracy (100%-Commission Error):
Agriculture: 77.46
Developed: 88.87
Forest: 88.93
Other: 81.72
Rangeland: 83.35

Users Error:
Agriculture: 0.14
Developed: 0.38
Forest: 0.09
Other: 0.39
Rangeland: 0.11

Producers Accuracy (100%-Omission Error):
Agriculture: 89.19
Developed: 37.31
Forest: 91.07
Other: 70.93
Rangeland: 81.25

Producers Error:
Agriculture: 0.11
Developed: 0.38
Forest: 0.08
Other: 0.43
Rangeland: 0.11

Number of Samples in each class:
Agriculture: 48074
Developed: 26132
Forest: 187755
Other: 22717
Rangeland: 65570

Observed
Agriculture Developed Forest Other Rangeland Users Acc Users SE
Predicted Agriculture 66628.46 2838.15 1894.62 107.21 14552.31 77.46 0.14
Developed 151.83 6118.47 339.82 8.57 266.11 88.87 0.38
Forest 1267.45 3505.47 109449.70 922.98 7922.11 88.93 0.09
Other 17.59 216.15 320.55 7840.21 1199.69 81.72 0.39
Rangeland 6639.14 3718.64 8181.59 2174.25 103720.71 83.35 0.11
Producers Acc 89.19 37.31 91.07 70.93 81.25 None
Producers SE 0.11 0.38 0.08 0.43 0.11 None 

<h3>LCMS v2024-10 AK Change Level 1 Accuracy </h3><p>Overall Accuracy: 97.03 +/- 0.07<br>
Balanced Accuracy: 64.97 +/- 1.44<br>
Kappa: 0.54<br>
<br>
Users Accuracy (100%-Commission Error): <br>
Stable: 98.42<br>
Loss: 56.39<br>
Gain: 55.47<br>
<br>
Users Error: <br>
Stable: 0.05<br>
Loss: 2.71<br>
Gain: 1.26<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
Stable: 98.54<br>
Loss: 36.81<br>
Gain: 59.57<br>
<br>
Producers Error: <br>
Stable: 0.05<br>
Loss: 2.13<br>
Gain: 1.29<br>
<br>
Number of Samples in each class: <br>
Stable: 55984<br>
Loss: 551<br>
Gain: 1478<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>Stable</td>
      <td>Loss</td>
      <td>Gain</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>Stable</td>
      <td>55267.98</td>
      <td>311.86</td>
      <td>577.52</td>
      <td>98.42</td>
      <td>0.05</td>
    </tr>
    <tr>
      <td></td>
      <td>Loss</td>
      <td>141.70</td>
      <td>189.50</td>
      <td>4.85</td>
      <td>56.39</td>
      <td>2.71</td>
    </tr>
    <tr>
      <td></td>
      <td>Gain</td>
      <td>675.35</td>
      <td>13.51</td>
      <td>858.07</td>
      <td>55.47</td>
      <td>1.26</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>98.54</td>
      <td>36.81</td>
      <td>59.57</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.05</td>
      <td>2.13</td>
      <td>1.29</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 CONUS Change Level 1 Accuracy </h3><p>Overall Accuracy: 89.50 +/- 0.05<br>
Balanced Accuracy: 67.10 +/- 0.36<br>
Kappa: 0.50<br>
<br>
Users Accuracy (100%-Commission Error): <br>
Stable: 94.41<br>
Loss: 50.61<br>
Gain: 54.14<br>
<br>
Users Error: <br>
Stable: 0.04<br>
Loss: 0.56<br>
Gain: 0.27<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
Stable: 93.92<br>
Loss: 50.53<br>
Gain: 56.86<br>
<br>
Producers Error: <br>
Stable: 0.04<br>
Loss: 0.56<br>
Gain: 0.28<br>
<br>
Number of Samples in each class: <br>
Stable: 310309<br>
Loss: 8060<br>
Gain: 31879<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>Stable</td>
      <td>Loss</td>
      <td>Gain</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>Stable</td>
      <td>290830.28</td>
      <td>3505.11</td>
      <td>13718.17</td>
      <td>94.41</td>
      <td>0.04</td>
    </tr>
    <tr>
      <td></td>
      <td>Loss</td>
      <td>3707.68</td>
      <td>4037.60</td>
      <td>232.48</td>
      <td>50.61</td>
      <td>0.56</td>
    </tr>
    <tr>
      <td></td>
      <td>Gain</td>
      <td>15131.31</td>
      <td>448.36</td>
      <td>18390.79</td>
      <td>54.14</td>
      <td>0.27</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>93.92</td>
      <td>50.53</td>
      <td>56.86</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.04</td>
      <td>0.56</td>
      <td>0.28</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 AK Change Level 2 Accuracy </h3><p>Overall Accuracy: 96.80 +/- 0.07<br>
Balanced Accuracy: 23.97 +/- 6.33<br>
Kappa: 0.41<br>
<br>
Users Accuracy (100%-Commission Error): <br>
Desiccation: Too few samples to assess accuracy<br>
Fire: 82.76<br>
Veg-Growth: 51.33<br>
Harvest: 64.81<br>
Insect-Disease-Drought: 6.28<br>
Inundation: Too few samples to assess accuracy<br>
Mechanical: Too few samples to assess accuracy<br>
Other: 0.51<br>
Stable: 98.48<br>
Wind: Too few samples to assess accuracy<br>
<br>
Users Error: <br>
Desiccation: Too few samples to assess accuracy<br>
Fire: 4.42<br>
Veg-Growth: 1.47<br>
Harvest: 15.30<br>
Insect-Disease-Drought: 4.74<br>
Inundation: Too few samples to assess accuracy<br>
Mechanical: Too few samples to assess accuracy<br>
Other: 0.41<br>
Stable: 0.05<br>
Wind: Too few samples to assess accuracy<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
Desiccation: Too few samples to assess accuracy<br>
Fire: 53.08<br>
Veg-Growth: 55.37<br>
Harvest: 16.90<br>
Insect-Disease-Drought: 5.24<br>
Inundation: Too few samples to assess accuracy<br>
Mechanical: Too few samples to assess accuracy<br>
Other: 0.55<br>
Stable: 98.29<br>
Wind: Too few samples to assess accuracy<br>
<br>
Producers Error: <br>
Desiccation: Too few samples to assess accuracy<br>
Fire: 4.68<br>
Veg-Growth: 1.52<br>
Harvest: 6.13<br>
Insect-Disease-Drought: 3.98<br>
Inundation: Too few samples to assess accuracy<br>
Mechanical: Too few samples to assess accuracy<br>
Other: 0.44<br>
Stable: 0.05<br>
Wind: Too few samples to assess accuracy<br>
<br>
Number of Samples in each class: <br>
Desiccation: 2 (Too few samples to assess accuracy)<br>
Fire: 147<br>
Veg-Growth: 1477<br>
Harvest: 101<br>
Insect-Disease-Drought: 85<br>
Inundation: 9 (Too few samples to assess accuracy)<br>
Mechanical: 23 (Too few samples to assess accuracy)<br>
Other: 185<br>
Stable: 55984<br>
Wind: 0<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>Desiccation</td>
      <td>Fire</td>
      <td>Veg-Growth</td>
      <td>Harvest</td>
      <td>Insect-Disease-Drought</td>
      <td>Inundation</td>
      <td>Mechanical</td>
      <td>Other</td>
      <td>Stable</td>
      <td>Wind</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>Desiccation</td>
      <td>0.0</td>
      <td>0.00</td>
      <td>0.84</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>1.10</td>
      <td>36.78</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <td></td>
      <td>Fire</td>
      <td>0.0</td>
      <td>60.40</td>
      <td>4.94</td>
      <td>0.15</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>7.49</td>
      <td>0.0</td>
      <td>82.76</td>
      <td>4.42</td>
    </tr>
    <tr>
      <td></td>
      <td>Veg-Growth</td>
      <td>0.0</td>
      <td>2.49</td>
      <td>593.27</td>
      <td>4.45</td>
      <td>2.81</td>
      <td>0.00</td>
      <td>0.26</td>
      <td>0.61</td>
      <td>551.88</td>
      <td>0.0</td>
      <td>51.33</td>
      <td>1.47</td>
    </tr>
    <tr>
      <td></td>
      <td>Harvest</td>
      <td>0.0</td>
      <td>2.97</td>
      <td>0.00</td>
      <td>6.32</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.26</td>
      <td>0.07</td>
      <td>0.13</td>
      <td>0.0</td>
      <td>64.81</td>
      <td>15.3</td>
    </tr>
    <tr>
      <td></td>
      <td>Insect-Disease-Drought</td>
      <td>0.0</td>
      <td>0.00</td>
      <td>0.57</td>
      <td>2.82</td>
      <td>1.64</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>21.17</td>
      <td>0.0</td>
      <td>6.28</td>
      <td>4.74</td>
    </tr>
    <tr>
      <td></td>
      <td>Inundation</td>
      <td>0.0</td>
      <td>0.00</td>
      <td>0.84</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>44.00</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <td></td>
      <td>Mechanical</td>
      <td>0.0</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.29</td>
      <td>0.00</td>
      <td>0.97</td>
      <td>0.0</td>
      <td>23.08</td>
      <td>37.48</td>
    </tr>
    <tr>
      <td></td>
      <td>Other</td>
      <td>0.0</td>
      <td>0.00</td>
      <td>1.48</td>
      <td>1.13</td>
      <td>0.52</td>
      <td>0.07</td>
      <td>0.16</td>
      <td>1.58</td>
      <td>304.04</td>
      <td>0.0</td>
      <td>0.51</td>
      <td>0.41</td>
    </tr>
    <tr>
      <td></td>
      <td>Stable</td>
      <td>2.2</td>
      <td>47.94</td>
      <td>469.51</td>
      <td>22.43</td>
      <td>26.41</td>
      <td>4.95</td>
      <td>1.87</td>
      <td>284.34</td>
      <td>55522.07</td>
      <td>0.0</td>
      <td>98.48</td>
      <td>0.05</td>
    </tr>
    <tr>
      <td></td>
      <td>Wind</td>
      <td>0.0</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.07</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>0.0</td>
      <td>53.08</td>
      <td>55.37</td>
      <td>16.90</td>
      <td>5.24</td>
      <td>0.00</td>
      <td>10.24</td>
      <td>0.55</td>
      <td>98.29</td>
      <td>0.0</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.0</td>
      <td>4.68</td>
      <td>1.52</td>
      <td>6.13</td>
      <td>3.98</td>
      <td>0.00</td>
      <td>17.97</td>
      <td>0.44</td>
      <td>0.05</td>
      <td>0.0</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 CONUS Change Level 2 Accuracy </h3><p>Overall Accuracy: 92.48 +/- 0.05<br>
Balanced Accuracy: 29.42 +/- 2.97<br>
Kappa: 0.40<br>
<br>
Users Accuracy (100%-Commission Error): <br>
Desiccation: 17.92<br>
Fire: 71.90<br>
Veg-Growth: 49.87<br>
Harvest: 85.22<br>
Insect-Disease-Drought: 20.98<br>
Inundation: 14.41<br>
Mechanical: 25.44<br>
Other: 0.92<br>
Stable: 95.73<br>
Wind: 2.29<br>
<br>
Users Error: <br>
Desiccation: 3.09<br>
Fire: 3.70<br>
Veg-Growth: 0.38<br>
Harvest: 1.93<br>
Insect-Disease-Drought: 1.27<br>
Inundation: 3.27<br>
Mechanical: 4.05<br>
Other: 0.21<br>
Stable: 0.04<br>
Wind: 2.37<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
Desiccation: 39.80<br>
Fire: 36.87<br>
Veg-Growth: 42.79<br>
Harvest: 17.00<br>
Insect-Disease-Drought: 17.30<br>
Inundation: 23.39<br>
Mechanical: 6.11<br>
Other: 11.03<br>
Stable: 96.58<br>
Wind: 3.33<br>
<br>
Producers Error: <br>
Desiccation: 5.89<br>
Fire: 2.84<br>
Veg-Growth: 0.35<br>
Harvest: 0.91<br>
Insect-Disease-Drought: 1.07<br>
Inundation: 5.01<br>
Mechanical: 1.09<br>
Other: 2.35<br>
Stable: 0.03<br>
Wind: 3.43<br>
<br>
Number of Samples in each class: <br>
Desiccation: 134<br>
Fire: 398<br>
Veg-Growth: 31326<br>
Harvest: 3381<br>
Insect-Disease-Drought: 2634<br>
Inundation: 132<br>
Mechanical: 777<br>
Other: 299<br>
Stable: 301757<br>
Wind: 49<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>Desiccation</td>
      <td>Fire</td>
      <td>Veg-Growth</td>
      <td>Harvest</td>
      <td>Insect-Disease-Drought</td>
      <td>Inundation</td>
      <td>Mechanical</td>
      <td>Other</td>
      <td>Stable</td>
      <td>Wind</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>Desiccation</td>
      <td>27.53</td>
      <td>0.00</td>
      <td>1.27</td>
      <td>0.00</td>
      <td>1.09</td>
      <td>0.00</td>
      <td>0.55</td>
      <td>0.00</td>
      <td>123.22</td>
      <td>0.00</td>
      <td>17.92</td>
      <td>3.09</td>
    </tr>
    <tr>
      <td></td>
      <td>Fire</td>
      <td>0.00</td>
      <td>106.21</td>
      <td>21.51</td>
      <td>2.41</td>
      <td>1.12</td>
      <td>0.00</td>
      <td>0.17</td>
      <td>0.03</td>
      <td>16.27</td>
      <td>0.00</td>
      <td>71.9</td>
      <td>3.7</td>
    </tr>
    <tr>
      <td></td>
      <td>Veg-Growth</td>
      <td>2.43</td>
      <td>12.31</td>
      <td>8552.50</td>
      <td>133.78</td>
      <td>27.20</td>
      <td>3.53</td>
      <td>30.72</td>
      <td>3.35</td>
      <td>8382.84</td>
      <td>1.66</td>
      <td>49.87</td>
      <td>0.38</td>
    </tr>
    <tr>
      <td></td>
      <td>Harvest</td>
      <td>0.00</td>
      <td>5.44</td>
      <td>2.44</td>
      <td>288.19</td>
      <td>0.91</td>
      <td>3.19</td>
      <td>5.87</td>
      <td>0.34</td>
      <td>28.89</td>
      <td>2.90</td>
      <td>85.22</td>
      <td>1.93</td>
    </tr>
    <tr>
      <td></td>
      <td>Insect-Disease-Drought</td>
      <td>0.00</td>
      <td>17.16</td>
      <td>69.02</td>
      <td>19.06</td>
      <td>214.34</td>
      <td>0.00</td>
      <td>0.33</td>
      <td>3.81</td>
      <td>697.87</td>
      <td>0.03</td>
      <td>20.98</td>
      <td>1.27</td>
    </tr>
    <tr>
      <td></td>
      <td>Inundation</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.18</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>16.67</td>
      <td>0.55</td>
      <td>0.00</td>
      <td>98.27</td>
      <td>0.00</td>
      <td>14.41</td>
      <td>3.27</td>
    </tr>
    <tr>
      <td></td>
      <td>Mechanical</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.55</td>
      <td>6.75</td>
      <td>0.55</td>
      <td>0.18</td>
      <td>29.48</td>
      <td>0.00</td>
      <td>78.19</td>
      <td>0.17</td>
      <td>25.44</td>
      <td>4.05</td>
    </tr>
    <tr>
      <td></td>
      <td>Other</td>
      <td>5.19</td>
      <td>7.10</td>
      <td>43.65</td>
      <td>569.10</td>
      <td>33.55</td>
      <td>3.99</td>
      <td>58.99</td>
      <td>19.65</td>
      <td>1385.65</td>
      <td>5.29</td>
      <td>0.92</td>
      <td>0.21</td>
    </tr>
    <tr>
      <td></td>
      <td>Stable</td>
      <td>34.02</td>
      <td>139.87</td>
      <td>11295.43</td>
      <td>661.69</td>
      <td>958.81</td>
      <td>42.94</td>
      <td>353.18</td>
      <td>150.92</td>
      <td>305791.03</td>
      <td>16.41</td>
      <td>95.73</td>
      <td>0.04</td>
    </tr>
    <tr>
      <td></td>
      <td>Wind</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.34</td>
      <td>14.18</td>
      <td>1.25</td>
      <td>0.77</td>
      <td>2.44</td>
      <td>0.00</td>
      <td>19.99</td>
      <td>0.91</td>
      <td>2.29</td>
      <td>2.37</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>39.80</td>
      <td>36.87</td>
      <td>42.79</td>
      <td>17.00</td>
      <td>17.30</td>
      <td>23.39</td>
      <td>6.11</td>
      <td>11.03</td>
      <td>96.58</td>
      <td>3.33</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>5.89</td>
      <td>2.84</td>
      <td>0.35</td>
      <td>0.91</td>
      <td>1.07</td>
      <td>5.01</td>
      <td>1.09</td>
      <td>2.35</td>
      <td>0.03</td>
      <td>3.43</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 AK Land Cover Level 1 Accuracy </h3><p>Overall Accuracy: 95.35 +/- 0.09<br>
Balanced Accuracy: 95.10 +/- 0.15<br>
Kappa: 0.88<br>
<br>
Users Accuracy (100%-Commission Error): <br>
VEG: 98.17<br>
NON-VEG: 87.67<br>
<br>
Users Error: <br>
VEG: 0.07<br>
NON-VEG: 0.26<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
VEG: 95.60<br>
NON-VEG: 94.60<br>
<br>
Producers Error: <br>
VEG: 0.10<br>
NON-VEG: 0.19<br>
<br>
Number of Samples in each class: <br>
VEG: 48503<br>
NON-VEG: 9510<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>VEG</td>
      <td>NON-VEG</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>VEG</td>
      <td>41696.31</td>
      <td>779.23</td>
      <td>98.17</td>
      <td>0.07</td>
    </tr>
    <tr>
      <td></td>
      <td>NON-VEG</td>
      <td>1919.01</td>
      <td>13645.78</td>
      <td>87.67</td>
      <td>0.26</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>95.60</td>
      <td>94.60</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.10</td>
      <td>0.19</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 CONUS Land Cover Level 1 Accuracy </h3><p>Overall Accuracy: 95.71 +/- 0.03<br>
Balanced Accuracy: 81.89 +/- 0.25<br>
Kappa: 0.59<br>
<br>
Users Accuracy (100%-Commission Error): <br>
VEG: 98.15<br>
NON-VEG: 57.39<br>
<br>
Users Error: <br>
VEG: 0.02<br>
NON-VEG: 0.34<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
VEG: 97.30<br>
NON-VEG: 66.48<br>
<br>
Producers Error: <br>
VEG: 0.03<br>
NON-VEG: 0.35<br>
<br>
Number of Samples in each class: <br>
VEG: 317873<br>
NON-VEG: 32375<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>VEG</td>
      <td>NON-VEG</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>VEG</td>
      <td>322919.24</td>
      <td>6077.77</td>
      <td>98.15</td>
      <td>0.02</td>
    </tr>
    <tr>
      <td></td>
      <td>NON-VEG</td>
      <td>8949.47</td>
      <td>12055.29</td>
      <td>57.39</td>
      <td>0.34</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>97.30</td>
      <td>66.48</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.03</td>
      <td>0.35</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 AK Land Cover Level 2 Accuracy </h3><p>Overall Accuracy: 83.79 +/- 0.15<br>
Balanced Accuracy: 85.39 +/- 0.24<br>
Kappa: 0.75<br>
<br>
Users Accuracy (100%-Commission Error): <br>
TREE_VEG: 79.43<br>
NON-TREE_VEG: 85.38<br>
NON-VEG: 87.67<br>
<br>
Users Error: <br>
TREE_VEG: 0.28<br>
NON-TREE_VEG: 0.24<br>
NON-VEG: 0.26<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
TREE_VEG: 86.68<br>
NON-TREE_VEG: 74.89<br>
NON-VEG: 94.60<br>
<br>
Producers Error: <br>
TREE_VEG: 0.24<br>
NON-TREE_VEG: 0.28<br>
NON-VEG: 0.19<br>
<br>
Number of Samples in each class: <br>
TREE_VEG: 25183<br>
NON-TREE_VEG: 23320<br>
NON-VEG: 9510<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>TREE VEG</td>
      <td>NON-TREE VEG</td>
      <td>NON-VEG</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>TREE VEG</td>
      <td>17089.42</td>
      <td>4218.15</td>
      <td>206.29</td>
      <td>79.43</td>
      <td>0.28</td>
    </tr>
    <tr>
      <td></td>
      <td>NON-TREE VEG</td>
      <td>2491.10</td>
      <td>17897.63</td>
      <td>572.94</td>
      <td>85.38</td>
      <td>0.24</td>
    </tr>
    <tr>
      <td></td>
      <td>NON-VEG</td>
      <td>136.05</td>
      <td>1782.95</td>
      <td>13645.78</td>
      <td>87.67</td>
      <td>0.26</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>86.68</td>
      <td>74.89</td>
      <td>94.60</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.24</td>
      <td>0.28</td>
      <td>0.19</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 CONUS Land Cover Level 2 Accuracy </h3><p>Overall Accuracy: 87.20 +/- 0.06<br>
Balanced Accuracy: 80.74 +/- 0.21<br>
Kappa: 0.76<br>
<br>
Users Accuracy (100%-Commission Error): <br>
TREE_VEG: 90.47<br>
NON-TREE_VEG: 88.22<br>
NON-VEG: 57.39<br>
<br>
Users Error: <br>
TREE_VEG: 0.08<br>
NON-TREE_VEG: 0.07<br>
NON-VEG: 0.34<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
TREE_VEG: 85.22<br>
NON-TREE_VEG: 90.53<br>
NON-VEG: 66.48<br>
<br>
Producers Error: <br>
TREE_VEG: 0.10<br>
NON-TREE_VEG: 0.07<br>
NON-VEG: 0.35<br>
<br>
Number of Samples in each class: <br>
TREE_VEG: 202848<br>
NON-TREE_VEG: 115025<br>
NON-VEG: 32375<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>TREE VEG</td>
      <td>NON-TREE VEG</td>
      <td>NON-VEG</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>TREE VEG</td>
      <td>116843.92</td>
      <td>10762.01</td>
      <td>1549.40</td>
      <td>90.47</td>
      <td>0.08</td>
    </tr>
    <tr>
      <td></td>
      <td>NON-TREE VEG</td>
      <td>19003.98</td>
      <td>176309.33</td>
      <td>4528.37</td>
      <td>88.22</td>
      <td>0.07</td>
    </tr>
    <tr>
      <td></td>
      <td>NON-VEG</td>
      <td>1264.05</td>
      <td>7685.42</td>
      <td>12055.29</td>
      <td>57.39</td>
      <td>0.34</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>85.22</td>
      <td>90.53</td>
      <td>66.48</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.10</td>
      <td>0.07</td>
      <td>0.35</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 AK Land Cover Level 3 Accuracy </h3><p>Overall Accuracy: 72.05 +/- 0.19<br>
Balanced Accuracy: 76.38 +/- 0.43<br>
Kappa: 0.64<br>
<br>
Users Accuracy (100%-Commission Error): <br>
TREES: 79.43<br>
SHRUBS: 72.71<br>
GRASS: 43.40<br>
BARREN: 67.68<br>
SNOW: 90.59<br>
WATER: 96.83<br>
<br>
Users Error: <br>
TREES: 0.28<br>
SHRUBS: 0.47<br>
GRASS: 0.45<br>
BARREN: 0.62<br>
SNOW: 0.36<br>
WATER: 0.31<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
TREES: 86.68<br>
SHRUBS: 42.51<br>
GRASS: 61.02<br>
BARREN: 81.79<br>
SNOW: 96.68<br>
WATER: 89.60<br>
<br>
Producers Error: <br>
TREES: 0.24<br>
SHRUBS: 0.40<br>
GRASS: 0.53<br>
BARREN: 0.56<br>
SNOW: 0.23<br>
WATER: 0.52<br>
<br>
Number of Samples in each class: <br>
TREES: 25183<br>
SHRUBS: 14860<br>
GRASS: 8460<br>
BARREN: 4587<br>
SNOW: 2828<br>
WATER: 2095<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>TREES</td>
      <td>SHRUBS</td>
      <td>GRASS</td>
      <td>BARREN</td>
      <td>SNOW</td>
      <td>WATER</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>TREES</td>
      <td>17089.42</td>
      <td>3059.00</td>
      <td>1159.14</td>
      <td>145.11</td>
      <td>0.00</td>
      <td>61.18</td>
      <td>79.43</td>
      <td>0.28</td>
    </tr>
    <tr>
      <td></td>
      <td>SHRUBS</td>
      <td>1282.76</td>
      <td>6541.98</td>
      <td>1099.74</td>
      <td>73.43</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>72.71</td>
      <td>0.47</td>
    </tr>
    <tr>
      <td></td>
      <td>GRASS</td>
      <td>1208.34</td>
      <td>5064.13</td>
      <td>5191.78</td>
      <td>252.73</td>
      <td>0.00</td>
      <td>246.78</td>
      <td>43.4</td>
      <td>0.45</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN</td>
      <td>135.83</td>
      <td>725.69</td>
      <td>741.32</td>
      <td>3871.29</td>
      <td>188.61</td>
      <td>57.42</td>
      <td>67.68</td>
      <td>0.62</td>
    </tr>
    <tr>
      <td></td>
      <td>SNOW</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>264.89</td>
      <td>355.61</td>
      <td>5974.48</td>
      <td>0.00</td>
      <td>90.59</td>
      <td>0.36</td>
    </tr>
    <tr>
      <td></td>
      <td>WATER</td>
      <td>0.22</td>
      <td>0.00</td>
      <td>51.06</td>
      <td>35.03</td>
      <td>16.85</td>
      <td>3146.50</td>
      <td>96.83</td>
      <td>0.31</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>86.68</td>
      <td>42.51</td>
      <td>61.02</td>
      <td>81.79</td>
      <td>96.68</td>
      <td>89.60</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.24</td>
      <td>0.40</td>
      <td>0.53</td>
      <td>0.56</td>
      <td>0.23</td>
      <td>0.52</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 CONUS Land Cover Level 3 Accuracy </h3><p>Overall Accuracy: 79.11 +/- 0.07<br>
Balanced Accuracy: 71.77 +/- 3.20<br>
Kappa: 0.70<br>
<br>
Users Accuracy (100%-Commission Error): <br>
TREES: 90.47<br>
SHRUBS: 74.22<br>
GRASS: 74.10<br>
BARREN: 40.03<br>
SNOW: 82.65<br>
WATER: 93.95<br>
<br>
Users Error: <br>
TREES: 0.08<br>
SHRUBS: 0.17<br>
GRASS: 0.12<br>
BARREN: 0.41<br>
SNOW: 7.02<br>
WATER: 0.30<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
TREES: 85.22<br>
SHRUBS: 60.53<br>
GRASS: 86.90<br>
BARREN: 54.69<br>
SNOW: 62.30<br>
WATER: 81.00<br>
<br>
Producers Error: <br>
TREES: 0.10<br>
SHRUBS: 0.17<br>
GRASS: 0.10<br>
BARREN: 0.48<br>
SNOW: 7.80<br>
WATER: 0.46<br>
<br>
Number of Samples in each class: <br>
TREES: 202848<br>
SHRUBS: 36067<br>
GRASS: 78958<br>
BARREN: 21543<br>
SNOW: 840<br>
WATER: 9992<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>TREES</td>
      <td>SHRUBS</td>
      <td>GRASS</td>
      <td>BARREN</td>
      <td>SNOW</td>
      <td>WATER</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>TREES</td>
      <td>116843.92</td>
      <td>5987.33</td>
      <td>4774.68</td>
      <td>921.50</td>
      <td>0.35</td>
      <td>627.55</td>
      <td>90.47</td>
      <td>0.08</td>
    </tr>
    <tr>
      <td></td>
      <td>SHRUBS</td>
      <td>6578.43</td>
      <td>48374.23</td>
      <td>8398.92</td>
      <td>1828.75</td>
      <td>0.00</td>
      <td>0.87</td>
      <td>74.22</td>
      <td>0.17</td>
    </tr>
    <tr>
      <td></td>
      <td>GRASS</td>
      <td>12425.55</td>
      <td>19746.46</td>
      <td>99789.72</td>
      <td>2040.16</td>
      <td>0.00</td>
      <td>658.59</td>
      <td>74.1</td>
      <td>0.12</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN</td>
      <td>1161.78</td>
      <td>5792.38</td>
      <td>1645.11</td>
      <td>5833.12</td>
      <td>14.20</td>
      <td>124.02</td>
      <td>40.03</td>
      <td>0.41</td>
    </tr>
    <tr>
      <td></td>
      <td>SNOW</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>5.05</td>
      <td>24.04</td>
      <td>0.00</td>
      <td>82.65</td>
      <td>7.02</td>
    </tr>
    <tr>
      <td></td>
      <td>WATER</td>
      <td>102.28</td>
      <td>19.28</td>
      <td>228.65</td>
      <td>37.54</td>
      <td>0.00</td>
      <td>6017.32</td>
      <td>93.95</td>
      <td>0.3</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>85.22</td>
      <td>60.53</td>
      <td>86.90</td>
      <td>54.69</td>
      <td>62.30</td>
      <td>81.00</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.10</td>
      <td>0.17</td>
      <td>0.10</td>
      <td>0.48</td>
      <td>7.80</td>
      <td>0.46</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 AK Land Cover Level 4 Accuracy </h3><p>Overall Accuracy: 64.45 +/- 0.20<br>
Balanced Accuracy: 35.76 +/- 0.35<br>
Kappa: 0.57<br>
<br>
Users Accuracy (100%-Commission Error): <br>
TREES: 71.86<br>
TS-TREES: 0.00<br>
SHRUBS-TRE: 0.88<br>
GRASS-TREE: 1.81<br>
BARREN-TRE: nan<br>
TS: 57.69<br>
SHRUBS: 25.66<br>
GRASS-SHRU: nan<br>
BARREN-SHR: nan<br>
GRASS: 42.29<br>
BARREN-GRA: 2.56<br>
BARREN-IMP: 67.68<br>
SNOW: 90.59<br>
WATER: 96.83<br>
<br>
Users Error: <br>
TREES: 0.31<br>
TS-TREES: 0.00<br>
SHRUBS-TRE: 1.34<br>
GRASS-TREE: 0.66<br>
BARREN-TRE: nan<br>
TS: 0.64<br>
SHRUBS: 0.80<br>
GRASS-SHRU: nan<br>
BARREN-SHR: nan<br>
GRASS: 0.45<br>
BARREN-GRA: 2.41<br>
BARREN-IMP: 0.62<br>
SNOW: 0.36<br>
WATER: 0.31<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
TREES: 91.85<br>
TS-TREES: 0.00<br>
SHRUBS-TRE: 0.03<br>
GRASS-TREE: 0.74<br>
BARREN-TRE: 0.00<br>
TS: 63.39<br>
SHRUBS: 12.88<br>
GRASS-SHRU: 0.00<br>
BARREN-SHR: 0.00<br>
GRASS: 63.47<br>
BARREN-GRA: 0.20<br>
BARREN-IMP: 81.79<br>
SNOW: 96.68<br>
WATER: 89.60<br>
<br>
Producers Error: <br>
TREES: 0.21<br>
TS-TREES: 0.00<br>
SHRUBS-TRE: 0.04<br>
GRASS-TREE: 0.27<br>
BARREN-TRE: 0.00<br>
TS: 0.65<br>
SHRUBS: 0.43<br>
GRASS-SHRU: 0.00<br>
BARREN-SHR: 0.00<br>
GRASS: 0.54<br>
BARREN-GRA: 0.19<br>
BARREN-IMP: 0.56<br>
SNOW: 0.23<br>
WATER: 0.52<br>
<br>
Number of Samples in each class: <br>
TREES: 21599<br>
TS-TREES: 472<br>
SHRUBS-TRE: 1787<br>
GRASS-TREE: 1121<br>
BARREN-TRE: 204<br>
TS: 5407<br>
SHRUBS: 5641<br>
GRASS-SHRU: 3541<br>
BARREN-SHR: 271<br>
GRASS: 7843<br>
BARREN-GRA: 617<br>
BARREN-IMP: 4587<br>
SNOW: 2828<br>
WATER: 2095<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>TREES</td>
      <td>TS-TREES</td>
      <td>SHRUBS-TRE</td>
      <td>GRASS-TREE</td>
      <td>BARREN-TRE</td>
      <td>TS</td>
      <td>SHRUBS</td>
      <td>GRASS-SHRU</td>
      <td>BARREN-SHR</td>
      <td>GRASS</td>
      <td>BARREN-GRA</td>
      <td>BARREN-IMP</td>
      <td>SNOW</td>
      <td>WATER</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>TREES</td>
      <td>15129.01</td>
      <td>270.36</td>
      <td>1080.63</td>
      <td>537.81</td>
      <td>54.68</td>
      <td>1286.84</td>
      <td>1218.91</td>
      <td>294.46</td>
      <td>50.72</td>
      <td>932.56</td>
      <td>0.87</td>
      <td>136.69</td>
      <td>0.00</td>
      <td>61.18</td>
      <td>71.86</td>
      <td>0.31</td>
    </tr>
    <tr>
      <td></td>
      <td>TS-TREES</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>1.24</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <td></td>
      <td>SHRUBS-TRE</td>
      <td>9.10</td>
      <td>0.00</td>
      <td>0.43</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>7.23</td>
      <td>31.41</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.43</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.88</td>
      <td>1.34</td>
    </tr>
    <tr>
      <td></td>
      <td>GRASS-TREE</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>7.41</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>107.91</td>
      <td>29.38</td>
      <td>30.91</td>
      <td>222.40</td>
      <td>3.30</td>
      <td>7.99</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>1.81</td>
      <td>0.66</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN-TRE</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <td></td>
      <td>TS</td>
      <td>343.92</td>
      <td>167.11</td>
      <td>201.49</td>
      <td>67.29</td>
      <td>28.44</td>
      <td>3469.31</td>
      <td>1212.16</td>
      <td>103.87</td>
      <td>29.68</td>
      <td>386.46</td>
      <td>0.00</td>
      <td>3.67</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>57.69</td>
      <td>0.64</td>
    </tr>
    <tr>
      <td></td>
      <td>SHRUBS</td>
      <td>191.29</td>
      <td>49.96</td>
      <td>128.03</td>
      <td>100.90</td>
      <td>4.34</td>
      <td>466.73</td>
      <td>765.97</td>
      <td>405.57</td>
      <td>88.70</td>
      <td>704.70</td>
      <td>8.58</td>
      <td>69.76</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>25.66</td>
      <td>0.8</td>
    </tr>
    <tr>
      <td></td>
      <td>GRASS-SHRU</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN-SHR</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <td></td>
      <td>GRASS</td>
      <td>681.15</td>
      <td>25.27</td>
      <td>223.49</td>
      <td>271.98</td>
      <td>6.45</td>
      <td>219.03</td>
      <td>2356.03</td>
      <td>2439.96</td>
      <td>7.25</td>
      <td>5041.63</td>
      <td>149.05</td>
      <td>252.73</td>
      <td>0.00</td>
      <td>246.78</td>
      <td>42.29</td>
      <td>0.45</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN-GRA</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>41.86</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>1.10</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>2.56</td>
      <td>2.41</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN-IMP</td>
      <td>116.05</td>
      <td>0.00</td>
      <td>5.64</td>
      <td>10.69</td>
      <td>3.44</td>
      <td>22.91</td>
      <td>256.67</td>
      <td>367.86</td>
      <td>78.25</td>
      <td>364.84</td>
      <td>376.48</td>
      <td>3871.29</td>
      <td>188.61</td>
      <td>57.42</td>
      <td>67.68</td>
      <td>0.62</td>
    </tr>
    <tr>
      <td></td>
      <td>SNOW</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>239.51</td>
      <td>25.38</td>
      <td>355.61</td>
      <td>5974.48</td>
      <td>0.00</td>
      <td>90.59</td>
      <td>0.36</td>
    </tr>
    <tr>
      <td></td>
      <td>WATER</td>
      <td>0.22</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>51.06</td>
      <td>0.00</td>
      <td>35.03</td>
      <td>16.85</td>
      <td>3146.50</td>
      <td>96.83</td>
      <td>0.31</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>91.85</td>
      <td>0.00</td>
      <td>0.03</td>
      <td>0.74</td>
      <td>0.00</td>
      <td>63.39</td>
      <td>12.88</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>63.47</td>
      <td>0.20</td>
      <td>81.79</td>
      <td>96.68</td>
      <td>89.60</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.21</td>
      <td>0.00</td>
      <td>0.04</td>
      <td>0.27</td>
      <td>0.00</td>
      <td>0.65</td>
      <td>0.43</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.54</td>
      <td>0.19</td>
      <td>0.56</td>
      <td>0.23</td>
      <td>0.52</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 CONUS Land Cover Level 4 Accuracy </h3><p>Overall Accuracy: 67.18 +/- 0.08<br>
Balanced Accuracy: 40.41 +/- 2.27<br>
Kappa: 0.57<br>
<br>
Users Accuracy (100%-Commission Error): <br>
TREES: 82.35<br>
TS-TREES: Not Modeled<br>
SHRUBS-TRE: 10.68<br>
GRASS-TREE: 35.26<br>
BARREN-TRE: 26.15<br>
TS: Not Modeled<br>
SHRUBS: 39.11<br>
GRASS-SHRU: 38.32<br>
BARREN-SHR: 34.34<br>
GRASS: 73.74<br>
BARREN-GRA: 1.58<br>
BARREN-IMP: 40.03<br>
SNOW: 82.65<br>
WATER: 93.95<br>
<br>
Users Error: <br>
TREES: 0.11<br>
TS-TREES: Not Modeled<br>
SHRUBS-TRE: 1.04<br>
GRASS-TREE: 0.42<br>
BARREN-TRE: 3.65<br>
TS: Not Modeled<br>
SHRUBS: 0.40<br>
GRASS-SHRU: 0.24<br>
BARREN-SHR: 0.50<br>
GRASS: 0.12<br>
BARREN-GRA: 0.66<br>
BARREN-IMP: 0.41<br>
SNOW: 7.02<br>
WATER: 0.30<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
TREES: 93.59<br>
TS-TREES: Not Modeled<br>
SHRUBS-TRE: 1.50<br>
GRASS-TREE: 16.97<br>
BARREN-TRE: 1.63<br>
TS: Not Modeled<br>
SHRUBS: 21.32<br>
GRASS-SHRU: 40.25<br>
BARREN-SHR: 23.13<br>
GRASS: 88.40<br>
BARREN-GRA: 0.20<br>
BARREN-IMP: 54.69<br>
SNOW: 62.30<br>
WATER: 81.00<br>
<br>
Producers Error: <br>
TREES: 0.08<br>
TS-TREES: Not Modeled<br>
SHRUBS-TRE: 0.15<br>
GRASS-TREE: 0.23<br>
BARREN-TRE: 0.26<br>
TS: Not Modeled<br>
SHRUBS: 0.25<br>
GRASS-SHRU: 0.25<br>
BARREN-SHR: 0.37<br>
GRASS: 0.10<br>
BARREN-GRA: 0.08<br>
BARREN-IMP: 0.48<br>
SNOW: 7.80<br>
WATER: 0.46<br>
<br>
Number of Samples in each class: <br>
TREES: 163335<br>
TS-TREES: Not Modeled<br>
SHRUBS-TRE: 10900<br>
GRASS-TREE: 26494<br>
BARREN-TRE: 2119<br>
TS: Not Modeled<br>
SHRUBS: 13095<br>
GRASS-SHRU: 15831<br>
BARREN-SHR: 7141<br>
GRASS: 75723<br>
BARREN-GRA: 3235<br>
BARREN-IMP: 21543<br>
SNOW: 840<br>
WATER: 9992<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>TREES</td>
      <td>SHRUBS-TRE</td>
      <td>GRASS-TREE</td>
      <td>BARREN-TRE</td>
      <td>SHRUBS</td>
      <td>GRASS-SHRU</td>
      <td>BARREN-SHR</td>
      <td>GRASS</td>
      <td>BARREN-GRA</td>
      <td>BARREN-IMP</td>
      <td>SNOW</td>
      <td>WATER</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>TREES</td>
      <td>94704.14</td>
      <td>3394.83</td>
      <td>9587.66</td>
      <td>489.02</td>
      <td>1399.95</td>
      <td>1184.25</td>
      <td>31.53</td>
      <td>2759.18</td>
      <td>65.99</td>
      <td>810.22</td>
      <td>0.35</td>
      <td>581.85</td>
      <td>82.35</td>
      <td>0.11</td>
    </tr>
    <tr>
      <td></td>
      <td>SHRUBS-TRE</td>
      <td>310.94</td>
      <td>94.82</td>
      <td>109.76</td>
      <td>4.04</td>
      <td>203.43</td>
      <td>110.39</td>
      <td>16.06</td>
      <td>36.90</td>
      <td>0.00</td>
      <td>1.16</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>10.68</td>
      <td>1.04</td>
    </tr>
    <tr>
      <td></td>
      <td>GRASS-TREE</td>
      <td>2242.73</td>
      <td>835.58</td>
      <td>4624.44</td>
      <td>403.40</td>
      <td>1090.87</td>
      <td>1355.59</td>
      <td>495.61</td>
      <td>1824.39</td>
      <td>88.04</td>
      <td>110.13</td>
      <td>0.00</td>
      <td>43.29</td>
      <td>35.26</td>
      <td>0.42</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN-TRE</td>
      <td>0.37</td>
      <td>0.00</td>
      <td>4.33</td>
      <td>37.86</td>
      <td>34.66</td>
      <td>21.66</td>
      <td>43.32</td>
      <td>0.18</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>2.42</td>
      <td>26.15</td>
      <td>3.65</td>
    </tr>
    <tr>
      <td></td>
      <td>SHRUBS</td>
      <td>283.42</td>
      <td>387.61</td>
      <td>1152.19</td>
      <td>138.42</td>
      <td>5829.34</td>
      <td>4009.42</td>
      <td>1372.74</td>
      <td>1453.63</td>
      <td>36.62</td>
      <td>241.95</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>39.11</td>
      <td>0.4</td>
    </tr>
    <tr>
      <td></td>
      <td>GRASS-SHRU</td>
      <td>180.41</td>
      <td>623.53</td>
      <td>2576.79</td>
      <td>381.75</td>
      <td>10259.01</td>
      <td>15876.80</td>
      <td>4533.47</td>
      <td>5328.32</td>
      <td>945.74</td>
      <td>720.78</td>
      <td>0.00</td>
      <td>0.87</td>
      <td>38.32</td>
      <td>0.24</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN-SHR</td>
      <td>17.33</td>
      <td>134.30</td>
      <td>430.97</td>
      <td>271.72</td>
      <td>1282.94</td>
      <td>2172.26</td>
      <td>3038.24</td>
      <td>368.78</td>
      <td>265.83</td>
      <td>866.01</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>34.34</td>
      <td>0.5</td>
    </tr>
    <tr>
      <td></td>
      <td>GRASS</td>
      <td>2959.94</td>
      <td>843.68</td>
      <td>8473.98</td>
      <td>147.96</td>
      <td>6105.01</td>
      <td>12325.28</td>
      <td>971.54</td>
      <td>99037.43</td>
      <td>746.62</td>
      <td>2031.15</td>
      <td>0.00</td>
      <td>658.59</td>
      <td>73.74</td>
      <td>0.12</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN-GRA</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>104.34</td>
      <td>121.30</td>
      <td>118.98</td>
      <td>0.00</td>
      <td>5.66</td>
      <td>9.01</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>1.58</td>
      <td>0.66</td>
    </tr>
    <tr>
      <td></td>
      <td>BARREN-IMP</td>
      <td>403.66</td>
      <td>17.83</td>
      <td>293.72</td>
      <td>446.57</td>
      <td>1029.34</td>
      <td>2257.78</td>
      <td>2505.26</td>
      <td>999.90</td>
      <td>645.21</td>
      <td>5833.12</td>
      <td>14.20</td>
      <td>124.02</td>
      <td>40.03</td>
      <td>0.41</td>
    </tr>
    <tr>
      <td></td>
      <td>SNOW</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>5.05</td>
      <td>24.04</td>
      <td>0.00</td>
      <td>82.65</td>
      <td>7.02</td>
    </tr>
    <tr>
      <td></td>
      <td>WATER</td>
      <td>90.94</td>
      <td>3.64</td>
      <td>2.79</td>
      <td>4.91</td>
      <td>1.98</td>
      <td>11.28</td>
      <td>6.03</td>
      <td>226.13</td>
      <td>2.52</td>
      <td>37.54</td>
      <td>0.00</td>
      <td>6017.32</td>
      <td>93.95</td>
      <td>0.3</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>93.59</td>
      <td>1.50</td>
      <td>16.97</td>
      <td>1.63</td>
      <td>21.32</td>
      <td>40.25</td>
      <td>23.13</td>
      <td>88.40</td>
      <td>0.20</td>
      <td>54.69</td>
      <td>62.30</td>
      <td>81.00</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.08</td>
      <td>0.15</td>
      <td>0.23</td>
      <td>0.26</td>
      <td>0.25</td>
      <td>0.25</td>
      <td>0.37</td>
      <td>0.10</td>
      <td>0.08</td>
      <td>0.48</td>
      <td>7.80</td>
      <td>0.46</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 AK Land Use Level 1 Accuracy </h3><p>Overall Accuracy: 99.83 +/- 0.02<br>
Balanced Accuracy: 72.71 +/- 2.79<br>
Kappa: 0.59<br>
<br>
Users Accuracy (100%-Commission Error): <br>
Anthro: 85.26<br>
Non-Anthro: 99.85<br>
<br>
Users Error: <br>
Anthro: 3.84<br>
Non-Anthro: 0.02<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
Anthro: 45.44<br>
Non-Anthro: 99.98<br>
<br>
Producers Error: <br>
Anthro: 3.94<br>
Non-Anthro: 0.01<br>
<br>
Number of Samples in each class: <br>
Anthro: 2226<br>
Non-Anthro: 55787<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>Anthro</td>
      <td>Non-Anthro</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>Anthro</td>
      <td>72.53</td>
      <td>12.53</td>
      <td>85.26</td>
      <td>3.84</td>
    </tr>
    <tr>
      <td></td>
      <td>Non-Anthro</td>
      <td>87.10</td>
      <td>57868.17</td>
      <td>99.85</td>
      <td>0.02</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>45.44</td>
      <td>99.98</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>3.94</td>
      <td>0.01</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 CONUS Land Use Level 1 Accuracy </h3><p>Overall Accuracy: 90.70 +/- 0.05<br>
Balanced Accuracy: 88.25 +/- 0.09<br>
Kappa: 0.76<br>
<br>
Users Accuracy (100%-Commission Error): <br>
Anthro: 81.52<br>
Non-Anthro: 94.02<br>
<br>
Users Error: <br>
Anthro: 0.13<br>
Non-Anthro: 0.05<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
Anthro: 83.13<br>
Non-Anthro: 93.37<br>
<br>
Producers Error: <br>
Anthro: 0.12<br>
Non-Anthro: 0.05<br>
<br>
Number of Samples in each class: <br>
Anthro: 74206<br>
Non-Anthro: 276042<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>Anthro</td>
      <td>Non-Anthro</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>Anthro</td>
      <td>75736.91</td>
      <td>17168.65</td>
      <td>81.52</td>
      <td>0.13</td>
    </tr>
    <tr>
      <td></td>
      <td>Non-Anthro</td>
      <td>15364.43</td>
      <td>241731.78</td>
      <td>94.02</td>
      <td>0.05</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>83.13</td>
      <td>93.37</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.12</td>
      <td>0.05</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 AK Land Use Level 2 Accuracy </h3><p>Overall Accuracy: 84.93 +/- 0.15<br>
Balanced Accuracy: 73.97 +/- 3.73<br>
Kappa: 0.77<br>
<br>
Users Accuracy (100%-Commission Error): <br>
Agriculture: 75.75<br>
Developed: 91.97<br>
Forest: 83.08<br>
Other: 92.37<br>
Rangeland: 81.96<br>
<br>
Users Error: <br>
Agriculture: 7.09<br>
Developed: 3.83<br>
Forest: 0.27<br>
Other: 0.22<br>
Rangeland: 0.25<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
Agriculture: 75.62<br>
Developed: 37.70<br>
Forest: 84.70<br>
Other: 89.00<br>
Rangeland: 82.82<br>
<br>
Producers Error: <br>
Agriculture: 7.09<br>
Developed: 4.37<br>
Forest: 0.26<br>
Other: 0.26<br>
Rangeland: 0.24<br>
<br>
Number of Samples in each class: <br>
Agriculture: 1076<br>
Developed: 1150<br>
Forest: 24106<br>
Other: 9535<br>
Rangeland: 22146<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>Agriculture</td>
      <td>Developed</td>
      <td>Forest</td>
      <td>Other</td>
      <td>Rangeland</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>Agriculture</td>
      <td>27.72</td>
      <td>0.00</td>
      <td>6.07</td>
      <td>0.00</td>
      <td>2.80</td>
      <td>75.75</td>
      <td>7.09</td>
    </tr>
    <tr>
      <td></td>
      <td>Developed</td>
      <td>0.00</td>
      <td>46.36</td>
      <td>1.59</td>
      <td>2.33</td>
      <td>0.13</td>
      <td>91.97</td>
      <td>3.83</td>
    </tr>
    <tr>
      <td></td>
      <td>Forest</td>
      <td>0.82</td>
      <td>35.29</td>
      <td>16054.33</td>
      <td>157.39</td>
      <td>3077.25</td>
      <td>83.08</td>
      <td>0.27</td>
    </tr>
    <tr>
      <td></td>
      <td>Other</td>
      <td>0.00</td>
      <td>4.70</td>
      <td>69.24</td>
      <td>13358.38</td>
      <td>1029.99</td>
      <td>92.37</td>
      <td>0.22</td>
    </tr>
    <tr>
      <td></td>
      <td>Rangeland</td>
      <td>8.11</td>
      <td>36.62</td>
      <td>2823.23</td>
      <td>1490.92</td>
      <td>19807.05</td>
      <td>81.96</td>
      <td>0.25</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>75.62</td>
      <td>37.70</td>
      <td>84.70</td>
      <td>89.00</td>
      <td>82.82</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>7.09</td>
      <td>4.37</td>
      <td>0.26</td>
      <td>0.26</td>
      <td>0.24</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br><h3>LCMS v2024-10 CONUS Land Use Level 2 Accuracy </h3><p>Overall Accuracy: 83.93 +/- 0.06<br>
Balanced Accuracy: 73.95 +/- 0.27<br>
Kappa: 0.77<br>
<br>
Users Accuracy (100%-Commission Error): <br>
Agriculture: 77.46<br>
Developed: 88.87<br>
Forest: 88.93<br>
Other: 81.72<br>
Rangeland: 83.35<br>
<br>
Users Error: <br>
Agriculture: 0.14<br>
Developed: 0.38<br>
Forest: 0.09<br>
Other: 0.39<br>
Rangeland: 0.11<br>
<br>
Producers Accuracy (100%-Omission Error): <br>
Agriculture: 89.19<br>
Developed: 37.31<br>
Forest: 91.07<br>
Other: 70.93<br>
Rangeland: 81.25<br>
<br>
Producers Error: <br>
Agriculture: 0.11<br>
Developed: 0.38<br>
Forest: 0.08<br>
Other: 0.43<br>
Rangeland: 0.11<br>
<br>
Number of Samples in each class: <br>
Agriculture: 48074<br>
Developed: 26132<br>
Forest: 187755<br>
Other: 22717<br>
Rangeland: 65570<br>
<br>
</p><table border="1" class="dataframe">
  <tbody>
    <tr>
      <td></td>
      <td>Observed</td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
      <td></td>
    </tr>
    <tr>
      <td></td>
      <td></td>
      <td>Agriculture</td>
      <td>Developed</td>
      <td>Forest</td>
      <td>Other</td>
      <td>Rangeland</td>
      <td>Users Acc</td>
      <td>Users SE</td>
    </tr>
    <tr>
      <td>Predicted</td>
      <td>Agriculture</td>
      <td>66628.46</td>
      <td>2838.15</td>
      <td>1894.62</td>
      <td>107.21</td>
      <td>14552.31</td>
      <td>77.46</td>
      <td>0.14</td>
    </tr>
    <tr>
      <td></td>
      <td>Developed</td>
      <td>151.83</td>
      <td>6118.47</td>
      <td>339.82</td>
      <td>8.57</td>
      <td>266.11</td>
      <td>88.87</td>
      <td>0.38</td>
    </tr>
    <tr>
      <td></td>
      <td>Forest</td>
      <td>1267.45</td>
      <td>3505.47</td>
      <td>109449.70</td>
      <td>922.98</td>
      <td>7922.11</td>
      <td>88.93</td>
      <td>0.09</td>
    </tr>
    <tr>
      <td></td>
      <td>Other</td>
      <td>17.59</td>
      <td>216.15</td>
      <td>320.55</td>
      <td>7840.21</td>
      <td>1199.69</td>
      <td>81.72</td>
      <td>0.39</td>
    </tr>
    <tr>
      <td></td>
      <td>Rangeland</td>
      <td>6639.14</td>
      <td>3718.64</td>
      <td>8181.59</td>
      <td>2174.25</td>
      <td>103720.71</td>
      <td>83.35</td>
      <td>0.11</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers Acc</td>
      <td>89.19</td>
      <td>37.31</td>
      <td>91.07</td>
      <td>70.93</td>
      <td>81.25</td>
      <td></td>
      <td>None</td>
    </tr>
    <tr>
      <td></td>
      <td>Producers SE</td>
      <td>0.11</td>
      <td>0.38</td>
      <td>0.08</td>
      <td>0.43</td>
      <td>0.11</td>
      <td></td>
      <td>None</td>
    </tr>
  </tbody>
</table><br>