tree-canopy

We forecast forest canopy change using a binary classification target:

High Canopy Growth – The classification compares forest canopy change between 2015 and 2019 (a 4-year period), and counties with a ≥5% increase are labeled as high growth (1), others as 0. Stable/Declining – All other counties.

Forest Canopy Change as Target

Our Forest Canopy Colab prepares forest canopy data for use in our Run Models Colab. The dataset is retrieved using the DataCommons API and paired with county-level geographic information based on FIPS codes.

The Colab notebook generates a .csv file that classifies each U.S. county into two groups based on their relative forest cover growth over the past 4 years.

View .csv output: tree-canopy/targets

This dataset is based on Copernicus-derived forest land cover as a percentage, accessed through DataCommons for each U.S. county from the most recent available years (2015-2019).

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Processing Steps

1. County Metadata Collection:

• Retrieved all counties in the U.S. using get_places_in(["country/USA"], "County").
• Extracted county and state names for each region.

2. Forest Land Cover Data Retrieval:

• Queried DataCommons for the variable LandCoverFraction_Forest.
• Fetched multi-year data (e.g., 2015–2019) per county.

3. Determine Growth Window:

• Identified the most recent year of data.
• Compared it to data from years prior.

4. Merge and Clean Data:

• Cleaned FIPS codes and renamed columns for consistency.
• Removed “ County” suffix and standardized capitalization.

5. Calculate Relative Growth:

• Computed:

  relative_growth = ((recent - start) / start) * 100
  

• A county with a ≥5% increase in forest cover is labeled as 1 (high growth), otherwise 0.

6. Export Final Target File:

• Saved to:

  input/targets/forest_canopy_data_target.csv
  

• Columns:
  • Fips: U.S. county code
  • Target: Binary target

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How It Works

The .csv file is used as a target input for ML models predicting forest canopy trends based on other features (e.g., economic activity).

You can plug this into any modeling workflow by referencing the parameter YAML described below.

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🔎 Workflow Overview (with the CSV)

1. Target Formation
   • Extract U.S. county metadata via FIPS codes.
   • Retrieve Copernicus-derived forest cover data.
   • Calculate relative growth:

     relative_growth = ((recent - start) / start) * 100
     

   • Label counties as High Growth (1) or Stable/Declining (0).
2. Mathematical Analysis
   • Rank counties by canopy change.
   • Identify top 10 counties per state with largest decreases.
3. Preprocessing for EDA
   • Clean FIPS codes, merge metadata.
   • Drop unrelated features, handle missing values.
   • Reshape data into long format for time-series models.
4. Predictive Modeling
   • Linear Regression (sklearn) for trend fitting.
   • ARIMA (statsmodels) for 5-year canopy forecasts.
   • Rolling-window validation used for robustness.
5. Statewise Pipelines
   • Group counties by state for localized forecasting.
   • Outputs structured for visualization.
6. Interactive Dashboard (TODO)
   • Planned UI with dropdown menus for state + county selection.
   • Direct access to forecasts from pipelines.

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Target YAML Configuration

forest_canopy_config.yaml – YAML configuration for using this dataset as a model target.

```yaml folder: naics6-forestcanopy-counties-simple features: data: industries common: Fips path: https://raw.githubusercontent.com/ModelEarth/community-timelines/main/training/naics2/US/counties/2020/US-ME-training-naics2-counties-2020.csv targets: data: forest_canopy path: https://raw.githubusercontent.com/ModelEarth/tree-canopy/main/input/targets/forest_canopy_data_target.csv models: rbf