Fields of the World

Country-scale Sentinel-2 mosaics and AI-derived field boundary probability maps for Japan, Mexico, Rwanda, South Africa, and Switzerland, covering 4.76 million km². The dataset includes bi-temporal composites (planting and harvest seasons for 2023 and 2024) at 10m resolution and raw softmax model outputs (background, field, and field boundary probabilities) generated via U-Net/EfficientNet models during the FTW Phase 2 Model Bakeoff using the WherobotsAI platform. All data is stored in cloud-optimized Zarr format.

field-boundaries agriculture sentinel-2 zarr remote-sensing machine-learning earth-observation time-series

Dataset files (10 x .zarr)

Source Cooperative product page

## README.md
# Fields of the World: Country-Scale Field Boundary Predictions

[![Source Coop](https://img.shields.io/badge/Source%20Coop-Access%20Data-blue?style=for-the-badge&logo=sourcetree)](https://source.coop/wherobots/fields-of-the-world)
[![Format](https://img.shields.io/badge/Format-Zarr-purple?style=for-the-badge)](https://zarr.dev/)
[![Model](https://img.shields.io/badge/Models-FTW%20Baselines-green?style=for-the-badge&logo=github)](https://github.com/fieldsoftheworld/ftw-baselines)

**Country-scale Sentinel-2 mosaics and AI-derived field boundary probability maps for Japan, Mexico, Rwanda, South Africa, and Switzerland.**

This dataset contains the underlying raster data generated during the evaluation of models produced during the FTW Phase 2 Model Bakeoff using the **[WherobotsAI platform](https://wherobots.com)**. It includes both the input imagery (seasonally optimized mosaics) and the raw model outputs (softmax predictions) hosted in cloud-optimized **Zarr** format.

## 📰 Read the Blog

> **Read the Blog:** [Wherobots and Taylor Geospatial Engine Bring Fields-of-the-World Models to Production Scale](https://wherobots.com/blog/fields-of-the-world-taylor-geospatial-engine-agricultural-ai/)

---

## 🛠 Try the Model

The model architecture and training pipeline are fully open-source. You can run inference, reproduce our baselines, or train your own models using the **[Fields of The World (FTW)](https://github.com/fieldsoftheworld/ftw-baselines)** repository and the FTW CLI tool.

**Installation:**

```bash
pip install ftw-tools
```

**Documentation & Source Code:**
For tutorials and usage instructions, visit the **[ftw-baselines repository](https://github.com/fieldsoftheworld/ftw-baselines)**.

---

## 🌍 Dataset Coverage

The dataset covers five agricultural systems across 2 seasons and 2 years (2023 and 2024), totaling **4.76 million km²**. This data represents the inference stage of models produced during the FTW Phase 2 Model Bakeoff.

| Country          | Area Processed (M km²) | Median Prediction Field Area (ha) |
| :--------------- | :--------------------- | :-------------------------------- |
| **Mexico**       | 2.39                   | 0.09                              |
| **South Africa** | 1.60                   | 0.07                              |
| **Japan**        | 0.65                   | 0.19                              |
| **Switzerland**  | 0.09                   | 0.28                              |
| **Rwanda**       | 0.02                   | 0.06                              |

---

## 🏗 Model Architecture

These predictions were generated using models produced during the FTW Phase 2 Model Bakeoff, which include **U-Net** architectures with **EfficientNet** encoders, specifically optimized for deployment robustness at scale.

*   **Robustness:** Trained with **Log-Cosh Dice Loss** and targeted augmentations (channel shuffling, brightness, resizing) to handle atmospheric variations in large-scale mosaics.
*   **Input Processing:** Mosaics use latitude-based heuristics to select optimal planting and harvest windows, producing cloud-free, seasonally aligned composites.
*   **Scale:** The inference pipeline processes millions of tiles using Gaussian-weighted averaging to merge across patch overlaps, reducing tiling artifacts in predictions and producing seamless probability maps across country borders.

---

## ⚙ Inference Pipeline

The high-throughput inference pipeline runs on the **[WherobotsAI platform](https://wherobots.com)** and prioritizes spatial consistency and cloud-native efficiency.

1.  **Seasonally-Aligned Mosaicking:** Uses **latitude-dependent heuristics** to identify optimal *Planting* and *Harvest* windows for each tile. A greedy selection algorithm constructs cloud-free composites from Sentinel-2 imagery, minimizing temporal redundancy while maximizing coverage.
2.  **Sliding Window Inference with Apodization:** Processes data in **256×256** patches with **25% overlap**. **Gaussian-weighted averaging (apodization)** merges across patch overlaps to reduce tiling artifacts in predictions, suppressing lower-quality predictions near patch edges and prioritizing spatially consistent center predictions.
3.  **Parallel Zarr Construction:** Handles reprojection to **Web Mercator (EPSG:3857)** on-the-fly. Results are written in parallel directly to **Zarr** stores, generating seamless, cloud-optimized probability maps for entire countries in minutes.

---

## 💾 Data Structure (Zarr)

The data is hosted as cloud-optimized **Zarr** stores. Each store corresponds to a country and contains high-resolution raster arrays with time coordinates for 2023 and 2024:

1.  **Sentinel-2 Mosaics:**
    *   Bi-temporal composites (Planting & Harvest seasons)
    *   Resolution: 10m
    *   Bands: RGB + NIR
    *   Mosaics are "valid-pixel" composites, filtered by cloud cover

2.  **Model Predictions:**
    *   **Raw Softmax Probabilities:** Pixel-wise probabilities for background, field, and field boundary.
    *   Raw logits support custom thresholding, watershedding, or vectorization parameters.

---

## 🚀 Usage (Data Access)

The dataset uses cloud-native Zarr format which allows access to subsets for analysis without downloading the entire dataset using Python libraries like `xarray` and `zarr`.

### 1. Setup and Define AOI

Define a sample Area of Interest (AOI) over Japan:

```python
import rioxarray
import geopandas as gpd
import xarray as xr
import shapely
import matplotlib.pyplot as plt
import numpy as np

# Sample AOI and parameters
src_crs = "EPSG:3857"
planting_bands = ['s2med_planting:B04', 's2med_planting:B03', 's2med_planting:B02']
harvest_bands = ['s2med_harvest:B04', 's2med_harvest:B03', 's2med_harvest:B02']
norm = 3000

# Define a bounding box (Japan)
xmin, ymin= (140.029187, 39.520088)
xmax, ymax = (140.403203, 39.215519)
box = shapely.box(xmin, ymin, xmax, ymax)
aoi = gpd.GeoDataFrame(geometry=[box], crs="EPSG:4326")
aoi = aoi.to_crs(src_crs)
```

### 2. Open Zarr Stores

```python
# Open Mosaics
mosaic_path = "https://data.source.coop/wherobots/fields-of-the-world/mosaics/japan.zarr/"
mosaic = xr.open_zarr(mosaic_path)
mosaic
```

```python
# Open Predictions
pred_path = "https://data.source.coop/wherobots/fields-of-the-world/predictions/japan.zarr/"
predictions = xr.open_zarr(pred_path)
predictions
```

### 3. Slicing and Visualizing

Lazily load data within the AOI for a specific year and visualize inputs alongside model outputs.

```python
time = "2024-01-01"

# Slice and clipping to the AOI
p_crop = mosaic.sel(time=time, band=planting_bands).rio.clip_box(*aoi.geometry.total_bounds) / norm
h_crop = mosaic.sel(time=time, band=harvest_bands).rio.clip_box(*aoi.geometry.total_bounds) / norm
pred_crop = predictions.sel(time=time, band="field").rio.clip_box(*aoi.geometry.total_bounds)

p_crop = p_crop.clip(0, 1).to_array().squeeze()
h_crop = h_crop.clip(0, 1).to_array().squeeze()

# Plot
fig, axes = plt.subplots(1, 3, figsize=(15, 5))

p_crop.plot.imshow(ax=axes[0])
axes[0].axis("off")
axes[0].set_title("Planting Season Mosaic")

h_crop.plot.imshow(ax=axes[1])
axes[1].axis("off")
axes[1].set_title("Harvest Season Mosaic")

pred_crop.to_array().squeeze().plot.imshow(ax=axes[2], cmap="plasma")
axes[2].axis("off")
axes[2].set_title("Field Predictions")

plt.tight_layout()
plt.show()
```

### 4. Convert to Cloud-Optimized Geotiff (COG)

```python
pred_crop.rio.to_raster(
    "field_predictions.tif",
    compress="lzw",
    dtype="float32",
    tiled=True,
    blockxsize=256,
    blockysize=256,
    driver="COG"
)
```

### 5. Application: Field Change Detection

The dataset includes both 2023 and 2024 data. Perform basic change detection to identify where field boundaries shifted or disappeared.

```python
# Fetch predictions for both years
pred_2023 = predictions.sel(time="2023-01-01", band="field")
pred_2023 = pred_2023.rio.clip_box(*aoi.geometry.total_bounds).to_array().squeeze().to_numpy()

pred_2024 = predictions.sel(time="2024-01-01", band="field")
pred_2024 = pred_2024.rio.clip_box(*aoi.geometry.total_bounds).to_array().squeeze().to_numpy()

# Calculate difference
diff = pred_2024 - pred_2023

# Simple thresholding (>= 99th percentile)
pct = 99
threshold = np.percentile(diff[diff > 0], pct)
change = diff.clip(0, 1)
change[change >= threshold] = 1
change[change < threshold] = 0

# Plotting
fig, axes = plt.subplots(1, 4, figsize=(20, 5))

axes[0].imshow(pred_2023, vmin=0, vmax=1, cmap="plasma")
axes[0].set_title("2023 Field Probability")
axes[0].axis("off")

axes[1].imshow(pred_2024, vmin=0, vmax=1, cmap="plasma")
axes[1].set_title("2024 Field Probability")
axes[1].axis("off")

green_cmap = plt.cm.colors.ListedColormap([[0, 0, 0], [0, 1, 0]])
axes[2].imshow(change, vmin=0, vmax=1, cmap=green_cmap)
axes[2].set_title("Detected Change (>=99th percentile)")
axes[2].axis("off")

img = plt.cm.plasma(pred_2023)
img[change == 1, :3] = [0, 1, 0]
axes[3].imshow(img, vmin=0, vmax=1)
axes[3].set_title("Change Overlay on 2023")
axes[3].axis("off")

plt.tight_layout()
plt.show()
```

---

## Citation & Attribution

If you use this data in your research, please cite:

```bibtex
@inproceedings{kerner2025fields,
  title={Fields of the world: A machine learning benchmark dataset for global agricultural field boundary segmentation},
  author={Kerner, Hannah and Chaudhari, Snehal and Ghosh, Aninda and Robinson, Caleb and Ahmad, Adeel and Choi, Eddie and Jacobs, Nathan and Holmes, Chris and Mohr, Matthias and Dodhia, Rahul and others},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={27},
  pages={28151--28159},
  year={2025}
}
```

---

## 📄 License

*The current model checkpoints used to produce these predictions were trained on the full FTW dataset, which included CC-BY-NC labels, resulting in CC-BY-NC licensed predictions. We will be releasing predictions trained on only the CC-BY licensed labels in the next few weeks.*

**License:** [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)

providers: role [producer] for 'Fields of the World (FTW)' is an AI guess — FTW developed the models and benchmark dataset underlying these predictions; producer role inferred from citation authorship and model repository ownership.. Verify before flipping to _state: reviewed.


Global Human Modification Dataset (1990–2017)

This dataset provides a global, high-resolution (300-meter) view of how human activities have modified terrestrial landscapes from 1995 to 2017, covering all land areas except Antarctica. It offers a continuous, quantitative estimate of human modification (H) ranging from 0 (no modification) to 1 (complete modification).


US Structures from Oak Ridge National Laboratory

A comprehensive geospatial database of over 125 million building structures across the United States and its territories, covering every structure larger than 450 square feet. Produced by Oak Ridge National Laboratory (ORNL) using AI, high-performance computing, and over 1.1 petabytes of satellite and aerial imagery. Each record includes building footprint geometry, occupancy classification, property address, physical dimensions (height, floor area), and elevation data.

building-footprints remote-sensing land-use geoparquet pmtiles machine-learning aerial-imagery

geoparquet

geometry

[geometry] Building footprint polygon geometry in EPSG:4326

[placename] Property city name

[admin_code] Property state abbreviation code

[zipcode] Property ZIP code

FIPS

[admin_code] FIPS county or state code

USNG

[spatial_index] US National Grid reference for the building location

LONGITUDE

[latlon] Centroid longitude of building structure

LATITUDE

[latlon] Centroid latitude of building structure

bbox

STRUCT(xmin DOUBLE, ymin DOUBLE, xmax DOUBLE, ymax DOUBLE)

geohash

[spatial_index] Geohash spatial index for the building location

5 files sampled for this table

iceberg/data

[placename] Property city name

[zipcode] Property ZIP code

FIPS

[admin_code] FIPS county or state code

geom

[geometry] Building footprint geometry in OGC:CRS84

5 files sampled for this table

Source Cooperative product page

## README.md
# USA Structures Dataset

The **USA Structures Dataset** provides a comprehensive geospatial database of over 125 million building structures across the U.S. and its territories, capturing every structure larger than 450 square feet. Developed by Oak Ridge National Laboratory (ORNL) through the integration of advanced geospatial science, high-performance computing, and AI, this dataset processes over 1.1 petabytes of imagery. It serves as a valuable resource across various fields, supporting tasks from urban planning to emergency response with detailed location-based data. Its precise, accessible structure facilitates cross-agency collaboration, making it an essential tool for federal, local, state, tribal, and territorial organizations. The dataset accelerates tasks like damage assessments and recovery operations, aiding communities in recovering from disasters efficiently.

Special thank you to [Mark Litwintschik](https://www.linkedin.com/feed/update/urn:li:activity:7132210177289650176/?updateEntityUrn=urn%3Ali%3Afs_feedUpdate%3A%28V2%2Curn%3Ali%3Aactivity%3A7132210177289650176%29) and his [original post](https://tech.marksblogg.com/ornl-fema-buildings.html) that outlined the initial ETL process.

---

## Quickstart Guide

Access to this dataset is available in **GeoParquet**, **Wherobots Havasu (Iceberg)**, and **PMTiles**.

### GeoParquet

You can access the GeoParquet data directly as partitioned GeoParquet files. These are partitioned by the primary occupancy which includes:

- Education
- Residential
- Government
- Utility and Misc
- Industrial
- Agriculture
- Unclassified
- Assembly
- Commercial

These files are located in `/geoparquet`.

**Example code**

```python
df = sedona.read.format("geoparquet").load(YOUR_S3_SOURCE_COOP_URI + 'geoparquet/OCC_CLS=Residential/*.parquet)
df.printSchema()
```

### Wherobots Havasu

Havasu is a spatial table format built on top of Apache Iceberg. WherobotsDB can save its in-memory geometry and raster tables to the Havasu table format and store them on AWS S3, or any other cloud file systems or object stores.

Havasu supports ACID transactions, schema evolution, and time travel on spatial data including geometries and rasters. Havasu stores spatial data in Apache Parquet format on cloud storage, which is very cost effective and highly decoupled with computation.

To use these files first copy the files to an S3 bucket of your choosing. You can get access to a bucket by signing up for a Wherobots account [here](https://cloud.wherobots.com/auth/register).

Once you have done that you can add a new spatial catalog using [these instructions](https://docs.wherobots.com/latest/develop/storage-management/s3-storage-integration/?h=catalog#spatial-catalog).

If you are using your own S3 bucket please refer to [these instructions](https://docs.wherobots.com/latest/develop/storage-management/s3-storage-integration/).

## PMTiles

You can use the PMTiles URL in any application or tool that supports PMTiles such as Felt, leafmap, MapLibre, and the [PM Tiles Viewer](https://pmtiles.io/)

extent.spatial.bbox: omitted; spatial reference is categorical via [geoparquet.geometry (geometry), geoparquet.LATITUDE (latlon), geoparquet.LONGITUDE (latlon), geoparquet.PROP_CITY (placename), geoparquet.PROP_ST (admin_code), geoparquet.PROP_ZIP (zipcode), geoparquet.FIPS (admin_code), geoparquet.geohash (spatial_index), geoparquet.USNG (spatial_index), iceberg/data.geom (geometry), iceberg/data.LATITUDE (latlon), iceberg/data.LONGITUDE (latlon), iceberg/data.PROP_CITY (placename), iceberg/data.PROP_ST (admin_code), iceberg/data.PROP_ZIP (zipcode), iceberg/data.FIPS (admin_code), iceberg/data.USNG (spatial_index)]. Add bbox once the placename → bbox gazetteer covers it.


The Wildland Almanac - California

The Wildland Almanac - California is a 30 m, Landsat-derived annual time series of 18 biophysical properties across California's forests, shrublands, and grasslands for water years 1985–2025. Properties span six themes: vegetation cover and structure, hydrology, fire hazard (including FARSITE/FlamMap landscape inputs), carbon stocks and flux, drought-driven tree dieoff risk, and disturbance severity. All layers are delivered as Cloud-Optimized GeoTIFFs in EPSG:5070, masked to the California wildland extent, with a companion STAC catalog.

remote-sensing landsat time-series earth-observation hydrology cloud-optimized-geotiff ecology biodiversity wildfire conservation

University of California, Irvine — Department of Earth System Science (M.L. Goulden)

Dataset files (901 x .tif)

Source Cooperative product page

## README.md
# The Wildland Almanac - California

The California release of the Wildland Almanac: an open, 30 m, Landsat-derived record of how California's forests, shrublands, and grasslands have changed over time. Eighteen biophysical properties - vegetation cover and structure, hydrology, fuels and fire hazard, carbon, drought vulnerability, and disturbance - produced from the Landsat archive through a single cross-consistent pipeline, every water year from 1985 to 2025.

**Current release: `v2026.1` (June 2026).** Annual water years 1985 through 2025, California, all 18 layers. Released under CC BY 4.0.

> This directory is the **California** half of the Wildland Almanac. The contiguous-U.S. release - decadal snapshots for water years 1990, 2000, 2010, 2020, and 2025 - is the sibling dataset at [`source.coop/wildland-almanac/conus`](https://source.coop/wildland-almanac/conus). The two share methods and conventions; the main differences are temporal cadence (California annual vs. CONUS decadal snapshots), the disturbance encoding (annual vs. cumulative), and the mask (California applies a full wildland-extent mask; CONUS is water-only).

---

## Quick navigation

- **Current release:** [`v2026.1/`](https://source.coop/wildland-almanac/california/v2026.1/) - the actual data files (901 COGs across 18 layer directories)
- **Documentation PDF:** [`WildlandAlmanac_CA_Documentation.pdf`](https://data.source.coop/wildland-almanac/california/WildlandAlmanac_CA_Documentation.pdf) - the binding reference for units, methods, projections, the mask definition, and the UC disclaimer (§6)
- **STAC catalog:** [`v2026.1/catalog.json`](https://data.source.coop/wildland-almanac/california/v2026.1/catalog.json) - machine-readable inventory, with 18 collections and 901 items
- **Website (project documentation, how-to guides, In Use & Findings):** [`wildlandalmanac.org`](https://wildlandalmanac.org/)
- **Sibling release:** [`../conus/`](https://source.coop/wildland-almanac/conus) - CONUS coverage (decadal snapshots, water years 1990-2025)

---

## What's in this release

Eighteen properties, six themes, every water year 1985 through 2025 (October-September), 30 m resolution, EPSG:5070 (NAD83 / CONUS Albers). All files are Cloud-Optimized GeoTIFFs (COGs). Canopy height, cover, base height, and bulk density are provided as bands of `Fire_LCP`, not as standalone layers.

**Vegetation cover and structure (4)**

- `Veg_TreeFrac/` - fractional tree canopy cover
- `Veg_ShrubFrac/` - fractional shrub cover
- `Veg_HerbFrac/` - fractional herbaceous cover
- `Veg_BareFrac/` - fractional bare/non-vegetated cover

**Hydrology (6)**

- `WaterFlux_AETmax/` - maximum evapotranspiration (vegetation-driven, no drought limitation)
- `WaterFlux_AETrealized/` - realized evapotranspiration (accounting for precipitation)
- `WaterFlux_Soilmoisture/` - end-of-water-year soil moisture
- `WaterFlux_SoilmoistureFrac/` - end-of-water-year soil moisture as a fraction of maximum rooting-zone storage
- `WaterFlux_Runoff/` - annual discharge (precip minus AETrealized)
- `WaterFlux_PminusETmax_SPI0/` - vegetation-driven water yield at long-term-mean precipitation

**Fire hazard (3)**

- `Fire_LCP/` - the components of a FARSITE/FlamMap landscape, delivered as **eight single-band COGs**: five per-year (fuel model, canopy cover, canopy height, canopy base height, canopy bulk density) and three static (elevation, slope, aspect). Clip and stack the bands to assemble a landscape - see the [Use It](https://wildlandalmanac.org/use.html) page for the carve-and-stack recipe.
- `Fire_FlamMap_FL/` - FlamMap-predicted flame length (relative hazard, fixed run conditions)
- `Fire_FlamMap_ROS/` - FlamMap-predicted rate of spread (relative hazard, fixed run conditions)

**Carbon (2)**

- `Carbon_AGB/` - aboveground live biomass (metric tons / hectare)
- `Carbon_GPP/` - gross primary production (g C / m² / yr)

**Forest dieoff risk (1)**

- `Vulner_TreeDieoff_SPI-2/` - tree-canopy vulnerability under severe long-term drought (48-month SPI = −2)

**Disturbance severity (2)** - **annual** loss at disturbed pixels, water years 1986-2024 (boundary years 1985 and 2025 are not produced; see documentation §4.3)

- `Disturbance_TreeFrac/` - annual loss of tree fractional cover at disturbed pixels
- `Disturbance_AGB/` - annual loss of aboveground biomass at disturbed pixels

Filenames follow `WildlandAlmanac_CA_{Property}_{WaterYear}.tif`. The `Fire_LCP` bands follow `WildlandAlmanac_CA_Fire_LCP_{Band}_{WaterYear}.tif` (five per-year bands) and `WildlandAlmanac_CA_Fire_LCP_{Band}.tif` (three static topographic bands, no year). See the documentation PDF for full per-layer specifications, units, scaling conventions, methodology, and caveats.

> **Note on base names.** `Disturbance_TreeFrac` and `Disturbance_AGB` are **annual** here and **cumulative** in the CONUS release (where they carry a `_cumulative` suffix). Do not assume the two are directly comparable without accounting for that difference.

---

## Releases and versioning

The Almanac uses an **archival versioning model**:

- Each release is a complete reprocessing of the full 1985-onward time series under one code base.
- Releases live in their own version directory (`v2026.1/`, `v2027.1/`, …). Files at a published version URL are stable: once a version has been used in published work, its contents are preserved.
- Future annual releases are published as **siblings**, not replacements. Earlier versions remain accessible at their original URLs (and under their original DOIs, where assigned).
- Minor fixes may be made in place without a version bump. More significant corrections that change a quantitative result downstream will earn a new minor version (e.g. `v2026.2/`) with its own DOI, and the prior version will be retained.

**Cite the version you used.** Each release receives its own DOI. When citing the Almanac in a paper, EIR, plan, or other document where future readers may need to verify the exact values you relied on, cite the **specific version** (and DOI), not the dataset as a whole. This is what makes the reference chain reproducible.

For analysis within one release: **use one version end-to-end.** Do not splice years from different versions - every release reprocesses the full series, so values for a given year may differ between versions.

---

## How to use the data

The data are Cloud-Optimized GeoTIFFs served over both S3 and HTTPS. Three access patterns, in increasing order of effort:

**Stream it** - most common, no download. GIS tools (ArcGIS Pro, QGIS, rasterio, terra) can read COGs directly from cloud storage, transferring only the bytes needed for your current window.

- ArcGIS Pro: `s3://us-west-2.opendata.source.coop/wildland-almanac/california/v2026.1/{Layer}/WildlandAlmanac_CA_{Layer}_{Year}.tif`
- QGIS / GDAL / rasterio / terra: `/vsicurl/https://data.source.coop/wildland-almanac/california/v2026.1/{Layer}/WildlandAlmanac_CA_{Layer}_{Year}.tif`

**Download one file** - `curl`, `wget`, browser, or PowerShell `Invoke-WebRequest` against the HTTPS URL above. To clip a lat/long box without downloading the whole file, use `gdalwarp -te ... -te_srs EPSG:4326 /vsicurl/<url> clip.tif` - GDAL fetches only the tiles overlapping your box.

**Bulk download** - AWS CLI with `--no-sign-request` (no account needed):
```
aws s3 sync s3://us-west-2.opendata.source.coop/wildland-almanac/california/v2026.1/{Layer}/ ./{Layer}/ --no-sign-request
```

**Building a fire-behavior landscape from `Fire_LCP`** - the eight bands are single-band COGs; clip each to your area of interest, then stack them in LCP band order (elevation, slope, aspect, fuel model, canopy cover, canopy height, canopy base height, canopy bulk density). Modern FlamMap reads the GeoTIFF stack directly. The worked recipe is on the [Use It](https://wildlandalmanac.org/use.html) page.

**Full how-to with worked examples** - ArcGIS Pro, QGIS, Python (rasterio), R (terra), and the cloud-native workflow background - is on the website's [Use It](https://wildlandalmanac.org/use.html) page. That page is the authoritative how-to reference.

---

## About the quality of these data

The Wildland Almanac is built to be as useful as the underlying observations allow, and the layers compare favorably with comparable products and with the published literature. But much of this work remains very difficult, and the dataset has real limits. Some pixels are wrong in ways that are known and documented; others are wrong in ways that have not yet been identified. Some layers are better than others; some years are more or less constrained than others; some regions and vegetation types are better characterized than others.

Anyone using these data for a specific decision should look critically at the values for their area of interest, compare against on-the-ground knowledge and independent observations when possible, and consider the documentation's notes on methods and caveats. The California layers are masked to the California wildland extent - water, areas outside California, urban/agricultural/barren land, and a set of non-target ecoregions (Central Valley and specified deserts) are excluded (see documentation §4.4). The decision to release the data publicly reflects our judgment that it uses the best information of its kind available, that we believe it has reached the point where it can aid planning and research, and that use-with-feedback is the path to improvement. Reports of what looks wrong - pixel-level errors, structural issues, or systematic problems - are the most valuable contribution a user can make.

---

## License

Released under [Creative Commons Attribution 4.0 (CC BY)](https://creativecommons.org/licenses/by/4.0/). Free to use, share, and adapt with attribution.

**University of California disclaimer.** These data are a University of California product, provided "as is" with no warranty; the user assumes all risk of use. Use of the data implies consent to the full University of California disclaimer, reproduced in §6 of the [documentation PDF](https://data.source.coop/wildland-almanac/california/WildlandAlmanac_CA_Documentation.pdf).

---

## Citation

> Goulden, M.L. (2026). *The Wildland Almanac - California* (Version v2026.1). Source Cooperative. DOI: [pending - EZID]. Released under CC BY.

When citing analyses based on these data, cite the **specific version** (above). Different versions reprocess the full time series and may differ in detail; the version DOI is what makes your analysis reproducible.

---

## Feedback and contact

**Found something wrong, or have a use case to share?** Please file an issue on GitHub: [github.com/wildland-almanac/wildland-almanac-site/issues](https://github.com/wildland-almanac/wildland-almanac-site/issues). Reports of errors and notes on real-world use are tracked there and folded into future releases.

**Contact:** [mgoulden@uci.edu](mailto:mgoulden@uci.edu) · Department of Earth System Science, University of California, Irvine. The data are offered with no promise of technical support, but feedback on errors and use cases is welcome and shapes future releases.

---

*The Wildland Almanac is an outgrowth of the Center for Ecosystem Climate Solutions (CECS), a multi-year University of California research program supported by California's Strategic Growth Council. Earlier work and publications referencing CECS share the same project lineage.*

extent.temporal: None (probe sample) widened to ['1985-01-01T00:00:00Z', '2025-01-01T00:00:00Z'] using README-stated coverage ['1985', '2025'].


The Wildland Almanac - CONUS

The Wildland Almanac - CONUS is a 30-meter, Landsat-derived record of wildland ecosystem properties across forests, shrublands, and grasslands of the conterminous United States. Seventeen biophysical properties spanning vegetation cover and structure, hydrology, fire hazard, carbon, and disturbance severity are delivered as Cloud-Optimized GeoTIFFs at decadal water-year snapshots for 1990, 2000, 2010, 2020, and 2024. All layers are produced through a single cross-consistent pipeline from the Landsat archive at 30 m resolution in EPSG:5070 (NAD83 / CONUS Albers).

remote-sensing landsat time-series earth-observation land-cover hydrology cloud-optimized-geotiff environmental-monitoring

University of California, Irvine — Department of Earth System Science

Dataset files (118 x .tif)

Source Cooperative product page

## README.md
# The Wildland Almanac - CONUS

The CONUS release of the Wildland Almanac: an open, 30 m, Landsat-derived record of how the conterminous United States' forests, shrublands, and grasslands have changed over time. Seventeen biophysical properties - vegetation cover and structure, hydrology, fuels and fire hazard, carbon, and disturbance - produced from the Landsat archive through a single cross-consistent pipeline, at decadal snapshots.

**Current release: `v2026.1` (June 2026).** Water years 1990, 2000, 2010, 2020, and 2024, conterminous U.S., all 17 layers. Released under CC BY 4.0.

> This directory is the **CONUS** half of the Wildland Almanac. The California release - annual water years 1985–2025 - is the sibling dataset at [`source.coop/wildland-almanac/california`](https://source.coop/wildland-almanac/california). The two share methods and conventions; the main differences are temporal cadence (California annual vs. CONUS decadal snapshots), the disturbance encoding (annual vs. cumulative), and the mask (CONUS masks open water, the area outside the conterminous U.S., and agricultural land).

---

## Quick navigation

- **Current release:** [`v2026.1/`](https://source.coop/wildland-almanac/conus/v2026.1/) - the actual data files (108 COGs across 17 layer directories)
- **Documentation PDF:** [`WildlandAlmanac_CONUS_Documentation.pdf`](https://data.source.coop/wildland-almanac/conus/WildlandAlmanac_CONUS_Documentation.pdf) - the binding reference for units, methods, projections, the cumulative-window definitions, and the UC disclaimer (§6)
- **STAC catalog:** [`v2026.1/catalog.json`](https://data.source.coop/wildland-almanac/conus/v2026.1/catalog.json) - machine-readable inventory, with 17 collections and 108 items
- **Website (project documentation, how-to guides, In Use & Findings):** [`wildlandalmanac.org`](https://wildlandalmanac.org/)
- **Sibling release:** [`../california/`](https://source.coop/wildland-almanac/california) - California coverage (annual, water years 1985–2025)

---

## What's in this release

Seventeen properties, five themes, water years 1990, 2000, 2010, 2020, and 2024 (October–September), 30 m resolution, EPSG:5070 (NAD83 / CONUS Albers). All files are Cloud-Optimized GeoTIFFs (COGs). Canopy height, cover, base height, and bulk density are provided as bands of `Fire_LCP`, not as standalone layers.

**Vegetation cover and structure (4)**

- `Veg_TreeFrac/` - fractional tree canopy cover
- `Veg_ShrubFrac/` - fractional shrub cover
- `Veg_HerbFrac/` - fractional herbaceous cover
- `Veg_BareFrac/` - fractional bare/non-vegetated cover

**Hydrology (6)**

- `WaterFlux_AETmax/` - maximum evapotranspiration (vegetation-driven, no drought limitation)
- `WaterFlux_AETrealized/` - realized evapotranspiration (accounting for precipitation)
- `WaterFlux_Soilmoisture/` - end-of-water-year soil moisture
- `WaterFlux_SoilmoistureFrac/` - end-of-water-year soil moisture as a fraction of maximum rooting-zone storage
- `WaterFlux_Runoff/` - annual discharge (precip minus AETrealized)
- `WaterFlux_PminusETmax_SPI0/` - vegetation-driven water yield at long-term-mean precipitation

**Fire hazard (3)**

- `Fire_LCP/` - the components of a FARSITE/FlamMap landscape, delivered as **eight single-band COGs**: five per-snapshot-year (fuel model, canopy cover, canopy height, canopy base height, canopy bulk density) and three static (elevation, slope, aspect). Clip and stack the bands to assemble a landscape - see the [Use It](https://wildlandalmanac.org/use.html) page for the carve-and-stack recipe.
- `Fire_FlamMap_FL/` - FlamMap-predicted flame length (relative hazard, fixed run conditions)
- `Fire_FlamMap_ROS/` - FlamMap-predicted rate of spread (relative hazard, fixed run conditions)

**Carbon (2)**

- `Carbon_AGB/` - aboveground live biomass (metric tons / hectare)
- `Carbon_GPP/` - gross primary production (g C / m² / yr)

**Disturbance severity (2)** - **cumulative** loss summed over unequal-length windows ending in each snapshot year (5 / 10 / 10 / 10 / 4 years for 1990 / 2000 / 2010 / 2020 / 2024; see documentation §4.3)

- `Disturbance_TreeFrac_cumulative/` - cumulative loss of tree fractional cover at disturbed pixels
- `Disturbance_AGB_cumulative/` - cumulative loss of aboveground biomass at disturbed pixels

Filenames follow `WildlandAlmanac_CONUS_{Property}_{WaterYear}.tif`. The `Fire_LCP` bands follow `WildlandAlmanac_CONUS_Fire_LCP_{Band}_{WaterYear}.tif` (five per-year bands) and `WildlandAlmanac_CONUS_Fire_LCP_{Band}.tif` (three static topographic bands, no year). See the documentation PDF for full per-layer specifications, units, scaling conventions, methodology, and caveats.

> **Note on base names.** `Disturbance_TreeFrac` and `Disturbance_AGB` are **annual** in the California release and **cumulative** here (hence the `_cumulative` suffix). Because the CONUS windows differ in length, do not compare values across snapshots without normalizing to a per-year rate.

---

## Releases and versioning

The Almanac uses an **archival versioning model**:

- Each release is a complete reprocessing of the full snapshot set under one code base.
- Releases live in their own version directory (`v2026.1/`, `v2027.1/`, …). Files at a published version URL are stable: once a version has been used in published work, its contents are preserved.
- Future releases are published as **siblings**, not replacements. Earlier versions remain accessible at their original URLs (and under their original DOIs, where assigned).
- Minor fixes may be made in place without a version bump. More significant corrections that change a quantitative result downstream will earn a new minor version (e.g. `v2026.2/`) with its own DOI, and the prior version will be retained.

**Cite the version you used.** Each release receives its own DOI. When citing the Almanac in a paper, EIR, plan, or other document where future readers may need to verify the exact values you relied on, cite the **specific version** (and DOI), not the dataset as a whole. This is what makes the reference chain reproducible.

For analysis within one release: **use one version end-to-end.** Do not splice snapshots from different versions - every release reprocesses the full set, so values for a given year may differ between versions.

---

## How to use the data

The data are Cloud-Optimized GeoTIFFs served over both S3 and HTTPS. Three access patterns, in increasing order of effort:

**Stream it** - most common, no download. GIS tools (ArcGIS Pro, QGIS, rasterio, terra) can read COGs directly from cloud storage, transferring only the bytes needed for your current window.

- ArcGIS Pro: `s3://us-west-2.opendata.source.coop/wildland-almanac/conus/v2026.1/{Layer}/WildlandAlmanac_CONUS_{Layer}_{Year}.tif`
- QGIS / GDAL / rasterio / terra: `/vsicurl/https://data.source.coop/wildland-almanac/conus/v2026.1/{Layer}/WildlandAlmanac_CONUS_{Layer}_{Year}.tif`

**Download one file** - `curl`, `wget`, browser, or PowerShell `Invoke-WebRequest` against the HTTPS URL above. To clip a lat/long box without downloading the whole file, use `gdalwarp -te ... -te_srs EPSG:4326 /vsicurl/<url> clip.tif` - GDAL fetches only the tiles overlapping your box. (Because the CONUS grid is large - 105,000 × 160,000 pixels - clipping to an area of interest rather than downloading whole layers is usually the right approach.)

**Bulk download** - AWS CLI with `--no-sign-request` (no account needed):
```
aws s3 sync s3://us-west-2.opendata.source.coop/wildland-almanac/conus/v2026.1/{Layer}/ ./{Layer}/ --no-sign-request
```

**Building a fire-behavior landscape from `Fire_LCP`** - the eight bands are single-band COGs; clip each to your area of interest, then stack them in LCP band order (elevation, slope, aspect, fuel model, canopy cover, canopy height, canopy base height, canopy bulk density). Modern FlamMap reads the GeoTIFF stack directly. The worked recipe is on the [Use It](https://wildlandalmanac.org/use.html) page.

**Full how-to with worked examples** - ArcGIS Pro, QGIS, Python (rasterio), R (terra), and the cloud-native workflow background - is on the website's [Use It](https://wildlandalmanac.org/use.html) page. That page is the authoritative how-to reference.

---

## About the quality of these data

The Wildland Almanac is built to be as useful as the underlying observations allow, and the layers compare favorably with comparable products and with the published literature. But much of this work remains very difficult, and the dataset has real limits. Some pixels are wrong in ways that are known and documented; others are wrong in ways that have not yet been identified. Some layers are better than others; some snapshots are more or less constrained than others; some regions and vegetation types are better characterized than others.

Anyone using these data for a specific decision should look critically at the values for their area of interest, compare against on-the-ground knowledge and independent observations when possible, and consider the documentation's notes on methods and caveats. The CONUS layers are masked for open water, areas outside the conterminous U.S., and agricultural land (NLCD cultivated crops and pasture/hay); developed/urban and barren pixels are retained and should be screened by the user where they are not of interest (see documentation §4.4). The decision to release the data publicly reflects our judgment that it uses the best information of its kind available, that we believe it has reached the point where it can aid planning and research, and that use-with-feedback is the path to improvement. Reports of what looks wrong - pixel-level errors, structural issues, or systematic problems - are the most valuable contribution a user can make.

---

## License

Released under [Creative Commons Attribution 4.0 (CC BY)](https://creativecommons.org/licenses/by/4.0/). Free to use, share, and adapt with attribution.

**University of California disclaimer.** These data are a University of California product, provided "as is" with no warranty; the user assumes all risk of use. Use of the data implies consent to the full University of California disclaimer, reproduced in §6 of the [documentation PDF](https://data.source.coop/wildland-almanac/conus/WildlandAlmanac_CONUS_Documentation.pdf).

---

## Citation

> Goulden, M.L. (2026). *The Wildland Almanac - CONUS* (Version v2026.1). Source Cooperative. DOI: [pending - EZID]. Released under CC BY.

When citing analyses based on these data, cite the **specific version** (above). Different versions reprocess the full snapshot set and may differ in detail; the version DOI is what makes your analysis reproducible.

---

## Feedback and contact

**Found something wrong, or have a use case to share?** Please file an issue on GitHub: [github.com/wildland-almanac/wildland-almanac-site/issues](https://github.com/wildland-almanac/wildland-almanac-site/issues). Reports of errors and notes on real-world use are tracked there and folded into future releases.

**Contact:** [mgoulden@uci.edu](mailto:mgoulden@uci.edu) · Department of Earth System Science, University of California, Irvine. The data are offered with no promise of technical support, but feedback on errors and use cases is welcome and shapes future releases.

---

*The Wildland Almanac is an outgrowth of the Center for Ecosystem Climate Solutions (CECS), which was supported by California's Strategic Growth Council. CECS was a multi-year California research program; the CONUS release extends the same methodology nationally. Earlier work and publications referencing CECS share the same project lineage.*

extent.temporal: None (probe sample) widened to ['1990-01-01T00:00:00Z', '2024-01-01T00:00:00Z'] using README-stated coverage ['1990', '2024'].


A counterfactual raster dataset showing the ecosystem conditions expected if any of 24 generic forest and fuel treatments were applied to the observed 2024 western conterminous U.S. landscape at 30 m resolution. Six modeled layers are provided per treatment: potential evapotranspiration, gross primary production, aboveground live biomass, drought-dieoff risk, flame length, and rate of spread. The dataset is intended for comparing treatment effects and screening where impacts are large, not as site-specific predictions.

cloud-optimized-geotiff ecology biodiversity conservation hydrology climate environmental-monitoring wildfire forest-management

Dataset files (144 x .tif)

Source Cooperative product page

## README.md
# The Wildland Almanac - Treatment Scenarios

A counterfactual ("what-if") companion to the Wildland Almanac: for each of 24 generic forest and fuel treatments, the ecosystem condition that would be expected **if** that treatment were applied to the observed 2024 landscape, across the western conterminous United States at 30 m. Four cover-driven properties - potential evapotranspiration, productivity, biomass, and drought-dieoff risk - plus two treated fire-behavior layers: flame length and rate of spread. Every layer is modeled, not observed; the `Undisturbed` treatment reproduces the observed Wildland Almanac exactly.

**Current release: `v2026.1` (June 2026).** Western CONUS, a single 2024 baseline water year, 24 treatments, EPSG:5070, 30 m. Released under CC BY 4.0.

> **These are scenarios, not observations.** This dataset acts *on* the observed Wildland Almanac - the measured baseline at [`source.coop/wildland-almanac/conus`](https://source.coop/wildland-almanac/conus) (and the annual California release at [`source.coop/wildland-almanac/california`](https://source.coop/wildland-almanac/california)) - applying a fixed library of generic treatments to the 2024 condition. The treatments are uniform intensities, not site-specific prescriptions, and no treatment is asserted to have occurred anywhere. Use the layers to compare treatments and screen where effects are large, not as predictions for a specific project. See the documentation PDF for the full caveats.

---

## Quick navigation

- **Current release:** [`v2026.1/`](https://source.coop/wildland-almanac/treatment-scenarios/v2026.1/) - the data files (144 COGs across six layer directories)
- **Documentation PDF:** [`WildlandAlmanac_TreatmentScenarios_Documentation.pdf`](https://data.source.coop/wildland-almanac/treatment-scenarios/WildlandAlmanac_TreatmentScenarios_Documentation.pdf) - the binding reference for units, properties, treatments, methods, projection, the counterfactual caveats, and the UC disclaimer
- **Treatment library:** [`ManagementDescriptions_v2026_1.csv`](https://data.source.coop/wildland-almanac/treatment-scenarios/ManagementDescriptions_v2026_1.csv) - the 24 treatments and the factors that define them (the file key in each filename comes from this table)
- **STAC catalog:** [`v2026.1/catalog.json`](https://data.source.coop/wildland-almanac/treatment-scenarios/v2026.1/catalog.json) - machine-readable inventory
- **Observed Almanac:** [`../conus/`](https://source.coop/wildland-almanac/conus) - the measured baseline these scenarios are built on
- **Website:** [`wildlandalmanac.org`](https://wildlandalmanac.org/)

---

## What's in this release

Six layer directories, western conterminous U.S., a single 2024 baseline water year, 30 m resolution, EPSG:5070 (NAD83 / CONUS Albers). All files are Cloud-Optimized GeoTIFFs (COGs), single-band int16, with missing/masked pixels set to -9999. Each treated layer carries one file per treatment; the file key replaces the water year that the observed Almanac uses.

**Cover-driven properties (4)**

- `WaterFlux_AETmax/` - maximum (vegetation-driven, no drought limitation) evapotranspiration under the treated canopy
- `Carbon_GPP/` - gross primary production under the treated canopy
- `Carbon_AGB/` - aboveground live biomass remaining after the treatment
- `Vulner_TreeDieoff_SPI-2/` - relative index of tree-canopy vulnerability to severe drought, after the treatment (a western index; see documentation)

**Fire behavior (2)**

- `Fire_FL/` - treated flame length
- `Fire_ROS/` - treated rate of spread

The fire layers are computed from per-treatment fire-behavior landscapes built from the treated vegetation, run at fixed conditions, and are best read as a **relative hazard** for comparing treatments rather than an absolute prediction. The treated landscapes themselves are an internal modeling step and are **not distributed**.

That is **144 COGs**: 96 cover-driven (4 properties x 24 treatments) and 48 fire-behavior (2 layers x 24 treatments).

**The 24 treatments** (the file key in each filename). Definitions and the factors that set each one are in the documentation (§3.1) and the `ManagementDescriptions_v2026_1.csv` table in this directory:

- **Baseline:** `Undisturbed`
- **Timber harvest:** `TimberHighPrivate`, `TimberMediumPrivate`, `TimberLowPrivate`, `TimberMediumPublic`, `TimberLowPublic`
- **Prescribed fire:** `PrescribedFirePrivate`, `PrescribedFirePublic`
- **Mechanical fuel reduction:** `MechanicalFuelPrivate`, `MechanicalFuelPublic`
- **Stewardship:** `StewardshipPrivate`, `StewardshipPublic`
- **Helicopter logging:** `HeliLoggingLow`, `HeliLoggingMedium`
- **Salvage:** `SalvageLoggingHighBeetle`, `SalvageLoggingMediumBeetle`, `SalvageLoggingHighFire`
- **Pile burn:** `PileBurn`
- **Mastication / thinning:** `ShrubMast`, `ThinBelowLowMast`, `ThinBelowHighMast`
- **Combined:** `MultiTreat_MediumThinMastRXburn`, `MultiTreat_HighThinMastRXburn`, `MultiTreat_HighThinMast`

Filenames follow `WildlandAlmanac_CONUSwest_{Property}_2024_{Treatment}.tif`, where `{Property}` is one of the six layer directories above. See the documentation PDF for full per-layer specifications, units, scaling conventions, and caveats.

---

## Reading these layers

A few things to keep in front of you; the documentation has the rest.

- **The `Undisturbed` treatment reproduces the observed Almanac.** Every treated surface is anchored to the observed 2024 layer, so the no-treatment case matches the published Wildland Almanac (`WaterFlux_AETmax`, `Carbon_GPP`, `Carbon_AGB`) pixel-for-pixel, and all treatments share that observed geography. Differences between two treatments are directly comparable; differences from `Undisturbed` are the treatment effect.
- **`WaterFlux_AETmax` is potential ET, not water yield.** It is the canopy's potential evapotranspiration, not realized water use or streamflow. Its change under a treatment is the canopy-driven change in water *demand* - an upper bound on the water a treatment could free, not a runoff forecast. In water-limited country the water actually freed is less than that change.
- **`Fire_FL` and `Fire_ROS` are a relative hazard.** They are computed at fixed run conditions to make treatments comparable, not calibrated to any particular fire-weather scenario. Read them as "how much does this treatment move modeled fire behavior relative to no treatment," not as an operational flame-length or spread-rate forecast.
- **First-order by design.** The treatment response is a transparent, prebaked, first-order estimate - a common reference for comparing treatments and a benchmark for more detailed models, not a calibrated prediction.

---

## Releases and versioning

The Almanac uses an **archival versioning model**:

- Each release lives in its own version directory (`v2026.1/`, `v2027.1/`, …). Files at a published version URL are stable: once a version has been used in published work, its contents are preserved.
- Future releases are published as **siblings**, not replacements. Earlier versions remain accessible at their original URLs (and under their original DOIs, where assigned).
- Minor fixes may be made in place without a version bump. More significant corrections that change a quantitative result downstream will earn a new minor version (e.g. `v2026.2/`) with its own DOI, and the prior version will be retained.

**Cite the version you used.** Each release receives its own DOI. When citing the scenarios in a paper, plan, or other document where future readers may need to verify the exact values you relied on, cite the **specific version** (and DOI), not the dataset as a whole.

---

## How to use the data

The data are Cloud-Optimized GeoTIFFs served over both S3 and HTTPS. Three access patterns, in increasing order of effort:

**Stream it** - most common, no download. GIS tools (ArcGIS Pro, QGIS, rasterio, terra) read COGs directly from cloud storage, transferring only the bytes needed for your current window.

- ArcGIS Pro: `s3://us-west-2.opendata.source.coop/wildland-almanac/treatment-scenarios/v2026.1/{Property}/WildlandAlmanac_CONUSwest_{Property}_2024_{Treatment}.tif`
- QGIS / GDAL / rasterio / terra: `/vsicurl/https://data.source.coop/wildland-almanac/treatment-scenarios/v2026.1/{Property}/WildlandAlmanac_CONUSwest_{Property}_2024_{Treatment}.tif`

**Download one file** - `curl`, `wget`, browser, or PowerShell `Invoke-WebRequest` against the HTTPS URL above. To clip a lat/long box without downloading the whole file, use `gdalwarp -te ... -te_srs EPSG:4326 /vsicurl/<url> clip.tif` - GDAL fetches only the tiles overlapping your box. (The western grid is large, so clipping to an area of interest rather than downloading whole layers is usually the right approach.)

**Bulk download** - AWS CLI with `--no-sign-request` (no account needed):
```
aws s3 sync s3://us-west-2.opendata.source.coop/wildland-almanac/treatment-scenarios/v2026.1/{Property}/ ./{Property}/ --no-sign-request
```

---

## About the quality of these data

These scenarios are built on the observed Wildland Almanac and inherit its limits; the treatment response adds a further layer of simplification on top. They are a first-order screen, not a site-specific prediction. Anyone using them for a specific decision should look critically at the values for their area of interest, compare against on-the-ground knowledge, and read the documentation's notes on methods and caveats - in particular the counterfactual framing, the interpretation of `WaterFlux_AETmax`, and the relative-hazard reading of the fire layers. The decision to release the data publicly reflects our judgment that it can aid planning and research and that use-with-feedback is the path to improvement. Reports of what looks wrong are the most valuable contribution a user can make.

---

## License

Released under [Creative Commons Attribution 4.0 (CC BY)](https://creativecommons.org/licenses/by/4.0/). Free to use, share, and adapt with attribution.

**University of California disclaimer.** These data are a University of California product, provided "as is" with no warranty; the user assumes all risk of use. Use of the data implies consent to the full University of California disclaimer, reproduced in the [documentation PDF](https://data.source.coop/wildland-almanac/treatment-scenarios/WildlandAlmanac_TreatmentScenarios_Documentation.pdf).

---

## Citation

> Goulden, M.L. (2026). *The Wildland Almanac - Treatment Scenarios* (Version v2026.1). Source Cooperative. DOI: [pending - EZID]. Released under CC BY.

When citing analyses based on these data, cite the **specific version** (above). Different versions may differ in detail; the version DOI is what makes your analysis reproducible.

---

## Feedback and contact

**Found something wrong, or have a use case to share?** Please file an issue on GitHub: [github.com/wildland-almanac/wildland-almanac-site/issues](https://github.com/wildland-almanac/wildland-almanac-site/issues). Reports of errors and notes on real-world use are tracked there and folded into future releases.

**Contact:** [mgoulden@uci.edu](mailto:mgoulden@uci.edu) · Department of Earth System Science, University of California, Irvine. The data are offered with no promise of technical support, but feedback on errors and use cases is welcome and shapes future releases.

---

*The Wildland Almanac is an outgrowth of the Center for Ecosystem Climate Solutions (CECS), which was supported by California's Strategic Growth Council. The Treatment Scenarios extend the Almanac with a counterfactual layer; earlier work and publications referencing CECS share the same project lineage.*

providers: role [processor] for 'Blue Forest (blueforest.org)' is an AI guess — Listed as design and build collaborator; assumed processor role since no explicit role is stated.. Verify before flipping to _state: reviewed.

extent.temporal: None (probe sample) widened to ['2024-01-01T00:00:00Z', '2024-01-01T00:00:00Z'] using README-stated coverage ['2024', '2024'].


Global Rangelands Raster

A global raster map (version 1) delineating the extent of rangelands worldwide, compiled and created by Dr. Mika Tosca at Woodwell Climate Research Center. The dataset consists of 12 GeoTIFF files covering the full global extent at approximately 486 MB total.

land-use land-cover rangelands grasslands global-coverage

Dataset files (12 x .tif)

Source Cooperative product page

license: AI returned 'unknown' (README does not state a license). Verify with publisher before flipping to _state: reviewed.

providers: role [producer] for 'Woodwell Climate Research Center' is an AI guess — Assumed producer based on API description attributing creation to Dr. Mika Tosca at this institution; licensor role not stated. Verify before flipping to _state: reviewed.


Secondary Forest Brazil — YbYrá-BR v8.1 (from MapBiomas Collection 10.1, 1986–2024)

Annual maps of secondary forest dynamics in Brazil (1986–2024) derived from MapBiomas Collection 10.1 land-use and land-cover data processed in Google Earth Engine at 30 m resolution. Four products are provided: age (years of continuous secondary forest since last disturbance), extent (binary presence/absence), increment (new forest pixels per year), and loss (cleared pixels per year), covering Forest Formation and Flooded Forest classes.

remote-sensing land-use land-cover secondary-forest forest-dynamics deforestation conservation

Dataset files (155 x .tif)

Source Cooperative product page

## README.md
# Brazilian Secondary Forest Dynamics (1986–2024)

Annual maps of increment, extent, age, and loss of secondary forests in Brazil, derived from MapBiomas Collection 10.1 land-use and land-cover data using Google Earth Engine. The **Forest Formation** and **Flooded Forest** classes are included.

## Products

| Product | Description | Period |
|:---|:---|:---:|
| **Age** | Years of continuous secondary forest since last disturbance | 1986–2024 |
| **Extent** | Binary presence/absence of secondary forest | 1986–2024 |
| **Increment** | New secondary forest pixels per year | 1986–2024 |
| **Loss** | Secondary forest pixels cleared per year | 1987–2024 |

## Technical Specifications

| Parameter | Value |
|:---|:---:|
| Spatial resolution | 30 m |
| CRS | EPSG:4326 (WGS84) |
| Format | Cloud-Optimized GeoTIFF (COG) |
| Input data | MapBiomas Collection 10.1 |
| Forest classes | Forest Formation, Flooded Forest |
| Version | v8.1 |

## GEE Assets

Multi-band assets (one band per year, named `classification_YYYY`) are publicly available:

```javascript
var age       = ee.Image('users/ybyrabr/public/secondary_forest_age_collection10_1_v8_1');
var extent    = ee.Image('users/ybyrabr/public/secondary_forest_extent_collection10_1_v8_1');
var increment = ee.Image('users/ybyrabr/public/secondary_forest_increment_collection10_1_v8_1');
var loss      = ee.Image('users/ybyrabr/public/secondary_forest_loss_collection10_1_v8_1');
```

## Code & STAC

- **GitHub:** https://github.com/celsohlsj/gee_brazil_sv
- **STAC Catalog:** https://celsohlsj.github.io/ybyrastac/

## Citation

```bibtex
@article{silvajunior2020,
  author  = {Silva-Junior, Celso H. L. and Heinrich, Viola H. A. and Freire, Ana T. G.
             and Broggio, Igor S. and Rosan, Thais M. and Doblas, Juan and Anderson,
             Liana O. and Rousseau, Guillaume X. and Shimabukuro, Yosio E. and
             Silva, Carlos A. and House, Joanna I. and Aragão, Luiz E. O. C.},
  title   = {Benchmark maps of 33 years of secondary forest age for {Brazil}},
  journal = {Scientific Data},
  year    = {2020},
  doi     = {10.1038/s41597-020-00600-4}
}
```

## Funding & License

Produced by the **YBYRÁ-BR Project**, funded by CNPq (Process 401741/2023-0).
Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

providers: role [producer] for 'MapBiomas' is an AI guess — Produced the Collection 10.1 LULC input data; role inferred from README attribution as upstream data source, not explicitly stated.. Verify before flipping to _state: reviewed.


Cloud-Native Geocoding

This project showcases the integration of DuckDB with Lowcoder to perform efficient and serverless geocoding within a cloud-native geospatial formats like geoparquet. (the current data is for Egypt)

geoparquet geocoding cloud-native duckdb lowcoder


European Ground Motion Cloud Native Data

Ground deformation measurements for Europe derived from synthetic aperture radar data, provided by the Copernicus European Ground Motion Service. Data has been converted to cloud-native Parquet Geo and PMTiles formats for scalable analysis and web mapping.

remote-sensing ground-motion deformation-monitoring synthetic-aperture-radar

Dataset files (48 x .parquet)

Source Cooperative product page

## README.md
# European Ground Motion Service Data Conversion

This repository contains data converted from the original Copernicus European Ground Motion Service datasets. The source data was provided as zipped CSV files and has been transformed into cloud native geospatial formats.

## Data Sources

- **Original Data**: [Copernicus European Ground Motion Service](https://land.copernicus.eu/en/products/european-ground-motion-service)
- **Formats Provided**:
  - **Parquet Geo** – Optimized for scalable geospatial analytics.
  - **PMTiles** – Designed for efficient, cloud-native tile serving.

## Conversion Process

1. **Extraction**: Unzipping and parsing CSV files.
2. **Transformation**: Converting CSV data into geospatial datasets.
3. **Output**: Generating Parquet Geo files for analysis and PMTiles(with only pid and geom and filename) for mapping.

## Usage

- **Parquet Geo**: Use with cloud-based analytics platforms for fast querying.
Example using DuckDB:
```sql
SELECT * FROM read_parquet('https://data.source.coop/youssef-harby/egms-copernicus/L2a/parquet/CY/EGMS_L2a_087_0205_IW2_VV_2019_2023_1.parquet') LIMIT 10;
```
- **PMTiles**: Serve directly to web maps or GIS applications for interactive visualization like [here](https://pmtiles.io/?url=https%3A%2F%2Fdata.source.coop%2Fyoussef-harby%2Fegms-copernicus%2FL2a%2Fpmtiles%2Fcy.pmtiles#map=9.36/34.9833/33.293).

## License & Acknowledgements

Please refer to the original data license provided by Copernicus for usage terms. This conversion project builds on the high-quality datasets provided by the European Ground Motion Service.


EXIOBASE 3

EXIOBASE 3 is a global, environmentally extended multi-regional input-output (EE-MRIO) database covering 44 countries and five rest-of-world regions across 200 sectors from 1995 to 2022 (with nowcasts through 2022). It quantifies economic transactions between regions and sectors alongside environmental pressures including greenhouse gas emissions, energy use, land, water, and material flows. Data are stored in partitioned Apache Parquet files organized by year, format (ixi/pxp), and matrix type.

input-output-tables environmental-footprint emissions trade economic-sectors multi-regional

f_impacts_units_1995_ixi.parquet

f_impacts_units_1995_pxp.parquet

f_impacts_units_1996_ixi.parquet

f_impacts_units_1996_pxp.parquet

f_impacts_units_1997_ixi.parquet

data.parquet

[admin_code] Country/region code for the supply-side region in Z and Y matrices (e.g. 'CN', 'US')

[admin_code] Country/region code for the demand-side region in Z and Y matrices

Source Cooperative product page

## README.md
# EXIOBASE 3: Global Input-Output Database

EXIOBASE 3 is a global, multi-regional input-output database, offering a detailed view of the world economy and its environmental impacts. It covers 49 regions and 200 sectors from 1995 to 2022. This dataset is ideal for analyzing trade, economic structures, and environmental footprints.

## Data Structure

The data is stored in Parquet format and partitioned by `year`, `format` (ixi/pxp), and `matrix` type (Z, Y, F_satellite, F_impacts) to allow for efficient querying.

```
s3://us-west-2.opendata.source.coop/youssef-harby/exiobase-3/
└── 4588235/
    ├── metadata/
    │   ├── f_impacts_units_1995_ixi.parquet
    │   ├── ... (112 files)
    └── parquet/
        ├── year={1995,1996,...,2022}/
        │   ├── format={ixi,pxp}/
        │   │   ├── matrix={F_impacts,F_satellite,Y,Z}/
        │   │   │   └── data.parquet
```

## Quickstart with DuckDB

The following examples demonstrate how to effectively query the EXIOBASE 3 dataset using DuckDB.

### A Note on Performance

For complex queries, such as time-series analysis or combining large matrices, direct queries may time out. A more robust approach is to break down the query into smaller parts and combine the results in your client application (e.g., Python with Pandas, R, etc.). The examples below follow this best practice.

### Top 10 Exporters to the US (2022)

To calculate the total exports to the United States, you should query the inter-industry (`Z`) and final demand (`Y`) matrices separately and then sum the results.

**Step 1: Get Exports from Inter-Industry Transactions**
```sql
SELECT origin_region, SUM(value) AS total_z
FROM 's3://us-west-2.opendata.source.coop/youssef-harby/exiobase-3/4588235/parquet/year=2022/format=ixi/matrix=Z/data.parquet'
WHERE dest_region = 'US' AND origin_region <> 'US' AND origin_region NOT LIKE 'W%'
GROUP BY origin_region
ORDER BY total_z DESC
LIMIT 20;
```

**Step 2: Get Exports from Final Demand**
```sql
SELECT origin_region, SUM(value) AS total_y
FROM 's3://us-west-2.opendata.source.coop/youssef-harby/exiobase-3/4588235/parquet/year=2022/format=ixi/matrix=Y/data.parquet'
WHERE dest_region = 'US' AND origin_region <> 'US' AND origin_region NOT LIKE 'W%'
GROUP BY origin_region
ORDER BY total_y DESC
LIMIT 20;
```
*Combine the `total_z` and `total_y` results for each `origin_region` in your client to get the final ranking.*

### China-US Trade (1995-2022)

Similarly, for time-series analysis, query each matrix across all years and then join the results.

**Step 1: Get Time-Series from Inter-Industry Transactions**
```sql
SELECT year, SUM(value) AS z_value
FROM read_parquet('s3://us-west-2.opendata.source.coop/youssef-harby/exiobase-3/4588235/parquet/year=*/format=ixi/matrix=Z/data.parquet', hive_partitioning=1)
WHERE origin_region = 'CN' AND dest_region = 'US' AND year BETWEEN 1995 AND 2022
GROUP BY year
ORDER BY year;
```

**Step 2: Get Time-Series from Final Demand**
```sql
SELECT year, SUM(value) AS y_value
FROM read_parquet('s3://us-west-2.opendata.source.coop/youssef-harby/exiobase-3/4588235/parquet/year=*/format=ixi/matrix=Y/data.parquet', hive_partitioning=1)
WHERE origin_region = 'CN' AND dest_region = 'US' AND year BETWEEN 1995 AND 2022
GROUP BY year
ORDER BY year;
```
*Join the results on the `year` column and sum `z_value` and `y_value` to get the total annual exports.*

### Top 20 CO2 Emitters (2022)

This query identifies the top 20 regions by total CO2 emissions, summing all CO2-related stressors from the `F_satellite` matrix.

```sql
SELECT
    region,
    SUM(value) AS total_co2,
    FIRST(unit) AS unit
FROM 's3://us-west-2.opendata.source.coop/youssef-harby/exiobase-3/4588235/parquet/year=2022/format=ixi/matrix=F_satellite/data.parquet'
WHERE stressor LIKE '%CO2%'
GROUP BY region
ORDER BY total_co2 DESC
LIMIT 20;
```

providers: role [producer, licensor] for 'EXIOBASE Consortium' is an AI guess — Original authors of the EXIOBASE database; licensor assumed from consortium ownership of the underlying data. Verify before flipping to _state: reviewed.

providers: role [processor] for 'Youssef Harby' is an AI guess — Converted EXIOBASE source data to Apache Parquet and published to Source Cooperative; inferred from S3 account path and API description. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [data.origin_region (admin_code), data.dest_region (admin_code), data.region (admin_code)]. Add bbox once a placename → bbox gazetteer is available.


Overture Maps STAC

This is a demo of the [Overture Maps Data](https://overturemaps.org/download/) STAC based on https://beta.source.coop/cholmes/overture

overture maps
geoparquet
stac
catalog
metadata


Weather Station Real-Time in Parquet

High-resolution environmental monitoring data collected by a Raspberry Pi with Enviro+ sensor and streamed directly to cloud storage in Parquet format. Each record captures timestamped atmospheric measurements including temperature, pressure, humidity, air quality gases (oxidised, reducing, NH3), light intensity, and particulate matter (PM1, PM2.5, PM10) across one or more named stations. The dataset is organized by station and date, enabling real-time and historical analysis of local atmospheric conditions.

weather air-quality particulate-matter environmental-monitoring real-time climate-change

2025_03_19.parquet

2025_03_20.parquet

2025_03_21.parquet

2025_03_22.parquet

2025_03_23.parquet

data_2355.parquet

latitude

[latlon] Station latitude in decimal degrees

longitude

[latlon] Station longitude in decimal degrees

data_0214.parquet

data_0219.parquet

data_0220.parquet

Source Cooperative product page

providers: role [producer, licensor] for 'OpenSensor Space (opensensor.space)' is an AI guess — Inferred as producer and licensor from the project URL in the API description; no explicit statement in README.. Verify before flipping to _state: reviewed.

extent.spatial.bbox: omitted; spatial reference is categorical via [data_2355.latitude (latlon), data_2355.longitude (latlon)]. Add bbox once a placename → bbox gazetteer is available.


Tanzania Crop Survey

This dataset was collected by YouthMappers students in three regions of Tanzania: Arusha, Dodoma and Mwanza. It includes estimated field centroid coordinates, primary crop, secondary crop and estimated field size. The data was collected in a 2023 pilot study supported by USDA’s Foreign Agricultural Service and the USAID/GeoCenter to see if university students could conduct field visits and collect ground-truth data for training machine or deep learning models applied to remotely sensed imagery.

youthmappers tanzania ground-truth crops


Example GeoZarr Stores

GeoZarr stores used to test interoperability of GeoZarr data.


LandCoverNet - Zarr

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2, and Landsat-8 missions in 2018.
This dataset is a Zarr-ified version of LandCoverNet, utilising the V3 Zarr standard.

land cover
segmentation
zarr
sentinel-1
sentinel-2
landsat 8


NYC Citi Bike

New York City Citibike bike-sharing trip data converted to geoparquet format, partitioned by year and month. The dataset includes station locations, pickup times, ride durations, and ridership patterns. Original data inconsistencies have been standardized into two distinct schemas.

bike-sharing urban-mobility trip-patterns station-locations

Dataset files (156 x .parquet)

Source Cooperative product page

## README.md
**Description**:

This dataset is a copy of the New York City Citibike data, which can be found here: https://citibikenyc.com/system-data, made available in the cloud-native geoparquet format partitioned by year and month.

The original data scehma exhibits inconsistencies across source files,particularly in data type definitions.

This dataset has been standardized and divided into twodistinct schemas ('new' and 'old') to best reflect the official Citibike data schema variations.

Specific data transformation steps are documented in my Medium blog post: https://medium.com/@calvinluozhengpei/  from-wall-street-to-central-park-decoding-nycs-citi-bike-patterns-part-1-3155569278cf

Feel free to read and provide feedback on how to make the process more efficient!

**Example Usage**:

Load data with Duckdb in Python:

```
con = duckdb.connect(database = ":memory:")
con.sql("""
SELECT * FROM read_parquet('s3://us-west-2.opendata.source.coop/zluo43/citibike/old_schema_combined_with_geom.parquet/**/*.parquet')
""")

```

Basic EDA in Python:

```
df=con.sql("""
SELECT * FROM read_parquet('s3://us-west-2.opendata.source.coop/zluo43/citibike/old_schema_combined_with_geom.parquet/**/*.parquet')
""").df()
df.info()

```