vertify/forest-forward-biomass-dataset-goa

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Multi-Source Satellite Imagery for Biomass Estimation: Dataset Card

Dataset Summary

This dataset combines multi-temporal satellite imagery with ground-truth above-ground biomass (AGB) measurements across several forest ecosystems in India and Thailand. It integrates optical, radar, and topographic data to enable deep learning approaches for biomass estimation and forest carbon stock assessment.

Dataset Details

Dataset Description

This dataset consists of co-registered satellite imagery and corresponding biomass measurements. The data has been processed into 224×224 pixel patches with normalized spectral values and corresponding log-transformed biomass targets.

• Satellite Data: 59-band imagery combining Sentinel-1, Sentinel-2, Landsat, PALSAR, and topographic data
• Ground Truth: Field-measured above-ground biomass values (Mg/ha)
• Temporal Coverage: Multi-seasonal imagery from 2020
• Spatial Resolution: Mixed (10-30m depending on source sensor)
• Data Format: Processed NumPy arrays with normalized values
• Dataset Size: ~5,100 valid image patches

Data Sources

Primary Input Features

1. Sentinel-2 Multispectral Imagery (30 bands total)

   • 10 bands (B2-B8, B8A, B11, B12) across 3 seasons
   • Spatial resolution: 10-20m
   • Captures spectral signature of vegetation

2. Landsat Surface Reflectance (18 bands total)

   • 6 spectral bands across 3 seasons
   • Spatial resolution: 30m
   • Complementary to Sentinel-2 with slightly different wavelengths

3. Sentinel-1 SAR Backscatter (6 bands total)

   • VV and VH polarization bands across 3 seasons
   • Spatial resolution: 10m
   • Penetrates cloud cover, sensitive to structure

4. PALSAR L-band SAR (2 bands)

   • HH and HV polarization
   • Spatial resolution: 25m
   • Deep canopy penetration, sensitive to biomass

5. Topographic and Derived Data (3 bands)

   • Digital Elevation Model (DEM)
   • Slope
   • Canopy Height Model (CHM)

Ground Truth Data

Above-ground biomass measurements derived from:

• Field inventory plots
• Allometric equations specific to forest types
• Quality-controlled and validated biomass maps

Geographic Coverage

The dataset covers four distinct forest ecosystems:

1. Yellapur, Karnataka, India

   • Western Ghats humid subtropical forests
   • High biodiversity area
   • Biomass range: 50-350 Mg/ha

2. Betul, Madhya Pradesh, India

   • Central Indian dry deciduous forests
   • Seasonal climate with distinct dry periods
   • Biomass range: 30-250 Mg/ha

3. Achanakmar, Chhattisgarh, India

   • Mixed deciduous and evergreen forests
   • Part of a tiger reserve with protected status
   • Biomass range: 40-300 Mg/ha

4. Khao Yai National Park, Thailand

   • Tropical rainforest ecosystem
   • UNESCO World Heritage site
   • Biomass range: 100-400 Mg/ha

Dataset Creation

Data Preprocessing

1. Satellite Data Preparation:

   • Atmospheric correction for optical imagery
   • Radiometric calibration for radar data
   • Co-registration of all data sources
   • Masking of clouds, cloud shadows, and invalid pixels

2. Chip Extraction:

   • Original patch size: 32×32 pixels
   • Extraction stride: 16 pixels (50% overlap)
   • Filtering for patches with ≥70% valid data
   • Resizing to 224×224 pixels for model training

3. Feature Normalization:

   • Robust scaling of each band: (x - median) / IQR
   • Zero-centering of input features
   • Handling of outliers with robust approach
   • Zero-filling of invalid pixels

4. Biomass Target Processing:

   • Log transformation: y = log(1+x)
   • Filtering of non-positive values
   • Calculation of mean biomass per patch from valid pixels

Data Validation

• Extensive quality checking of inputs and outputs
• Visual inspection of randomly sampled patches
• Statistical analysis of band distributions
• Verification of spatial alignment between sources

Dataset Structure

Data Format

The dataset is stored as a collection of NumPy arrays and accompanying metadata:

• X_{timestamp}.npy: Feature array of shape (N, 59, 224, 224) where N is number of samples
• y_{timestamp}.npy: Target array of shape (N,) containing log-transformed biomass values
• masks_{timestamp}.npy: Boolean validity masks of shape (N, 32, 32)
• coordinates_{timestamp}.pkl: List of (x, y) pixel coordinates for each patch
• metadata_{timestamp}.json: Dataset metadata and processing information
• normalization_params.json: Parameters used for feature normalization

Data Splits

The dataset uses a geographic quadrant-based split:

• Training Set (~65%): Patches from three geographic quadrants
• Validation Set (~17.5%): Half of the patches from the fourth quadrant
• Test Set (~17.5%): Remaining half of patches from the fourth quadrant

This approach ensures spatial independence between training and evaluation data.

Dataset Usage

Intended Use Cases

This dataset is designed for:

• Training and evaluation of deep learning models for biomass estimation
• Research on multi-source remote sensing fusion
• Development of carbon stock assessment methods
• Benchmark comparisons of different biomass estimation approaches

Example Code Snippet

# Load the dataset
import numpy as np
import pickle

# Load features and targets
X = np.load("processed_data/X_20250428_134150.npy")
y = np.load("processed_data/y_20250428_134150.npy")
masks = np.load("processed_data/masks_20250428_134150.npy")

# Load coordinates
with open("processed_data/coordinates_20250428_134150.pkl", "rb") as f:
    coordinates = pickle.load(f)

# Basic data inspection
print(f"Feature shape: {X.shape}")  # (N, 59, 224, 224)
print(f"Target shape: {y.shape}")   # (N,)
print(f"Number of samples: {len(y)}")

# Convert back from log scale if needed
biomass_original = np.expm1(y)
print(f"Biomass range: {biomass_original.min():.1f} - {biomass_original.max():.1f} Mg/ha")

Important Notes on Biomass Values

• The dataset contains log-transformed biomass values
• This transformation was applied using: y_transformed = np.log1p(biomass)
• When making predictions, models will output values on this log scale
• To convert back to original biomass units (Mg/ha): biomass_original = np.expm1(y_predicted)
• Evaluation metrics should be calculated on both scales for comprehensive assessment

Dataset Limitations

Known Issues and Limitations

1. Temporal Consistency:

   • Different acquisition dates within seasons may introduce phenological variations
   • Some areas may have missing data due to cloud cover in optical imagery

2. Spatial Limitations:

   • Limited to four specific forest ecosystems
   • May not generalize well to other forest types or regions
   • Edge effects at the boundaries of forest patches

3. Technical Limitations:

   • Saturation of optical and radar signals in very high biomass areas
   • Mixed pixel effects at lower resolutions
   • Co-registration errors between different sensor sources

4. Ground Truth Uncertainties:

   • Allometric equation uncertainties propagate to biomass values
   • Spatial interpolation introduces smoothing effects
   • Plot-level measurements may not capture fine-scale variations

Bias and Fairness Considerations

• The dataset primarily focuses on tropical and subtropical forests in South/Southeast Asia
• Underrepresented forest types include boreal, temperate, and montane forests
• May perform differently across different forest management regimes (protected vs. managed)

Dataset Maintenance and Updates

Version Information

• Current Version: 1.0.0
• Release Date: May 2025
• Maintenance Status: Active

Future Plans

• Expansion to include additional geographic regions and forest types
• Integration of higher resolution imagery sources
• Addition of temporal data to capture forest dynamics
• Inclusion of uncertainty estimates for ground truth data

Ethical Considerations

Environmental Impact

The dataset is intended to support:

• Improved forest carbon stock assessment
• Better understanding of forest ecosystem services
• Supporting conservation and sustainable management
• Climate change mitigation through improved carbon accounting

Privacy and Licenses

• Satellite data sourced according to respective usage policies:
  • Sentinel data: Copernicus Open Access License
  • Landsat data: USGS Public Domain
  • PALSAR data: JAXA research use provisions
• Ground truth data collected with appropriate permissions from forest departments

Citations and Acknowledgements

Data Sources Acknowledgement

This dataset incorporates data from multiple sources:

• Sentinel-1 and Sentinel-2 data provided by ESA/Copernicus
• Landsat Surface Reflectance courtesy of USGS
• PALSAR data courtesy of JAXA
• Field inventory data collected in collaboration with local forest departments

Recommended Citation

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

@dataset{biomass_dataset_2025,
  author    = {Your Name},
  title     = {Multi-Source Satellite Imagery for Biomass Estimation},
  year      = {2025},
  publisher = {Your Organization},
  url       = {https://example.com/dataset}
}

Contact Information

For questions, issues, or collaboration related to this dataset, please contact:

• Email: your.email@example.com
• GitHub Issues: https://github.com/username/biomass-ensemble/issues