Geoskill: Land Use Carbon Accounting

Land Use Carbon Accounting

Computes carbon stock changes, emissions/removals, and uncertainty from multi-temporal land cover data using IPCC Tier 1/2 carbon factors.

Trigger

Use when the user wants to:

• Compute carbon stock changes from multi-temporal land cover data
• Estimate CO2e emissions/removals from land use transitions
• Generate carbon accounting reports with uncertainty analysis
• Analyze deforestation or afforestation carbon impacts
• Produce transition matrices and carbon change rasters

CLI Usage

# Synthetic demo mode (no input files needed)
python scripts/land_use_carbon_accounting.py --output-dir ./luca-output

# With input GeoTIFF land cover rasters
python scripts/land_use_carbon_accounting.py \
  --before-landcover ./data/before.tif \
  --after-landcover ./data/after.tif \
  --eco-zone subtropical \
  --pools BAG BBG SOC \
  --output-dir ./luca-output

# With custom carbon factors and Monte Carlo settings
python scripts/land_use_carbon_accounting.py \
  --before-landcover ./data/before.tif \
  --after-landcover ./data/after.tif \
  --eco-zone temperate \
  --pools BAG BBG DW LT SOC \
  --mc-iterations 5000 \
  --mc-seed 42 \
  --confidence 0.95 \
  --output-dir ./luca-output

# With bounding box for area computation
python scripts/land_use_carbon_accounting.py \
  --before-landcover ./data/before.tif \
  --after-landcover ./data/after.tif \
  --bbox 116.0 39.5 116.5 40.0 \
  --output-dir ./luca-output

Parameters

Output

Carbon Pools

Land Cover Classes (IPCC)

Ecological Zones

Key Algorithms

Stock-Difference Method

Computes carbon stock change using the IPCC stock-difference approach: ΔC = Σ(A_ij × (C_after_j - C_before_i))

Where A_ij is the area transitioning from class i to j, and C is the carbon density (tC/ha) for each class.

Transition Matrix

Counts pixel-level transitions between before and after land cover classes. Handles nodata values (0, 255) by exclusion.

Monte Carlo Uncertainty

Samples carbon factors from normal distributions using coefficient of variation (CV) from the factors registry. Computes confidence intervals from the distribution of total carbon change.

Pixel Area Computation

• Projected CRS: Uses transform directly (pixel width × height)
• Geographic CRS: Applies latitude correction (cos(lat) × 111320)

Exit Codes

Limitations

• Tier 1/2 approach only; not certified for MRV/VERRA/Gold Standard
• Carbon factors are regional averages; local calibration recommended
• Does not account for time-dependent carbon dynamics (Tier 3)
• Assumes instantaneous change between two time points
• Pixel resolution affects area accuracy for heterogeneous landscapes

References

• IPCC 2006 Guidelines for National Greenhouse Gas Inventories
• IPCC 2019 Refinement to the 2006 Guidelines
• GFOI 2016 Integrating remote-sensing and ground-based observations

数据下载

本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):

python land_use_carbon_accounting.py --bbox 116,39,117,40 --date-range 2024-06-01,2024-06-30 --output-dir 

• --bbox W,S,E,N: WGS-84 边界框 (西, 南, 东, 北)
• --date-range START,END: 日期范围 (YYYY-MM-DD,YYYY-MM-DD)
• --aoi-file : 替代 --bbox 的 GeoJSON 多边形
• --cache-dir : 缓存目录 (默认 ~/.geoskill_cache)

当用户只给 --bbox + --date-range (没有 --image) 时，skill 自动下载数据。 当用户给 --image 时，走原文件路径 (向后兼容)。