Geoskill: Land Use Carbon Accounting

Compute carbon stock changes, emissions/removals, and uncertainty from multi-temporal land cover data using IPCC Tier 1/2 carbon factors. Use when analyzing land use change carbon budgets, estimating CO2e emissions from deforestation, or generating carbon accounting reports.

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

bash

# 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

Parameter	Default	Description
--before-landcover	None	Before period land cover GeoTIFF
--after-landcover	None	After period land cover GeoTIFF
--eco-zone	subtropical	Ecological zone for carbon factors
--pools	BAG BBG SOC	Carbon pools to include
--carbon-factors	None	Path to custom carbon factors JSON
--source-system	auto	Source classification: auto, from_glc_fcs30, from_esri_lulc, from_copernicus
--mc-iterations	1000	Monte Carlo iterations
--mc-seed	42	Monte Carlo random seed
--confidence	0.95	Confidence level for uncertainty
--bbox	None	Bounding box: xmin ymin xmax ymax
--output-dir	./luca-output	Output directory

Output

File	Description
transition_matrix.csv	Land cover transition matrix (pixel counts)
land_transition.tif	Transition type raster
carbon_change.tif	Pixel-level carbon change raster (tC/ha)
carbon_summary.csv	Per-transition carbon change summary
uncertainty.json	Monte Carlo uncertainty analysis
request.json	Analysis request metadata
dataset-manifest.json	Dataset inventory and mapping info
output-manifest.json	Output file inventory and carbon results
qa.json	Quality assurance checks

Carbon Pools

Code	Name	Description
BAG	Above-ground Biomass	Living vegetation above ground
BBG	Below-ground Biomass	Living roots and rhizomes
DW	Dead Wood	Standing and fallen dead wood
LT	Litter	Leaf litter and fine debris
SOC	Soil Organic Carbon	Topsoil organic carbon

Land Cover Classes (IPCC)

Code	Name	Description
FL	Forest Land	Forest and woodland
CL	Cropland	Cropland and pasture
GL	Grassland	Natural grassland
WL	Wetlands	Wetlands and peatlands
SL	Settlements	Built-up areas
OL	Other Land	Barren, ice, water

Ecological Zones

Zone	Description
tropical	Tropical forest and savanna
subtropical	Subtropical and warm temperate
temperate	Cool temperate and boreal
arid	Arid and semi-arid

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

Code	Meaning
0	Success
2	Argument error
3	Dependency missing
6	Data validation failure
7	Processing failure

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):

bash

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

• --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 <path.geojson>: 替代 --bbox 的 GeoJSON 多边形
• --cache-dir <path>: 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)。