forest-carbon-estimate

Estimate forest carbon stock from remote sensing data using multiple methods with uncertainty analysis.

Features

• BEF Method: Biomass Expansion Factor from AGB
• Allometric Equations: AGB = a × H^b from forest height
• IPCC Tier 1/2: Default factors by forest type
• Monte Carlo Uncertainty: Propagate input uncertainties
• Raster Processing: Direct GeoTIFF input/output
• Tabular Processing: CSV with plot-level data
• Multiple Forest Types: Tropical, temperate, boreal, mangrove

Usage

# Single-point estimate (allometric)
python scripts\forest-carbon-estimate.py estimate --method allometric --height 15 --forest-type tropical

# Single-point estimate (BEF)
python scripts\forest-carbon-estimate.py estimate --method bef --agb 200 --forest-type temperate

# IPCC Tier 1 default
python scripts\forest-carbon-estimate.py estimate --method ipcc --forest-type boreal --area-ha 100

# Raster processing
python scripts\forest-carbon-estimate.py estimate --input height.tif --method allometric --output carbon.tif

# Uncertainty analysis
python scripts\forest-carbon-estimate.py uncertainty --method allometric --height 15 --iterations 5000

# Report from CSV
python scripts\forest-carbon-estimate.py report --input carbon_stock.csv

Parameters

Parameter	Description	Default
--input	Input GeoTIFF or CSV	None (single-point)
--method	Estimation method	allometric
--forest-type	Forest type for default factors	default
--height	Forest height (m) for allometric	None
--agb	Above-ground biomass (t/ha) for BEF	None
--area-ha	Area in hectares (IPCC)	1.0
--agb-band	Band number for raster input	1
--iterations	Monte Carlo iterations	1000
--output	Output file path	Auto-generated

Calculation Chain

AGB (Above-ground biomass)
  ↓
BGB = AGB × root_shoot_ratio (default 0.26)
  ↓
Total biomass = AGB + BGB
  ↓
Carbon stock = Total biomass × carbon_fraction (default 0.47)

Methods

Method	Input	Description
BEF	AGB (t/ha)	Total biomass = AGB × BEF
Allometric	Height (m)	AGB = a × H^b
IPCC	Forest type	Default density from IPCC tables

Installation

pip install requests>=2.28.0 tqdm numpy scipy rasterio
# Or: pip install -r scripts/requirements.txt

Dependencies

Package	Purpose
numpy	Numerical computation and Monte Carlo
rasterio	GeoTIFF I/O for raster mode
scipy	Statistical functions
requests	Data download (if applicable)
tqdm	Progress bars

Data Source

• IPCC Guidelines for National Greenhouse Gas Inventories (2006, 2019 Refinement)
• IPCC EFDB (Emission Factor Database)

BEF Values per Forest Type

Forest Type	BEF Range
Tropical	1.5 – 3.0
Temperate	1.2 – 1.8
Boreal	1.0 – 1.5
Mangrove	1.2 – 1.8

Default BEF = 1.32 (temperate mixed forest). Adjust with --bef parameter.

Allometric Coefficients

For AGB = a × H^b (H = forest height in m):

Forest Type	a	b
Tropical	0.0673	0.976
Temperate	0.0592	1.030
Boreal	0.0450	1.050

These are DBH-based defaults. For height-based allometry, use --coeff-a and --coeff-b to override.

IPCC Default Wood Density

Forest Type	Wood Density (g/cm³)
Tropical	0.57 – 0.69
Temperate	0.41 – 0.56
Boreal	0.38 – 0.51

Default: 0.55 g/cm³. Override with --wood-density.

Method Selection Guidance

Method	Best For	Input Required
BEF	Forest inventory data	AGB (t/ha)
Allometric	Remote sensing (LiDAR/InSAR height)	Forest height (m)
IPCC Tier 1	Quick estimates, no field data	Forest type + area

Output Units

Carbon stock is reported in Mg C/ha (megagrams of carbon per hectare), equivalent to t C/ha.

For total stock: multiply by area (ha) → total Mg C.

Nodata Handling

Nodata pixels in input rasters are skipped. Output GeoTIFF uses the same nodata value as input. No interpolation is performed on nodata areas.

Custom Parameters

# Custom carbon fraction and root-shoot ratio
python scripts\forest-carbon-estimate.py estimate --method bef --agb 200 --forest-type temperate --carbon-fraction 0.45 --root-shoot-ratio 0.28

Parameter	Default	Description
--carbon-fraction	0.47	Carbon fraction of dry biomass
--root-shoot-ratio	0.26	Root-to-shoot ratio
--bef	1.32	Biomass expansion factor

Uncertainty Output Structure

{
  "mean": 125.3,
  "std": 18.7,
  "CI95_lower": 88.6,
  "CI95_upper": 162.0
}

Field	Description
mean	Mean carbon stock estimate (Mg C/ha)
std	Standard deviation from Monte Carlo
CI95_lower	95% confidence interval lower bound
CI95_upper	95% confidence interval upper bound

Data Acquisition Guidance

Obtain forest height/AGB rasters from:

• Global Forest Watch (https://www.globalforestwatch.org/) — AGB maps
• NASA GEDI (https://gedi.umd.edu/) — Forest height from spaceborne LiDAR
• ESA Biomass Mission — P-band SAR forest height
• National forest inventory — Plot-level AGB data

Validation / Quality Assessment

• Compare with field-measured carbon stock plots
• Cross-validate with IPCC default values for the same forest type
• Check that uncertainty range (CI95) is reasonable (< 50% of mean)
• Report method, forest type, and input data source in publications

Citation

@book{ipcc2006guidelines,
  title={2006 IPCC Guidelines for National Greenhouse Gas Inventories},
  author={{IPCC}},
  year={2006},
  publisher={Institute for Global Environmental Strategies},
  url={https://www.ipcc-nggip.iges.or.jp/public/2006gl/}
}
@book{ipcc2019refinement,
  title={2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories},
  author={{IPCC}},
  year={2019},
  publisher={IPCC},
  url={https://www.ipcc-nggip.iges.or.jp/public/2019rf/}
}

Visualization Guidance

import rasterio
import matplotlib.pyplot as plt
import numpy as np

with rasterio.open("carbon.tif") as src:
    carbon = src.read(1)
    nodata = src.nodata

carbon_plot = np.where(carbon == nodata, np.nan, carbon)

fig, ax = plt.subplots(figsize=(10, 8))
im = ax.imshow(carbon_plot, cmap="Greens", vmin=0, vmax=200)
cbar = plt.colorbar(im, ax=ax, shrink=0.8)
cbar.set_label("Carbon Stock (Mg C/ha)")
ax.set_title("Forest Carbon Stock")
ax.axis("off")
plt.tight_layout()
plt.savefig("carbon_map.png", dpi=200)

Troubleshooting

Error	Cause	Solution
ConnectionError	Network issue	Check internet, retry
HTTP 429	Rate limit	Wait 60s, retry
ValueError	Invalid input	Check parameter format
Empty output	No data	Try different parameters
ModuleNotFoundError	Missing dep	Run pip install

---

Advanced Usage

Batch Raster Processing

for year in 2020 2021 2022 2023; do
  python scripts\forest-carbon-estimate.py estimate     --input agb_${year}.tif --method allometric     --output carbon_${year}.tif
done

CI/CD Integration (GitHub Actions)

# .github/workflows/carbon-update.yml
name: Forest Carbon Update
on:
  schedule:
    - cron: '0 0 1 1 *'  # Yearly
jobs:
  estimate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install numpy rasterio
      - run: |
          python scripts\forest-carbon-estimate.py estimate \
            --input data/agb_latest.tif \
            --method allometric \
            --output data/carbon_latest.tif

PostGIS Raster Import

raster2pgsql -s 4326 -I -C carbon_latest.tif public.forest_carbon | psql -d gis_db

Performance Tips

• --method bef is fastest for regional estimates; --method allometric for species-specific
• Use --carbon-fraction 0.47 to match local species (default 0.47 = IPCC default)
• For large rasters, process in tiles using --window parameter

---

中文说明

基于遥感数据估算森林碳储量，支持 BEF、异速生长方程、IPCC Tier 1/2 三种方法，含蒙特卡洛不确定性分析。

安装

pip install requests>=2.28.0 tqdm numpy scipy rasterio
# 或: pip install -r scripts/requirements.txt

依赖

包	用途
numpy	数值计算和蒙特卡洛
rasterio	栅格模式 GeoTIFF 读写
scipy	统计函数
requests	数据下载（如适用）
tqdm	进度条

各森林类型 BEF 值

森林类型	BEF 范围
热带	1.5 – 3.0
温带	1.2 – 1.8
寒带	1.0 – 1.5
红树林	1.2 – 1.8

默认 BEF = 1.32（温带混交林）。使用 --bef 参数调整。

异速生长系数

AGB = a × H^b（H = 树高，单位 m）：

森林类型	a	b
热带	0.0673	0.976
温带	0.0592	1.030
寒带	0.0450	1.050

这些是基于 DBH 的默认值。树高异速生长使用 --coeff-a 和 --coeff-b 覆盖。

IPCC 默认木材密度

森林类型	木材密度 (g/cm³)
热带	0.57 – 0.69
温带	0.41 – 0.56
寒带	0.38 – 0.51