forest-carbon-estimate

Estimate forest carbon stock from remote sensing data using BEF, allometric equations, or IPCC Tier 1/2 methods. Includes Monte Carlo uncertainty analysis. Supports raster (GeoTIFF) and tabular (CSV) inputs.

ruiduobao@ruiduobao

Install

openclaw skills install @ruiduobao/forest-carbon-estimate

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

bash

# 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

text

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

bash

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

bash

# 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

json

{
  "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

bibtex

@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

python

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

bash

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)

yaml

# .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

bash

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

bash

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

默认：0.55 g/cm³。使用 --wood-density 覆盖。

方法选择指南

方法	适用场景	所需输入
BEF	森林资源清查数据	AGB (t/ha)
异速生长	遥感（LiDAR/InSAR 树高）	树高 (m)
IPCC Tier 1	快速估算，无野外数据	森林类型 + 面积

输出单位

碳储量以 Mg C/ha（兆克碳/公顷）报告，等同于 t C/ha。

总储量：乘以面积（ha）→ 总 Mg C。

NoData 处理

输入栅格中的 NoData 像元被跳过。输出 GeoTIFF 使用与输入相同的 nodata 值。不对 NoData 区域进行插值。

自定义参数

bash

# 自定义碳分数和根冠比
python scripts\forest-carbon-estimate.py estimate --method bef --agb 200 --forest-type temperate --carbon-fraction 0.45 --root-shoot-ratio 0.28

参数	默认值	说明
`--carbon-fraction`	0.47	干生物量碳分数
`--root-shoot-ratio`	0.26	根冠比
`--bef`	1.32	生物量扩展因子

不确定性输出结构

json

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

字段	说明
`mean`	碳储量估算均值 (Mg C/ha)
`std`	蒙特卡洛标准差
`CI95_lower`	95% 置信区间下界
`CI95_upper`	95% 置信区间上界

数据获取指南

从以下渠道获取森林树高/AGB 栅格：

Global Forest Watch (https://www.globalforestwatch.org/) — AGB 地图
NASA GEDI (https://gedi.umd.edu/) — 星载 LiDAR 森林树高
ESA Biomass Mission — P 波段 SAR 森林树高
国家森林资源清查 — 样地 AGB 数据

验证/质量评估

与野外实测碳储量样地对比
用同森林类型 IPCC 默认值交叉验证
检查不确定性范围（CI95）是否合理（< 均值的 50%）
在论文中报告方法、森林类型和输入数据来源

引用格式

bibtex

@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/}
}

可视化指南

python

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("碳储量 (Mg C/ha)")
ax.set_title("森林碳储量")
ax.axis("off")
plt.tight_layout()
plt.savefig("carbon_map.png", dpi=200)

故障排除

错误	原因	解决方案
`ConnectionError`	网络问题	检查网络，重试
`HTTP 429`	速率限制	等待 60 秒后重试
`ValueError`	无效输入	检查参数格式
空输出	无数据	尝试不同参数
`ModuleNotFoundError`	缺少依赖	运行 pip install

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。

功能特性

BEF 法：生物量扩展因子从地上生物量推算
异速生长方程：AGB = a × H^b，从树高推算
IPCC Tier 1/2：按森林类型的默认因子
蒙特卡洛不确定性：输入不确定性传播分析
栅格处理：直接输入输出 GeoTIFF
表格处理：CSV 样地数据
多森林类型：热带、温带、寒带、红树林

使用方法

bash

# 单点估算（异速生长）
python scripts\forest-carbon-estimate.py estimate --method allometric --height 15 --forest-type tropical

# 单点估算（BEF）
python scripts\forest-carbon-estimate.py estimate --method bef --agb 200 --forest-type temperate

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

# 栅格处理
python scripts\forest-carbon-estimate.py estimate --input height.tif --method allometric --output carbon.tif

# 不确定性分析
python scripts\forest-carbon-estimate.py uncertainty --method allometric --height 15 --iterations 5000

# 从 CSV 生成报告
python scripts\forest-carbon-estimate.py report --input carbon_stock.csv

计算链

text

AGB（地上生物量）
  ↓
BGB = AGB × 根冠比（默认 0.26）
  ↓
总生物量 = AGB + BGB
  ↓
碳储量 = 总生物量 × 碳分数（默认 0.47）

方法对比

方法	输入	说明
BEF	AGB (t/ha)	总生物量 = AGB × BEF
异速生长	树高 (m)	AGB = a × H^b
IPCC	森林类型	IPCC 默认密度

数据来源

IPCC 国家温室气体清单指南（2006，2019 修订）
IPCC EFDB（排放因子数据库）