Geoskill: Grassland Degradation Monitor

Monitor grassland degradation and recovery trends from multi-temporal vegetation cover, phenology, bare ground and climate baselines. Outputs management zones for restoration prioritization. Use when assessing grassland health, identifying degraded areas, evaluating restoration effectiveness, or generating management recommendations.

Grassland Degradation Monitor

Identifies grassland degradation and recovery trends from multi-temporal remote sensing data. Separates climate-driven from management-driven vegetation change and outputs management zones with restoration priorities.

Trigger

Use when the user wants to:

• Identify degrading grassland areas from multi-temporal imagery
• Evaluate restoration effectiveness (e.g., grazing exclusion, reseeding)
• Separate climate-driven from management-driven vegetation change
• Generate management zone maps with restoration priorities
• Conduct BACI (Before-After-Control-Impact) analysis for management assessment
• Monitor grassland health trends over years to decades

CLI Usage

bash

# Basic analysis with default parameters (10 years, Theil-Sen, climate normalized)
python scripts/grassland_degradation_monitor.py --output-dir ./gdm-output

# Custom years and trend method
python scripts/grassland_degradation_monitor.py \
  --years 15 \
  --trend-method ols \
  --output-dir ./gdm-output

# Without climate normalization (raw trend only)
python scripts/grassland_degradation_monitor.py \
  --years 10 \
  --no-climate-normalize \
  --output-dir ./gdm-output

# With custom degradation schema
python scripts/grassland_degradation_monitor.py \
  --years 10 \
  --degradation-schema references/degradation_schema.json \
  --output-dir ./gdm-output

Parameters

Parameter	Default	Description
--input-ndvi	None	Input NDVI time series GeoTIFF (multi-band, optional)
--years	10	Number of years for analysis
--trend-method	theil-sen	Trend estimation: theil-sen, ols, mann-kendall
--climate-normalize	True	Apply climate normalization (residual trend)
--no-climate-normalize	-	Disable climate normalization
--degradation-schema	built-in	Custom degradation schema JSON
--output-dir	./gdm-output	Output directory

Output

File	Description
degradation_status.tif	Raster map of degradation/recovery status codes
trend.tif	Raster map of trend slopes
priority_areas.geojson	Point features for priority restoration areas
management_summary.csv	Area statistics and recommendations per class
timeseries.csv	Mean NDVI, precipitation, temperature per year
request.json	Analysis request metadata
output-manifest.json	Output file inventory and area statistics
qa.json	Quality assurance checks

Degradation Status Codes

Code	Name	Description	Recommendation
3	severe_degradation	NDVI declining >0.02/yr, 3+ years	Immediate restoration
2	moderate_degradation	NDVI declining 0.01-0.02/yr, 3+ years	Priority restoration
1	light_degradation	NDVI declining 0.005-0.01/yr, 3+ years	Preventive management
0	stable	No significant trend	Sustainable use
-1	light_recovery	NDVI increasing 0.005-0.01/yr, 2+ years	Monitor and maintain
-2	moderate_recovery	NDVI increasing 0.01-0.02/yr, 2+ years	Continue current practices
-3	significant_recovery	NDVI increasing >0.02/yr, 2+ years	Success case, replicate

Key Algorithms

Climate Normalization (Residual Trend)

Regresses NDVI against precipitation and temperature anomalies, then computes the trend of residuals. This separates climate-driven variation from management-driven change.

Trend Estimation

• Theil-Sen: Robust non-parametric slope (median of pairwise slopes). Recommended for noisy remote sensing data.
• OLS: Ordinary Least Squares. Efficient for clean data.
• Mann-Kendall: Normalized Kendall tau. Non-parametric trend strength.

Degradation Classification

Combines three factors:

1. Trend slope: Direction and magnitude of change
2. Persistence: Minimum consecutive years meeting threshold
3. Absolute state: Current NDVI level (very low NDVI escalates severity)

BACI Analysis

Before-After-Control-Impact design for management effectiveness: BACI = (treat_after - treat_before) - (ctrl_after - ctrl_before)

Exit Codes

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

Limitations

• Grazing intensity data typically unavailable
• Grassland type differences complicate threshold transfer
• Remote sensing productivity proxies do not replace field biomass measurement
• Climate normalization assumes linear climate-vegetation relationship
• Short time series (< 5 years) reduces trend reliability

References

• Fensholt & Proud 2012, Remote Sensing of Environment (residual trend)
• Ivits et al. 2013, Remote Sensing (European grassland trends)
• BGC ChinaGrass dataset for grassland classification

数据下载

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

bash

python grassland_degradation_monitor.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)。