Aquaculture Pond Mapping

Identifies coastal and inland aquaculture ponds from multi-temporal satellite imagery. Separates aquaculture ponds from natural water bodies and paddy fields using shape, texture, water frequency, and adjacency features. Outputs pond locations, aquaculture zones, change detection, and area statistics.

Trigger

Use when the user wants to:

• Map aquaculture pond distribution from satellite imagery
• Monitor pond expansion or abandonment over time
• Detect conversion between aquaculture and wetlands/cropland
• Generate pond inventories for fisheries management
• Assess environmental impacts of aquaculture development
• Identify illegal pond construction in protected areas

CLI Usage

# Basic analysis with default parameters (rules method, single year)
python scripts/aquaculture_pond_mapping.py --output-dir ./apm-output

# Multi-year analysis with custom minimum area
python scripts/aquaculture_pond_mapping.py \
  --years 2020 2021 2022 2023 \
  --min-area 1000 \
  --output-dir ./apm-output

# ML-based classification (requires training data)
python scripts/aquaculture_pond_mapping.py \
  --method ml \
  --training-data ./training_samples.geojson \
  --output-dir ./apm-output

# With custom input NDWI stack
python scripts/aquaculture_pond_mapping.py \
  --input-ndwi ./ndwi_stack.tif \
  --years 2020 2021 2022 \
  --output-dir ./apm-output

Parameters

Parameter	Default	Description
--input-ndwi	None	Input NDWI time series GeoTIFF (multi-band, optional)
--method	rules	Classification method: rules, ml
--years	[2023]	Years for analysis (space-separated)
--min-area	500.0	Minimum pond area in m² (default: 500)
--output-dir	./apm-output	Output directory

Output

File	Description
ponds.geojson	Point features for detected aquaculture ponds with confidence
aquaculture_zones.geojson	Point features for aquaculture cluster zones
change.geojson	Point features showing new/abandoned/stable ponds
area_by_admin.csv	Area statistics per classification class
accuracy.json	Classification summary and confidence metrics
request.json	Analysis request metadata
dataset-manifest.json	Input data and parameters manifest
output-manifest.json	Output file inventory and area statistics
qa.json	Quality assurance checks

Classification Classes

Code	Name	Description
1	aquaculture_pond	Managed aquaculture pond (rectangular, embanked, clustered)
2	natural_water	Natural water body (irregular shape, permanent, no embankments)
3	paddy_field	Rice paddy (small, rectangular, seasonal flooding)
0	non_water	Non-water or below minimum area

Change Detection Codes

Code	Name	Description
1	new_pond	Pond present in latest period but not baseline
-1	abandoned_pond	Pond present in baseline but not latest
0	stable_pond	Pond present in both periods

Key Algorithms

Two-Step Classification

1. Water Extraction: NDWI/MNDWI thresholding to identify water candidates
2. Pond Classification: Rule-based scoring using shape, texture, water frequency, and adjacency features to separate aquaculture from natural water

Shape Features

• Rectangularity: area / bounding box area (ponds are rectangular)
• Compactness: 4π·area / perimeter² (moderate for ponds)
• Aspect Ratio: elongation (ponds have moderate ratios)

Texture Features (Embankment Detection)

• Local variance and edge density detect embankments (堤埂) around ponds
• Higher texture scores indicate human-constructed boundaries

Water Frequency

• Seasonal water (intermediate frequency) suggests managed aquaculture
• Permanent water suggests natural lakes/rivers

Adjacency Features

• Clustered ponds indicate aquaculture areas
• Isolated water bodies more likely natural

Change Detection

• Compares pond masks between baseline and latest periods
• Identifies new, abandoned, and stable ponds

Exit Codes

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

Limitations

• Paddy fields and salt pans may be confused with aquaculture ponds
• Very small ponds (< 2 pixels) may be missed
• Seasonal drying can cause false negatives in single-date imagery
• Cloud cover affects optical remote sensing accuracy
• Ground truth data recommended for accuracy assessment
• ML method requires representative training samples

References

• McFeeters 1996 - NDWI for open water delineation
• Xu 2006 - MNDWI for enhanced water detection
• Two-step water-then-pond classification approach

数据下载

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

python aquaculture_pond_mapping.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)。