wetlandmapper 1.0.0

Automatic wetland detection, dynamics classification, and Wetland Cover Type characterisation from multispectral time-series data.

WetlandMapper

Automatic wetland detection, temporal dynamics classification, and Wetland Cover Type characterisation from multispectral satellite time-series data.

WetlandMapper is a Python library that operationalises two peer-reviewed remote-sensing frameworks:

Module	Method	Output
classify_dynamics	MNDWI time-series aggregation (Singh & Sinha 2022, RSL)	6 temporal dynamics classes
classify_wct / classify_wct_ema	MNDWI + NDVI + NDTI combination (Singh et al. 2022, EMA)	5 biophysical cover types

Both methods work on any multispectral archive (Landsat 4–9, Sentinel-2, etc.) and require no labelled training data. Data can be supplied by the user or fetched directly from Google Earth Engine using any Landsat mission or Sentinel-2.

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Wetland Dynamics Classes

Code	Class	Description
10	Persistent	Wet ≥ threshold % of all observations
6	Intermittent	Wet enough, but no directional trend
5	Intensifying	Increasing wet frequency over time
4	Diminishing	Decreasing wet frequency over time
3	Lost	Wet historically, dry in recent period
2	New	Dry historically, wet in recent period
0	Non-wetland	Below minimum wet-frequency threshold

Wetland Cover Types (WCTs)

Code	Class	Key Signal
1	Open Clear Water	High MNDWI, low NDVI, low NDTI
2	Turbid / Sediment-laden Water	High MNDWI, low NDVI, high NDTI
3	Submerged Aquatic Vegetation	High MNDWI, moderate NDVI
4	Emergent / Floating Vegetation	Moderate MNDWI, high NDVI
5	Moist / Waterlogged Soil	Low–moderate MNDWI, low NDVI
0	Non-wetland	Below water threshold

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Installation

Minimal (core algorithms only):

pip install wetlandmapper

With plotting support:

pip install "wetlandmapper[plot]"

With Google Earth Engine support (includes earthengine-api, rasterio, xee, dask, geopandas):

pip install "wetlandmapper[gee]"
earthengine authenticate  # one-time setup

Full development install:

git clone https://github.com/manudeo-singh/wetlandmapper
cd wetlandmapper
pip install -e ".[all]"

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Quick Start

Wetland Dynamics from your own data

import xarray as xr
from wetlandmapper import compute_mndwi, classify_dynamics

# Load a multi-temporal multispectral stack (any xarray-compatible format)
ds = xr.open_dataset("landsat_timeseries.nc")

# Step 1: compute MNDWI (Landsat 8/9 bands: green=B3, swir1=B6)
mndwi = compute_mndwi(ds, green_band="B3", swir_band="B6")

# Step 2: classify into dynamics classes
dynamics = classify_dynamics(
    mndwi,
    nYear=3,               # years/scenes per temporal window
    thresholdWet=25,       # minimum wet-frequency (%) to count as wetland
    thresholdPersis=75,    # wet-frequency (%) for Persistent class
)
dynamics.rio.to_raster("wetland_dynamics.tif")

Wetland Cover Types from your own data

from wetlandmapper import compute_indices, classify_wct_ema

indices = compute_indices(
    ds_composite,
    green_band="B3", red_band="B4",
    nir_band="B5",   swir_band="B6",  # Landsat 8/9
)
wct = classify_wct_ema(indices)
wct.rio.to_raster("wetland_cover_types.tif")

Retrieve data from Google Earth Engine

from wetlandmapper.gee import fetch
from wetlandmapper import classify_dynamics, classify_wct_ema

# AOI accepts a shapefile path, GeoJSON file path, or GeoJSON dict
aoi = "study_area/chilika.shp"           # shapefile
# aoi = "study_area/chilika.geojson"     # GeoJSON file
# aoi = {"type": "Polygon", ...}         # GeoJSON dict

# Long-record annual composites — merges all available Landsat missions
mndwi = fetch(
    aoi, start="1984-01-01", end="2023-12-31",
    sensor="LandsatAll",
    temporal_aggregation="annual",
    use_slc_off=False,     # exclude Landsat 7 post-SLC-failure images
)
dynamics = classify_dynamics(mndwi, nYear=3, thresholdWet=25, thresholdPersis=75)

# Single-sensor post-monsoon WCT composite
indices = fetch(
    aoi, start="2022-10-01", end="2022-12-31",
    sensor="Landsat8", index=["MNDWI", "NDVI", "NDTI"],
)
wct = classify_wct_ema(indices.isel(time=0))

Temporal aggregation

from wetlandmapper import aggregate_time

mndwi_annual   = aggregate_time(mndwi_ts, freq="annual",   method="median")
mndwi_seasonal = aggregate_time(mndwi_ts, freq="seasonal", method="median")
mndwi_monthly  = aggregate_time(mndwi_ts, freq="monthly",  method="median")

Visualisation

from wetlandmapper.plotting import plot_dynamics, plot_wct

fig, ax = plot_dynamics(dynamics)
fig.savefig("dynamics_map.png", dpi=150, bbox_inches="tight")

fig, ax = plot_wct(wct)
fig.savefig("wct_map.png", dpi=150, bbox_inches="tight")

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Google Earth Engine — Sensor Reference

All Landsat collections are Collection 2 Level-2 surface reflectance.

sensor=	GEE collection	Operational dates	Note
"Landsat4"	LT04/C02/T1_L2	1982–1993	TM
"Landsat5"	LT05/C02/T1_L2	1984–2013	TM
"Landsat7"	LE07/C02/T1_L2	1999–2022	ETM+; SLC failure 2003-06-01
"Landsat8"	LC08/C02/T1_L2	2013–present	OLI (default)
"Landsat9"	LC09/C02/T1_L2	2021–present	OLI-2
"LandsatAll"	all 5 merged	1982–present	auto-harmonised band names
"Sentinel2"	S2_SR_HARMONIZED	2015–present	MSI

"Landsat" is a backward-compatible alias for "Landsat8".

Use use_slc_off=False (default) to exclude Landsat 7 post-SLC-failure images. Use use_slc_off=True to include them (covers the 2003–2012 gap before Landsat 8).

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Band Name Reference (for local data)

Sensor	Green	Red	NIR	SWIR1
Landsat 4/5/7 TM/ETM+ (C02 L2)	SR_B2	SR_B3	SR_B4	SR_B5
Landsat 8/9 OLI (C02 L2)	SR_B3	SR_B4	SR_B5	SR_B6
Sentinel-2 L2A	B3	B4	B8	B11
MODIS MOD09GA	sur_refl_b04	sur_refl_b01	sur_refl_b02	sur_refl_b06

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Running Tests

pip install -e ".[dev]"
pytest
pytest --cov=wetlandmapper --cov-report=term-missing

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Citing

If you use WetlandMapper in your research, please cite the two underlying methods and the software:

Dynamics classification:

Singh, M., & Sinha, R. (2022). A basin-scale inventory and hydrodynamics of floodplain wetlands based on time-series of remote sensing data. Remote Sensing Letters, 13(1), 1–13. https://doi.org/10.1080/2150704X.2021.1980919

Wetland Cover Types:

Singh, M., Allaka, S., Gupta, P. K., Patel, J. G., & Sinha, R. (2022). Deriving wetland-cover types (WCTs) from integration of multispectral indices based on Earth observation data. Environmental Monitoring and Assessment, 194(12), 878. https://doi.org/10.1007/s10661-022-10541-7

Software (JOSS paper — pending publication):

Singh, M. (2026). WetlandMapper: A Python package for automatic wetland mapping, dynamics classification, and cover-type characterisation. Journal of Open Source Software. https://doi.org/10.5281/zenodo.XXXXXXX

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License

GPL-3.0-or-later — see LICENSE.

Contact

Manudeo Singh
Department of Geography and Earth Science, Aberystwyth University, Aberystwyth, Wales, UK
[e-mail] · ORCID: 0000-0002-3511-8362