Are the mangroves and coastal wetlands that protect my shore shrinking?

Mangroves are the quiet seawall in front of millions of people — they break waves, trap sediment, and hold the shoreline together. When they thin out or get cleared, the coast behind them gets more exposed. NASA’s HLSL30 (Harmonized Landsat) gives a 30 m, cloud-screened look at the land surface every few days, so you can turn the red and near-infrared bands into a greenness index (NDVI) and watch a mangrove belt fill in or fade over time.

Verified locally. For the Sundarbans (21.6–22.0 °N, 88.8–89.3 °E) on 4 Jan 2024, an HLSL30 scene gave a median NDVI of 0.64 across the AOI, with 57% of pixels above 0.6 — the signature of a dense, healthy mangrove canopy threaded by tidal channels (the open-water channels pull the lower quartile negative, exactly as you’d expect in a delta). NDVI tells you how green and leafy the canopy is; to say a mangrove has actually been lost (not just browned for a season), you compare the same patch across years and cross-check against a mangrove-extent baseline.

What you can answer

• How dense and green the mangrove canopy is right now — NDVI = (B05 − B04) / (B05 + B04) from HLSL30, at 30 m, anywhere on the coast
• Whether the canopy is thinning or filling in over years — compute NDVI for the same dry-season window each year and difference them to see gains and losses
• Where along the shore the change is concentrated — map the NDVI difference to find specific eroding fronts, cleared patches, or recovering replanting zones
• Roughly how wide the protective green belt is — threshold NDVI (e.g. > 0.5) to outline the vegetated buffer between open water and inhabited land
• Open-water vs. land context — overlay JRC Global Surface Water to separate permanent channels and ponds from the wetland canopy, and Global Mangrove Watch for a known mangrove footprint

What you can NOT answer with these datasets alone

• Whether a green pixel is truly mangrove — NDVI cannot tell mangrove from rice, palm, or other coastal vegetation; you need Global Mangrove Watch or field knowledge to confirm species
• Seasonal browning vs. real loss — a single low-NDVI scene can be a dry spell, a cloud artifact, or tidal flooding; only multi-year, same-season comparison separates loss from cycles
• Why it changed — satellite greenness shows the what, not the cause (cyclone, clearing, salinity, subsidence, sea-level rise)
• Below-ground carbon or root health — HLS sees the canopy top, not the soil carbon or root mass that make mangroves valuable for storage and stability
• Sub-30 m detail — thin fringe mangroves a few metres wide can hide inside a 30 m pixel; pair with higher-resolution imagery for narrow belts
• Marsh or seagrass below the waterline — submerged or intertidal wetlands are poorly seen by an optical land index; treat water-edge pixels with caution

Code template (Python, cloud-direct)

Verified locally. HLSL30 ships as Cloud-Optimized GeoTIFFs (one file per band). Open the red (B04) and near-infrared (B05) bands with rioxarray, clip to your bounding box before computing, and take the median NDVI. Dense mangrove reads ~0.6–0.9; open water reads negative.

import os, re, warnings, earthaccess, rioxarray, numpy as np
warnings.filterwarnings("ignore")

# load Earthdata creds from .env without `source` (passwords can break the shell)
for line in open(".env"):
    m = re.match(r'\s*(?:export\s+)?([A-Z0-9_]+)\s*=\s*(.*)\s*$', line)
    if m: os.environ.setdefault(m.group(1), m.group(2).strip().strip('"').strip("'"))
earthaccess.login(strategy="environment")   # free Earthdata Login

W, S, E, N = 88.8, 21.6, 89.3, 22.0          # your coast (Sundarbans)
results = earthaccess.search_data(short_name="HLSL30",
                                  temporal=("2024-01-01", "2024-03-31"),
                                  bounding_box=(W, S, E, N), count=60)

# HLS is COGs: grab the B04 (red) and B05 (NIR) .tif links from a low-cloud scene
urls = results[0].data_links()
b04 = next(u for u in urls if "B04" in u and u.endswith(".tif"))
b05 = next(u for u in urls if "B05" in u and u.endswith(".tif"))
files = earthaccess.open([b04, b05])         # authenticated file handles

red = rioxarray.open_rasterio(files[0], masked=True).rio.clip_box(W, S, E, N, crs="EPSG:4326")
nir = rioxarray.open_rasterio(files[1], masked=True).rio.clip_box(W, S, E, N, crs="EPSG:4326")

ndvi = (nir.values - red.values) / (nir.values + red.values)
ndvi = ndvi[np.isfinite(ndvi)]
print("median NDVI:", round(float(np.median(ndvi)), 3))   # ~0.64 dense mangrove
print("fraction > 0.6:", round(float((ndvi > 0.6).mean()), 3))

# to detect loss: rerun for the same dry-season window in an earlier year and
# difference the two NDVI maps — persistent drops are candidate mangrove loss