Trend map: Swat valley NDVI 2001–2024

Theil–Sen trend of growing-season NDVI (May–September median of MODIS Terra MOD13A1 v6.1 16-day composites at 500 m, pixel reliability 0–1, at least three good composites per year), 2001–2024, around the Swat valley (pre-2026 district boundary): the former Swat district (before January 2026), now Swat and Bar Swat (geoBoundaries). Browning is terracotta and greening slate teal. Colours are limited to ±0.08 decade⁻¹ (98th percentile of |slope|); 1.9 % of pixels exceed this. Dots: at least 50 % of 500 m pixels significant (Mann–Kendall, p < 0.05). Per-pixel Mann–Kendall tests assume serial independence and are not adjusted for multiple comparisons. Grey with diagonal hatching: mean NDVI below 0.1 over the record (not vegetated; 14.9 % of the pixels with data); every pixel with data has a valid trend, and pixels without data are left white. The caption sentences on colour limits, dots, test assumptions and grey come from fig.eofigs_layout["caption"]. Browning runs along the valley floor from Mingora to Bahrain; greening is widespread and strongest in the lower valley. MODIS data: NASA LP DAAC, accessed through the Microsoft Planetary Computer. Boundaries and names shown do not imply any opinion on the legal status of any country or territory.

Script PDF PNG, 600 dpi

from pathlib import Path

import pandas as pd
import rioxarray

import eofigs

DATA = Path(__file__).resolve().parents[1] / "docs" / "data" / "swat"


def swat_ndvi():
    """Load the annual growing-season NDVI cube (one band per year) with a time axis."""
    raw = rioxarray.open_rasterio(DATA / "swat_ndvi_growing_season_500m.tif", mask_and_scale=True)
    years = [int(year) for year in raw.attrs["long_name"]]  # band descriptions
    cube = raw.rename(band="time").assign_coords(
        time=pd.to_datetime([f"{year}-07-01" for year in years])
    )
    cube.name = "ndvi"
    cube.attrs = {"long_name": "NDVI", "units": "1"}
    return cube


eofigs.set_style("elsevier")
fig = eofigs.trend_map(
    swat_ndvi(),
    aoi=DATA / "swat_boundary.geojson",
    aoi_label="Swat valley\n(pre-2026 district boundary)",
    width="onehalf",
)


# README card: the lower valley, with the north arrow and scale bar
readme_card = [(fig.axes[0], (0.0, 0.0, 1.0, 0.38))]

if __name__ == "__main__":
    print(fig.eofigs_layout["caption"])
    print(eofigs.save_figure(fig, "trend_map_swat"))

Figure check

Figure check (elsevier): passed
  PASS  size: 140.0 x 180.0 mm (onehalf width).
  PASS  resolution: 600 dpi (at least 500 for figures with text and images).
  PASS  font size: Smallest text 7 pt (minimum 7 pt).
  PASS  fonts: Fonts in use: Arial.
  PASS  colour-blind safety: 4 categorical colours, all distinguishable.
  PASS  colour maps: Colour maps: eofigs.terra_slate (diverging).