DOI: 10.3390/land15081513 ISSN: 2073-445X

An Integrated Earth Observation Assessment of Land-Cover Change, Settlement Expansion, and Tropospheric NO2 in East Kazakhstan

Igor Klein, Emma Garcia Boadas, Coen Rouppe van der Voort, Nurgul Raissova, Zhanar Abilda, Dias Daurov, Malika Shamekova, Kabyl Zhambakin

The East Kazakhstan Region is characterized by heterogeneous mountain, steppe, agricultural, and urban–industrial landscapes. However, spatially integrated assessments of land-cover conditions, agricultural dynamics, settlement expansion, and atmospheric trace-gas patterns remain limited. This study combines open multi-source Earth observation datasets and products to assess regional land cover, cropland dynamics from 2003 to 2019, built-up-area expansion from 1985 to 2025, and tropospheric nitrogen dioxide (NO2) column density from 2019 to 2024. The land-cover classification was based on Sentinel-2 imagery and ancillary geospatial datasets. Its random forest component achieved an overall accuracy of 90.17%, a Cohen’s kappa coefficient of 0.892, and a macro-averaged F1 score of 0.888. Grassland was the largest mapped land-cover class, covering 39.3% of the study region, followed by dense vegetation (16.2%), rock (14.9%), and sparse vegetation (10.6%). Mapped cropland extent increased from 4771.6 km2 in 2003 to 5043.9 km2 in 2019, representing a net increase of 272.3 km2 (5.7%), although a minor decrease occurred after 2015. The harmonized built-up area time series showed expansion within all nine major settlements, with the largest absolute increase observed in Öskemen (Ust-Kamenogorsk). Newly detected built-up pixels during 2000–2024 were mostly associated with transition from grassland and dense shrubs. Annual Sentinel-5P observations showed recurring elevated tropospheric NO2 column densities in northwestern East Kazakhstan, particularly around Öskemen. Over the built-up footprint, the area-weighted mean increased from 24.1 µmol m−2 in 2019 to 29.2 µmol m−2 in 2024. The integrated framework provides a spatially consistent regional baseline while identifying descriptive patterns that require further process-based investigation in future.

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