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Palabras clave – provistas por la editorial

bfast; Mann–Kendall; Sen’s slope; East Africa; NDVI; breakpoint analysis; vegetation trends; greening; browning; Kenya; Uganda; trend analysis; land use; land cover; spatial heterogeneity; mining development; geographically weighted regression (GWR); Mann-Kendall; arid and semi-arid areas; salinization; irrigated systems; Niger River basin; salinity index; vegetation index; TI-NDVI; Sentinel-2 images; high temporal resolution; wind erosion modeling; RWEQ; GEE; central Asia; spatial-temporal variation; land degradation; archetypes; self-organizing maps; drivers; savannah; Nigeria; reference levels; REDD+; greenhouse gas emissions; Xishuangbanna; monitoring and reporting; Normalised Difference Vegetation Index (NDVI); Vegetation Condition Index (VCI); drought; land use-land cover; remote sensing; Botswana; developing countries; Google Earth Engine; Landsat time series analysis; semi-arid areas; sustainable land management programmes; precipitation; breakpoints and timeseries analysis; ecosystem structural change; BFAST; land degradation neutrality; SDG; land productivity; Landsat; vegetation-precipitation relationship; soil organic carbon; Kobresia pygmaea community; unmanned aerial vehicle; Gaofen satellite; spatial distribution; aridity index; satellite-based aridity index; remote sensing index; salinized land degradation index (SDI); Amu Darya delta (ADD); satellite imagery; gully mapping; machine learning; random forest; support vector machines; South Africa; semi-arid environment; shrub encroachment; slangbos; Earth observation; time series; Sentinel-1; Sentinel-2; Synthetic Aperture Radar (SAR); Soil Adjusted Vegetation Index (SAVI); Kyrgyzstan; pastures; MODIS; land surface phenology; drought impacts; drought adaptation; drought index; vegetation resilience; drought vulnerability; standardized precipitation evapotranspiration index; AVHRR; n/a