Land Degradation Assessment with Earth Observation

Edited by
May 2022
368 pages
  • ISBN978-3-0365-4227-0 (Hardback)
  • ISBN978-3-0365-4228-7 (PDF)

This book is a reprint of the Special Issue Land Degradation Assessment with Earth Observation that was published in

Environmental & Earth Sciences

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
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; vegetation trends; mining development; geographically weighted regression (GWR); Sen’s slope; 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; land degradation; 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; land degradation; Landsat time series analysis; semi-arid areas; sustainable land management programmes; NDVI; precipitation; drought; breakpoints and timeseries analysis; ecosystem structural change; BFAST; land degradation neutrality; SDG; land productivity; land cover; NDVI; Landsat; vegetation-precipitation relationship; soil organic carbon; Google Earth Engine; Kobresia pygmaea community; unmanned aerial vehicle; Gaofen satellite; spatial distribution; aridity index; drought; land degradation; remote sensing; satellite-based aridity index; land degradation; salinization; 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; land degradation; Earth observation; time series; Sentinel-1; Sentinel-2; Synthetic Aperture Radar (SAR); Soil Adjusted Vegetation Index (SAVI); machine learning; Kyrgyzstan; pastures; Landsat; MODIS; land surface phenology; drought impacts; NDVI; drought adaptation; drought index; vegetation resilience; drought vulnerability; standardized precipitation evapotranspiration index; AVHRR; land degradation; n/a