Land Degradation Assessment with Earth Observation

• Autore: Symeonakis Elias
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (368 p.)
• Soggetto topico: Research & information: general
• Soggetto non controllato: Amu Darya delta (ADD); archetypes; arid and semi-arid areas; aridity index; AVHRR; bfast; BFAST; Botswana; breakpoint analysis; breakpoints and timeseries analysis; browning; central Asia; developing countries; drivers; drought; drought adaptation; drought impacts; drought index; drought vulnerability; Earth observation; East Africa; ecosystem structural change; Gaofen satellite; GEE; geographically weighted regression (GWR); Google Earth Engine; greenhouse gas emissions; greening; gully mapping; high temporal resolution; irrigated systems; Kenya; Kobresia pygmaea community; Kyrgyzstan; land cover; land degradation; land degradation neutrality; land productivity; land surface phenology; land use; land use-land cover; Landsat; Landsat time series analysis; machine learning; Mann-Kendall; mining development; MODIS; monitoring and reporting; n/a; NDVI; Niger River basin; Nigeria; Normalised Difference Vegetation Index (NDVI); pastures; precipitation; random forest; REDD+; reference levels; remote sensing; remote sensing index; RWEQ; salinity index; salinization; salinized land degradation index (SDI); satellite imagery; satellite-based aridity index; savannah; SDG; self-organizing maps; semi-arid areas; semi-arid environment; Sen's slope; Sentinel-1; Sentinel-2; Sentinel-2 images; shrub encroachment; slangbos; Soil Adjusted Vegetation Index (SAVI); soil organic carbon; South Africa; spatial distribution; spatial heterogeneity; spatial-temporal variation; standardized precipitation evapotranspiration index; support vector machines; sustainable land management programmes; Synthetic Aperture Radar (SAR); TI-NDVI; time series; trend analysis; Uganda; unmanned aerial vehicle; Vegetation Condition Index (VCI); vegetation index; vegetation resilience; vegetation trends; vegetation-precipitation relationship; wind erosion modeling; Xishuangbanna
• Persona (resp. second.): SymeonakisElias
• Sommario/riassunto: 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.
• Titolo autorizzato: Land Degradation Assessment with Earth Observation
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910576873503321
• Lo trovi qui: Univ. Federico II