TY - BOOK ID - 137904064 TI - Land Degradation Assessment with Earth Observation PY - 2022 PB - Basel MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - bfast KW - Mann–Kendall KW - Sen’s slope KW - East Africa KW - NDVI KW - breakpoint analysis KW - vegetation trends KW - greening KW - browning KW - Kenya KW - Uganda KW - trend analysis KW - land use KW - land cover KW - spatial heterogeneity KW - mining development KW - geographically weighted regression (GWR) KW - Mann-Kendall KW - arid and semi-arid areas KW - salinization KW - irrigated systems KW - Niger River basin KW - salinity index KW - vegetation index KW - TI-NDVI KW - Sentinel-2 images KW - high temporal resolution KW - wind erosion modeling KW - RWEQ KW - GEE KW - central Asia KW - spatial-temporal variation KW - land degradation KW - archetypes KW - self-organizing maps KW - drivers KW - savannah KW - Nigeria KW - reference levels KW - REDD+ KW - greenhouse gas emissions KW - Xishuangbanna KW - monitoring and reporting KW - Normalised Difference Vegetation Index (NDVI) KW - Vegetation Condition Index (VCI) KW - drought KW - land use-land cover KW - remote sensing KW - Botswana KW - developing countries KW - Google Earth Engine KW - Landsat time series analysis KW - semi-arid areas KW - sustainable land management programmes KW - precipitation KW - breakpoints and timeseries analysis KW - ecosystem structural change KW - BFAST KW - land degradation neutrality KW - SDG KW - land productivity KW - Landsat KW - vegetation-precipitation relationship KW - soil organic carbon KW - Kobresia pygmaea community KW - unmanned aerial vehicle KW - Gaofen satellite KW - spatial distribution KW - aridity index KW - satellite-based aridity index KW - remote sensing index KW - salinized land degradation index (SDI) KW - Amu Darya delta (ADD) KW - satellite imagery KW - gully mapping KW - machine learning KW - random forest KW - support vector machines KW - South Africa KW - semi-arid environment KW - shrub encroachment KW - slangbos KW - Earth observation KW - time series KW - Sentinel-1 KW - Sentinel-2 KW - Synthetic Aperture Radar (SAR) KW - Soil Adjusted Vegetation Index (SAVI) KW - Kyrgyzstan KW - pastures KW - MODIS KW - land surface phenology KW - drought impacts KW - drought adaptation KW - drought index KW - vegetation resilience KW - drought vulnerability KW - standardized precipitation evapotranspiration index KW - AVHRR KW - n/a KW - Sen's slope UR - https://www.unicat.be/uniCat?func=search&query=sysid:137904064 AB - 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. ER -