Avoiding, reducing, and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on Earth. To halt and reverse the current trends in land degradation, there is an immediate need to enhance national capacities to undertake quantitative assessments and mapping of their degraded lands, as required by the Sustainable Development Goals (SDGs), in particular, the SDG indicator 15.3.1 (“proportion of land that is degraded over total land area”). Earth Observations (EO) can play an important role both for generating this indicator as well as complementing or enhancing national official data sources. Implementations like Trends.Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR. Consequently, there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data (e.g. Landsat or Sentinels). In response to this issue, this paper presents an initial overview of an innovative approach to monitor land degradation at the national scale in compliance with the SDG15.3.1 indicator using Landsat observations using a data cube but further work is required to improve the calculation of the three sub-indicators.

When defining indicators on the environment, the use of existing initiatives should be a priority rather than redefining indicators each time. From an Information, Communication and Technology perspective, data interoperability and standardization are critical to improve data access and exchange as promoted by the Group on Earth Observations. GEOEssential is following an end-user driven approach by defining Essential Variables (EVs), as an intermediate value between environmental policy indicators and their appropriate data sources. From international to local scales, environmental policies and indicators are increasingly percolating down from the global to the local agendas. The scientific business processes for the generation of EVs and related indicators can be formalized in workflows specifying the necessary logical steps. To this aim, GEOEssential is developing a Virtual Laboratory the main objective of which is to instantiate conceptual workflows, which are stored in a dedicated knowledge base, generating executable workflows. To interpret and present the relevant outputs/results carried out by the different thematic workflows considered in GEOEssential (i.e. biodiversity, ecosystems, extractives, night light, and food-water-energy nexus), a Dashboard is built as a visual front-end. This is a valuable instrument to track progresses towards environmental policies.

Exacerbated by climate change, Europe has experienced series of hot and dry summer since the beginning of the 21st century. The importance of land conditions became an international concern with a dedicated sustainable development goal (SDG), the SDG 15. It calls for developing and finding innovative solutions to follow and evaluate impacts of changing land conditions induced by various driving forces. In Switzerland, drought risk will significantly increase in the coming decades with severe consequences on agriculture, energy production and vegetation. In this paper, we used a 35-year satellite-derived annual and seasonal times-series of normalized difference water index (NDWI) to follow vegetation water content evolution at different spatial and temporal scales across Switzerland and related them to temperature and precipitation to investigate possible responses of changing climatic conditions. Results indicate that there is a small and slow drying tendency at the country scale with a NDWI mean decreasing slope of −0.22%/year for the 23% significant pixels across Switzerland. This tendency is mostly visible below 2000 m above sea level (m.a.s.l.) and in all biogeographical regions. The Southern Alps regions appear to be more responsive to changing drying conditions with a significant and slight negative NDWI trend (−0.39%/year) over the last 35 years. Moreover, NDWI values are mostly a function of temperature at elevations below the tree line rather than precipitation. Findings suggest that multi-annual and seasonal NDWI can be a valuable indicator to monitor vegetation water content at different scales, but other components such as land cover type and evapotranspiration should be considered to better characterize NDWI variability. Satellite Earth Observations data can provide valuable complementary observations for national statistics on the ecological state of vegetation to support SDG 15 to monitor land affected by drying conditions.

Small-scale placer mining in Colombia takes place in rural areas and involves excavations resulting in large footprints of bare soil and water ponds. Such excavated areas comprise a mosaic of challenging terrains for cloud and cloud-shadow detection of Sentinel-2 (S2A and S2B) data used to identify, map, and monitor these highly dynamic activities. This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale mining areas, one which places an emphasis on the reduction of misclassification of mining sites as clouds or shadows. The first step is comprised of a supervised support-vector-machine classification identifying clouds, cloud shadows, and clear pixels. The second step is a geometry-based improvement of cloud-shadow detection where solar-cloud-shadow-sensor geometry is used to exclude commission errors in cloud shadows. The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of cloud shadow for each cloud projection. The approach does not require supplementary data on cloud-top or bottom heights nor cloud-top ruggedness. It assumes that the location of dense clouds is mainly impacted by meteorological conditions and that cloud-top and cloud-base heights vary in a predefined manner. The methodology has been tested over an intensively excavated and well-studied pilot site and shows 50% more detection of clouds and shadows than Sen2Cor. Furthermore, it has reached a Specificity of 1 in the correct detection of mining sites and water ponds, proving itself to be a reliable approach for further related studies on the mapping of small-scale mining in the area. Although the methodology was tailored to the context of small-scale mining in the region of Antioquia, it is a scalable approach and can be adapted to other areas and conditions.