The Swiss data cube, analysis ready data archive using earth observations of Switzerland.

Since the opening of Earth Observation (EO) archives (USGS/NASA Landsat and EC/ESA Sentinels), large collections of EO data are freely available, offering scientists new possibilities to better understand and quantify environmental changes. Fully exploiting these satellite EO data will require new approaches for their acquisition, management, distribution, and analysis. Given rapid environmental changes and the emergence of big data, innovative solutions are needed to support policy frameworks and related actions toward sustainable development. Here we present the Swiss Data Cube (SDC), unleashing the information power of Big Earth Data for monitoring the environment, providing Analysis Ready Data over the geographic extent of Switzerland since 1984, which is updated on a daily basis. Based on a cloud-computing platform allowing to access, visualize and analyse optical (Sentinel-2; Landsat 5, 7, 8) and radar (Sentinel-1) imagery, the SDC minimizes the time and knowledge required for environmental analyses, by offering consistent calibrated and spatially co-registered satellite observations. SDC derived analysis ready data supports generation of environmental information, allowing to inform a variety of environmental policies with unprecedented timeliness and quality.

Priority list of biodiversity metrics to observe from space.

Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.

Mapping precipitation-corrected NDVI trends across Namibia.

Savannas comprise a major component of the Earth system and contribute ecosystem services and functions essential to human livelihoods. Monitoring spatial and temporal trends in savanna vegetation and understanding change drivers is therefore crucial. Widespread greening has been identified across southern Africa; yet its drivers and manifestations on the ground remain ambiguous. This study removes the effects of precipitation on an NDVI time-series, thereby identifying trends not driven by rainfall. It utilizes the significant correlation between vegetation and precipitation as captured using MODIS and rainfall estimates. A linear regression between variables was used to derive its residual (corrected) time-series, and the rate and spatial extent of trends were evaluated in relation to biomes. A random sample-based qualitative interpretation of high spatial resolution imagery was then used to evaluate the nature of the trend on the ground. 23.25% of the country, including all biomes exhibited positive trends. We propose that greening may be related to a reduction in woody species richness, loss of the large trees and a shift towards drought tolerant shrub species, as has been shown in other sub-Saharan environments. 3.23% of the country exhibited negative trends, which were mostly associated with more humid (forested) regions pointing to deforestation as a cause; these manifested as vegetation clearing, identifiable using high resolution multi-temporal imagery. Greening trends could not be identified using this approach; instead, they point to the occurrence of gradual vegetation change caused by indirect drivers.