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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 solu- tions to follow and evaluate impacts of changing land condi- tions induced by various driving forces. In Switzerland, drought risk will significantly increase in the coming decades with severe consequences on agriculture, energy production and vegeta- tion. 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 func- tion 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 obser- vations for national statistics on the ecological state of vegeta- tion to support SDG 15 to monitor land affected by drying conditions.

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., Measurement(s)surface reflectance • backscatterTechnology Type(s)satellite imagingSample Characteristic - LocationSwitzerland Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14635485

Coastal and marine ecosystems are supplying a wide range of services. With accelerated Sea Level Rise, intensification of waves and storm surge severity and increasing anthropogenic pressures, these areas are under multiple threats and society may not receive the same level of ecosystems services. This study aims at measuring the trend of beach erosion and at identifying and quantifying the role of some coastal and marine ecosystems in mitigating beach erosion in the region of Negril (Jamaica). In this location, the tourism industry provides the main source of economic revenue. Even at the national level, the two beaches are important assets linked with 5% of the national revenue as 25% of the hotel rooms are located around Negril. In Jamaica, the tourism industry is a significant component of national GDP. 25% of hotel rooms are located around the two beaches of Negril, which have lost an average of 23.4 m of width since 1968. Given the importance of Negril’s beaches to their economy, the Government of Jamaica asked UNEP to conduct a study to identify causes of beach erosion in Negril and potential solutions to address trends of beach erosion, in the context of future sea level rise scenarios induced by climate change. This paper addresses the current beach erosion status and future trends under different climate scenarios. We explain how, by using remote sensing, GIS, wave modelling and multiple regressions analysis associated with national, local and community consultations, we were able to identify and quantify the role of ecosystems for mitigating beach erosion. We show that larger widths of coral and seagrass meadows reduce beach erosion.

This paper presents estimates of the geographical and technical potentials for solar electricity generation in rural areas of West Africa (ECOWAS region). The study is performed by application of Geographic Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods. We study both opportunities for large-scale grid-connected solar systems (photovoltaic, and concentrated solar power technologies), and for off-grid solar systems (photovoltaic). Locations are evaluated according to their suitability for solar systems’ deployment according to topographical, legal, and social constraints, as well as factors that could facilitate or impede solar generation development. The study is conducted on a regional scale. The results can be used for identification of potential areas of interest for solar generation deployment, and as a support for integration between electricity grid expansion and off-grid electrification policies.

Global environmental changes are mostly induced by human activities (e.g., food and energy production, urbanization, mining activities). To assess and understand these changes that are occurring all around the planet, regular and continuous monitoring is an essential condition. However, due to the potentially large area spread over numerous locations that need to be followed, this usually leads to a low frequency of monitoring of environmental changes of only a few selected sites at best. With the increasing number of freely and openly accessible big remotely-sensed Earth Observations (EO) Data repositories and the increasing capabilities of open and interoperable software solutions it is now possible to automate various EO data processing tasks to monitor environmental changes at large scale. This paper presents the Live Monitoring of Earth Surface (LiMES) framework that helps to automate image processing tasks in transforming raw data into information and knowledge through workflows using interoperable processing service chains for monitoring environmental changes. Both benefits and limitations are demonstrated and discussed through the implementation of a prototype to facilitate the update on the status of some of the 278 UNEP Environmental Hotspots. We believe that such a framework can help to reduce the gap between massive volumes of EO data and the users such as International Organizations (IO) in order to help them better fulfil their environmental monitoring mandates by bringing raw data to a level which can be used by non-remote sensing experts for basic impacts assessments.

Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability. Some of these environmental issues can be monitored using remotely sensed Earth Observations (EO) data that are increasingly available from a number of freely and openly accessible repositories. However, the full information potential of EO data has not been yet realized. They remain still underutilized mainly because of their complexity, increasing volume, and the lack of e cient processing capabilities. EO Data Cubes (DC) are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form. Systematic and regular provision of Analysis Ready Data (ARD) will signi cantly reduce the burden on EO data users. Nevertheless, ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task. This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains. The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.

Mountainous regions are particularly vulnerable to climate change, and the impacts are already extensive and observable, the implications of which go far beyond mountain boundaries and the environmental sectors. Monitoring and understanding climate and environmental changes in mountain regions is, therefore, needed. One of the key variables to study is snow cover, since it represents an essential driver of many ecological, hydrological and socioeconomic processes in mountains. As remotely sensed data can contribute to filling the gap of sparse in-situ stations in high-altitude environments, a methodology for snow cover detection through time series analyses using Landsat satellite observations stored in an Open Data Cube is described in this paper, and applied to a case study on the Gran Paradiso National Park, in the western Italian Alps. In particular, this study presents a proof of concept of the preliminary version of the snow observation from space algorithm applied to Landsat data stored in the Swiss Data Cube. Implemented in an Earth Observation Data Cube environment, the algorithm can process a large amount of remote sensing data ready for analysis and can compile all Landsat series since 1984 into one single multi-sensor dataset. Temporal filtering methodology and multi-sensors analysis allows one to considerably reduce the uncertainty in the estimation of snow cover area using high-resolution sensors. The study highlights that, despite this methodology, the lack of available cloud-free images still represents a big issue for snow cover mapping from satellite data. Though accurate mapping of snow extent below cloud cover with optical sensors still represents a challenge, spatial and temporal filtering techniques and radar imagery for future time series analyses will likely allow one to reduce the current cloud cover issue.

The relation between the fraction of snow cover and the spectral behavior of the surface is a critical issue that must be approached in order to retrieve the snow cover extent from remotely sensed data. Ground-based cameras are an important source of datasets for the preparation of long time series concerning the snow cover. This study investigates the support provided by terrestrial photography for the estimation of a site-specific threshold to discriminate the snow cover. The case study is located in the Italian Alps (Falcade, Italy). The images taken over a ten-year period were analyzed using an automated snow-not-snow detection algorithm based on Spectral Similarity. The performance of the Spectral Similarity approach was initially investigated comparing the results with different supervised methods on a training dataset, and subsequently through automated procedures on the entire dataset. Finally, the integration with satellite snow products explored the opportunity offered by terrestrial photography for calibrating and validating satellite-based data over a decade.

Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability. The key to sustainable development is achieving a balance between the exploitation of natural resources for socioeconomic development and maintaining ecosystem services that are critical to human’s wellbeing and livelihoods. Some of these environmental issues can be monitored using remotely sensed Earth Observations (EO) data that are increasingly available from freely and openly accessible repositories. Hereafter, we present the Swiss Data Cube, a unique Analysis Ready Data archive of satellite imagery and some use cases to monitor Sustainable Development Goals.

With more than half of the world’s population living in towns and cities, urban areas get more and more into the focus of humanitarian relief organisations such as ICRC, Médecins sans Frontières (MSF), or SOS Children’s Villages. A key information required for almost any intervention is an estimation of the population numbers for the towns and cities where these organisations operate in. As census data are usually not available or outdated, population numbers have to be estimated by alternative methods such as remote sensing. To do that built-up densities are estimated from high-resolution image data and population numbers are disaggregated proportional to the densities in a top-down approach. Alternatively, population counts per density unit can be aggregated following a bottom-up approach. Both approaches were tested applying normalised Digital Surface Models (nDSM) derived from tri-stereo Pléiade images for Salzburg, Austria and Port-au-Prince, Haiti; the former for testing the quality and stability of the approach in a well-known setting, the latter for testing the approach in a critical environment. Key findings are that satellite-derived nDSMs provide sufficient accuracy for estimating population distributions, as long as reliable information is available for the separation of residential and non-residential urban areas.

Earth Observations Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with Earth Observations (EO) data. They can provide access to large spatio-temporal data in analysis ready format. Systematic and regular provision of Analysis Ready Data (ARD) can significantly reduce the burden on EO data users by minimizing the time and scientific knowledge required to access and prepare remotely-sensed data having consistent and spatially aligned calibrated surface reflectance observations. Currently, Sentinel-2 ARD are not commonly generated by the Copernicus program and consequently getting uniform and consistent Sentinel-2 ARD remains a challenging task. This paper presents an approach to generate Sentinel-2 ARD using an automated processing services chain. The approach has been tested and validated to complete the Swiss Data Cube with Sentinel-2 data.

Snow accumulation is one of the most important forms of water storage. The natural cycle of water is being increasingly influenced by climate change and will continue to change in the future. To understand the evolution of snow cover and to perfect its accurate detection UN Environment/GRID-Geneva and the University of Geneva have developed a Snow Detection tool called “Snow Observations from Space” for the Swiss Data Cube. The Snow Detection tool uses the C Function of Mask to identify snow pixels and then subsequently produces a normalized detection raster. Through further development, this tool will reach its full potential as an accurate method of detecting snow cover change for Switzerland.

Assessments of tropical cyclone risk trends are typically based on reported losses, which are biased by improvements in information access. Now research based on thousands of physically observed events and contextual factors shows that, despite projected reductions in tropical cyclone frequency, projected increases in demographic pressure and tropical cyclone intensity can be expected to exacerbate disaster risk.

Forests represent important habitats for species and provide multiple ecosystem services for human well-being. Preserving forests and other terrestrial ecosystems has become crucial to halt desertification, land degradation, and biodiversity loss worldwide, and is also one of the Sustainable Development Goals (SDGs) to be achieved by 2030. Remote sensing could greatly contribute to measuring progress toward SDGs by providing consistent and repetitive coverage of large areas, as well as information in various wavelengths, which facilitates the monitoring of environmental trends at various scales. This paper focuses on SDG indicator 15.1.1&mdash, &ldquo, Forest area as a percentage of total land area&rdquo, to demonstrate the potential of Earth Observation Data Cubes for SDGs. The approach presented here uses Landsat Analysis Ready Data (ARD) stored in the Swiss Data Cube, and offers a complementary method to ground-based approaches to monitor Switzerland&rsquo, s forest extent based on the Normalized Difference Vegetation Index (NDVI). The proposed method performs time-series analyses to extract a forest/non-forest map and a graph representing the trend of SDG 15.1.1 indicator over time. Preliminary results suggest that this approach can identify similar forest extent and growth patterns to observed trends, and can therefore help monitor progress toward the selected SDG indicator more effectively.

The tsunami that deeply impacted the North Indian Ocean shores on 26 December 2004, called for urgent rehabilitation of coastal infrastructures to restore the livelihood of local populations. A spatial and statistical analysis was performed to identify what geomorphological and biological configurations (mangroves forests, coral and other coastal vegetation) are susceptible to decrease or increase coastal vulnerability to tsunami. The results indicate that the width of flooded land strip was, in vast majority, influenced by the distance to fault lines as well as inclination and length of proximal slope. Areas covered by seagrass beds were less impacted, whereas areas behind coral reefs were more affected. The mangroves forests identified in the study were all located in sheltered areas, thus preventing to address the potential protecting role of mangroves forests.

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.

Environmental sustainability is nowadays a major global issue that requires efficient and effective responses from governments. Essential variables (EV) have emerged in different scientific communities as a means to characterize and follow environmental changes through a set of measurements required to support policy evidence. To help track these changes, our planet has been under continuous observation from satellites since 1972. Currently, petabytes of satellite Earth observation (EO) data are freely available. However, the full information potential of EO data has not been yet realized because many big data challenges and complexity barriers hinder their effective use. Consequently, facilitating the production of EVs using the wealth of satellite EO data can be beneficial for environmental monitoring systems. In response to this issue, a comprehensive list of EVs that can take advantage of consistent time-series satellite data has been derived. In addition, a set of use-cases, using an Earth Observation Data Cube (EODC) to process large volumes of satellite data, have been implemented to demonstrate the practical applicability of EODC to produce EVs. The proposed approach has been successfully tested showing that EODC can facilitate the production of EVs at different scales and benefiting from the spatial and temporal dimension of satellite EO data for enhanced environmental monitoring.