Snow Observations from Space: an Approach to Map Snow Cover from Three Decades of Landsat Imagery Across Switzerland

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

SwissEnvEO: A FAIR National Environmental Data Repository for Earth Observation Open Science

Environmental scientific research is highly becoming data-driven and dependent on high performance computing infrastructures to process ever increasing large volume and diverse data sets. Consequently, there is a growing recognition of the need to share data, methods, algorithms, and infrastructure to make scientific research more effective, efficient, open, transparent, reproducible, accessible, and usable by different users. However, Earth Observations (EO) Open Science is still undervalued, and different challenges remains to achieve the vision of transforming EO data into actionable knowledge by lowering the entry barrier to massive-use Big Earth Data analysis and derived information products. Currently, FAIR-compliant digital repositories cannot fully satisfy the needs of EO users, while Spatial Data Infrastructures (SDI) are not fully FAIR-compliant and have difficulties in handling Big Earth Data. In response to these issues and the need to strengthen Open and Reproducible EO science, this paper presents SwissEnvEO, a Spatial Data Infrastructure complemented with digital repository capabilities to facilitate the publication of Ready to Use information products, at national scale, derived from satellite EO data available in an EO Data Cube in full compliance with FAIR principles

Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors

ABSTRACTEnvironmental changes are significantly modifying terrestrial vegetation dynamics, with serious consequences on Earth system functioning and provision of ecosystem services. Land conditions are an essential element underpinning global sustainability frameworks, such as the Sustainable Development Goals (SDGs), requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces. At the global scale, long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate. However, greening trends at the national scale have received less attention, although countries like Switzerland are prone to important changing climate conditions. Hereby, we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index (NDVI) to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature, precipitation, and land cover to investigate possible responses of changing climatic conditions. Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61% significant pixels across Switzerland. In particular, the seasonal mean NDVI shows an important break for winter, autumn and spring seasons starting from 2010, potentially indicating a critical point of changing land conditions. At biogeographical scale, we observed an apparent clustering (Jura-Plateau; Northern-Southern Alps; Eastern-Western Alps) related to landscape characteristics, while forested land cover classes are more responsive to NDVI changes. Finally, the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation. The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions. This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale

Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors

Environmental changes are significantly modifying terrestrial vegetation dynamics, with serious consequences on Earth system functioning and provision of ecosystem services. Land conditions are an essential element underpinning global sustainability frameworks, such as the Sustainable Development Goals (SDGs), requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces. At the global scale, long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate. However, greening trends at the national scale have received less attention, although countries like Switzerland are prone to important changing climate conditions. Hereby, we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index (NDVI) to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature, precipitation, and land cover to investigate possible responses of changing climatic conditions. Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61% significant pixels across Switzerland. In particular, the seasonal mean NDVI shows an important break for winter, autumn and spring seasons starting from 2010, potentially indicating a critical point of changing land conditions. At biogeographical scale, we observed an apparent clustering (Jura-Plateau; Northern-Southern Alps; Eastern-Western Alps) related to landscape characteristics, while forested land cover classes are more responsive to NDVI changes. Finally, the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation. The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions. This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale

Normalized Difference Vegetation Index (NDVI) - Annual Mean - Switzerland

AbstractThis dataset is an annual time-serie of Landsat Analysis Ready Data (ARD)-derived Normalized Difference Vegetation Index (NDVI) computed from Landsat 5 Thematic Mapper (TM) and Landsat 8 Opeational Land Imager (OLI). To ensure a consistent dataset, Landsat 7 has not been used because the Scan Line Correct (SLC) failure creates gaps into the data. NDVI quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs) using this generic formula: (NIR - R) / (NIR + R) For Landsat sensors, this corresponds to the following bands: Landsat 5, NDVI = (Band 4 – Band 3) / (Band 4 + Band 3). Landsat 8, NDVI = (Band 5 – Band 4) / (Band 5 + Band 4). NDVI values ranges from -1 to +1. NDVI is used to quantify vegetation greenness and is useful in understanding vegetation density and assessing changes in plant health. Standard Deviation is also provided for each time step. Data format: GeoTiff This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch

Normalized Difference Water Index (NDWI) - Seasonal Standard Deviation - Switzerland

AbstractThis dataset is a seasonal standard deviation time-series of Landsat Analysis Ready Data (ARD)-derived Normalized Difference Water Index (NDWI) computed from Landsat 5 Thematic Mapper (TM) and Landsat 8 Opeational Land Imager (OLI). It complements the seasonal mean dataset. To ensure a consistent dataset, Landsat 7 has not been used because the Scan Line Correct (SLC) failure creates gaps into the data. NDWI quantifies plant water content by measuring the difference between Near-Infrared (NIR) and Short Wave Infrared (SWIR) (or Green) channels using this generic formula: (NIR - SWIR) / (NIR + SWIR) For Landsat sensors, this corresponds to the following bands: Landsat 5, NDVI = (Band 4 – Band 2) / (Band 4 + Band 2). Landsat 8, NDVI = (Band 5 – Band 3) / (Band 5 + Band 3). NDWI values ranges from -1 to +1. NDWI is a good proxy for plant water stress and therefore useful for drought monitoring and early warning. NDWI is sometimes alos refered as Normalized Difference Moisture Index (NDMI) Spring: March-April_May (_MAM) Summer: June-July-August (_JJA) Autumn: September-October-November (_SON) Winter: December-January-February (_DJF) Data format: GeoTiff This dataset has been generated with the Swiss Data Cube (http://www.swissdatacube.ch

Normalized Difference Water Index (NDWI) - Annual Mean - Switzerland

AbstractThis dataset is an annual time-serie of Landsat Analysis Ready Data (ARD)-derived Normalized Difference Water Index (NDWI) computed from Landsat 5 Thematic Mapper (TM) and Landsat 8 Opeational Land Imager (OLI). To ensure a consistent dataset, Landsat 7 has not been used because the Scan Line Correct (SLC) failure creates gaps into the data. NDWI quantifies plant water content by measuring the difference between Near-Infrared (NIR) and Short Wave Infrared (SWIR) (or Green) channels using this generic formula: (NIR - SWIR) / (NIR + SWIR) For Landsat sensors, this corresponds to the following bands: Landsat 5, NDVI = (Band 4 – Band 2) / (Band 4 + Band 2). Landsat 8, NDVI = (Band 5 – Band 3) / (Band 5 + Band 3). NDWI values ranges from -1 to +1. NDWI is a good proxy for plant water stress and therefore useful for drought monitoring and early warning. NDWI is sometimes alos refered as Normalized Difference Moisture Index (NDMI) Standard Deviation is also provided for each time step. Data format: GeoTiff This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch

Normalized Difference Vegetation Index (NDVI) - Seasonal Mean - Switzerland

AbstractThis dataset is a seasonal time-series of Landsat Analysis Ready Data (ARD)-derived Normalized Difference Vegetation Index (NDVI) computed from Landsat 5 Thematic Mapper (TM) and Landsat 8 Opeational Land Imager (OLI). To ensure a consistent dataset, Landsat 7 has not been used because the Scan Line Correct (SLC) failure creates gaps into the data. NDVI quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs) using this generic formula: (NIR - R) / (NIR + R) For Landsat sensors, this corresponds to the following bands: Landsat 5, NDVI = (Band 4 – Band 3) / (Band 4 + Band 3). Landsat 8, NDVI = (Band 5 – Band 4) / (Band 5 + Band 4). NDVI values ranges from -1 to +1. NDVI is used to quantify vegetation greenness and is useful in understanding vegetation density and assessing changes in plant health. Standard Deviation is provided in a separate dataset for each time step. Spring: March-April_May (_MAM) Summer: June-July-August (_JJA) Autumn: September-October-November (_SON) Winter: December-January-February (_DJF) Data format: GeoTiff This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch

Normalized Difference Water Index (NDWI) - Seasonal Mean - Switzerland

AbstractThis dataset is a seasonal time-series of Landsat Analysis Ready Data (ARD)-derived Normalized Difference Water Index (NDWI) computed from Landsat 5 Thematic Mapper (TM) and Landsat 8 Opeational Land Imager (OLI). To ensure a consistent dataset, Landsat 7 has not been used because the Scan Line Correct (SLC) failure creates gaps into the data. NDWI quantifies plant water content by measuring the difference between Near-Infrared (NIR) and Short Wave Infrared (SWIR) (or Green) channels using this generic formula: (NIR - SWIR) / (NIR + SWIR) For Landsat sensors, this corresponds to the following bands: Landsat 5, NDVI = (Band 4 – Band 2) / (Band 4 + Band 2). Landsat 8, NDVI = (Band 5 – Band 3) / (Band 5 + Band 3). NDWI values ranges from -1 to +1. NDWI is a good proxy for plant water stress and therefore useful for drought monitoring and early warning. NDWI is sometimes alos refered as Normalized Difference Moisture Index (NDMI) Standard Deviation is provided in a separate dataset for each time step. Spring: March-April_May (_MAM) Summer: June-July-August (_JJA) Autumn: September-October-November (_SON) Winter: December-January-February (_DJF) Data format: GeoTiff This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch