A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

ABSTRACT

For evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.     

Contribution of the Solar Constant Variations to Calculations of Insolation for the Holocene Period

Geomagnetism and Aeronomy - Insolation is one of the most important factors that affect the changes in the global climate and weather. Therefore, its accurate calculation is a relevant question of...     

Problems in Forecasting of the Decennial Solar Activity in Terms of TSI by the Method of Analogs

Geomagnetism and Aeronomy - Reconstruction and forecasting the data series of total solar irradiation (TSI) is one of the most urgent scientific problems at the moment. In this paper, we review...     

Long-term Changes in Total Solar Irradiance and Their Predictions

Geomagnetism and Aeronomy - The Total Solar Irradiance (TSI) index of solar activity attracts the attention of a wide scientific audience due to its direct influence on the Earth’s climate. A...     

Grid system for flood extent extraction from satellite images

Floods are among the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is a flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a new method to the flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. We implemented our approach in a Grid system that was used to process data from three different satellite sensors: ERS-2/SAR during the flooding on the river Tisza, Ukraine and Hungary (2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 during the flooding on the river Huaihe, China (2007).