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| 1 | Drying conditions in Switzerland-indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs显示文摘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. | Charlotte Poussin Alexandrine Massot Christian Ginzler Dominique Weber Bruno Chatenoux Pierre Lacroix Thomas Piller Liliane Nguyen Gregory Giuliani | 2021 | Big Earth Data2021，5，4： | 1 |
| 2 | Virtual earth cloud: a multi-cloud framework for enabling geosciences digital ecosystems显示文摘Humankind is facing unprecedented global environmental and social challenges in terms of food,water and energy security,resilience to natural hazards,etc.To address these challenges,international organizations have defined a list of policy actions to be achieved in a relatively short and medium-term timespan.The development and use of knowledge platforms is key in helping the decision-making process to take significant decisions(providing the best available knowledge)and avoid potentially negative impacts on society and the environment.Such knowledge platforms must build on the recent and next coming digital technologies that have transformed society–including the science and engineering sectors.Big Earth Data(BED)science aims to provide the methodologies and instruments to generate knowledge from numerous,complex,and diverse data sources.BED science requires the development of Geoscience Digital Ecosystems(GEDs),which bank on the combined use of fundamental technology units(i.e.big data,learning-driven artificial intelligence,and network-based computing platform)to enable the development of more detailed knowledge to observe and test planet Earth as a whole.This manuscript contributes to the BED science research domain,by presenting the Virtual Earth Cloud:a multi-cloud framework to support GDE implementation and generate knowledge on environmental and social sustainability. | Mattia Santoro Paolo Mazzetti Stefano Nativi | 2023 | International Journal of Digital Earth2023，16，1： | 1 |
| 3 | Forest status assessment in China with SDG indicators based on high-resolution satellite data显示文摘To assess the status and change trend of forest in China,an indicator framework was developed using SDG sub-indicators.In this paper,we propose an improved methodology and a set of workflows for calculating SDG indicators.The main modification include the use of moderate and high spatial resolution satellite data,as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass(AGB).This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover,while time series Landsat data is used to estimate forest AGB across the whole territory of China.The study calculate two SDG sub-indicators:SDG_(15.1.1) for forest area and SDG_(15.2.1) for sustainable forest management.The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021,accounting for about 23.51%of the land area.The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha,and the overall trend of forest AGB change in China was positive,albeit with some spatial differences. | Xiaomei Zhang Guojin He Shijie Yan Tengfei Long Xueli Peng Zhaoming Zhang Guizhou Wang | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
| 4 | Global degradation trends of grassland and their driving factors since 2000显示文摘Grassland is the second largest terrestrial ecosystem and a fundamental land resource for human survival and development.Although grassland degradation is a recognized and crucial ecological problem,there is no consensus on the area,scope,and degree of its global degradation trends,making the implementation of Sustainable Development Goals(SDG)15.3 for achieving a land degradation-neutral world uncertain.This study quantitatively explored global grassland degradation trends from 2000 to 2020 by coupling vegetation growth and its response to climate change.Furthermore,the driving factors behind these trends were analyzed,especially in hotspots.Results show that the improvement in global grassland has been remarkable since 2000,with a 1.92 times larger area than degrading grassland,amounting to 372.47×10^(4) and 193.57×10^(4) km^(2),respectively.Africa and Asia lead in global grassland degradation and improvement,respectively.Globally,the combined effects of climate change and human activities are the main driving factors for grassland degradation and improvement,accounting for 84.72 and 87.76%,respectively.Notably,human activities played a crucial role in reversing the trend of grassland degradation in some hotspots.Finally,this study provides an essential scientific reference and support for realizing SDG 15.3 on global and regional scales. | Ziyu Yan Zhihai Gao Bin Sun Xiangyuan Ding Ting Gao Yifu Li | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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