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Nataliia Kussul Andrii Kolotii Sergii Skakun Olena Rakoid Leonid Shumilo 《International Journal of Digital Earth》2020,13(2):309-321
ABSTRACTFor 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. 相似文献
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Increasing water losses from snow captured in the canopy of boreal forests: A case study using a 30 year data set 
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下载免费PDF全文 Nataliia Kozii Hjalmar Laudon Mikaell Ottosson‐Löfvenius Niles J. Hasselquist 《水文研究》2017,31(20):3558-3567
Water losses from snow intercepted by forest canopy can significantly influence the hydrological cycle in seasonally snow‐covered regions, yet how snow interception losses (SIL) are influenced by a changing climate are poorly understood. In this study, we used a unique 30 year record (1986–2015) of snow accumulation and snow water equivalent measurements in a mature mixed coniferous (Picea abies and Pinus sylvestris ) forest stand and an adjacent open area to assess how changes in weather conditions influence SIL. Given little change in canopy cover during this study, the 20% increase in SIL was likely the result of changes in winter weather conditions. However, there was no significant change in average wintertime precipitation and temperature during the study period. Instead, mean monthly temperature values increased during the early winter months (i.e., November and December), whereas there was a significant decrease in precipitation in March. We also assessed how daily variation in meteorological variables influenced SIL and found that about 50% of the variation in SIL was correlated to the amount of precipitation that occurred when temperatures were lower than ?3 °C and to the proportion of days with mean daily temperatures higher than +0.4 °C. Taken together, this study highlights the importance of understanding the appropriate time scale and thresholds in which weather conditions influence SIL in order to better predict how projected climate change will influence snow accumulation and hydrology in boreal forests in the future. 相似文献
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Ian McCallum Carsten Montzka Bagher Bayat Stefan Kollet Andrii Kolotii Nataliia Kussul 《International Journal of Digital Earth》2020,13(2):299-308
ABSTRACTThere is a growing recognition of the interdependencies among the supply systems that rely upon food, water and energy. Billions of people lack safe and sufficient access to these systems, coupled with a rapidly growing global demand and increasing resource constraints. Modeling frameworks are considered one of the few means available to understand the complex interrelationships among the sectors, however development of nexus related frameworks has been limited. We describe three open-source models well known in their respective domains (i.e. TerrSysMP, WOFOST and SWAT) where components of each if combined could help decision-makers address the nexus issue. We propose as a first step the development of simple workflows utilizing essential variables and addressing components of the above-mentioned models which can act as building-blocks to be used ultimately in a comprehensive nexus model framework. The outputs of the workflows and the model framework are designed to address the SDGs. 相似文献
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Recent decades have seen a change in the runoff characteristics of the Suntar River basin in the mountainous, permafrost, hard-to-reach region of Eastern Siberia. This study aims to investigate and simulate runoff formation processes, including the factors driving recent changes in hydrological response of the Suntar River, based on short-term historical observations of a range of hydrological, climatological and landscape measurements conducted in 1957–1959. The hydrograph model is applied as it has the advantage of using observed physical properties of landscapes as its parameters. The developed parametrization of the goltsy landscape (rocky-talus) is verified by comparison of the results of simulations of variable states of snow and frozen ground with observations carried out in 1957–1959. Continuous simulations of streamflow on a daily time step are conducted for the period 1957–2012 in the Suntar River (area 7680 km2, altitude 828–2794 m) with mean and median values of Nash–Sutcliff criteria reaching 0.58 and 0.67, respectively. The results of simulations have shown that the largest component of runoff (about 70%) is produced in the high-altitude area which comprises only 44% of the Suntar River basin area. The simulated streamflow reproduces the patterns of recently observed changes, including the increase in low flows, suggesting that the increase in the proportion of liquid precipitation in autumn due to air temperature rise is an important factor in driving streamflow changes in the region. The data presented are unique for the vast mountainous parts of North-Eastern Eurasia which play an important role in the global climate system. The results indicate that parameterizing a hydrological model based on observations allows the model to be used in studying the response of river basins to climate change with greater confidence. 相似文献
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Gohar Ghazaryan Olena Dubovyk Valerie Graw Nataliia Kussul Jürgen Schellberg 《地理信息系统科学与遥感》2020,57(5):704-718
ABSTRACT Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought. 相似文献
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Grid system for flood extent extraction from satellite images 总被引：4，自引：2，他引：2
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). 相似文献
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