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1.
ABSTRACT

The United Nations' 2030 Agenda for Sustainable Development sets seventeen Sustainable Development Goals (SDGs) to be achieved by 2030. Earth observation are needed that can support the development and validation of transformation policies to make progress towards the SDGs. A participatory and inclusive goal-based approach (GBA) is introduced that links societal goals, targets and indicators to Essential Transformation Variables (ETVs) of the human and non-human environment. The GBA is complementary to the widely used expert-based approach. The GBA is applied to the SDGs at the goal, target and indicator levels. The high-level conceptual model used for the SDGs is humanity embedded in the Earth's life-support system (ELSS). At the goal level, very few of the SDGs are directly focusing on the ELSS and its physiology. Most of the SDG Targets focus on transformations in society and the built environment. Having targets that explicitly focus on the physiology of the ELSS would be important for sustainability. Most of the current indicator measure the built environment and the embedded social fabric. Sustainable development requires a functioning ELSS, and to ensure this, complementary indicators that bring environmental aspects to the monitoring of SDG targets are needed.  相似文献   

2.
A digital earth platform for sustainability   总被引：1，自引：1，他引：0  
ABSTRACT

Based on the experience of the International Society for Digital Earth (ISDE), this paper describes some challenges foreseen in order to develop a Digital Earth platform that can support the implementation of the Sustainable Development Goals. The use of ready-to-use derived geospatial information is essential. Future Earth’s methodology of ‘co-design’ aims to bring together natural, social scientists and decision makers to plan and carry out research for sustainability. Sustainability implies transdisciplinary research, but in order for scientists of different disciplines to work together, they will need to be able to share, access and use common data. This is by far not simple! While the good will to share data might exist, the associated technological, ethical and privacy issues are difficult to solve. An adequate e-infrastructure will be required. ISDE could consider to use the SDGs is the basis to develop the desired Digital Earth platform. This paper, by no means, covers everything for a Digital Earth platform, it aims to trigger research discussions and to have a good view about a starting point.  相似文献   

3.
顾及地理空间视角的区域SDGs综合评估方法与示范   总被引：1，自引：0，他引：1  
目前世界各国正积极落实联合国《2030年可持续发展议程》及其17项可持续发展目标(Sustainable Development Goals,英文缩写为SDGs),重要举措之一是利用统计和地理信息进行SDGs进展评估监测。就总体而言,国内外这方面研究尚处于概念设计、方法探讨和单指标、小范围试点阶段。究其原因,主要是涉及因素众多、技术过程复杂,既面临全球指标体系的科学理解、海量时空数据的融合处理、顾及地理视角的指标计算、基于事实的SDGs分析评估等诸多技术难题,还要实现跨学科的综合分析、多机构的沟通协调等。针对这一国际前沿课题,笔者研究提出了统计和地理信息相结合的综合评估方法,完成了浙江省德清县践行2030议程情况的定量综合评估。既为总结当地践行SDGs经验、发现存在问题、制定改进方案提供了重要科学依据,也为国内外其他区域开展SDGs定量评估监测提供了可借鉴的方法与范例。  相似文献   

4.
ABSTRACT

In 2015, it was adopted the 2030 Agenda for Sustainable Development to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. The year after, 17 Sustainable Development Goals (SDGs) officially came into force. In 2015, GEO (Group on Earth Observation) declared to support the implementation of SDGs. The GEO Global Earth Observation System of Systems (GEOSS) required a change of paradigm, moving from a data-centric approach to a more knowledge-driven one. To this end, the GEO System-of-Systems (SoS) framework may refer to the well-known Data-Information-Knowledge-Wisdom (DIKW) paradigm. In the context of an Earth Observation (EO) SoS, a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data – e.g. social and economic datasets. These elements are: Essential Variables (EVs), Indicators and Indexes, Goals and Targets. Their generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social Ecosystem. This includes: collect, formalize, publish, access, use, and update knowledge. ConnectinGEO project analysed the knowledge necessary to recognize, formalize, access, and use EVs. The analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains.  相似文献   

5.
ABSTRACT

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.  相似文献   

6.
“一带一路”区域可持续发展生态环境遥感监测   总被引：2，自引：1，他引：1  
2013年9月和10月,习近平主席在出访中亚和东南亚国家期间,先后提出了共建"丝绸之路经济带"和"21世纪海上丝绸之路"(简称"一带一路")的重大倡议。要全面保护"一带一路"区域生态环境,实现2030年可持续发展目标,是一个具有挑战性的问题。遥感技术对生态环境监测与评价发挥着十分重要的作用。本研究利用多尺度、多源遥感数据,对2015年"一带一路"区域的生态环境状况进行监测和分析,旨在提供可持续发展目标生态环境遥感监测的本底。本文选取了几个重要的生态环境方面开展监测与分析,主要包括宏观生态系统结构和植被状况、太阳能资源分布、水资源平衡、主要生态环境限制因素对经济走廊建设的影响、主要城市生态环境质量等。监测区域覆盖亚洲、非洲、欧洲和大洋洲的陆上区域。研究结果为生态环境评价与保护提供了有效的决策依据,有助于"一带一路"建设积极推进。  相似文献   

7.
The availability of freely available moderate-to-high spatial resolution (10–30 m) satellite imagery received a major boost with the recent launch of the Sentinel-2 sensor by the European Space Agency. Together with Landsat, these sensors provide the scientific community with a wide range of spatial, spectral, and temporal properties. This study compared and explored the synergistic use of Landsat-8 and Sentinel-2 data in mapping land use and land cover (LULC) in rural Burkina Faso. Specifically, contribution of the red-edge bands of Sentinel-2 in improving LULC mapping was examined. Three machine-learning algorithms – random forest, stochastic gradient boosting, and support vector machines – were employed to classify different data configurations. Classification of all Sentinel-2 bands as well as Sentinel-2 bands common to Landsat-8 produced an overall accuracy, that is 5% and 4% better than Landsat-8. The combination of Landsat-8 and Sentinel-2 red-edge bands resulted in a 4% accuracy improvement over that of Landsat-8. It was found that classification of the Sentinel-2 red-edge bands alone produced better and comparable results to Landsat-8 and the other Sentinel-2 bands, respectively. Results of this study demonstrate the added value of the Sentinel-2 red-edge bands and encourage multi-sensoral approaches to LULC mapping in West Africa.  相似文献   

8.
Main objective of this study was to establish a relationship between land cover and land surface temperature (LST) in urban and rural areas. The research was conducted using Landsat, WorldView-2 (WV-2) and Digital Mapping Camera. Normalised difference vegetation index and normalised difference built-up index were used for establishing the relation between built-up area, vegetation cover and LST for spatial resolution of 30 m. Impervious surface and vegetation area generated from Digital Mapping Camera from Intergraph and WV-2 were used to establish the relation between built-up area, vegetation cover and LST for spatial resolutions of 0.1, 0.5 and 30 m. Linear regression models were used to determine the relationship between LST and indicators. Main contribution of this research is to establish the use of combining remote sensing sensors with different spectral and spatial resolution for two typical settlements in Vojvodina. Correlation coefficients between LST and LST indicators ranged from 0.602 to 0.768.  相似文献   

9.
结合高分遥感和多源数据的高原湖泊流域土地利用分析   总被引：1，自引：0，他引：1  
高分一号卫星是为提升我国高分辨率数据自给率自主发射的卫星，在土地利用监测方面具有重要的应用价值。将GF-1卫星影像与多源数据影像进行对比能够挖掘各数据源在土地利用动态监测方面的差异性。本文以云南省杞麓湖流域为研究区，选取最新的Sentinel-2、GF-1和Landsat 8卫星遥感影像进行土地利用分类，以第二次全国土地利用调查数据为基期数据，结合野外实地调研和土地利用转移矩阵开展土地利用现状和演变分析，得出以下结论：①杞麓湖流域的水域、建设用地、耕地和林地自中心向外呈现出圈层分布的特征，与第二次全国土地调查结果相比，水域、耕地、林地3种类型的自然景观面积减少，而建设用地和其他用地受人类活动的影响，面积大幅增加，变化主要集中在杞麓湖周边的纳古镇和河西镇。②Sentinel-2卫星影像与GF-1卫星影像均具有较高的空间分辨率，二者对土地利用变化评估的结果相近，均优于Landsat 8卫星影像，其中GF-1卫星影像具有较高的应用价值。  相似文献   

10.
In the context of growing populations and limited resources, the sustainable intensification of agricultural production is of great importance to achieve food security. As the need to support management at a range of spatial scales grows, decision-support tools appear increasingly important to enable the timely and regular assessment of agricultural production over large areas and identify priorities for improving crop production in low-productivity regions. Understanding productivity patterns requires the timely provision of gapless, spatial information about agricultural productivity. In this study, dense 30-m time series covering the 2004–2014 period were generated from Landsat and MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images over the irrigated cropped area of the Fergana Valley, Central Asia. A light-use efficiency model was combined with machine learning classifiers to assess the crop yield at the field level. The classification accuracy of land cover maps reached 91% on average. Crop yield and acreage estimates were in good agreement (R2 = 0.812 and 0.871, respectively) with reported yields and acreages at the district level. Several indicators of cropland intensity and productivity were derived on a per-field basis and used to highlight homogeneous regions in terms of productivity by means of clustering. Results underlined that regions with lower water-use efficiency were not only located further away from irrigation canals and intake points, but also had limited access to markets and roads. The results underline that yield could be increased by roughly 1.0 and 1.4 t/ha for cotton and wheat, respectively, if the access to water would be optimized in some of the regions. The minimum calibration requirement of the method and the fusion of multi-sensor data are keys to cope with the constraints of operational crop monitoring and guarantee a sustained and timely delivery of the agricultural indicators to the user community. The results of this study can form the baseline to support regional land- and water-resource management.  相似文献   

11.
Land degradation is a critical issue globally requiring immediate actions for protecting biodiversity and associated services provided by ecosystems that are supporting human quality of life. The latest Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services Landmark Assessment Report highlighted that human activities are considerably degrading land and threating the well-being of approximately 3.2 billion people.In order to reduce and ideally reverse this prevailing situation, national capacities should be strengthened to enable effective assessments and mapping of their degraded lands as recommended by the United Nations Sustainable Development Goals (SDGs). The indicator 15.3.1 (“proportion of land that is degraded over total land area”) requires regular data production by countries to inform and assess it through space and time. Earth Observations (EO) can play an important role both for generating the indicator in countries where it is missing, as well complementing or enhancing national official data sources.In response to this issue, this paper presents an innovative, scalable and flexible approach to monitor land degradation at various scales (e.g., national, regional, global) using various components of the Global Earth Observation System of Systems (GEOSS) platform to leverage EO resources for informing SDG 15.3.1. The proposed approach follows the Data-Information-Knowledge pattern using the Trends.Earth model (http://trends.earth) and various data sources to generate the indicator. It also implements additional components for model execution and orchestration, knowledge management, and visualization.The proposed approach has been successfully applied at global, regional and national scales and advances the vision of (1) establishing data analytics platforms that can potentially support countries to discover, access and use the necessary datasets to assess land degradation; and (2) developing new capacities to effectively and efficiently use EO-based resources.  相似文献   

12.
ABSTRACT

In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency’s Sentinel missions have been used in various remote sensing applications. However, there is a lack of studies that utilize these data to assess the performance of machine learning algorithms in complex boreal landscapes. In this article, I compare the classification performance of four non-parametric algorithms: support vector machines (SVM), random forests (RF), extreme gradient boosting (Xgboost), and deep learning (DL). The study area chosen is a complex mixed-use landscape in south-central Sweden with eight land-cover and land-use (LCLU) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 covering spring, summer, autumn and winter conditions. Using stratified random sampling, each LCLU class was allocated 1477 samples, which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, but primarily overall accuracy was used in allocating algorithm hierarchy. A two-proportion Z-test was used to compare the proportions of correctly classified pixels of the algorithms and a McNemar’s chi-square test was used to compare class-wise predictions. The results show that the highest overall accuracy was produced by support vector machines (0.758 ± 0.017), closely followed by extreme gradient boosting (0.751 ± 0.017), random forests (0.739 ± 0.018), and finally deep learning (0.733 ± 0.0023). The Z-test comparison of classifiers showed that a third of algorithm pairings were statistically different. On a class-wise basis, McNemar’s test results showed that 62% of class-wise predictions were significant from one another at the 5% level or less. Variable importance metrics show that nearly half of the top twenty Sentinel-2 bands belonged to the red edge (25%) and shortwave infrared (23%) portions of the electromagnetic spectrum, and were dominated by scenes from spring (38%) and summer (40%). The results are discussed within the scope of recent studies involving machine learning and Sentinel-2 data and key knowledge gaps identified. The article concludes with recommendations for future research.  相似文献   

13.
The 17 Sustainable Development Goals (SDGs) aim to end extreme poverty and create a healthy, sustainable world by the year 2030. Goal 7 is of interest to this study as it targets access to clean and affordable energy. However, in this study we show that the energy created in South Africa is not necessary clean. South Africa has numerous coal-fired power station located in the Mpumalanga (MP), Gauteng (GP) and Limpopo (LP) provinces. These power station produce tons of toxic pollutants including sulphur dioxide (SO2), nitrogen dioxide (NO2) and sulphates (SO4). These pollutants are known to have a negative impact on human health, climate and the environment. In this study we use the sequential Mann-Kendall test to investigate the 39 year (1980–2019) trends of SO2, NO2 and SO4 from these source areas. We also report for the first time on the observations of SO2 and NO2 from the Sentinel-5 P sensor over South Africa. Increasing trends of SO2 were observed in the MP, LP and GP regions. The increase was mostly due to the emissions from coal-fired power stations. Moreover, the increase of SO2 over the years could be due to the increasing demand in electricity, aging power stations and the low quality of coal used. Sentinel-5 P observations of SO2 and NO2 over South Africa were observed in the MP, GP and LP regions as a result of coal-fired power stations. Dispersion of SO2 and NO2 over South Africa were observed in the winter months, while confined SO2 and NO2 in the source region were observed in the summer months.  相似文献   

14.
Abstract

A methodology is presented for estimating percent coverage of impervious surface (IS) and forest cover (FC) within Landsat thematic mapper (TM) pixels of urban areas. High-resolution multi-spectral images from Quickbird (QB) play a key role in the sub-pixel mapping process by providing information on the spatial distributions of ISs and FCs at 2.4 m ground sampling intervals. Thematic classifications, also derived from the Landsat imagery, have then been employed to define relationships between 30 m Landsat-derived greenness values and percent IS and FC. By also utilizing land cover/land use classification derived from Landsat and defining unique relationships for urban sub-classes (i.e. residential, commercial/industrial, open land), confusion between impervious and fallow agricultural lands has been overcome. Test results are presented for Ottawa-Gatineau, an urban area that encompasses many aspects typical of the North American urban landscape. Multiple QB scenes have been acquired for this urban centre, thereby allowing us to undertake an in-depth study of the error budgets associated with the fractional inference process.  相似文献   

15.
Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250-m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25-km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not related to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68% was found between the rainfall-corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ‘severe’ vegetation productivity losses under high land-use intensities.  相似文献   

16.
ABSTRACT

The presence of green spaces within city centres has been recognized as a valuable component of the city landscape. Vegetation provides a variety of benefits including energy saving, improved air quality, reduced noise pollution, decreased ambient temperature and psychological restoration. Evidence also shows that the amount of vegetation, known as ‘greenness’, in densely populated areas, can also be an indicator of the relative wealth of a neighbourhood. The ‘grey-green divide’, the contrast between built-up areas with a dominant grey colour and green spaces, is taken as a proxy indicator of sustainable management of cities and planning of urban growth. Consistent and continuous assessment of greenness in cities is therefore essential for monitoring progress towards the United Nations Sustainable Development Goal 11. The availability of multi-temporal greenness information from Landsat data archives together with data derived from the city centres database of the Global Human Settlement Layer (GHSL) initiative, offers a unique perspective to quantify and analyse changes in greenness across 10,323 urban centres all around the globe. In this research, we assess differences between greenness within and outside the built-up area for all the urban centres described by the city centres database of the GHSL. We also analyse changes in the amount of green space over time considering changes in the built-up areas in the periods 1990, 2000 and 2014. The results show an overall trend of increased greenness between 1990 and 2014 in most cities. The effect of greening is observed also for most of the 32 world megacities. We conclude that using simple yet effective approaches exploiting open and free global data it is possible to provide quantitative information on the greenness of cities and its changes over time. This information is of direct interest for urban planners and decision-makers to mitigate urban related environmental and social impacts.  相似文献   

17.
The North Peixian mining area of China has rich coal resources, with total proven reserves of 2.37 billion tons. However, the underground coal mining activities have resulted in ground collapse, which has caused serious harm to the environment and threatened the lives and properties of local residents. In this study, 12 Sentinel-1A terrain observation by progressive scans (TOPS) mode acquisitions between 30 July 2015 and 13 May 2016 over the abandoned mining area in North Peixian were analyzed using the interferometric synthetic aperture radar (InSAR) time series method to detect the ground subsidence, with the maximum ground subsidence reaching 83 mm/a and an average value of about 12.7 mm/a. The subsidence results derived from the Sentinel-1A TOPS mode dataset were proven to be effective in investigating and monitoring the ground subsidence in the North Peixian mining area. Compared to the rapid deformation during the ongoing period of mining excavation, the ground subsides slowly in abandoned mining areas and shows a linear relationship with time over a relatively long period of time. Spatial correlation between the subsidence distribution and land cover was found, in that the magnitude of the subsidence in urban areas was smaller than that in rural areas, which is associated with the controlled coal mining activities under buildings, railways, and water bodies. The results demonstrate that Sentinel-1A TOPS SAR images can be used to effectively and accurately detect and monitor ground subsidence in a mining area, which is critically important when investigating land subsidence in a large-scale mining area.  相似文献   

18.
Total evaporation is of importance in assessing and managing long-term water use, especially in water-limited environments. Therefore, there is need to account for water utilisation by different land uses for well-informed water resources management and future planning. This study investigated the feasibility of using multispectral Landsat 8 and moderate resolution imaging spectroradiometer (MODIS) remote sensing data to estimate total evaporation within the uMngeni catchment in South Africa, using surface energy balance system. The results indicated that Landsat 8 at 30 m resolution has a better spatial representation of total evaporation, when compared to the 1000 m MODIS. Specifically, Landsat 8 yielded significantly different mean total evaporation estimates for all land cover types (one-way ANOVA; F4.964?=?87.011, p < 0.05), whereas MODIS failed to differentiate (one-way ANOVA; F2.853?=?0.125, p = 0.998) mean total evaporation estimates for the different land cover types across the catchment. The findings of this study underscore the utility of the Landsat 8 spatial resolution and land cover characteristics in deriving accurate and reliable spatial variations of total evaporation at a catchment scale.  相似文献   

19.
Changes in forest composition impact ecological services, and are considered important factors driving global climate change. A hybrid sampling method along with a modelling approach to map current and past land cover in Kunming, China is reported. MODIS land cover (2001–2011) data-sets were used to detect pixels with no apparent change. Around 3000 ‘no change points’ were systematically selected and sampled using Google Earth’s high-resolution imagery. Thirty-five per cent of these points were verified and used for training and validation. We used Random forests to classify multi-temporal Landsat imagery. Results show that forest cover has had a net decrease of 14385?ha (1.3% of forest area), which was primary converted to shrublands (11%), urban and barren land (2.7%) and agriculture (2.5%). Our validation indicates an overall accuracy (Kappa) of 82%. Our methodology can be used to consistently map the dynamics of land cover change in similar areas with minimum costs.  相似文献   

20.
Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.  相似文献   

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