可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

TO–YOLOX: a pure CNN tiny object detection model for remotesensing images

Remote sensing and deep learning are being widely combined in tasks such as urban planning and disaster prevention.However,due to interference occasioned by density,overlap,and coverage,the tiny object detection in remote sensing images has always been a difficult problem.Therefore,we propose a novel TO–YOLOX(Tiny Object–You Only Look Once)model.TO–YOLOX possesses a MiSo(Multiple-in-Singleout)feature fusion structure,which exhibits a spatial-shift structure,and the model balances positive and negative samples and enhances the information interaction pertaining to the local patch of remote sensing images.TO–YOLOX utilizes an adaptive IOU-T(Intersection Over Uni-Tiny)loss to enhance the localization accuracy of tiny objects,and it applies attention mechanism Group-CBAM(group-convolutional block attention module)to enhance the perception of tiny objects in remote sensing images.To verify the effectiveness and efficiency of TO–YOLOX,we utilized three aerial-photography tiny object detection datasets,namely VisDrone2021,Tiny Person,and DOTA–HBB,and the following mean average precision(mAP)values were recorded,respectively:45.31%(+10.03%),28.9%(+9.36%),and 63.02%(+9.62%).With respect to recognizing tiny objects,TO–YOLOX exhibits a stronger ability compared with Faster R-CNN,RetinaNet,YOLOv5,YOLOv6,YOLOv7,and YOLOX,and the proposed model exhibits fast computation.

地球大数据监测和评估可持续发展指标

联合国可持续发展目标(SDGs)涵盖经济、社会、环境三大领域,为各国全面转向可持续发展指明了方向.然而,数据缺失、发展不均衡、目标间关联且相互制约等问题阻碍了SDGs实现.地球大数据为SDGs实现提供了创新解决方案,其宏观、动态监测能力可为SDGs进展评估研究提供关键支撑.中国科学院战略性先导科技专项“地球大数据科学工程”围绕6个SDGs及其指标进行研究,取得了一系列成果.本文重点介绍了地球大数据在可持续发展指标监测和评估中的研究进展,包括模型和算法、决策支持分析方法、数据产品等,并综合分析了在2010~2020年期间6个SDGs的动态变化情况,发现中国在6个SDGs方面正稳步推进,近年来改善幅度显著.

地球大数据:促进可持续发展目标实现的创新实践

The 2030 Agenda for Sustainable Development is a programmatic document for future global development.In the past five years,all countries of the world have made great efforts to achieve the United Nations Sustainable Development Goals(SDGs).

A standardized dataset of built-up areas of China’s cities with populations over 300,000 for the period 1990-2015

China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land,population,and economic impact.However,due to the lack of consistent and harmonized data,little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades.Along with the implementation of the 2030 Agenda for Sustainable Development,a standardized dataset for assessing the sustainability of urbanization in China is needed.In this paper,we used remote sensing data from multiple sources(time-series of Landsat and Sentinel images)to map the impervious surface area(ISA)at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more.This dataset was produced following the well-established rules adopted by the United Nations(UN).Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy(OA),producer’s accuracy(PA)and user’s accuracy(UA)were 91.24%,92.58%and 89.65%,respec-tively.Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China,the World Bank and UN-habitat indicated that our dataset,namely the stan-dardized urban built-up area dataset for China(SUBAD-China),provides an improved description of the spatiotemporal character-istics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas.Potential applications of this dataset include combin-ing the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1.The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China.

Assessing 40 years of spatial dynamics and patterns in megacities along the Belt and Road region using satellite imagery

The Belt and Road(B&R)region,a vital area with historical,economic,cultural and political significance,has undergone rapid urbanization in the past several decades,especially in the form of urban expansion.In this study,20 megacities in the B&R region were selected to explore different spatiotemporal patterns of urban expansion.Object-oriented support vector machines(SVM),annual growth rate(AGR)models,and landscape metrics were employed to delineate the urban areas and characterize spatiotemporal characteristics and landscape patterns of these megacities during 1975–2015.All urban maps presented high overall accuracies(80.70%–95.90%)and overall Kappa coefficients(0.76–0.95).The study revealed that megacities in the B&R region have undergone different types of urban sprawl,mainly adopting a‘concentric circle’pattern in inland areas and a‘sector’pattern in coastal areas.Besides,six expansion modes were summarized according to the AGRs of individual megacities.Differences existed in megacities of the developing and developed countries and among five sub-regions.Moreover,‘dispersion,gathering,and re-dispersion’and‘coalescence’were two major landscape patterns of megacities in developing and developed countries.Results of this study can provide a scientific reference for urban planning and aid in sustainable development of local areas.

A novel image-fusion method based on the un-mixing of mixed MS sub-pixels regarding high-resolution DSM

A major reason for the spectral distortions of fused images generated by current image-fusion methods is that the fused versions of mixed multispectral(MS)sub-pixels(MSPs)corresponding to panchromatic(PAN)pure pixels remain mixed.The MSPs can be un-mixed spectrally to pure pixels having the same land cover classes in a fine classification map during the fusion process.Since it is difficult to produce such a land cover classification map using only MS and PAN images,a Digital Surface Model(DSM)derived from airborne Light Detection And Ranging data were employed in this study to facilitate the classification.In a novel fusion method proposed in this paper,MSPs near and across boundaries between vegetation and non-vegetation are identified using MS,PAN,and normalized Digital Surface Model(nDSM).The identified MSPs then are fused to pure pixels with respect to the corresponding land cover class in the classification map.In a test on WorldView-2 images over an urban area and the corresponding nDSM,the fused image generated by the proposed method was visually and quantitatively compared with fused images obtained using common image-fusion methods.The fused images generated by the proposed method yielded minimal spectral distortions and sharpened boundaries between vegetation and non-vegetation.

Improved alpine grassland mapping in the Tibetan Plateau with MODIS time series: a phenology perspective

The Tibetan Plateau is primarily composed of alpine grasslands.Spatial distributions of alpine grasses,however,are not well documented in this remote,highly uninhabited region.Taking advantage of the frequently observed moderate resolution imaging spectroradiometer(MODIS)images(500-m,8-day)in 2010,this study extracted the phenological metrics of alpine grasses from the normalized difference vegetation index time series.With the Support Vector Machine,a multistep classification approach was developed to delineate alpine meadows,steppes,and desert grasses.The lakes,permanent snow,and barren/desert lands were also classified with a MODIS scene acquired in the peak growing season.With ground data collected in the field and aerial experiments in 2011,the overall accuracy reached 93%when alpine desert grasses and barren lands were not examined.In comparison with the recently published national vegetation map,the alpine grassland map in this study revealed smoother transition between alpine meadows and steppes,less alpine meadows in the southwest,and more barren/deserts in the high-cold Kunlun Mountain in the northeast.These variations better reflected climate control(e.g.precipitation)of different climatic divisions on alpine grasslands.The improved alpine grassland map could provide important base information about this cold region under the pressure of rapidly changing climate.

Research on SAR data integrated processing methodology oriented on earth environment factor inversions

As an important advanced technique in the field of Earth observations,Synthetic Aperture Radar(SAR)plays a key role in the study of global environmental change,resources exploration,disaster mitigation,urban environments,and even lunar exploration.However,studies on imaging,image processing,and Earth factor inversions have often been conducted independently for a long time,which significantly limits the application effectiveness of SAR remote sensing due to the lack of an overall integrated design scheme and integrated information processing.Focusing on this SAR application issue,this paper proposes and describes a new SAR data processing methodology–SAR data integrated processing(DIP)oriented on Earth environment factor inversions.The simple definition,typical integrated modes and overall implementation ideas are introduced.Finally,focusing on building information extraction(man-made targets)and sea ice classification(natural targets)applications,three SAR DIP methods and experiments are conducted.Improved results are obtained under the guidance of the SAR DIP framework.Therefore,the SAR DIP theoretical framework and methodology represent a new SAR science application mode that has the capability to improve the SAR remote sensing quantitative application level and promote the development of new theories and methodologies.

Big earth data for achieving the sustainable development goals in the belt and road region

The Belt and Road(B&R)is an important region with historical,economic,cultural,and political significance,including 75%of the global population and numerous social activ-ities.However,many countries along the B&R region are experiencing developmental challenges such as rapid urbanization,land degradation,water shortages,water and food security,frequent disasters,and large-scale ecosystem changes.The UN’s 17 Sustainable Development Goals(SDGs)provide a universal call to action to end poverty,protect the planet,and ensure that all people enjoy peace and prosperity by 2030 and to achieve economic,social,and environmental sustainability at global,regional,and national scales.