The Agulhas Return Current (ARC) transports warm tropical and subtropical waters eastward into the southern Indian Ocean. It plays a crucial role in the oceanographic connections between the Indian, Atlantic, and Southern oceans. Modern oceanographic observations show that the latitudinal position of the ARC varies interannually. However, its historical positional variations remain poorly understood. Calcareous nannofossils can be a good indicator of ancient current migration, although their record in the Southwest Indian Ocean is poorly studied. This research aims to understand the characteristics and downcore variation of the calcareous nannofossil assemblages and trace the record of the ancient ARC. To achieve these goals, this study analyzed pelagic sediments of multicore 34IV-SWIR-S021MC03, 34IV-SWIR-S032MC04, and 34IV-SWIR-S040MC05 from the Southwest Indian Ocean. A total of 13 nannofossil species have been identified. The assemblages belong to the subtropical convergence zone regime. They are characterized by warm and cold water species, dominated by Emiliania huxleyi, Calcidiscus leptoporus, Gephyrocapsa muellerae, and Florisphaera profunda. According to the AMS14C age model and phytoplankton ecological signatures, this study establishes a calcareous nannofossil indicator to trace the migration of the ancient ARC during the last 40 kyr. The result shows three periods of migration: 40–22 kyr, the ancient ARC was in the far north and was moving southward; 22–14 kyr, a transitional period, the ancient ARC was moving northward; 14–3 kyr, the ARC was moving southward. This further suggests that the migration of the ancient ARC is more complex than the two recognized phases, and there were essential turning points around the last glacial maximum period. It also acknowledges that the ancient ARC is sensitive to interglacial periods and can be influenced simultaneously by the Southern Hemisphere monsoon and westerly winds.

Ground objects in high-resolution remote sensing images are often closely related to the scenecategories. If the constraint information of the scene on the ground object can be usefully employed, it isexpected to improve further the performance of object detection. Considering the relationship between sceneinformation and objects, a scene constrained object detection method in high-resolution remote sensing imagesby Relation-aware Global Attention (RGA) is proposed. First, the global scene features are learned by addingthe global relational attention to the basic network in Feature fusion and Scaling-based Single Shot Detector(FS-SSD). Then, object is predicted by combining the oriented response convolution module with the multiscalefeature module under the constraints of learned global scene features. Finally, two loss functions are used tooptimize jointly the network to achieve object detection. Four experiments are conducted on NWPU VHR-10dataset and better object detection performance is achieved under the constraints of scene information

Effective identification of the sources and biogeochemical processes of dissolved sulfate in groundwater is an important prerequisite for ensuring drinking water safety and aquatic ecological security and is of significance to manage and protect groundwater resources. In this review, the sources of groundwater sulfate and the typical range of δ34S and δ18O isotope from different sulfate sources are summarized by reviewing the literature; the identification of sulfate sources and S biogeochemical cycles by δ34S and δ18O isotope in sulfate is reviewed, and the existing problems and development trends are proposed. The source apportionment of groundwater sulfate sources has gone through the processes of hydrochemistry analysis→δ34S isotope→dual isotope→qualitative identification of multiple isotopes and tracers→quantitative evaluation. Due to the differences in sulfur and oxygen isotopes and the biogeochemical transformation processes in a specific region, there is still a larger uncertainty in the determination of groundwater sulfate sources.It is proposed to arrange the sampling points for collecting pollution sources and groundwater samples on the framework of groundwater flow systems and land use distributions and to analyze the hydrochemical components and the sulfur and oxygen isotope values of sulfate and other complementary tracer isotope values and/or concentrations in a specific area. The sources and their contributions of groundwater sulfate are analyzed using multidisciplinary and multi-methods based on the full integration of hydrogeochemistry, seepage field, land use and other information in a study area for the scientific implementation of groundwater resource protection and pollution prevention.

基于生态优先原则,生态资产价值评估对国土空间规划中精准识别生态极重要区域具有重要意义.以广西涠洲岛为例,综合考虑生态资产的流量和存量,建立陆海统筹的生态资产价值评估指标体系,并利用GF-2图像及InVEST模型,形成生态保护重要性分布格局.结果 表明:(1) 2018年涠洲岛生态资产总价值约为32.10亿元,海洋生态资产总价值占比最高,林地和岛内水域的单位价值较高,均超过4015万元/平方千米;(2)生态保护极重要区域环状分布于岛屿沿海,特别是岛南北部两端,重要区域与一般区域集中分布于环岛公路以内的岛屿中部,具备开发潜力.基于研究,建议加强保护高价值生态资产,科学规划陆海统筹的高质量空间,绿色发展和向海发展并重,促进乡村振兴,建设美丽海岛.

Due to the complex background and spatial distribution, it brings great challenge to object detection in high-resolution remote sensing images. In view of the characteristics of various scales, arbitrary orientations, shape variations, and dense arrangement, a multiscale object detection method in high-resolution remote sensing images is proposed by using rotation invariance deep features driven by channel attention. First, a channel attention module is added to our feature fusion and scaling-based single shot detector (FS-SSD) to strengthen the long-term semantic dependence between objects for improving the discriminative ability of the deep features. Then, an oriented response convolution is followed to generate feature maps with orientation channels to produce rotation invariant deep features. Finally, multiscale objects are predicted in a high-resolution remote sensing image by fusing various scale feature maps with multiscale feature module in FS-SSD. Five experiments are conducted on NWPU VHR-10 dataset and achieve better detection performance compared with the state-of-the-art methods.

The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene.

以贵州松桃整装勘查区为研究对象,运用全生命周期评估方法对锰矿资源开采和利用过程中存在的环境问题进行了理论探讨和实证分析.从资源开采、运输流通、深加工3个阶段,对锰矿资源开发中的生态系统服务价值、区域固碳能力、碳排放等进行评估,并分析了2003-2015年研究区锰矿开采造成的环境负面影响的演化路径.结果 表明,研究区锰矿资源开发对区域生态环境造成了较严重的破坏,3个阶段都存在不同程度的环境负面效应,其中生态系统服务损失主要集中在矿石开采环节,碳排放的负面影响主要集中在产品运输和电解锰生产环节.随着对锰矿石资源利用规模和强度的增大,区域生态环境受到的负面影响逐渐加深.基于此,提出通过整合矿权设置、提高电解锰企业的集中度、改进开采工艺和进一步加强环境规制等措施来减少锰矿资源开发的环境负面影响.

The Openness based on DEM emphasizes the terrain convexity and concavity. It facilitates the interpretation of detailed landforms on the Earth’s surface. Compared with the layer stacking of ETM+ with less three-dimensionality and visualizability and with indefinite details of linear images in the deep cutting or deep covered region, the Openness is used for accurate interpretation of tectonic geomorphic units and linear structures. In this paper, the ETM+ images (741 RGB) and RRIM based on Openness combined with the field geological investigation are used to trace the escaping structure in SE Asia. The east boundary is Ailaoshan shear zone and the west boundary is Uttaradit-Dien Bien Phu fault, which together form the southwards extruding wedge block. The arc boundary surface of the southern Khorat Plateau is jutted to the north. The NW and NE sides of Khorat Plateau are traversed by Uttaradit-Dien Bien Phu fault and Thakhek-Da Nang fault, respectively, resulting in a blocked escaping structure. The SE margins of Truong Son structure belt and Song Ma structure belt are both arcs jutting to SE. These arc structures clamped by faults or related to the fault on one side indicating the material flow direction obviously, are the most specific manifestation of escaping structures. Moreover, these push units are extruded from south to north successively.