The current status as well as the potential absorption capability of the forest carbon sinks in terrestrial ecosystems are in urgent need of further studies. The ground observation-based methods are labor-intensive, and the resulting statistics from samples are difficult to evaluate, while inversion methods based on remote sensing data lack theoretical explanation and universality. This paper proposes a pixel-level, multi-scale, high-precision Explicit Forest carbon stock Model (EFM) that is universal and adaptive. First, four key variables were used in the construction of the EFM: remote sensing image resolution, forest canopy density, terrain slope, and forest height; Second, simulated forest scene were generated based on the growth characteristics of individual trees, and EFM parameters were solved and analyzed by these simulated pixels; Third, the EFM was tested at various scales and forest saturation levels to verify its accuracy, robustness, and applicability, the simulation and real-life experiments show that the correlation coefficient is greater than 0.98 and the relative error is about 20%. The EFM solves the problem that the existing methods are lack in theoretical interpretation and universal applicability, thus can be used to map forest carbon stocks at high resolution and large scale and even monitor forest carbon dynamics at global scale.
In the western of China, the deep mining area with super-thick and weak cementation overburden is vast, sparsely populated and the ecological environment is extremely fragile. With the large-scale exploitation of deep coal resources, it is inevitable to face green mining problem, whose essence is the surface subsidence control. Therefore, it is necessary to study the control technology for the regional mining based on the evolution law of subsidence movement and energy-polling of super-thick and weak cementation overburden, and put forward the economically design scheme that can control strata movement and surface subsidence in a certain degree. Based on the key strata control theory, this paper puts forward the subsidence control scheme of partial filling -partial caving in multi-working face coordinated mining, and further studies its control mechanism through the numerical simulation and then analyzes the control effect of the strata movement and energy-polling in the fully caving mining, backfill mining, wide strip skip- mining and mixed filling mining method etc., the following conclusions are detailed as follows: (1) The maximum value of energy-polling occurs on the coal pillars or on both sides of goaf. With the width of goaf, the maximum value of energy-polling increases in a parabola. (2) In the partial filling-partial caving multiple working faces coordinated mining based on the main key stratum, the stress distribution of the composite backfill in the filling working face is parabolic, and it is high on both sides and low in the middle. Moreover, in the composite backfill, the stress concentration degree of a outside coal pillar is greater than that of the inside coal pillar. (3) The control mechanism of partial filling-partial caving harmonious mining based on main key layer structure is the double-control cooperative deformation system, formed by the composite backfill and the main and sub-key layers structure. They jointly control the movement and energy accumulation of overlying strata by greatly reducing the effective space to transmit upward, and absorb the wave subsidence trend of the overburden until it develops into a single flat subsidence basin. (4) Considering the recovery rate, pillar rate, area filling rate, technical difficulty and subsidence coefficient etc., the partial fillingpartial caving multiple working faces coordinated mining based on the main key stratum is the most cost-effective mining method to control surface subsidence. This paper takes a guiding role in controlling the regional strata movement and surface subsidence of deep mining with super-thick and weak cementation overburden.
With the rapid development of multi-mode and multi-frequency GNSSs (including GPS, GLONASS, BDS, Galileo, and QZSS), more observations for research on ionosphere can be provided. The Global Ionospheric Map (GIM) products are generated based on the observation of multi-mode and multi-frequency GNSSs, and comparisons with other GIMs provided by the ionosphere analysis centers are provided in this paper. Taking the CODE (Center of Orbit Determination in Europe) GIM as a reference during 30 days in January 2019, for the GIMs from JPL (Jet Puls Laboratory), UPC (Technical University of Catalonia), ESA (European Space Agency), WHU (Wuhan University), CAS (Chinese Academy of Sciences), and MMG (The multi-mode and multi-frequency GNSS observations used in this paper), the mean bias with respect to CODE products is 1.87, 1.30, −0.10, 0.01, −0.02, and −0.71 TECu, and the RMS is 2.12, 2.00, 1.33, 0.88, 0.88, and 1.30 TECu, respectively. The estimated multi-type DCB is also in good agreement with the DCB products provided by the MGEX.
Small-scale events involve interactive human movement in limited space and time. Social media platforms possibly generate large amount of geospatially-referenced information related to small-scale events. It benefits individuals, management departments, and urban systems if small-scale events can be timely detected from social media platforms, where measuring the abnormal patterns of human movement to discover events and analyzing associated texts to interpret the reasons behind abnormal movement are two keys. Through investigating how people move as different events occur and measuring the patterns on social media platforms, small-scale events can be generally classified into two types, namely type I events with abrupt patterns and type II events with random occurrence of key factors, where social events and traffic events are representative correspondingly.Despite many studies have been conducted to detect social events and traffic events usinggeosocial media data, there still are some un-answered questions requiring further research. Mostexisting studies did not identify occurring events from a full coverage of spatial, temporal, andsemantic perspectives. Studies concerning social event detection lack efficient semantic analysis summarizing event content to infer the reasons driving the abnormal movement. The typicalclassification-based method regarding traffic event detection lacks investigation on how the spatiotemporal distribution of traffic relevant posts associate with the occurring traffic events, andsimply assigns the detected events with predefined categories, missing events that indicate trafficanomalies but go beyond the predetermined categories.In this thesis, spatial-temporal-semantic approaches are proposed to measure spatiotemporalpatterns of posts and users of social media platforms to capture abnormal human movement, andanalyze the content of associated posts to mine the reasons driving the movement. A variety oftechniques including machine learning, natural language processing, and spatiotemporal analysisare adopted to realize effective detection. Based on one-year Twitter data collected in Toronto,2014 Toronto International Film Festival and traffic anomaly detection are selected as two casestudies to evaluate the performance of proposed approaches. Through comparing with the groundtruth data, the result reveals that more than 80% of the detected events do refer to real-world events,which illustrates the feasibility and efficiency of proposed approaches.Keywords: Small-scale event, Event detection, Geosocial media data, Traffic event, Social event,Twitter, Spatiotemporal clustering
The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model the statistical distributions of pixel intensities. In this study, an innovative high-resolution remote sensing image segmentation algorithm is proposed based on a flexible hierarchical GMM (HGMM). The components are first defined by the weighted sums of elements, in order to accurately model the complicated distributions of pixel intensities in object regions. The elements of components are defined by Gaussian distributions to model the distributions of pixel intensities in local regions of the object region. Following the Bayesian theorem, the segmentation model is then built by combining the HGMM and the prior distributions of parameters. Finally, a novel birth or death Markov chain Monte Carlo (BDMCMC) is designed to simulate the segmentation model, which can automatically determine the number of elements and flexibly model complex distributions of pixel intensities. Experiments were implemented on simulated and real high-resolution remote sensing images. The results show that the proposed algorithm is able to flexibly model the complicated distributions and accurately segment images.
Traditional neural network methods for building change detection tend to produce the saw-tooth boundaries, and they are difficult to accurately identify change boundaries in dense building areas. To address that, this paper proposes a change detection method based on main body, edge decomposition and reorganization network. The proposed method performs change detection by respectively modeling body and edges features of buildings, which is on the basis on the characteristics of strong similarity between the body pixels and weak similarity between the edge pixels. In the definition of the proposed method, we first yield dual-temporal multi-scale difference features using a Siamese ResNet structure, and then separate the body features and edge features of buildings by learning a flow field. Subsequently, a feature optimization structure is designed to refine the body and edge features using the body and edge tags. Finally, the optimized body and edge features are reorganized to generate an end-to-end change detection model. Experiments have been performed by using the publicly available building dataset LEVIR-CD, and the results show that the proposed method can accurately identify the boundaries of changing buildings, and obtain better results compared with the methods based on U-Net network and these combining spatial-temporal attention.
青藏高原的构造变形模式及动力学机制一直以来都是地学界争论的焦点之一,其中以两大端元学说:“大陆逃逸（块体变形）”和“地壳增厚（连续变形）”最为著名。虽然有研究指出现有的地学资料还无法明确区分青藏高原的运动学机理到底属于哪种变形模式,但至少可以基于已有的大地测量数据来探究高原的构造变形是倾向于块体模型的“聚集式”变形,
设施配置空间优化旨在形成设施空间布局和调度的规划方案,是以地理信息为研究基础,以运筹建模为方法内核,以城市规划为应用导向的交叉研究问题,是一种典型高维多峰NP-Hard组合优化问题.设计并改进设施配置空间优化算法对提升规划方案适应度具有重要价值.本文剖析设施配置空间优化基本特征,引入实数编码量子进化算法,并重点构造四倍体量子染色体编码算子、总量约束算子,形成面向设施配置空间优化的量子进化算法(quantum evolutionary algorithm for spatial optimization of facility allocation,QEA-SOFA).基于急救设施配置空间优化实例分析,QEA-SOFA算法可有效提升急救服务设施重定位优化公平性,较实数编码遗传算法提高66％.结果表明QEA-SOFA算法在高维多峰空间优化问题上全局搜索能力更强,且对空间异质区域局部搜索具有更大探测尺度,也揭示了量子进化机制在地理空间优化问题中的巨大潜力.
Robust estimation is a basic technology in geometric processing and survey adjustment. Traditional iteratively reweighted least squares (IRLS) cannot handle problems with high outlier rates (≥50%); Random sampling consensus (RANSAC) type algorithms can only obtain approximate solutions and are time consuming. This paper proposes a progressively optimized scale-adaptive Cauchy robust estimation model. First, a scale parameter is introduced into the typical Cauchy kernel function to control its robustness. Second, the proposed method uses the control parameter to filter out some observations with the large residuals in each iteration and reduce the true outlier rate. Then, a "coarse to fine" IRLS method is used for optimization in a progressive manner. In the iterative process, the control parameter is continuously reduced to improve the robustness. This paper also applies the proposed model in several important tasks of photogrammetry, including mismatch removal, image orientation, and point cloud registration. Extensive experiments show that the proposed model is robust to more than 80% outliers when the gross errors conform to an approximately uniform or random distribution, and is 2~3 orders of magnitude faster than RANSAC.
相对于传统的无电离层组合模型,近年发展起来的非组合PPP可直接利用GNSS原始观测值,在未放大观测噪声的前提下,保留了电离层延迟等有效信息,且更适合当前及未来多系统多频率数据融合解算.然而,国内外对于非组合PPP的研究还比较有限,特别是在当前多系统多频率的大背景下,如何建立更为精确的函数模型和随机模型,如何高效地改正各类型观测值上的硬件延迟偏差和不同系统间的基准偏差,如何在受到电离层延迟残差影响严重的情况下有效地分离卫星端相位硬件延迟偏差和得到最优模糊度固定解等方面仍是研究难点.针对上述问题,论文主要研究内容和成果如下.
On-orbit geometric calibration is a key link for satellites to achieve high-precision positioning. In this paper, based on the 1∶2000 digital calibration test field in Ningxia, the calibration parameters of the dual-line-array cameras are calculated as a whole by the alternate iteration of forward intersection and backward intersection, and the high-precision on-orbit geometric calibration of the dual-line-array cameras of the GF-14 satellite is achieved. The calibration results were tested by using many testing fields around the world. The test results show that after high-precision geometric calibration, the accuracy of the direct forward intersection of the GF-14 satellite image can reach 2.34 m in plane and 1.97 m in elevation without ground control.
Cloud detection is a critical stage in remote sensing image preprocessing. However, when there is snow on the underlying surface of scenes, the general cloud detection methods wouldbe easily affected. As a result, the cloud detection accuracy of these methods would reduce.Furthermore, most available cloud detection datasets are of medium-resolution and do not focus on the cloud and snow coexistence study areas. As a result, a cloud detection dataset has been created and released based on high-resolution cloud-snow coexistence remote sensing images.Meanwhile, this study suggests a convolution neural network termed RDC-Net for cloud detection in high-resolution cloud and snow coexistence images. The RDC-Net contains the reconstructible multiscale feature fusion module for multiscale cloud feature extraction, the dual adaptive feature fusion module for effective cloud feature representation reconstruction, and the controllably deep gradient guidance flows module for unbiased network gradient descent guidance. Benefiting from the above technical components, the network can enhance the robustness of cloud detection in complicated regions and facilitate lightweight deployment of the network. The experimental results show that the RDC-Net has an excellent anti-interference capacity for highlighted ground objects and has outstanding detection performance for thin clouds and clouds over snow. Furthermore, the RDC-Net has fewer parameters and floating-point operations, making it acceptable for industrial production and application.
As the release of China's first generation 40 a (1979—2018) global atmosphere and land reanalysis (CRA40) by China meteorological administration (CMA) in December 2020, the suitability, method and performance of CRA40 in tropospheric delay ray-tracing is initially investigated in this paper. Tropospheric delays in zenith and 5° elevation directions at 231 international GNSS service (IGS) and 213 crustal movement observation network of China (CMONOC) stations during the year of 2018 from CRA40 and two ECMWF reanalysis (ERA-Interim and ERA5) are ray-traced and evaluated by GNSS zenith total delay (ZTD) products and inter-comparison in globe and China area, respectively. The change law from the CRA40 zenith wet delay (ZWD) and slant wet delay (SWD) accuracy is also analyzed over China. The results show that the CRA40 ZTD difference RMS is about 1.40 cm, and it is slightly better than ERA-Interim in globe and similar to ERA-Interim in China area. By taking the ERA5 slant total delay (STD) as a reference, the CRA40 STD difference RMS is about 10.83 and 12.30 cm in globe and China area, respectively, which is not significantly different from ERA-Interim. The CRA40 ZWD and SWD accuracy over China is related to the climatic types, and the accuracy during winter is obviously better than that during summer in monsoon climate areas.
Coastal settlement monitoring usually uses the Global Navigation Satellite System (GNSS) positioning technology to measure at present. However, it only reflects the sediment settlement below the station base, and the settlement information above the station base cannot be obtained. In coastal areas, sediments accumulate rapidly, and settlement changes greatly under compaction and alluvium. Therefore, it is necessary to monitor the settlement above the base in order to obtain the overall coastal settlement information. With the continuous development of GNSS, a new GNSS interactive reflectometry (GNSS-IR) technology has been proved to be able to use multipath effect for reflector height monitoring. Because the base of GNSS station is deep and the base length remains unchanged, the ground height change obtained by GNSS-IR technology can reflect the settlement above the base. Therefore, the paper use GNSS-IR technology to measure the subsidence changes above the base; At the same time, the settlement below the base change is obtained by using GNSS positioning technology, and then the total settlement change is obtained by using GNSS-IR and GNSS positioning technology. The Mississippi River Delta with large sediment thickness is selected as the test area, and the data of FSHS, GRIS and MSIN stations are selected for analysis. The results show that GNSS-IR can be used to measure the settlement rate above the base, and the corrected total settlement rate is equivalent to the relative sea level rise rate, which can better estimate the flood susceptibility and land loss.
基于内容的遥感影像检索是解决遥感大数据"数据海量、信息淹没"问题的有效方法,但面对海量的遥感数据,存在两个方面的严峻挑战:第一,遥感影像具有数据海量、尺度依赖、地物种类繁多和场景复杂等特点,基于单一或组合低层视觉特征的检索很难取得满意的检索结果;第二,设计一种适用于不同传感器影像的特征描述方法是不切实际的,传统的人工设计特征的策略不再适用.深度学习通过构造多层网络结构对图像内容进行逐级特征表达,能够实现特征的自适应学习.论文研究基于深度学习对复杂的遥感影像进行场景分析,通过自适应特征学习实现海量遥感影像的精确、快速检索,主要工作和贡献如下.
Aiming at the problems of the current non-navigational TIN-DDM automatic generalization algorithm cannot fully take into account the accuracy of seabed topographic forms recognition and the adequacy of seabed terrain features maintenance, based on the analysis of the concept of TIN-DDM rolling ball transformation, this paper introduces the concept of topographic forms recognition range into the correlation model of TIN-DDM point topographic type and rolling ball radius, and through the micro (macro) scale of TIN-DDM point topography quantitative identification and evaluation, and a non-navigational TIN-DDM automatic generalization algorithm considering topographic forms and features is proposed. First, apply the local Delaunay influence domain to the range constraints of TIN-DDM point topographic forms recognition, and establish an association model of topographic forms and rolling ball radius for micro-topography; Then, the sampling points are classified into the type of topography on a macro scale by analyzing the numerical change law of the critical rolling ball radius, and the correlation model between the topographic forms and the critical rolling ball radius for the macro-topography is established. Finally, using the critical rolling ball radius as the link, the correlation between the topographic type determination of TIN-DDM points and the continuous expression of topographic forms is demonstrated, and a quantitative evaluation index for seabed terrain features of TIN-DDM points is designed for submarine topographic forms recognition, and a TIN-DDM automatic generalization model based on TIN-DDM points to evaluation index is established. The experimental results show that the algorithm can effectively maintain the features of the seabed terrain on the basis of identifying topographic forms.
目前基于载波相位的实时动态差分(RTK)和实时精密单点定位(RT-PPP)技术是GNSS实时高精度导航定位应用中最为常见的两种技术.RTK面临的主要问题是随着基线距离的增长大气误差相关性降低.相比RTK,RT-PPP需额外依赖高精度的轨道、钟差、硬件延迟产品,快速PPP的关键是尽可能削弱实时轨道、钟差误差的影响,提高PPP模糊度固定(PPP-AR)的可靠性.另外,随着GNSS基准站网的不断发展,基于区域参考站网的大气产品可以为RTK和RT-PPP用户提供快速精密定位服务,然而目前针对两种用户的服务系统相对独立.此外,在实时高精度应用中还需考虑通信延迟的影响.
近年来,大量学者致力于行人导航定位系统的研究,多数导航定位系统依赖于专业设备支撑,价格昂贵,且在复杂环境中(比如室内、城市峡谷)定位精度较差.那么如何兼具系统定位性能与成本,且适用于大众,成为我们所面临的问题.智能终端已经成为人们日常生活的重要部分,其内置的MEMS传感器具有质量轻、体积小、功耗低、成本低、易集成等优点,这使得基于MEMS传感器的导航定位技术成为理想的行人导航定位手段之一.但基于MEMS传感器的导航定位系统单独工作时,定位误差会随时间迅速增长,最终致使系统无法正常工作,因此,须融合其他信息来修正系统误差进而辅助导航定位.论文研究以智能手机为平台,结合MEMS传感器、GNSS、iBeacon、地图进行融合定位算法研究.围绕这个核心目标,本文在以下4个方面做了深入研究并取得了相应的成果.
GF-14 is one of the highest mapping accuracy satellites in China, and is adopted advanced multi-load integrated for earth observation technology, which is mainly used for high accuracy location and mapping 1∶10 000 geographic information products on a global wide. In this paper, the payload, ground processing flow and its performance were briefly introduced, then the geometric performance of the satellite images was evaluated using different fields including domestic and foreign areas. As a result, the location accuracy without ground control points (GCPs) of single strip can reach 1.8 m in horizontal and 0.80 m in vertical elevation in domestic areas and 1.76 m in horizontal and 0.82 m in vertical elevation in foreign areas, which can reach the best known level in international optical photogrammetry on location accuracy without GCPs.
