基于无人机多源遥感数据海岛植被叶面积指数协同反演研究

无人机多源遥感数据的获取、融合以及应用是当今研究的热点和难点。文中以城洲岛为例,针对海岛特殊的地理生态环境,获取无人机多源遥感数据。结合无人机多光谱遥感数据定量分析各遥感植被指数与植被叶面积指数(Leaf Area Index, LAI)的响应关系,构建单因子遥感反演模型;基于无人机激光LiDAR点云提取海岛植被冠层高度模型(Canopy Height Model,CHM),并将其作为自变量引入到多源统计回归分析中,从而构建多源遥感数据协同反演模型,对区域尺度下海岛叶面积指数(LAI)进行估算,开展验证和精度评价。结果显示,加入植被冠层高度因子的协同反演模型的判定系数R2为0.92,绝对平均误差系数为12.29%,预测精度要优于单因子反演模型(判定次数R2为0.86,绝对平均误差系数19.95%)。研究表明,加入了植被冠层高度因子的协同反演模型能在一定程度上提高乔木植被LAI的预测精度。实践证明,无人机多源遥感技术在生态学定量研究中具有巨大的潜力和广阔的应用前景。     

雅浦海沟南缘铁锰结核矿物与地球化学特征及其成因研究

本文采用X射线衍射、全岩地球化学和电子探针等测试方法,对雅浦海沟南部附近海域获得的铁锰结核样品进行了显微构造、矿物和地球化学分析,并探讨了其成因。结果表明:铁锰结核的显微构造主要包括平行纹层构造、柱状构造、叠层状构造和同心环状构造;显微构造和探针结果显示铁锰结核在生长初期处于底部海洋动力比较强烈的环境,后期生长环境逐渐趋于稳定;铁锰结核的矿物组分以水羟锰矿、钠水锰矿、石英和钙十字石为主;样品中Fe、Mn元素含量较高且含量比较接近,Cu、Co、Ni和REE相对富集,REE分布模式整体比较平缓并都出现较强的Ce正异常和重稀土元素亏损现象;文中两块铁锰结核都为水成成因,成矿物质主要来自同期的海水沉淀,同时也受一定海底火山物质和陆源风尘物质的影响。     

Northwest Africa 11024—A heated and dehydrated unique carbonaceous (CM) chondrite

Based on the high abundance of fine‐grained material and its dark appearance, NWA 11024 was recognized as a CM chondrite, which is also confirmed by oxygen isotope measurements. But contrary to known CM chondrites, the typical phases indicating aqueous alteration (e.g., phyllosilicates, carbonates) are missing. Using multiple analytical techniques, this study reveals the differences and similarities to known CM chondrites and will discuss the possibility that NWA 11024 is the first type 3 CM chondrite. During the investigation, two texturally apparent tochilinite–cronstedtite intergrowths were identified within two thin sections. However, the former phyllosilicates were recrystallized to Fe‐rich olivine during a heating event without changing the textural appearance. A peak temperature of 400–600 °C is estimated, which is not high enough to destroy or recrystallize calcite grains. Thus, calcites were never constituents of the mineral paragenesis. Another remarkable feature of NWA 11024 is the occurrence of unknown clot‐like inclusions (UCLIs) within fine‐grained rims, which are unique in this clarity. Their density and S concentration are significantly higher than of the surrounding fine‐grained rim and UCLIs can be seen as primary objects that were not formed by secondary alteration processes inside the rims. Similarities to chondritic and cometary interplanetary dust particles suggest an ice‐rich first‐generation planetesimal for their origin. In the earliest evolution, NWA 11024 experienced the lowest degree of aqueous alteration of all known CM chondrites and subsequently, a heating event dehydrated the sample. We suggest to classify the meteorite NWA 11024 as the first type 3 CM chondrite similar to the classification of CV3 chondrites (like Allende) that could also have lost their matrix phyllosilicates by thermal dehydration.     

Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

Depression filling is a critical step in distributed hydrological modeling using digital elevation models (DEMs). The traditional Priority‐Flood (PF) approach is widely used due to its relatively high efficiency when dealing with a small‐sized DEM. However, it seems inadequate and inefficient when dealing with large high‐resolution DEMs. In this work, we examined the relationship between the PF algorithm calculation process and the topographical characteristics of depressions, and found significant redundant calculations in the local micro‐relief areas in the conventional PF algorithm. As such calculations require more time when dealing with large DEMs, we thus propose a new variant of the PF algorithm, wherein redundant points and calculations are recognized and eliminated based on the local micro‐relief water‐flow characteristics of the depression‐filling process. In addition, depressions and flatlands were optimally processed by a quick queue to improve the efficiency of the process. The proposed method was applied and validated in eight case areas using the Shuttle Radar Topography Mission digital elevation model (SRTM‐DEM) with 1 arc‐second resolution. These selected areas have different data sizes. A comparative analysis among the proposed method, the Wang and Liu‐based PF, the improved Barnes‐based PF, the improved Zhou‐based PF, and the Planchon and Darboux (P&D) algorithms was conducted to evaluate the accuracy and efficiency of the proposed algorithm. The results showed that the proposed algorithm is 43.2% (maximum) faster than Wang and Liu's variant of the PF method, with an average of 31.8%. In addition, the proposed algorithm achieved similar performance to the improved Zhou‐based PF algorithm, though our algorithm has the advantage of being simpler. The optimal strategies using the proposed algorithm can be employed in various landforms with high efficiency. The proposed method can also achieve good depression filling, even with large amounts of DEM data.     

An appearance‐preserving simplification method for complex 3D building models

The simplification of 3D building models to effectively reduce model complexity and improve rendering efficiency is an important component of 3D GIS. To reduce the data volume while preserving the model appearance, this article proposes a novel simplification method for complex 3D building models. Texture discontinuities are addressed by developing a new data model that records the mapping relation between the texture coordinates of each vertex and its neighboring triangles. The surface mesh of the building model is then segmented into regions, guided by topology and appearance. Finally, the mesh segmentation information is used to derive an improved error metric that considers both geometric and texture errors, and the texture coordinates are adjusted after each simplification operation. A series of comparative experiments alongside traditional methods demonstrates that our approach achieves a good balance between geometric fidelity and texture preservation, and produces simplified 3D building models with better visual quality.     

A method for the extraction of partition lines from long and narrow patches that account for structural features

The extraction of partition lines for long and narrow patches (LN patches) is an important yet difficult problem in the generalization of thematic data. When current methods are used to process polygons with irregular shapes or complex branch convergence zones, the extracted line structural features tend to be inaccurate and topologically erroneous. In this article, we propose an improved partition lines extraction algorithm of constrained Delaunay triangulation to counter these issues. The proposed method aims to maintain consistency between the extracted line structure characteristics and the actual object structure, especially for complex branch convergence zones. First, we describe three types of aggregation patterns (Type A, B, and C aggregation zones) that occur in partition line extractions for LN patches of complex branch convergence zones using Delaunay triangulation. Then, a partition line extraction algorithm that accounts for the direction between the edges of triangles and the distance of nodes in aggregation zones is proposed. Finally, we test our method for a dataset relating to Guizhou Province, China. Compared with the current method that uses quantitative indicators and visualization, the results indicate that our method not only has applicability for simple situations but also is superior for preserving structural features of complex branch convergence zones.     

Robust abnormality detection methods for spatial search of radioactive materials

Radiological dirty bombs and improvised nuclear devices pose a significant threat to both public health and national security. Growing networks of radiation sensors have been deployed by a number of offices within the U.S. and international agencies. Detecting such threats while minimizing false alarm rates presents a considerable challenge to homeland security and public health. This research aims to achieve a higher probability of detection with a lower probability of false alarms. It focuses on the local spatial instability of radiation levels in order to detect radioactive materials based on robust outlier detection methods. Our approach includes a three‐step abnormality detection method consisting of one‐dimensional robust outlier detection for all gamma‐ray counts, a density‐based clustering analysis, and a two‐dimensional robust outlier detection method using a bagplot, based on spatial associations. The effectiveness of the method proposed is demonstrated through a case study, wherein radioactive materials are detected in urban environments, and its performance is compared with alternative methods employing a k‐sigma approach, local Getis–Ord () statistic, and the goodness of fit of the Poisson distribution.     

River centerline extraction using the multiple direction integration algorithm for mixed and pure water pixels

The river centerline is a basic hydrological characteristic. Most prior studies have used remote sensing data to extract the river centerline from the open water region in a pure water pixel region. Extracting this type of river is relatively easy. However, extracting the centerline of a micro-river, which is mainly composed of mixed water pixels, is challenging. This paper presents a novel method, called the Multiple Direction Integration Algorithm (MDIA), to extract the river centerline using an image-enhancing method combined with river morphology. MDIA can be applied to regions mainly composed of pure water pixels, as well as to regions consisting of mixed water pixels in the index image. The method first calculates the normalized difference vegetation index (NDVI) and enhances the river linear structure using a Hessian matrix. Second, a small window is constructed as a circular structural element. In the window region, the local threshold is automatically obtained using water-oriented clustering segmentation and prior river knowledge to judge the pixel type. After completing the river centerline extraction in the current window, the next detecting window is generated to continue judgment. The structural element automatically executes river centerline judgment until the entire river centerline is extracted. The Landsat 8 images of six regions with different geomorphologies were chosen to analyze the method’s performance. The test sites include high mountain region, low mountain region, plains region with farmland and a residential region. The experimental results show that the optimal threshold of the processing results ranged from 0.2 to 0.3. In this range, the user’s accuracy is 0.813 to 0.997, and the producer’s accuracy is 0.981 to 1. The MDIA effectively and correctly extracts the river network in mixed-pixel regions. The presented method provides an effective algorithm for river centerline extraction that can be used to expand and update river datasets and provide reliable river centerline data for relevant hydrology studies.     

自动化监测系统在深基坑监测中的可靠性分析

深基坑监测对于城市工程的安全建设具有重要的指导意义。为了探究自动化监测系统在城市深基坑监测中的可行性,根据全站仪的测量原理利用Matlab进行三维仿真,从理论上证明了自动化监测系统的平面精度能够达到规范要求。采用尺度变形分析及水平位移对比分析,论证了在城市深基坑监测中利用自动化监测系统进行水平位移监测的可靠性。