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。     

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.     

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.     

基于加权破产博弈模型的跨境流域水资源分配研究

基于公平合理利用视角,将多准则决策模型与破产理论相结合探讨跨境水资源分配问题.通过梳理国际水法,设计了跨境流域水资源公平合理分配的指标体系,采用投影法解决这一多准则决策问题,并依此加权调整流域国用水需求.在加权调整用水需求和考虑议价能力的基础上,基于破产博弈模型探讨跨境流域水资源分配问题,以提高分配方案的公平合理性和可...     

中国北方磷矿矿床类型及成矿规律

通过对中国北方内生磷灰石、变质及沉积型磷矿成矿特征及典型矿床的研究,建立了各种类型磷矿床的成矿模式和找矿模型、确定了各类型矿床的时空分布规律以及矿床的共生规律,为开展磷矿的找矿工作具有重要参考价值。     

中国北方磷矿成矿预测

在中朝准地台、塔里木准地台边缘深断裂附近和台中隆起带以及天山-兴安、祁连-秦岭、额尔古纳几个地槽褶皱区的中间地带,不同程度地具备中国北方三种主要类型磷矿的成矿条件。研究分析认为中国北方的内生磷矿具有良好的找矿前景,绿岩带型磷矿和古元古代沉积变质磷矿显示一定的找矿潜力,沉积型磷矿一般品位低、规模小。通过归纳,在中国北方划分出磷矿的二级城矿区4个、三级成矿带15个、四级成矿亚带19个。圈定成矿预测区A级2个,B级C级各23个。确定了21处可开展进一步工作的地区。     

深水钻井中浅水流灾害问题及其地球物理识别技术

在深水钻井中,浅水流是一种常见的地质灾害。中国正在进行深水油气勘探,十分有必要开展这方面的地质灾害研究。介绍了浅水流的形成机制及地球物理特性。目前用于识别和预测浅水流的手段有测井和反射地震两大类方法。主要介绍了几种用于参数提取的地震反演方法,如传统Dix反演、叠后振幅反演、层析反演、叠前振幅反演等。     

Using otolith weight to predict the age of <Emphasis Type="Italic">Pennahia macrocephalus</Emphasis> in the mouth of the Beibu Gulf

The relation between otolith weight (OW) and the age of marine fish is studied. A total of 222 individuals of bighead white croaker, Pennahia macrocephalus were sampled seasonally in the mouth of the Beibu Gulf, the South China Sea, in 2007. Since there are no significant differences in sagittal OW between otolith in pairs (P≥0.05), the undamaged left sagittal otolith is used for age determination. The highest correlations among standard length, OW and fish ages are confirmed by linear, exponential and multinomial regression. Results show that sagittal OW overlaps only occasionally among age groups, and to individuals with similar standard length, the older and slower-growing fish has a heavier otolith because of the continued otolith material deposition. There are differences in sagittal OW among different age groups and significant positive linear relationship with age (P<0.05). The age readings can be verified by plotting the sagittal OW versus the standard length for age groups, and the individuals with similar standard length but in different ages can be separated by sagittal OW frequency analysis. Mostly, the predicted ages using the regression between sagittal OW and ages are closed to the observed ages by counting annulus on scale. It indicates that the sagittal OW analysis is a useful technique for validating the accuracy of age determination by annuli counts, especially for individuals of similar size. Furthermore, the technique is applied for Pennahia macrocephalus with discussion in this paper. Supported by the National Natural Science Foundation of China (No. 30771653) and Bureau of Fisheries, Ministry of Agriculture     

Impacts of soil fauna on litter decomposition at different succession stages of wetland in Sanjiang Plain, China

Litter decomposition is the key process in nutrient recycling and energy flow. The present study examined the impacts of soil fauna on decomposition rates and nutrient fluxes at three succession stages of wetland in the Sanjiang Plain, China using different mesh litterbags. The results show that in each succession stage of wetland, soil fauna can obviously increase litter decomposition rates. The average contribution of whole soil fauna to litter mass loss was 35.35%. The more complex the soil fauna group, the more significant the role of soil fauna. The average loss of three types of litter in the 4mm mesh litterbags was 0.3–4.1 times that in 0.058mm ones. The decomposition function of soil fauna to litter mass changed with the wetland succession. The average contribution of soil fauna to litter loss firstly decreased from 34.96% (Carex lasiocapa) to 32.94% (Carex meyeriana), then increased to 38.16% (Calamagrostics angustifolia). The contributions of soil fauna to litter decomposition rates vary according to the litter substrata, soil fauna communities and seasons. Significant effects were respectively found in August and July on C. angustifolia and C. lasiocapa, while in June and August on C. meyeriana. Total carbon (TC), total nitrogen (TN) and total phosphorus (TP) contents and the C/N and C/P ratios of decaying litter can be influenced by soil fauna. At different wetland succession stages, the effects of soil fauna on nutrient elements also differ greatly, which shows the significant difference of influencing element types and degrees. Soil fauna communities strongly influenced the TC and TP concentrations of C. meyeriana litter, and TP content of C. lasiocapa. Our results indicate that soil fauna have important effects on litter decomposition and this influence will vary with the wetland succession and seasonal variation. Foundation item: Under the auspices of State Key Development Program for Basic Research of China (No. 2009CB421103), Key Program of National Natural Science Foundation of China (No. 40830535/D0101), Knowledge Innovation Programs of Chinese Academy of Sciences (No. KZCX2-YW-BR-16, KSCX2-YW-N-46-06)     

Ecosystem health assessment of Honghu Lake Wetland of China using artificial neural network approach

Honghu Lake, located in the southeast of Hubei Province, China, has suffered a severe disturbance during the past few decades. To restore the ecosystem, the Honghu Lake Wetland Protection and Restoration Demonstration Project (HLWPRDP) has been implemented since 2004. A back propagation (BP) artificial neural network (ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland. And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project. Particularly, 12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs. The output is one layer of ecosystem health index. After training and testing the BP ANNs, an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland. The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP (in 2002) to middle health after the implementation of the HLWPRDP (in 2005). It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.