Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

Influence of Cyclic Wetting Drying on Collapse Behaviour of Compacted Residual Soil

The climatic zones where residual soils occur are often characterized by alternate wet and dry seasons. Laboratory studies of earlier workers have established that the alternate wetting and drying process affects the swell-shrink potentials, water content, void ratio and particle cementation of expansive soils. The influence of cyclic wetting and drying on the collapse behaviour of residual soils has not been examined. This paper examines the influence of alternate wetting and drying on the collapse behaviour of compacted residual soil specimens from Bangalore District. Results of such a study are useful in anticipating changes in collapse behaviour of compacted residual soil fills. Experimental results indicated that the cyclic wetting and drying process increased the degree of expansiveness of the residual soils and reduced their collapse tendency. Changes in the swell/collapse behaviour of compacted residual soil specimens from wetting drying effects are attributed to reduction in water content, void ratio and possible growth of cementation bonds.     

滇西北亚高山针叶林、硬叶栎类林的树种多样性

在滇西北香格里拉地区,以长苞冷杉(Abiesgeorgei)林、丽江云杉(Picealikiangensis)林作为亚高山针叶林的代表,以川滇高山栎(Quercusaquifolioides)林作为硬叶栎类林的代表,分别选择了6块样地,采用Shannon-Wiener多样性指数(H)、Simpson多样性指数的倒数(D')、Pielou均匀度指数(JH、JD)分别测定其树种多样性,并进行了比较研究。结果表明,三个森林类型的Shannon Wiener指数、Simpson指数的倒数、物种丰富度的顺序均为:长苞冷杉林>川滇高山栎林>丽江云杉林;而前两类森林的Pielou均匀度指数差异不大,但都高于丽江云杉林的均匀度指数值。滇西北的川滇高山栎具有很强的萌生能力。人工种植的亚高山针叶林的树种多样性较原始林低,需要较长时间的恢复过程。亚高山针叶林的存在极大地丰富了云南省的树种多样性,使云南植被的垂直地带性得到了充分体现。     

树冠叶面积体密度和叶面积指数的间接估值方法研究

本文论述了根据计算机断层成像的原理，对树冠的叶面积体密度和叶面积指数等构造参数进行间接估值的方法。说明了对树冠多角度底视数据的获取，对树冠外形的反投影重构，和获得冠层内叶面积体密度的空间分布的原理和方法。总结了测量工作经验，并用测量数据对估值方法进行了检验。     

钢铁厂周围不同污染介质的磁学性质及环境意义

为了通过钢铁厂周围不同污染介质的磁学性质研究,实现对环境污染的有效监测,本文系统地采集了河北兴隆和湖南娄底两个钢铁厂周围的树皮和草表面的飞灰、土壤及不同树种的年轮样品.其中前三者用来反映现今污染源,而树木年轮用来追踪长时间尺度的污染历史.通过综合岩石磁学实验分析（包括热磁曲线、磁滞性质和低温实验等）、电子扫描电镜(SEM)和能谱(EDX)以及X射线衍射(XRD)分析,发现钢铁厂周围现今污染源中的主要粗颗粒磁性矿物为磁铁矿、赤铁矿和纯铁.其中8~50 μm的球形磁铁矿颗粒和呈棱角状的铁颗粒是钢铁厂污染物的特征磁性矿物.对不同树种来说,其中香樟树和枫树树木年轮中的磁性矿物以顺磁性物质为主, 不适合用于环境磁学的污染监测.而柳树中的磁性矿物（主要为磁铁矿）与现今污染源的磁性矿物相符.以上研究表明多参数岩石磁学测量并辅以微观分析等手段,可以精确地提取磁性矿物的种类及粒度特征, 判别重工业区污染源,为应用磁学方法来监测环境污染提供依据.     

Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data

Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition.     

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.     

改进的最小生成树自适应空间点聚类算法

针对传统的最小生成树聚类算法存在使用全局不变阈值确定噪声边,聚类需要用户根据经验确定初始化聚类参数,如“边权值倍数容差”,“边长变化因子”等,聚类不能发现局部噪声的问题,本文提出了一种改进的最小生成树自适应空间点聚类算法。该算法在无需用户输入参数的前提下,克服主观因素的影响,根据最小生成树边长的数理统计特征定义裁剪因子。算法首先从宏观层面对最小生成树进行首轮删枝操作,消除全局环境下的噪声边,进而根据各子树的边长统计情况,自适应设定局部裁剪因子,进行第二轮删枝操作,消除局部环境下的噪声边。最后,采用1个模拟数据和1个实际应用验证算法的有效性,结果表明本文提出的改进算法在无需人为提供经验参数的环境下能够发现任意形状、不同密度的簇,能够准确的识别出空间点中的噪声数据,从而能够实现空间点数据背后隐藏信息的自动挖掘。     

最大树法模糊聚类分析在台湾地区地震预报中的应用

运用最大树法模糊聚类分析,将台湾地区1936年至1989年间的35个地震事件分成了三类6型,并运用聚类结果对1990年至2000年的11个地震事件进行检验,得出了比较合理的检验结果,最后用聚类结果对台湾地区2001年的地震趋势进行了预测,得出了与其它手段一致的预报结果.