小波理论在遥感图像融合中应用

小波变换用于多源遥感图像融合已得到了国内外学者的广泛关注,并且又提出了很多小波变换与传统融合方法结合的融合算法。本文在对这些算法进行总结的基础上,讨论了基于小波包变换和最优树变换以及多进制小波变换在融合中的应用,分析他们在融合中各自的特色,以便使用时根据具体情况进行选择。     

Modèles géostatistiques de concentrations ou de débits le long des cours d'eau

Estimating concentrations or flow rates along a stream network requires specific models. Two classes of models, recently proposed in the literature, are generalized, to the intrinsic case in particular. We present a global construction by ‘streams’, i.e. on the whole set of paths between sources and outlet. Combining stationary or intrinsic one-dimensional random functions leads to stationary or intrinsic models on segments, with discontinuities at the forks. A construction from outlet to sources, leads to stationary or intrinsic models on each stream, without any discontinuity at the forks. The linear variogram is found as a particular case. The extension to the linear model of coregionalization is immediate, allowing a multivariate modelling of concentrations. To cite this article: C. de Fouquet, C. Bernard-Michel, C. R. Geoscience 338 (2006).     

运用树木活力度进行城市生态环境质量分区--以吉林市为例

城市生态环境质量是影响城市景观和居民身心健康的重要因素，环境质量分区是进行生态环境评价的基础性工作。在以往生态环境质量评价中，通常采用单一的环境污染指标，难以全面综合的反应生态环境质量状况。植被的面貌是一定时期内大气、土壤、水分、人类活动综合作用的结果。由于树木有生命而又不能移动，它的健康状态是一定时期因素内，综合地反映其生活环境条件的最好尺度。借鉴国外环境诊断的研究方法，提出“树木活力度”，即树木的枝、叶、梢、树形等各个部位的生长状况和健康程度这一新的综合生态指标，并结合实际，以吉林市为例，对运用这一指标进行城市生态环境质量评价的方法、原理、问题等进行了初步探讨。指出运用树木活力度进行城市生态环境质量分区，具有迅速目测、计算简便，快速经济的特点，是当前较为流行的一种景观诊断的简便易行的方法。     

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。     

Boreal temperature variability inferred from maximum latewood density and tree-ring width data, Wrangell Mountain region, Alaska

Variations in both width and density of annual rings from a network of tree chronologies were used to develop high-resolution proxies to extend the climate record in the Wrangell Mountain region of Alaska. We developed a warm-season (July–September) temperature reconstruction that spans A.D. 1593–1992 based on the first eigenvector from principal component analysis of six maximum latewood density (MXD) chronologies. The climate/tree-growth model accounts for 51% of the temperature variance from 1958 to 1992 and shows cold in the late 1600s–early 1700s followed by a warmer period, cooling in the late 1700s–early 1800s, and warming in the 20th century. The 20th century is the warmest of the past four centuries. Several severely cold warm-seasons coincide with major volcanic eruptions. The first eigenvector from a ring-width (RW) network, based on nine chronologies from the Wrangell Mountain region (A.D. 1550–1970), is correlated positively with both reconstructed and recorded Northern Hemisphere temperatures. RW shows a temporal history similar to that of MXD by increased growth (warmer) and decreased growth (cooler) intervals and trends. After around 1970 the RW series show a decrease in growth, while station data show continued warming, which may be related to increasing moisture stress or other factors. Both the temperature history based on MXD and the growth trends from the RW series are consistent with well-dated glacier fluctuations in the Wrangell Mountains and some of the temperature variations also correspond to variations in solar activity.     

基于改进随机蕨算法的GIS增强现实应用研究

随机蕨算法由于其简单高效,实时性好的特点,在计算机视觉领域得到了广泛的应用。但是其在构造二值特征过程中采用的基于像素点灰度值比较的方法对图像变形的适应性较差。本文基于方向梯度直方图对光照变化,图像变形鲁棒性较好的特点,利用统计方向梯度直方图取代像素灰度值进行比较,以此构造特征点二值特征。基于改进的随机蕨算法,本文设计了一种将增强现实技术(AR)应用于GIS的框架模型。实验结果表明,经过改进的随机蕨算法对目标识别的正确率得到了有效提升,且算法的实时性较好,适用于GIS的增强现实应用。     

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。