面向对象的黑河下游河岸林植被覆盖信息分类!

地表植被覆盖是描述区域生态系统的基础数据,也是全球及区域陆面过程、生态与水文众多模型中所需的重要地表参数。对于黑河下游额济纳绿洲,以Landsat 30m分辨率为主的遥感影像难以真实提取下游绿洲河岸林植被覆盖信息,而高分辨率影像目标地物轮廓清晰、空间细节信息丰富,有利于干旱背景下景观破碎、异质性强的植被覆盖信息分类。基于黑河下游额济纳绿洲QuickBird影像,通过面向对象的分类方法提取耕地、胡杨、柽柳、草地和裸地等主要植被覆盖类型,分类总体精度和Kappa系数分别为84.71%和0.7986。结果表明:利用面向对象分类方法对高分辨率影像进行植被覆盖信息分类,分类结果较好,能够满足精度要求。     

基于加速度传感器的可扩展手势识别

为了提高基于加速度传感器的动态手势识别算法的性能,并且增强系统的可扩展性,提出了一种有效结合机器学习模型与模板匹配的方法.将手势分为基本手势和复杂手势两大类,其中复杂手势可分割为基本手势组成的序列;根据手势运动的特点提取有效的特征量,并利用基本手势样本训练随机森林模型,然后用其对基本手势序列进行分类预测;将预测结果进行约翰逊编码,再与标准模板序列进行相似度匹配.实验结果表明,该方法获得了99.75%的基本手势识别率以及100%的复杂手势识别率.算法既保证了手势识别的精度,也提高了系统的可扩展性.     

基于无线网络的森林防火系统的研究与改进

论文针对现行的无线传感器技术方式的森林防火系统中的弊端,提出了一种改进的系统设计方案.改进后的方案主要采用二级Ad-Hoc模式的网络,对传统的无线传感器进行有效的改造的同时,也增加了稳定可靠的中心网络节点,无线通信网络采用改进后的AODV作为多跳路由协议,中心节点的数据处理采用卡尔曼滤波预处理,数据发送采用ALOHA机制进行随机延迟发送.系统经过反复实验,最终验证并总结出一套可以有效应用于实际工程中的系统部署方案.     

雨洪资源利用进展与利用模式探索

对雨洪及雨洪资源利用的概念进行了界定,明确了雨洪资源开发利用的对象。在明确雨洪资源概念的基础上,概括了近20年来国内外在雨洪资源利用方面的研究和进展,并对国外在此利用技术和管理方面所取得的经验进行了总结。接下来用较多的篇幅对雨洪资源利用工程和非工程的措施进行了的介绍,在对每种利用方式加以剖析之后,分别引用了相应利用方式中比较典型的实例。最后对这些利用方式在未来的发展趋势做了初步的展望。     

Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR

Remote sensing of invasive species is a critical component of conservation and management efforts, but reliable methods for the detection of invaders have not been widely established. In Hawaiian forests, we recently found that invasive trees often have hyperspectral signatures unique from that of native trees, but mapping based on spectral reflectance properties alone is confounded by issues of canopy senescence and mortality, intra- and inter-canopy gaps and shadowing, and terrain variability. We deployed a new hybrid airborne system combining the Carnegie Airborne Observatory (CAO) small-footprint light detection and ranging (LiDAR) system with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) to map the three-dimensional spectral and structural properties of Hawaiian forests. The CAO-AVIRIS systems and data were fully integrated using in-flight and post-flight fusion techniques, facilitating an analysis of forest canopy properties to determine the presence and abundance of three highly invasive tree species in Hawaiian rainforests.

The LiDAR sub-system was used to model forest canopy height and top-of-canopy surfaces; these structural data allowed for automated masking of forest gaps, intra- and inter-canopy shadows, and minimum vegetation height in the AVIRIS images. The remaining sunlit canopy spectra were analyzed using spatially-constrained spectral mixture analysis. The results of the combined LiDAR-spectroscopic analysis highlighted the location and fractional abundance of each invasive tree species throughout the rainforest sites. Field validation studies demonstrated < 6.8% and < 18.6% error rates in the detection of invasive tree species at  7 m² and  2 m² minimum canopy cover thresholds. Our results show that full integration of imaging spectroscopy and LiDAR measurements provides enormous flexibility and analytical potential for studies of terrestrial ecosystems and the species contained within them.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

Smart Client技术实现林权证管理系统的离线应用

林权证管理系统对于提高林权证管理的效率和科学性具有重要意义。对当前林权证管理系统存在的问题进行了分析,提出采用Smart Client技术进行林权证管理系统的开发,分析了离线状态下林权证办理的实现技术,为保障林权证业务的正常开展提供了一个有效途径。     

林木场景漫游系统的研究与实现

由于林木本身结构复杂,因此在模拟中几何模型的面片数多。如何在保证一定的视觉真实感的前提下,尽量简化场景模型,实现林木场景的快速绘制,一直是计算机图形学领域的热点问题。提出了一种适合包含大面积林木的自然场景漫游系统的实现方法,即利用3维建模软件3ds max构建场景模型,然后基于虚拟开发工具Open Scene Graph（OSG）实现场景的实时绘制和漫游。充分利用软件的特点和功能,实现了包含大面积林木的自然场景的漫游,同时还根据相交测试原理实现了漫游过程中的碰撞检测。实验结果表明,此系统具有较高的绘制速度,能满足包含大面积林木的自然场景实时漫游的要求,同时能达到一定的场景真实感。     

基于RF-BiLSTM的柔直阀冷入阀水温预测及冷却能力评估

为实现柔性直流(voltage sourced converter-high voltage direct current,VSC-HVDC)换流阀冷却系统入阀水温的智能预测,文中提出一种基于随机森林(random forest,RF)和双向长短时记忆(bi-directional long short-term memory,BiLSTM)网络混合的柔直换流阀冷却系统入阀水温的预测模型,并以此为基础对柔直换流站阀冷系统的冷却能力进行评估。首先,采用RF算法对由阀冷系统监测变量组成的高维特征集进行重要性分析,筛选出影响入阀水温的重要特征,与历史入阀水温构成输入特征向量。然后,将特征向量输入到BiLSTM预测模型,对模型进行训练并实现对入阀水温的准确预测和冷却能力定量评估。最后,以广东电网某柔直换流站为实例对所提方法进行分析,验证了所提出的基于RF-BiLSTM的混合模型预测精度优于BiLSTM模型、RF模型、支持向量机(support vector machine,SVM)模型和自回归滑动平均模型(auto-regressive and moving average,ARMA)模型,并且实现了冷却能力的定量评估。结果表明该换流站冷却裕量达98%,存在过度冷却、能源浪费的问题,与换流站现场运行情况相符,验证了文中所提方法的有效性和准确性。