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.     

Development of a coupled land-atmosphere satellite data assimilation system for improved local atmospheric simulations

This study developed a coupled land-atmosphere satellite data assimilation system as a new physical downscaling approach, by coupling a mesoscale atmospheric model with a land data assimilation system (LDAS). The LDAS consists of a land surface scheme as the model operator, a radiative transfer model as the observation operator, and the simulated annealing method for minimizing the difference between the observed and simulated microwave brightness temperature. The atmospheric model produces forcing data for the LDAS, and the LDAS produces better initial surface conditions for the modelling system. This coupled system can take into account land surface heterogeneities through assimilating satellite data for a better precipitation prediction. To assess the effectiveness of the new system, 3-dimensional numerical experiments were carried out in a mesoscale area of the Tibetan Plateau during the wet monsoon season. The results show significant improvement compared with a no assimilation regional atmospheric model simply nested from the global model. The surface soil moisture content and its distribution from the assimilation system were more consistent to in situ observations. These better surface conditions affect the land-atmosphere interactions through convection systems and lead to better atmospheric predictability as confirmed by satellite-based cloud observations and in situ sounding observations. Through the use of satellite brightness temperature, the developed coupled land-atmosphere assimilation system has shown potential ability to provide better initial surface conditions and its inputs to the atmosphere and to improve physical downscaling through regional models.     

Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin

Spatially distributed estimates of evaporative fraction and actual evapotranspiration are pursued using a simple remote sensing technique based on a remotely sensed vegetation index (NDVI) and diurnal changes in land surface temperature. The technique, known as the triangle method, is improved by utilizing the high temporal resolution of the geostationary MSG-SEVIRI sensor. With 15 min acquisition intervals, the MSG-SEVIRI data allow for a precise estimation of the morning rise in land surface temperature which is a strong proxy for total daytime sensible heat fluxes. Combining the diurnal change in surface temperature, dT_s with an interpretation of the triangular shaped dT_s − NDVI space allows for a direct estimation of evaporative fraction. The mean daytime energy available for evapotranspiration (R_n − G) is estimated using several remote sensors and limited ancillary data. Finally regional estimates of actual evapotranspiration are made by combining evaporative fraction and available energy estimates. The estimated evaporative fraction (EF) and actual evapotranspiration (ET) for the Senegal River basin have been validated against field observations for the rainy season 2005. The validation results showed low biases and RMSE and R² of 0.13 [−] and 0.63 for EF and RMSE of 41.45 W m^− 2 and R² of 0.66 for ET.     

Kelvin Probe measurements of polymer coated gold substrates: Mechanism studies

The origin of work-function changes induced in polymer/metal structures (polyacrylic acid (PAA), ammonium and sodium polyacrylate) upon ammonia exposure is studied. To understand and explain the mechanism systematic measurements of uncoated and polymer coated metals are performed in Kelvin Probe (KP) setups. Additional data have been acquired through gravimetric and electrochemical experiments (cyclic voltammetry (CV)). The results obtained confirm that the field effect sensitive processes are taking place at the metal/polymer interface and allow their modelling. The intrinsic sensitivity of metallic samples upon ammonia exposure is influenced by the presence of coating polymers, by their ability to sorb water and to keep the metal surface wet or dry.     

遥感图像分形特征提取与分割

分形理论由B.B.Mandelbrot于20世纪70年代中期创立,现已被广泛地应用于自然科学和社会科学的几乎所有领域.本文在前人研究的基础上,利用双毯法(Double Blanket Method)提取出图像的分形特征并用于图像分割,进一步证实了分形在此领域的可行性和有效性.首先,通过比较局部分形维数偏移全局分形维数的标准差来确定适合该方法的最优滑动窗口.其次,考虑到单尺度分维特征的局限性,提取出多尺度的特征值并建立分形维数谱.然后,以模拟图像为例,分析图像中各区域的分维谱,选择适当尺度的分形特征,利用最大似然法对图像进行分割.最后,将分形理论应用于遥感图像中,与传统的基于灰度值特征的图像分割方法相比,加入图像的空间分形纹理特征后分割精度明显提高.研究结果表明:分维值的大小和变化趋势可以表示不同地物的空间复杂度,结合地物的光谱以及灰度信息能有效地识别目标地物.     

用风云二号C星进行热点监测的快速算法

用卫星影像资料监测热点已经有较长的历史.静止卫星由于影像采集时间间隔短,能显著提高热点监测的及时性,但由于其星下点分辨率低,影像覆盖范围广,监测热点较为困难.文中提出了一种利用风云二号C星影像资料进行热点监测的快速算法,首先对影像资料进行辐射定标并生成中红外与长红外通道的差值影像图,而后将中红外影像图与差值影像图分割为20×20像元的小块,分别计算各小块的最大值、平均值及标准差,认为两幅影像相对应的像元块内最大值均大于平均值3倍标准差即认为该块内可能含有热点;然后结合地表真实分布情况对含有热点的小块进行综合识别处理;最后将含有热点的局部影像进行几何校正投影输出.通过实例资料进行检验,认为这种方法计算简单、快速,可极大提高热点监测的准确性与时效性.     

卫星通信TCP特性及改进

文章就卫星通信TCP性能改进进行了研究,分析了船载卫通站卫星通信通过TCP协议传播的一些不利于工作效率的特性,针对部分特性提出了改进方法,并进行了简单的测试,TCP协议作为一种可靠的通信协议,本身采取了一些保护数据安全传输的措施,但是这些措施往往是应用于有线网络,并不能很好地适应船载卫通站的通信保障需求。本文提出的改进方法主要是采用分析数据包丢失现象改进TCP传输的拥塞控制和慢启动策略。     

基于MC9S12NE64的以太网测控系统的设计

嵌入式测控系统是当前工业自动化测控应用领域研究的热点之一。本文利用Freescale公司的MC9S12NE64MCU,运用其内部10位A/D模块,进行数据采集,并通过其集成的以太网网络模块实现了数据上传和远程控制。     

基于Internet的电梯ARS监控系统的设计与实现

电梯ARS（自动逃生）系统是在电梯供电中断等紧急情况下,帮助乘客逃生的自动化系统。在整个监控系统中,包括本地和远程监控两部分。该文介绍了监控系统的功能,设计与实现。本地监控通过串口通信与下位机进行通信来实现数据的采集,远程的实现是应用微软公司开发的语言C#,以网络编程核心Windows Sockets和客户／服务器模式为基础,运用ADO．NET类融合数据库Access和．NET Remoting网络技术,完成一个远程分布式数据库查询系统,以实现电梯ARS远程监控功能。