基于无人机的高寒草甸地表温度监测及影响因素研究

地表温度直接影响地表感热、潜热及辐射能量传输过程，是研究寒区植被生态水文过程的重要参数。以疏勒河源区高寒草甸为研究对象，利用无人机搭载普通及热红外相机获取6块样地植被盖度及地表温度数据，用以评估热红外相机在高寒草甸地表温度监测中的应用精度，分析高寒草甸地表温度变化特征，并结合气象因子及植被盖度数据，探究地表温度的影响因素。结果表明：热红外影像地表温度与地面实测值具有较高一致性（R² ≈ 0.75），平均误差5 ℃以内；高寒草甸地表温度在9： 00-18： 00时段内表现为快速升温达到峰值继而波动下降的趋势，观测期内（7月4日-8月17日）未表现出显著日际变化趋势；气象因子中，太阳辐射和空气温度与地表温度呈显著正相关，起到增温作用，而空气湿度抑制地表增温，表现为显著负相关；连续降水降低裸土温度，植被覆盖度与地表温度呈现出正相关变化趋势。无人机热红外遥感技术可以快速、精准获取高分辨率地表温度数据，为高寒草甸干旱监测、土壤水分及蒸散发等反演提供数据支持。     

从景观的角度分析植物模型的表现

基于景观构成和植物特性,对当前建筑动画和游戏场景中植物模型的配置进行了分析,发现存在许多问题:植物模型的随意摆放,植物树种以及植物图片色彩与具体地域的不协调,植物模型体量与周围环境失调等等。从景观的角度出发,提出了具体的措施来构成植物景观,衬托环境,演绎时间,表达意境。以期在景观辅助设计中更好的表现设计者意图,达到自然的效果。     

基于BP神经网络的针阔混交林TM遥感图像自动分类技术研究

在对标准BP神经网络试验分析的基础上，通过输入矢量归一化处理、主成分分析、增加验证集、改进训练学习算法、扩大隐层和输出层规模等措施，对BP神经网络自动分类系统进行改进；利用改进后的BP系统对吉林省汪清林业局的典型针阔混交林TM遥感图像进行辩识、分类试验研究。结果表明：改进后的BP网络分类系统自动分类精度提高了19．14％，比传统无监督自动分类精度提高8．55％，达到了区分森林类型的分类要求。研究还显示了该改进系统应用于针阔混交林TM遥感图像自动分类识别的精度随网络规模增大而提高。     

Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data

Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m^–2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.     

洱海流域植被覆盖度遥感估算与变化分析

植被覆盖度是刻画地表植被覆盖的一个重要参数,也是指示生态环境变化的重要指标之一.以洱海流域1990年和2006年TM影像为数据源,利用NDVI的像元二分模型法对洱海流域1990年和2006年植被覆盖度进行遥感估算,并进行变化分析.结果显示,近17年来洱海流域植被覆盖度总体上有所提高,无植被覆盖区域面积明显下降,高植被覆盖占全植被覆盖区域面积比大幅提高,二者面积比近50％;在空间分布上,洱海流域上游地区、东部部分地区植被覆盖度相对较低,对地区生态环境稳定构成重大威胁,将是今后洱海流域生态建设和整治的重点区域.     

光学影像纹理信息在林业领域的最新应用进展

随着光学卫星影像空间分辨率的不断提高,影像纹理特征的重要性日益凸显。然而,纹理是一个非常复杂的空间属性,会随着太阳/观测角度、地形、感兴趣目标及其所处环境的不同而发生显著的变化。此外,不同纹理变量的选择及相应输入参数的设置,如窗口大小、像元间距、方向以及量化等级等都可能在一定程度上决定着影像纹理的利用价值。如何有效利用纹理量及其优化组合是一个值得深入探讨的问题。鉴于此,本文首先全面回顾了影像纹理特征在森林分类、森林结构参数反演以及森林生物量与碳储量的遥感估算等方面的最新研究与应用,并从不同角度肯定了光学影像纹理在林业遥感领域的应用潜力。此外,从纹理变量及其4大输入参数的选择和最优变量组合的判别方面,总结并剖析了当前研究领域中所存在的关键问题,权衡利弊并给出了相应的建议,为相关研究人员将影像纹理信息更有效地应用于林业领域提供参考。      

汉诗意象及其英译

由于汉诗悠久的传统，导致汉诗独特的意象系统，具体表现在汉诗意象的象征义方面，并不同于英诗的意象系统。因此，汉诗意象的英译，应该采取以意为主、以意为先的翻译策略。具体而言，对于名词意象的英译，应注意其联想义；对于动词意象的英译，应注意其精确到位。译诗应避免浅层理解之上的浅层之译，并努力做到深层理解之上的深层之译。     

Estimating forest carbon fluxes for large regions based on process-based modelling, NFI data and Landsat satellite images

The aim of this study was to develop and evaluate a new approach for estimating forest carbon fluxes for large regions based on climate-sensitive process-based model, national forest inventory (NFI) data and satellite images. The approach was tested for Central Finland and Lapland with NFI field data and daily weather data from 2004 to 2008.The approach combines (1) a light use efficiency (LUE) model, (2) a process-based summary model for estimating gross primary production (GPP) and net primary production (NPP), and (3) the Yasso07 soil carbon model, which together allow the estimation of net ecosystem exchange (NEE). Landsat TM 5 satellite images were utilized to generalize the carbon fluxes obtained for field sample plots for all forested areas using the k-NN imputation method. The accuracy of the imputations was examined by leave-one-out cross validation and by comparing the imputed and simulated values with Eddy covariance (EC) measurements.RMSE of the k-NN imputations was slightly better in Central Finland than in Lapland, the bias staying at a similar level. Based on the EC comparisons, the approach seemed to work rather well with GPP estimates in both areas, but in the north the NEE estimates were remarkably biased. The main advantages of the approach include its applicability to basic NFI data and a high output resolution (30 m).The method proved to be a promising way to produce carbon flux estimates based on large-scale forest inventory data and could therefore be easily applied to the whole of Northern Europe. However, there are still drawbacks to the approach, such as lacking parameters for peat lands. One of the future goals is to integrate the approach with an interactive mapping framework, which could thereafter be utilized, for example, in climate change research.     

基于扫描图像RGB分析的生物质炭吸附特性研究

以花生壳和玉米秸秆为原料,利用自主研发的无轴螺旋连续热解装置在300、400、500℃的热解温度下反应10 min制备生物质炭,对生物质炭进行工业分析和热值测量,分析其组成成分和热值;开展了生物质炭亚甲基蓝吸附与碘吸附特性研究,结合扫描仪和取色软件获取生物质炭的RGB数据并进行灰度转化,探究生物质炭的吸附特性与RGB值、灰度的相关关系。结果表明:随着热解温度的升高,生物质炭中挥发分的含量降低,固定碳和灰分的含量升高,热值升高;较低热解温度的生物质炭的吸附效果优于较高温度热解的生物质炭;生物质炭的吸附值与R、G、B值均随着热解温度的升高而降低,两者之间存在强正相关关系,相关系数r为0.582~0.944;生物质炭的灰度与吸附值存在强正相关关系,相关系数r为0.685~0.977。     

Binary Images Processing Based on Adaptation to Mixture Transform Coding

To enhance quality of image processing, it is demanded that coding algorithm should be simplified and the speed of image processing should be faster,and information loss on image compressed should be minimum.Adaptive segmentation is given for image which to be processed.Region coding is combined with threshold coding.Adaptation to mixture transform coding is fulfilled.Results of simulation shows that with this method coding loss witch images are compressed is mined and quantity of image processing is good.