西鄂尔多斯荒漠化动态分析

以20世纪80年代末和2005年两期TM影像作信息源,对西鄂尔多斯鄂托克旗(以下简称鄂旗)境内的荒漠化土地现状与动态变化进行了监测研究。结果表明:近30年来,鄂旗荒漠化土地总面积变化较大,增长6.9%,荒漠化程度也明显加重,中度荒漠化面积明显增加,其中中度沙质地荒漠化面积增长了20.80%,中度盐渍化荒漠化面积增加了35.65%。     

高分辨率遥感影像在台塬区土地整治项目设计中的应用

土地整治工程的实施已成为缓解和改善耕地数量不足的重要方式,但由于耕地后备资源数量不断减少,集中连片程度的不断降低,项目前期立项和设计工作所需数据信息获取难度越来越大,尤其是渭北台塬区主要为丘陵沟壑地貌,部分区域甚至已无法通过人力踏勘的方式去完成,给设计工作带来了巨大的困难。结合现代计算机技术以及移动通讯技术等相关技术的发展,高精度卫星遥感影像为土地整治类项目项目区范围确定、地块信息获取、工程整体布局以及规划设计工作带来了极大的便利,本文以宝鸡市陇县LJB村土地整治项目为例,对高精度遥感影像在土地整治项目中规划设计工作的应用进行研究分析,以期为高精度遥感影像在土地整治设计工作的应用研究提供案例。     

Contrast-enhanced fluid-attenuated inversion recovery vs. contrast-enhanced spin echo T1-weighted brain imaging

In humans, contrast-enhanced fluid-attenuated inversion recovery (FLAIR) imaging plays an important role in detecting brain disease. The aim of this study was to define the clinical utility of contrast-enhanced FLAIR imaging by comparing the results with those with contrast-enhanced spin echo T1-weighted images (SE T1WI) in animals with different brain disorders. Forty-one dogs and five cats with a clinical suspicion of brain disease and 30 normal animals (25 dogs and five cats) were evaluated using a 0.2 T permanent magnet. Before contrast medium injection, spin echo T1-weighted, SE T1WI, and FLAIR sequences were acquired in three planes. SE T1WI and FLAIR images were also acquired after gadolinium injection. Sensitivity in detecting the number, location, margin, and enhancement pattern and rate were evaluated. No lesions were found in a normal animal. In affected animals, 48 lesions in 34 patients were detected in contrast-enhanced SE T1WI whereas 81 lesions in 44 patients were detected in contrast-enhanced FLAIR images. There was no difference in the characteristics of the margins or enhancement pattern of the detected lesions. The objective enhancement rate, the mean value between lesion-to-white matter ratio and lesion-to-gray matter ratio, although representing an overlap of T1 and T2 effects and not pure contrast medium shortening of T1 relaxation, was better in contrast-enhanced FLAIR images. These results suggest a superiority of contrast-enhanced FLAIR images as compared with contrast-enhanced SE T1WI in detecting enhancing brain lesions.     

Process-based approach to automated classification of forest structures using medium format digital aerial photos and ancillary GIS information

The methods of forest inventory data acquisition, based on the analysis of remotely sensed images have been well tested and implemented during the last decade. The predominant visual interpretation and pixel-based automated techniques are now being gradually replaced by the object-based image classification at multiple levels. This paper describes an experiment using medium-format digital aerial imagery for the purpose of automated updating of the existing GIS forest management database (LHPO). The method emphasises the pre-processing phase, where various image transforms and additional channels i.e. spectral ratios and vegetation indices (NDVI), low-pass filters, Sobel edge and GLCM (grey level co-occurrence matrix) texture measures are derived from the original dataset. The layer stack is then transferred into the object-oriented classification environment together with the existing thematic vector layer, and analysed on three hierarchical object levels. The classification involves the recognition of the successional stage of forest compartments and the estimation of tree species composition in terms of area coverage. In addition, age information on the GIS forestry management map can be updated and the spatial distribution of classes corrected using the multi-scale object relations of the former analysis. The advances of the automated procedure based on sequential processing of image objects are partially covered. Moreover, aspects of utilisation of the medium-format colour infra-red images (CIR) as an alternative to traditional aerial photos and very high resolution (VHR) satellite data, were considered.     

内蒙古鄂托克前旗地区苦豆子植被指数信息提取的研究

苦豆子是一种兼具药用、经济、生态等多方面价值的灌草,本文利用NDVI、DVI和MSAVI等7种植被指数(Vegetation Indices)对内蒙古鄂托克前旗地区的苦豆子资源进行遥感分类提取,并且利用分类精度评价和信噪比评价2种方法比较了这几种植被指数在苦豆子分类上的优劣。经过对比分析,认为修正土壤调节植被指数(MSAVI)可以根据植被的郁闭度来调节土壤调节参数,在苦豆子遥感分类提取上具有明显的优势。     

基于超高空间分辩率无人机影像的面向对象土地利用分类方法

为明确基于无人机超高空间分辨率影像的土地利用分类方法,尤其是有效特征和算法的选择,该研究获取吉林省德惠市一农耕区超高分无人机影像,获取区域正射影像图和数字表面模型,计算地形指标,采用面向对象方法进行土地利用分类研究。首先,采用随机森林算法,以光谱特征为基础,依次引入指数、形态、地形、纹理特征,建立5种特征选择方案,分析各类特征对分类效果的影响。其次,以Boruta特征选择算法获取的优化特征集为基础,采用随机森林算法、朴素贝叶斯算法、逻辑回归算法和支持向量机算法分类,分析不同算法的分类效果。结果表明:采用5种特征选择方案分类,引入形态特征时总体精度降低,引入其他特征时总体精度逐渐提高。5种特征共同参与的分类效果最佳,总体精度为98.04%,Kappa系数为0.980。错分主要发生在裸地和宅基地,漏分主要发生在草地、裸地、水渠和道路。错分和漏分主要是因为这几种类型对象具有相似的光谱、形态、纹理特征或相似的分布位置。采用优化特征集分类时,相比其他算法,随机森林算法更擅长处理高维特征集,获得最高的总体精度98.19%,最低的错分和漏分误差,分类效果最佳。借助无人机超高空间分辨率影像提取地形信息、形态信息,可以有效辅助土地利用分类,并能提高传统分类方法精度。     

基于遥感影像植被指数变化量分析的冬小麦长势动态监测

为进一步探究利用中低分辨率影像监测小麦苗情的机理，丰富小麦长势动态监测的模式，结合2017-2018年定点观测试验，以GF-WFV数据为遥感影像源，研究了孕穗-开花期冬小麦主要长势变化量参数和产量及其与植被指数变化量间的定量关系，以逐步回归方法筛选目标长势变化量参数，分别构建及评价基于GF-WFV影像遥感植被指数变化量的孕穗-开花期叶片含氮量变化量和叶绿素含量变化量监测模型。结果表明，冬小麦叶片含氮量变化量（ΔLNC）和叶绿素含量变化量（ΔCHL）与产量密切相关，而孕穗-开花期的归一化植被指数变化量（ΔNDVI）、比值植被指数变化量（ΔRVI）分别与ΔLNC和ΔCHL相关性最好，因此选择这两个植被指数变化量作为敏感参量构建冬小麦长势监测模型。经验证，基于ΔNDVI和ΔRVI构建的长势线性模型可靠且精度高，其决定系数分别为0.70和0.64，均方根误差分别为0.39%和0.08 mg·L^-1FW。基于预测模型和实测数据分级量化表达冬小麦长势的空间分布状况，能够很好实现了基于GF-WFV时相影像长势不同等级的遥感监测。     

长时序高光谱图像清晰度影响因素分析

目的 清晰度是评价对地观测成像仪影像数据质量的典型指标之一,可以反映成像仪对地物边缘变化的敏锐程度。已有的对地观测成像仪在轨测试及图像质量评价方法研究中,往往关注遥感影像清晰度是否达标,或监测其变化趋势,未对清晰度变化影响因素进行深入探讨。针对这一问题,本文主要对长时间序列的成像仪成像清晰度的变化以及影响因素进行探讨。方法 以天宫一号高光谱成像仪短波红外谱段0级数据作为研究对象,首先利用改进的基于边缘检测的清晰度算法计算出影像的清晰度,其次将各影像数据对应的成像仪工程参数进行筛选,然后利用Apriori算法对长时间序列高光谱影像的清晰度与成像时刻的工程参数进行关联规则挖掘,利用最小支持度阈值和最小置信度阈值筛选出强关联规则,并附加提升度和余弦对强关联规则进行验证,最后结合3维散点图对影响清晰度的主要因素进行定量分析。结果 经大量测试数据表明,天宫一号高光谱成像仪短波红外谱段影像清晰度较好,影响清晰度的主要因素有太阳高度角、拍摄积分时间以及平台稳定性(包括俯仰角、偏航角和滚动角的稳定性)。太阳高度角与图像清晰度呈正相关关系,即当太阳高度角大于65°时,影像清晰度较高,当太阳高度角小于30°时,影像清晰度较低;平台稳定性与图像清晰度呈正相关关系,即当太阳高度角大于30°且小于65°时,平台稳定性高倾向于得到清晰度较高的图像,平台稳定性低倾向于得到清晰度较低的图像;拍摄积分时间与图像清晰度呈负相关关系。结论 基于关联规则挖掘的长时序高光谱图像清晰度影响因素分析方法是一种有效的分析方法,可以挖掘出与影像清晰度强关联的工程参数。后续可扩大工程参数范围,利用此分析方法进一步研究遥感图像其他指标与工程参数的关联关系。     

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.     

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。