Scale Dependencies of Vegetation and Topography in a Mountainous Environment of Montana

This research examines the effects of spatial scale on estimating the relationship between vegetation biomass and topography within a portion of Glacier National Park, Montana. The Reflectance/Absorptance vegetation index, developed from processed Landsat Thematic Mapper digital data, is related to three topographic variables obtained through processed Digital Elevation Models: elevation, slope angle, and slope aspect. R² values between the vegetation index and the topographic variables are obtained from regression analyses at a series of aggregated spatial scales. The effective range of spatial scales within which the two sets of variables are spatially dependent and the degree of the spatial dependencies are characterized through semivariance and fractal analyses.     

基于多维度遥感影像的洪河国家级自然保护区沼泽湿地分类方法研究

以洪河国家级自然保护区为研究区,选取了多时相的Sentinel-1B和Sentinel-2A影像为数据源,制定出9种多时相主被动遥感数据组合方案,用于沼泽湿地遥感分类;分别对根据9种方案整合的多维数据集,进行基于尺度继承的多尺度分割,得到面向对象的分割影像,建立与不同方案对应的特征数据集;采用随机森林机器学习算法,对多...     

基于遥感与SRTM的青藏高原冰缘地貌信息提取方法——以1 ∶ 100万标准分幅拉萨幅(H46)为例

根据模型和分布函数,本文首先依据多年平均气温、地温和SRTM等数据对研究区域冰缘地貌的分布范围进行分别提取,并利用遥感数据和人工解译方式对其进行了修正。在此基础上,采用一定指标,利用SRTM数据对冰缘地貌次级类型(如起伏度、海拔高度和坡度等)进行了提取,从而完成研究区域冰缘地貌信息的提取。研究结果表明:①研究区域冰缘地貌总面积约5.15×10⁴km²,主要分布在研究区域的西北部和西南部,另外在东北部也有少量分布;通过提取,研究区域中最重要的冰缘地貌类型是冰缘作用的中起伏缓极高山,面积约0.82×10⁴km²,分布范围较广。②冰缘地貌的分布与海拔高度、气温和地温等有密切的关系,基于此提取的结果可为冰缘地貌的解译提供一定的参考;由于青藏高原气象站点较少,数据精度较低,自动提取精度受到很大限制,因此进行人工解译修正是非常重要和必不可少的。     

面向对象的湿地景观遥感分类——以杭州湾南岸地区为例

在ENVIEX软件的Feature Extraction平台上,利用LandsatTM影像数据,采用面向对象方法对杭州湾南岸地区湿地景观进行遥感影像分类;通过与基于最大似然法、人工神经网络法、支持向量机法等传统像元方法的相应分类结果进行比较,系统分析了面向对象方法在中低分辨率遥感影像的湿地景观生态分类中的有效性。研究结果表明:①较之单一依据像元光谱值进行分类的传统方法,面向对象方法综合考虑了对象的光谱、空间、纹理、色彩等多种属性特征,因而对于类型复杂多样、分布界限模糊、光谱混淆与混合像元现象严重的沿海滩涂、湖泊、河流等湿地景观具有更好的鉴别能力,也因此获得更高的分类精度(研究区景观分类总精度为88.80%,Kappa系数为0.8765);②面向对象方法在分类中提取的是由同质性像元组成的"对象",且在合理的影像分割下得到的对象破碎化程度较低,因而能在较大程度上减小分类结果中的"椒盐噪声"干扰;而基于像元方法提取的景观类型以离散像元形式组成,难以清晰表征景观的边界、形状等信息,所以分类结果中会有明显的噪声现象;③影像分割在运用面向对象方法进行遥感影像分类过程中具有重要影响,实验结果表明,60%的分割尺度和归并尺度组合较有利于中低分辨率影像的遥感分类;④面向对象分类过程中诸如影像分割精度的评价、最优分割尺度的选取、特征空间的优化等问题,则有待今后进一步探讨。     

Automated classification of landform elements using object-based image analysis

This paper presents an automated classification system of landform elements based on object-oriented image analysis. First, several data layers are produced from Digital Terrain Models (DTM): elevation, profile curvature, plan curvature and slope gradient. Second, relatively homogenous objects are delineated at several levels through image segmentation. These object primatives are classified as landform elements using a relative classification model, built both on the surface shape and on the altitudinal position of objects. So far, slope aspect was not used in classification. The classification has nine classes: peaks and toe slopes (defined by the altitudinal position or the degree of dominance), steep slopes and flat/gentle slopes (defined by slope gradients), shoulders and negative contacts (defined by profile curvatures), head slopes, side slopes and nose slopes (defined by plan curvatures). Classes are defined using flexible fuzzy membership functions. Results are visually analyzed by draping them over DTMs. Specific fuzzy classification options were used to obtain an assessment of output accuracy. Two implementations of the methodology are compared using (1) Romanian datasets and (2) Berchtesgaden National Park, Germany. The methodology has proven to be reproducible; readily adaptable for diverse landscapes and datasets; and useful in respect to providing additional information for geomorphological and landscape studies. A major advantage of this new methodology is its transferability, given that it uses only relative values and relative positions to neighboring objects. The methodology introduced in this paper can be used for almost any application where relationships between topographic features and other components of landscapes are to be assessed.     

遥感影像混合像元分解中的端元选择方法综述

端元选择是进行遥感影像混合像元分解的首要步骤,也是最关键的步骤,其直接影响混合像元分解的精度。该文对遥感影像混合像元分解中端元产生的特定背景、当前存在的端元选择途径和端元选择方法进行综述,并通过对当前端元选择方法的分析,提出了选择或构造端元选择方法应遵循的几个原则。     

Expert-driven semi-automated geomorphological mapping for a mountainous area using a laser DTM

In this paper a semi-automated method is presented to recognize and spatially delineate geomorphological units in mountainous forested ecosystems, using statistical information extracted from a 1-m resolution laser digital elevation dataset. The method was applied to a mountainous area in Austria. First, slope angle and elevation characteristics were determined for each key geomorphological unit occurring in the study area. Second, a map of slope classes, derived from the laser DTM was used in an expert-driven multilevel object-oriented approach. The resulting classes represent units corresponding to landforms and processes commonly recognized in mountain areas: Fluvial terrace, Alluvial Fan, Slope with mass movement, Talus slope, Rock cliff, Glacial landform, Shallow incised channel and Deep incised channel. The classification result was compared with a validation dataset of geomorphological units derived from an analogue geomorphological map. For the above mentioned classes the percentages of correctly classified grid cells are 69%, 79%, 50%, 64%, 32%, 61%, 23% and 70%, respectively. The lower values of 32% and 23% are mainly related to inaccurate mapping of rock cliffs and shallow incised channels in the analogue geomorphological map. The accuracy increased to 76% and 54% respectively if a buffer is applied to these specific units. It is concluded that high-resolution topographical data derived from laser DTMs are useful for the extraction of geomorphological units in mountain areas.     

澜沧江―湄公河地区地形地貌划分

地形地貌反映了地质构造运动特征,是影响和制约区域经济社会发展的基础。本研究以澜沧江-湄公河地区1:250 000TM遥感影像图为基础,考虑到本区新构造运动异常活跃以及微地貌的特殊性,结合其坡度形态及海拔高度,采用成因类型-成因形态-物质形态-坡度形态-微地貌形态的方法进行地貌类型划分研究。采用地势起伏度分级、野外查证等方法进一步验证了地貌划分成果,结果表明本次地形地貌划分研究较好地反映了客观实际。     

遥感图像纹理信息提取方法综述

纹理是遥感图像的重要特征，它提示了图像中辐射亮度值空间变化的重要信息。要利用图像空间信息提高分类精度，合理而有效地度量纹理至关重要。目前遥感图像纹理信息提取方法主要有：统计描述法、小波变换法、分维分形法和地统计学4类。分别就各种方法的优缺点、适用领域和应用情况进行了阐述，最后展望了遥感图像纹理信息提取方法的发展方向和研究热点。     

Automated construction of the boundaries of basin geosystems for the Volga Federal District

Using the Volga (Privolzhskii) Federal District as an example, we offer the solution of such a currently central scientific and methodological problem as automated identification of the boundaries of basin geosystems under different landscape and geomorphological conditions in terms of multiresolution digital elevation models. The main stages of work are described, namely the development of the digital elevation model, its hydrological adjustment, identification of the boundaries of basin geosystems, and validity assessment of results obtained. The technique has been developed for constructing a hydrologically valid digital elevation model with the purpose of identifying the boundaries of basin geosystems.     

基于Landsat 8 OLI数据与面向对象分类的昆嵛山地区土地覆盖信息提取

利用2015年Landsat 8 OLI遥感影像和DEM作为分类数据源,结合野外调查数据,采用面向对象的分类方法对昆嵛山地区土地覆盖信息进行提取,并对分类结果进行精度评价与比较分析。研究表明：面向对象分类方法提取的各地类连续且边界清晰,分类效果与实际情况基本吻合。昆嵛山地区占主导地位的土地覆盖类型是针叶林,面积为1 546.81 km²。研究区土地覆盖分类的总体精度和Kappa系数分别为91.5%和0.88,其中针叶林、草地、水体和建设用地的生产者精度均达到87%以上。相对于监督分类方法,本研究提出的土地覆盖信息提取方法的总体分类精度和Kappa系数分别提高14.7%和0.17。基于面向对象的中分辨率遥感影像,能够获取较高精度的土地覆盖信息,为大范围土地覆盖分类研究提供方法参考。     

基于卷积神经网络和高分辨率影像的湿地群落遥感分类——以洪河湿地为例

以洪河国家级自然保护区为研究对象,应用卷积神经网络（CNN）方法进行高分辨率湿地遥感影像的分类研究,并与基于光谱支持向量机（SP-SVM）的方法和基于纹理及光谱的支持向量机（TSP-SVM）的方法进行了对比。结果显示,对于所选取的2个研究区域,CNN分类方法的全局精度高于SP-SVM方法5.61%和5%,高于TSP-SVM方法4.18%和4.15%。尤其对于部分湿地植被的分类精度明显高于SP-SVM和TSP-SVM方法。研究表明,卷积神经网络为湿地识别的精细划分提供了有利的手段。     

Merged remotely sensed data for geomorphological investigations in deserts: examples from central Saudi Arabia

Image merging has gained acceptance in geological remote sensing, however it has rarely been applied in geomorphology. We report on the usefulness of principal components substitution (PCS) to merge IRS panchromatic data with multispectral Thematic Mapper (TM) imagery, to map commonly encountered desert geomorphological features, and in relative age dating of alluvial surfaces. The merged data were applied to the identification and mapping of geomorphological features along two geolo-gically different mountain fronts in central Saudi Arabia. Two types of geomorphological maps have been created. A morphogenetic map that distinguishes between aeolian landforms, fluvial landforms, desert pavements, and gypsum crusts. Second, a morphochronological map, which shows the relative age of four geomorphic surfaces developed on an alluvial fan. The construction of the two maps is supported by field observations and laboratory measurements. Using the optimum index factor (OIF), a TM band 1, 5 and 7 image (of 20 merged composites) was found to be the optimum colour composite image for the geomorphological features in this arid environment. We discuss our findings in the context of the spatial and spectral properties required for applied geomorphological remote sensing.     

基于光谱和纹理特征的ALOS影像土地利用信息提取

针对高分辨率遥感影像易于反映地物纹理特征的特点,综合利用地物的光谱和纹理特征进行分类,探讨适用于ALOS影像的土地利用信息提取方法。以川东丘陵地区影像为例,基于GLCM提取纹理信息,将提取的纹理特征向量采用赋权值法融合为一个综合纹理信息波段,然后采用面向对象法将其与光谱特征信息共同参与分类。与最大似然法的提取结果对比表明,考虑了纹理特征的面向对象分类方法能明显提高分类精度,Kappa精度提高了0.12;避免了椒盐现象,分割的地类边界具有更好的语义表达,更贴合地物实际分布特征;建筑用地和林地具有明显的纹理特征,而旱地纹理特征不明显。该方法不仅分出了6个基本地物类型,而且对于林地、建筑用地等类型还能进一步细分。     

基于MODIS数据的川西山区河道水体提取方法研究

MODIS数据时间分辨率较高,将其应用于河道监测优势明显.川西山区河道狭窄,水体像元多为混合像元,并且山区沟壑众多.山区阴影、混合雪盖和城镇区域的反射率特征与山区河道水体反射率特征相近,利用现有方法提取水体难度较大.为将MODIS数据的优势运用于山区河道监测,选定岷江上游区域作为典型研究区域,在对多日MODIS数据中的典型地物进行采样分析后,对归一化植被指数法(NDVI)进行了改进,将其与MODIS第1波段数据组合得到山区水体增强指数(MEWI),实现了图像中山区沟壑的模糊处理,突出了水体.在针对山区地物特点进行图像增强后,河道水体清晰可辨,能够应用于山区5级以上河流的监测.     

不同分辨率遥感图像混合像元线性分解方法研究

混合像元的存在是传统像元级遥感分类和面积量测精度难以达到实用要求的主要原因,为了提高遥感应用精度,须解决混合像元分解问题。传统的方法主要通过改进分解模型提高分解精度,该文在不改变线性分解模型的条件下,分析不同分辨率尺度对于线性分解精度的影响。实验中运用像元合并的方法,得到不同分辨率的TM系列遥感图像,分别选取植被、裸地、水体3种典型地物进行线性分解;以分辨率更高的Quickbird图像分类结果作为真值进行精度评价。实验结果表明:随着图像分辨率的降低,植被的RMSE值不断缩小,在30 m分辨率尺度上均值为0.36,在150 m尺度上均值为0.17,分解精度提高了1倍左右;但随着分辨率进一步降低,由于混合像元现象加剧,RMSE值上升,分解精度随之降低。     

基于GIS的斜坡单元划分方法改进与实现

斜坡单元已广泛应用于滑坡易发性制图和地质灾害评价。然而在山间盆地或大型宽谷处,常规方法划分出的斜坡单元与地貌背景难以匹配。依据高程及其衍生变量的基本形态系统和曲率的流域分割原理,基于ArcGIS技术,通过叠加曲率和反转曲率的流域边界,改进了斜坡单元划分方法。结果表明：与常规方法相比,改进方法不仅能够使用山脊线和山谷线以划分斜坡单元,还能利用台地边界和宽谷边界以分割水平地表与倾斜地表;划分出的单元大小相对均匀,单元形状总体介于圆形和正三角形之间。对于水平成分较多的地区,如黄土塬区和水库库区,该方法与传统方法相比,具有较好的应用前景。     

基于邻域扩展量化法的城市边界识别

城市空间分析的基本条件是可靠的测度,而城市的基本测度是规模。客观定义城市边界是有效确定城市规模的技术前提。近年来国内外学者提出几种城市边界识别的方法,其中能够定量反映城市内部实体空间组织关系的多采用矢量图像。但这些矢量数据的获取十分困难,且实时性差。因此,本文借鉴前人的研究成果,基于邻域扩展量化和标度思想,提出一种应用于遥感栅格图像上的城市边界识别方法。该方法的本质是一种空间邻域融合法,通过改变像元邻域的作用范围,可以得到不同的空间集群数目;借助搜索范围与集群数目的标度关系确定一个客观的半径,据此可以利用GIS技术确定城市边界。将该方法应用于北京地区多个年份的遥感图像,发现了像元的有效邻域作用范围。此方法以栅格图像为基础,数据实时性好并且获取容易,计算过程简便,在未来的城市边界研究过程中,可望与现有的方法相互补充。     

Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.     

基于地质统计学纹理特征的遥感影像分类方法研究

介绍地质统计学的原理与方法,论述了地质统计学应用于遥感影像描述及纹理提取的有效性,并将地质统计学变差函数得到的遥感影像纹理信息与其光谱信息相结合进行遥感影像分类试验,结果表明,辅以地质统计学纹理特征的遥感影像分类方法能够明显提高分类精度.