西藏冻融侵蚀的分级和评价

Freeze-thaw erosion is the third largest soil erosion type after water erosion and wind erosion. Restricted by many factors, few researches on freeze-thaw erosion have so far been done at home and abroad, especially those on the assessment method of freeze-thaw erosion. Based on the comprehensive analysis of impact factors of free-thaw erosion, this paper chooses six indexes, including the annual temperature range, annual precipitation, slope, aspect, vegetation and soil, to build the model for relative classification of freeze-thaw erosion using weighted and additive methods, and realizes the relative classification of the freeze-thaw erosion in Tibet with the support of GIS software. Then a synthetic assessment of freeze-thaw erosion in Tibet has been carried out according to the relative classification result. The result shows that the distribution of freeze-thaw eroded area is very extensive in Tibet, accounting for 55.3% of the total local land area; the spatial differentiation of freeze-thaw erosion with different intensities is obvious; and the difference in distribution among different regions is also obvious.     

Spatial variability in classification accuracy of agricultural crops in the Dutch national land-cover database

Variability in per cell classification accuracy is predominantly modelled with land-cover class as the explanatory variable, i.e. with users' accuracies from the error matrix. Logistic regression models were developed to include other explanatory variables: heterogeneity in the 3×3 window around a cell, the size of the patch and the complexity of the landscape in which a cell is located. It was found that per cell, the probability of correct classification was significantly (α=0.05) higher for cells with a less heterogeneous neighbourhood, for cells part of larger patches and for cells in regions with a less heterogeneous landscape. To validate the models, a leave-one-out procedure was applied in which the absolute difference between the actual and the model-estimated number of cells correctly classified was summarized over 55 regions in the Netherlands. The sum of differences reduced from 60.9 to 48.1 after adding the variables ‘patch size’ and ‘landscape dominance’ to the land-cover class model. Spatial variability thus modelled therefore led to a substantial improvement in the estimation of the per cell classification accuracy.     

基于地理信息元组的分类预测理论及应用——以中亚矿集区预测为例

地理信息科学的迅速发展为矿集区预测提供了新的理论依据,使地理信息理论、相应的技术方法组合及应用的整体研究成为可能。在大量理论和实验研究的基础上提出了基于地理信息元组的分类预测理论体系,该理论体系由六个概念、一个空间信息本质、两个空间信息特性以及六个空间信息相关方法和约定的构成。并在该理论体系基础上结合空间数据分析方法和探索式空间数据分析方法,建立了基于地理信息元组的分类预测模型。应用该模型在中亚区域实现了Au矿集区预测目标。为中亚成矿域中其它区域(如新疆)的矿集区预测研究奠定了理论与方法论基础。     

Exploring a combination of objective and subjective assessment in landscape classification: Side case from Turkey

Landscape is a product of interactions between human and nature that bring multiple characteristics to discrete geographic settings. Landscape character assessment (LCA) is a process of describing, mapping and evaluating distinct characters in the landscape. The aim of this study is to integrate objective and subjective assessment in landscape classification in the case of Side district in Antalya, Turkey. The methodology of the study is based on a holistic approach to combine map-based biophysical information and on-site visual landscape characteristics into the LCA process. Principal component and cluster analysis were used to understand relationships and spatial patterns between 29 landscape character areas and types which were previously defined by previous work. The main source of data was landscape characters, and 35 character attributes was processed as variables. Cluster analysis showed that landscape character areas and types in Side were gathered into two main cluster groups and five sub clusters. The majority of landscape character areas tended to constitute separate subclusters, while character types appeared to form large groups of clusters in which recognisable land-use patterns were the main activity. According to the cluster dendrogram, it was possible to interpret spatial linkages between the clusters of character areas and types and to delineate geographic classification of the main landscapes in Side. Scaling relations for LCA in a pattern-process-product framework provided an explicit understanding of the data layers in landscape classification and where the clustering can function. Biophysical characteristics comprised the pattern of the landscape, while visual characteristics demonstrated the condition of the landscape as a product. The process depends upon transformation between the objective and the subjective as a link between pattern and product. Further steps would be to conduct semistructured surveys to assess local perceptions and preferences about landscape characters for landscape quality objectives.     

Urban mapping,accuracy, & image classification: A comparison of multiple approaches in Tsukuba City,Japan

The rapid growth of urban space and its environmental challenges require precise mapping techniques to represent complex earth surface features more accurately. In this study, we examined four mapping approaches (unsupervised, supervised, fuzzy supervised and GIS post-processing) using Advanced Land Observing Satellite images to predict urban land use and land cover of Tsukuba city in Japan. Intensive fieldwork was conducted to collect ground truth data. A random stratified sampling method was chosen to generate geographic reference data for each map to assess the accuracy. The accuracies of the maps were measured, producing error matrices and Kappa indices. The GIS post-processing approach proposed in this research improved the mapping results, showing the highest overall accuracy of 89.33% as compared to other approaches. The fuzzy supervised approach yielded a better accuracy (87.67%) than the supervised and unsupervised approaches. The fuzzy supervised approach effectively dealt with the heterogeneous surface features in residential areas. This paper presents the strengths of the mapping approaches and the potentials of the sensor for mapping urban areas, which may help urban planners monitor and interpret complex urban characteristics.     

土地利用变化遥感监测精度评价系统——以随机抽样为基础的方法

中国正在采用遥感技术对 5 0万以上市区非农人口城市的建设用地扩展情况进行分期监测 ,监测结果的精度评价是该项工作的一个重要方面。本文提出以分层随机抽样的方法来获取对遥感监测的精度分析 ,内容包括监测精度评价的指标系统、分层随机抽样样本容量的确定和在各层的分配、抽样评价的工作步骤以及抽样调查的统计分析和结果表达 ,并以北京和重庆 2 0 0 1年的遥感监测数据和实地调查数据为基础进行了精度评价的实际演练。     

自然灾害风险评估与分级方法论探研——以山西省地震灾害风险为例

自然灾害发生的可能损失一直是灾害风险评估上的难题.本研究以自然灾害风险评估经典模型为基础,提出了新的自然灾害风险评估与风险等级划分方法,利用历史灾情和孕灾环境解决风险评价中致灾因子破坏力与灾害发生的可能,并以山西省地震灾害风险为例进行了定量化评估研究.主要改进在于:确定灾害损失率和环境参数以建立地震灾害可能损失计算模型...     

基于QUEST决策树的遥感影像土地利用分类——以云南省丽江市为例

土地利用分类精度直接决定土地利用／土地覆被变化相关研究的准确性,而基于决策树的遥感影像分类是近年来提高土地利用分类精度的重要方法。QUEST决策树在影像解译和空间表达方面,运算速度和分类精度均优于普通CART等决策树方法。本文以云南丽江地区为例,应用QUEST决策树分类方法,对该地区的LandsatTM5影像图进行分类,同时将地形因素、植被指数作为地学辅助数据的因子添加到分类波段中,进行不同特征融合,来处理目标类别间的非线性关系,该方法在处理图像理解知识方面具有更大的灵活性;同时与普通决策树分类法的遥感影像分类的结果相比较,Kappa系数值从原来的0．789提高到0．849。在地形复杂的山地地区,针对TM影像数据,选择基于QUEST决策树分类能够有效提高土地利用分类结果精度。     

Map-image registration accuracy using least-squares polynomials

Abstract

Results of a simulation study of map-image rectification accuracy are reported. Sample size, spatial distribution pattern and measurement errors in a set of ground control points, and the computational algorithm employed to derive the estimate of the parameters of a least-squares bivariate map-image transformation function, are varied in order to assess the sensitivity of the procedure. Standard errors and confidence limits are derived for each of 72 cases, and it is shown that the effects of all four factors are significant. Standard errors fall rapidly as sample size increases, and rise as the control point pattern becomes more linear. Measurement error is shown to have a significant effect on both accuracy and precision. The Gram-Schmidt orthogonal polynomial algorithm performs consistently better than the Gauss-Jordan matrix inversion procedure in all circumstances.     

基于粗糙集的不确定区域RCC拓扑关系研究

基于粗糙集定义了粗糙区域和粗糙区域的上、下近似区域,提出两种基于上、下近似区域表示粗糙区域拓扑关系的方法。一种方法用上、下近似区域的连接关系表达粗糙区域拓扑关系,得出两个粗糙区域之间的6种拓扑关系,并进一步利用上、下近似区域的RCC5关系表达粗糙区域之间的拓扑关系,得出两个粗糙区域之间的51种拓扑关系,然后分析两种表达之间的联系。另一种方法扩展RCC5模型,首先定义三值逻辑的部分关系≤和补运算~,然后用三值逻辑三元组(X≤Y,X≤~Y,Y≤X)描述两个粗糙区域X和Y的拓扑关系,并给出可能的8种拓扑关系。     

基于粗集均值滤波的Hyperion高光谱数据滤噪方法

高光谱图像滤噪处理直接影响后续分析结果的精确度乃至正确性,鉴于此,针对Hyperion高空间分辨率和高光谱分辨率的特征,在比较分析目前各种高光谱遥感数据噪声滤波方法的基础上提出一种基于粗集均值滤波的噪声滤除方法,即先用上下近似空间和门限值刻画边缘,再用均值滤波。将该方法的实验结果与常用的高光谱数据MNF噪声分离算法和低通滤波算法的结果进行比较,发现基于粗集概念的均值滤波具有较好的保边去噪效果,其误差小、光滑性好、自适应能力强以及能够较好的保留细节信息,但需要通过交互确定门限;在处理不完整性、不确定性等问题时,粗集算法比硬计算效果更好。     

基于粗糙集与突变级数法的土地利用景观分区研究

基于江苏宜兴市土地利用现状数据库,利用土地景观格局指数,采用粗糙集与突变级数法相结合的方法对宜兴市进行土地利用景观分区。分区结果表明:区域地貌特征影响的土地利用方式是土地利用景观格局分异的主要因素,随着水域、林地面积比重的增加,人类对土地系统的改造逐渐减弱,斑块破碎化程度降低,斑块形状复杂化程度增强,景观多样性减弱;结合粗糙集论通过知识约简挖掘数据中隐含的内在规则,依据属性重要性对评价因子进行重要性排序,无需确定权重,最终通过多指标集成的突变级数法进行土地利用景观分区。     

Transportation mode-based segmentation and classification of movement trajectories

The knowledge of the transportation mode used by humans (e.g. bicycle, on foot, car and train) is critical for travel behaviour research, transport planning and traffic management. Nowadays, new technologies such as the Global Positioning System have replaced traditional survey methods (paper diaries, telephone) because they are more accurate and problems such as under reporting are avoided. However, although the movement data collected (timestamped positions in digital form) have generally high accuracy, they do not contain the transportation mode. We present in this article a new method for segmenting movement data into single-mode segments and for classifying them according to the transportation mode used. Our fully automatic method differs from previous attempts for five reasons: (1) it relies on fuzzy concepts found in expert systems, that is membership functions and certainty factors; (2) it uses OpenStreetMap data to help the segmentation and classification process; (3) we can distinguish between 10 transportation modes (including between tram, bus and car) and propose a hierarchy; (4) it handles data with signal shortages and noise, and other real-life situations; (5) in our implementation, there is a separation between the reasoning and the knowledge, so that users can easily modify the parameters used and add new transportation modes. We have implemented the method and tested it with a 17-million point data set collected in the Netherlands and elsewhere in Europe. The accuracy of the classification with the developed prototype, determined with the comparison of the classified results with the reference data derived from manual classification, is 91.6%.     

基于粗糙集和层次分析法的农村居民旅游目的地选择研究

充分考虑主客观因素,运用粗糙集和层次分析法研究农村居民旅游目的地选择影响因子,确定农村居民旅游目的地选择指标体系,并通过评价方法将指标合理化。研究结果表明： ① 判断矩阵计算的各因素权重结果较为接近,比较理想。② 农村居民旅游目的地影响因子中的消费水平、旅游景观、旅游氛围均超过0.1,排在前3位,较以往学者研究结果有所变化。③ 农村居民出游对旅游资源偏好较以往有很大改观。④ 旅游目的地的旅游氛围营造等影响因子已经超过了之前农村居民优先考虑的距离因素。⑤ 对交通条件,住宿水平的影响因子考虑的较少。相对传统的农村居民旅游目的地出游影响因素的研究方法,该方法可以最大限度的避免层次分析法所依赖的专家经验,避免出游因素评价指标设计中出现的重复性。     

Identification and classification of resource-based cities in China

Journal of Geographical Sciences - Resource-dependent cities are cities whose economic development depends on the exploitation and processing of natural resources. Their transformation and...     

The accuracy of altimétric surfaces

Summary. Estimates of the shape of the sea surface as determined from satellite altiméters have become of increasing importance in geophysics, geodesy and physical oceanography. The most effective error reduction procedures used in producing these surfaces have been some version of a crossing-arc analysis. The problem with this procedure is the absence of an easy means of evaluating the noise structure of the final result. By recasting the problem as one of objective mapping (optimal estimation) we both optimize the error reduction and obtain an explicit estimate of the wave-number structure of the final altimétric surface. Examples, suitable to a small computer, have been generated to show that the error reduction along the sub-satellite tracks is very great, with most of the residual lying in long wavelengths.