基于目标分割和景观格局特征的城市土地利用分类

根据相同土地利用类型景观格局特征相似的原理,在传统遥感分类方法的基础上,结合景观生态学理论,建立了土地利用分类新方法; 应用SPOT遥感图像提取了北京市五环内的居民用地和非居民用地类型,总分类精度达到了85.9%,Kappa系数为71.1%.本研究结合学科交叉的优势,为遥感技术应用和土地利用信息提取提供了新思路.     

Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity

Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines – SVM), and hybrid (unsupervised–supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different depending on land use/cover classes. Early-growth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land use/cover classes were mapped with producer's and user's accuracies of ∼90%. Our classification approach seems very well suited to accurately map land use/cover of heterogeneous landscapes, thus having great potential to contribute to climate change mitigation schemes, conservation initiatives, and the design of management plans and rural development policies.     

Using geoinformatics and geomorphometrics to quantify the geodiversity of Crete,Greece

The geodiversity of Crete is quantified in this study, based on the classification of geomorphometric, geological and climatic factors. A number of geomorphometric variables, extracted from the ASTER Global Digital Elevation Model (ASTER G-DEM) in conjunction with geological and climatic information, are evaluated through various algorithms incorporated into Geographical Information System (GIS) software’s. The derived geoinformatic data sets are then analyzed to produce the geodiversity of Crete. The geodiversity map is used to quantify the geodiversity, by calculating landscape diversity and other spatial pattern indices. Those indices are evaluating the richness, evenness, fragmentation and shape of the landscape patch types. The outcome of this study has highlighted that western Crete is characterized by complex geodiversity with more irregular, elongated and fragmented landscape patterns relative to the eastern part of the island. The geodiversity indices provide insights into the processes shaping landscapes, particularly the “battle” between neotectonic landscape deformation and erosion/deposition. The methodology presented can be useful for decision makers when evaluating a regions geological heritage, planning the management of natural resources, or designating areas for conservation.     

地理国情普查外业调查技术要求

地理国情外业调查是地理国情普查工作十分重要的工作内容,是保证地理国情普查数据质量的关键环节。外业调查是对采集的地理国情要素和解译的地表覆盖分类成果以及内业无法定性的类型、边界和属性进行实地调查,同时采集遥感影像样本数据,为最终形成地理国情要素数据、地表覆盖分类数据成果和遥感影像解译样本数据库提供基础。     

Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier

In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification.     

Assessment of changes in urban green spaces of Mashad city using satellite data

Green spaces play important functions in urban environments. Reducing air pollution, providing shade and habitat for arboreal birds, producing oxygen, providing shelter against winds, recreational and aesthetic qualities and architectural applications are the main functions of urban green spaces. With the rapid change of urban area in Mashad city during the past decades, green spaces have been fragmented and dispersed causing impairment and dysfunction of these important urban elements. The objective of this study was to detect changes in extent and pattern of green areas of Mashad city and to analyze the results in terms of landscape ecology principles and functioning of the green spaces. In this research, we classified a Landsat TM and an IRS LISS-III image belonging to the years 1987 and 2006, respectively. We then used a post-classification comparison to determine the changes in green space areas of Mashad city during the 19 years covered by the images. Then, we applied landscape ecology calculations to derive metrics that quantified pattern of the changes in the green areas. The results showed that during 19 years from 1987, a significant decrease had occurred in the extent of urban green spaces with a concomitant fragmentation resulting in downgrading and destruction of the functions and services these areas provide. We conclude that the general quality of life in the central parts of the city has been diminished. We also state that a combination of remote sensing image classification, landscape metrics assessment and vegetation indices can provide a tool for assessing life quality and its trend for urban areas.     

基于GIS和景观生态学的土地整理景观研究

运用GIS技术和景观生态学理论方法,以安徽省淮南市潘集区市级投资重点土地整理项目为例,选取3类景观指标,对项目区内土地整理前后的土地利用现状和景观格局变化情况进行研究。结果表明： 景观类型中水田斑块占绝对优势,斑块分维数、形状指数呈下降趋势,表明斑块形状趋于规则和简单; 斑块数量和斑块密度降低,平均斑块面积和最大斑块面积增加,最大斑块指数增大,景观破碎度降低; 平均分维数和平均形状指数增大,表明景观形状较整理前变得规则,但总体形态变得复杂; 多样性指数和均匀度指数降低,表明在增加了景观分布均匀程度的同时降低了景观的多样性,景观类型有所减少。     

Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification

Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing.Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline.     

VEGETATION AS A CRITERION FOR INTERPRETING THE MOISTURE REGIME OF A LANDSCAPE

Vegetation signatures on aerial photographs and space imagery are used as indicators of soil moisture differences in a Siberian taiga landscape. The authors use remote sensing products to identify all major habitat types resulting from variable soil moisture regimes that were observed on the ground. These types are described, as are their interpretation keys and the effects of scale change on habitat discrimination. Translated from: Kosmicheskiye metody izucheniya prirodnoy sredy Sibiri i Dal'nego Vostoka, Novosibirsk, 1983, pp. 63-74.     

基于RS和GIS的深圳市景观格局时空变化研究

基于RS、GIS和景观生态学原理与方法,研究深圳市域景观的空间格局特征及其动态变化.采用ERDASIMAGINE遥感数据处理软件对1995年、1999年和2008年的TM遥感影像图进行数据预处理、监督分类以及分类后处理,并通过ArcMap对经过分类后处理的图像进行编辑,得到深圳市历年的景观分类图,应用FRAGSTATS...     

深圳市土地覆盖格局空间变化研究

以深圳市为研究对象,利用遥感影像和空间分析方法,研究了深圳市土地覆盖格局的梯度变化特征:各个土地覆盖类型的斑块形状指数沿梯度区呈先增大后减少的趋势,在城区内其值较低,城乡结合区其值较高;城区的斑块密度与边缘密度较小,聚集度较高,越靠近城乡结合处,斑块的密度越大.分析土地覆盖类型斑块平均形状指数和斑块聚集度的变化,可看出人为活动的干扰由市中心向郊区呈增强的趋势.     

An Analysis of Texture Measures in PCA-Based Unsupervised Classification of SAR Images

In single-band single-polarized SAR images, intensity and texture are the information source available for unsupervised land cover classification. Every textural feature measure identifies texture patterns by different approaches. For efficient land cover classification, textural measures have to be chosen suitably. Therefore, in this letter, the role of various intensity and textural measures is analyzed for their discriminative ability for unsupervised SAR image classification into various land cover types like water, urban, and vegetation areas. To make the algorithm adaptable, these textural features are fused using principal component analysis (PCA), and principal components are used for classification purposes. To highlight the effectiveness of PCA, the difference between PCA- and non-PCA-based classifications is also analyzed. Analysis of the role of texture measures for unsupervised classification of real-world SAR data with application of PCA is presented in this letter. The analysis of how every individual feature measure contributes for classification process is presented, and then, textural measures for a feature set are chosen according to their role in improving classification accuracy. By analysis, it is observed that the feature set comprising mean, variance, wavelet components, semivariogram, lacunarity, and weighted rank fill ratio provides good classification accuracy of up to 90.4% than by using individual textural measures, and this increased accuracy justifies the complexity involved in the process.     

Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®

This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS^® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS^® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP.     

Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS,Landsat 7 ETM+, SRTM and multiple Random Forests

Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil, the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery. In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape.The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eight landscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derived standard deviation in elevation.In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit.Burrows were successfully classified in all landscape units. In the ‘steppe on floodplain’ areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the ‘floodplain’ areas with a more heterogeneous vegetation cover, classification worked least well; there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively.In this study, an innovative stratification method using high- and medium resolution imagery was applied in order to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data set which can be used as input for epidemiological plague models. This is an important step in understanding the dynamics of plague.     

小区地表温度与下垫面结构关系研究

以北京市为例,选取了商业区、学校、生活区和公园4种典型小区24个,基于遥感及GIS的方法反演地表温度,获取下垫面覆盖信息;研究了不同类型小区地表温度热场与下垫面结构的关系.结果表明:水体、绿地具有明显的降温功能,建筑地面则增温效果明显,这3种地表所占的面积比例与小区平均地表温度关系密切;在所选取的绿地结构指数中,对温度影响从大到小依次为绿地覆盖率、分离度、缀块平均面积、连通指数、形状指数和分维数,其中,分离度与温度呈现正相关,其它指数与温度呈负相关;在所有类型小区中,温度从高到低依次为商业区、学校、生活区和公园;公园的温度分布在所有类型小区中最分散,商业区最集中.     

ANFIS Based Land Cover/Land Use Mapping of LISS IV Imagery Using Optimized Wavelet Packet Features

Digital image classification is the process of sorting all the pixels in an image into a finite number of individual classes. But, it is difficult to classify satellite images since they include both pure pixels and boundary pixels. The boundary pixels are ‘mixed’ pixels, representing an area occupied by more than one ground cover. That is, class boundaries represented by pixels, are not sharp but fuzzy. This paper discuses the application of Adaptive Neuro-Fuzzy inference system (ANFIS) for classification of remotely sensed images that contains mixed pixels. Decision making was performed in two stages: feature extraction using the Wavelet Packet Transforms (WPT) and the ANFIS trained with the back propagation gradient descent method in combination with the least squares method for classification. Genetic Algorithms (GA) based approach is analysed for the selection of a subset from the combination of Wavelet Packet Statistical Features (WPSF) and Wavelet Packet Co-occurrence (WPC) textural feature set, which are used to classify the LISS IV images. GA has been employed to reduce the complexity and increase the accuracy of classification. Four indices—user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experiments show that the proposed approach produces better results compared to the results obtained when classical classifiers are used.     

右江谷地景观格局空间分异特性遥感研究

利用1986年和2006年获取的TM图像,在GIS技术支持下,运用景观生态学原理,选取反应景观空间结构和景观异质性的景观指数,以右江谷地典型区广西平果县为研究区,分析该区近20 a间的景观格局及其动态变化特征.结果表明,1986～2006年间,平果县土地利用变化总趋势是林地、未利用地和居民地面积增加,草地、耕地和水域面...