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1.
K. O. Niemann 《国际地球制图》2013,28(1):13-30
Studies integrating digital elevation models (DEMs) with multispectral digital satellite data have typically concentrated on geographic areas characterized by moderate to high topographic relief. Variables such as elevation, slope gradient and aspect contribute most significantly to the zonation of vegetation in these environments. In areas where relief is low, vegetation zonation is based not on individual form elements but rather on physical processes. The purpose of this research was to investigate the potential of integrating multispectral and ancillary process data in such a low relief environment. For this a study area was chosen in the Boreal forest of west central Alberta where the zonation of vegetation is based, to a large extent, on landscape drainage. An initial classification of forest cover based on Landsat multispectral data yielded overall classification accuracies of 58%. A DEM was developed from a digitized 1:50,000 topographic map sheet. The differential geometry of the DEM was mapped as a series of coverages: slope, aspect, and directional curvatures (down ‐ and across slope). Two additional coverages, relief and flow paths, were also developed and mapped. A data set was extracted from the DEM through which landscape drainage could be evaluated. A univariate analysis of drainage using the form variables resulted in a 45% to 47% explanation of the observed variation. Multivariate analysis combining slope gradient, across and down slope curvatures, relief, and flow paths increased the explanation to 68%. The MSS data were reinterpreted integrating the DEM ‐ based landscape drainage model. The resulting classification accuracy was increased to 73%. 相似文献
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R. Suresh Kumar C. Menaka M. E. J. Cutler 《Journal of the Indian Society of Remote Sensing》2013,41(3):477-486
Albeit the advent of fast computing facilities, digital image classification of remotely sensed data is still remain the topic of research. This might be due to the reason that the ancillary information such as texture and topography is absent in image classification. Since two decades, texture is widely applied in image classification but there is no explicit icon in most popularly used remote sensing software. Hence the aim of this study is to classify the Landsat ETM+ captured in 2000 using spectral information, topographic information and texture information. This study helps to throw light into statistical texture analysis i.e., the effect window size i.e., 3?×?3 to 9?×?9, on image classification. The ability of Grey Run Length Matrix (GRLM), which is computationally complex compared to industrially well-known Grey Level Co-occurrence Matrix (GLCM) but encompasses greater potential to discriminate between two classes, is explored. Eight spectral bands, 11 texture parameters extracted from Landsat ETM+ data and elevation, slope, aspect extracted from DEM data are classified individually using Artificial Neural Network (ANN) and the individually classified information is integrated using endorsement theory. Validations of classified results are performed using Google Maps and Landmap services updated in 2009. The results are compared with Maximum Likelihood classification (MLC) and hence all the evidence (spectral, texture and topography) with 5?×?5 texture window provided maximum classification accuracy of 70.44 %. 相似文献
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The aim of this paper was to analyze the ground and low vegetation points of a Light Detection and Ranging (LiDAR) point cloud from the aspect of the generated digital terrain model (DTM). We determined the height difference between the surveyed surface and the DTM and the level of interspersion of ground and low vegetation points in a floodplain. Finally, we performed a supervised classification with topographic (elevation, slope and aspect) variables and an Normalized Difference Vegetation Index (NDVI) layer to identify swales and point bars as floodplain forms. Cross sections of field surveys provided reference data to express the magnitude of the bias on the DTM caused by the vegetation, and we proved that the bias can reach the 60% of the relative height and depth of the floodplain forms (mean error was 0.15 ± 0.12 m). A landscape metric, the Aggregation Index, provided an appropriate tool to analyze and quantify the interspersion of the ground and vegetation points: indicating a high level of interspersion of the classified points, i.e. proved that vegetation points where the last echoes reflected from the vegetation became ground points. Floodplain classification performed best with the common use of DTM, slope, aspect and NDVI coverages, with 71% overall accuracy. 相似文献
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Sarnam Singh T. P. Singh Gaurav Srivastava 《Journal of the Indian Society of Remote Sensing》2005,33(4):547-563
Improving image classification and its techniques have been of interest while handling satellite data especially in hilly
regions with evergreen forests particularly with indistinct ecotones. In the present study an attempt has been made to classify
evergreen forests/vegetation in Moulirig National Park of Arunachal Pradesh in Eastern Himalayas using conventional unsupervised
classification algorithms in conjunction with DEM. The study area represents climax vegetation and can be broadly classified
into tropical, subtropical, temperate and sub-alpine forests. Vegetation pattern in the study area is influenced strongly
by altitude, slope, aspect and other climatic factors. The forests are mature, undisturbed and intermixed with close canopy.
Rugged terrain and elevation also affect the reflectance. Because of these discrimination among the various forest/vegetation
types is restrained on satellite data. Therefore, satellite data in optical region have limitations in pattern recognition
due to similarity in spectral response caused by several factors. Since vegetation is controlled by elevation among other
factors, digital elevation model (DEM) was integrated with the LISS III multiband data. The overall accuracy improved from
40.81 to 83.67%. Maximum-forested area (252.80 km2) in national park is covered by sub-tropical evergreen forest followed
by temperate broad-leaved forest (147.09 km2). This is probably first attempt where detailed survey of remote and inhospitable areas of Semang sub-watershed, in and around
western part of Mouling Peak and adjacent areas above Bomdo-Egum and Ramsingh from eastern and southern side have been accessed
for detailed ground truth collection for vegetation mapping (on 1:50,000 scale) and characterization. The occurrence of temperate
conifer forests and Rhododendron Scrub in this region is reported here for the first time. The approach of DEM integrated
with satellite data can be useful for vegetation and land cover mapping in rugged terrains like in Himalayas. 相似文献
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利用卫星遥感数据制作复杂地形环境的植被图面临的最主要问题是精度,单纯对遥感数据(TM或SPOI)进行监督或非监督分类的精度低于50%。本文选择美国亚利桑那州SantaCatalina山脉的PuschRidge作为研究区,分析地理信息系统模型在改善植被分类精度中的作用。结果表明,通过结合辅助数据和应用地理信息系统模型,其精度可以从37.41%提高到71.67%(SPOT数据,非监督分类),或从50.07%提高到61.50%(TM数据,监督分类)。同时表明用SPOT数据进行山区植被制图的效果好于TM数据。 相似文献
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Spectrally similar nature of land covers in a glacierized terrain hampers their automated mapping from multispectral satellite data, which may be overcome by using multisource data. In the present study, an artificial neural network (ANN)-based information extraction approach was applied for mapping the Kolahoi glacier and adjoining areas, using Landsat TM (Thematic Mapper) data and several ancillary layers such as image transformations and topographic attributes. Results reveal that ANN (highest overall accuracy (OA): 83.74%) outperforms maximum likelihood classifier (highest OA: 66.90%) and the incorporation of ancillary data into the classification process significantly enhances the mapping accuracy (>9%), particularly the addition of Near Infrared Red/Short Wave Infrared (NIR/SWIR) data to the spectral data. A nine-band combination dataset (spectral data, slope, Red/NIR and decorrelation stretch) was found to be the best multisource dataset. Results of the Z-tests (at 95% confidence level) also corroborate and statistically validate the above findings. 相似文献
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机载激光雷达技术(LiDAR)作为一项先进的遥感技术,是植被覆盖区DEM获取的重要手段之一,而不同地形坡度条件及点云密度对DEM产品质量有重要影响。本文以辽宁省某市的机载LiDAR数据为基础,选取5种不同地形坡度的点云数据,通过随机、等间距及基于曲率3种不同的点云抽稀方法,按照点云保留率为80%、60%、40%、20%和10%共5个不同梯度的抽稀倍数对原始点云进行抽稀简化处理,生成与之对应的DEM并对其进行精度评价,以此研究地形坡度、点云抽稀方法、抽稀倍数对DEM精度的影响。结果表明,DEM精度与地形坡度呈负相关关系,即RMSE随地形坡度升高不断增加;基于曲率的抽稀方法在地形坡度>30°时,相较于其他两种方法RMSE较小,具有明显优势;40%的点云保留率是平衡DEM精度与数据存储效率的一个节点,当点云保留率<40%时,DEM的高程RMSE会迅速增大。该研究对于利用机载LiDAR进行大范围DEM生产具有一定的指导和借鉴意义。 相似文献
10.
结合纹理的SVM遥感影像分类研究 总被引:7,自引:0,他引:7
针对传统统计模式识别分类方法分类精度不高,分类时未加入像元灰度的空间分布和结构特征以及分类时样本不足等缺陷,采用一种结合纹理的支持向量机(SVM)遥感图像分类方法。该方法在对Landsat7 ETM遥感影像进行纹理特征提取的基础上,构建了结合纹理的SVM分类模型。以河南省汝阳县为试验区,利用此模型对该区域的土地利用类型进行分类研究,并将分类结果与最大似然法和单源数据(光谱)SVM分类结果进行定性和定量比较分析。研究结果表明:该方法能够有效地解决单数据源分类效果破碎、分类精度不高等问题;对高维输入向量具有较高的推广能力;总精度达到90%,比单源信息的SVM分类法提高了6%,而与最大似然法相比,总精度提高了近9%,取得了良好的效果。 相似文献
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Envisat ASAR的区域森林-非森林制图 总被引:2,自引:0,他引:2
凌飞龙 李增元 陈尔学 黄燕平 田昕 SCHMULLIUS Christin LEITERER Reik REICHE Johannes SANTORO Maurizio 《遥感学报》2012,16(5):1100-1113
Envisat卫星ASAR传感器的双极化数据对区域森林监测十分有效。通过分别采用SRTM DEM和Landsat TM图像对地形起伏区域和平坦区域的SAR图像进行地理编码,发展了一种SAR图像自动预处理方法。基于冬季单时相ASAR数据的HH(水平发射,水平接收)、HV(水平发射,垂直接收)极化比值和HV极化图像,提出了一种面向对象的森林-非森林分类方法。将之应用于中国东北森林/非森林制图,分类总体精度、森林用户精度和生产者精度分别为83.7%,85.6%和75.7%。结果表明,本文提出的方法十分适合区域森林-非森林制图的业务化运行。 相似文献
12.
Xia Zhang Rui Sun Bing Zhang Qingxi Tong 《ISPRS Journal of Photogrammetry and Remote Sensing》2008,63(4):476-484
Moderate Resolution Imaging Spectroradiometer (MODIS) data have played an important role in global environmental and resource research. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results. In this research, the high temporal resolution of MODIS was employed to improve the accuracy of land cover classification of the North China Plain using MODIS_EVI time series from 2003. Harmonic Analysis of Time Series (HANTS) was performed on the MODIS_EVI image time series to reduce cloud and other noise effects. The improved MODIS_EVI time series was then classified into 100 clusters by the Iterative Self-Organizing Data Analysis Technique (ISODATA). To distinguish ambiguous land cover classes, a decision tree was built on five phenological features derived from EVI profiles, Land Surface Temperature (LST) and topographic slope. The overall accuracy of the final land cover map was 75.5%, indicating the promise of using MODIS EVI time series and decision trees for broad area land cover classification. 相似文献
13.
Impact of Topography on Accuracy of Land Cover Spectral Change Vector Analysis Using AWiFS in Western Himalaya 总被引:1,自引:0,他引:1
J. K. Sharma V. D. Mishra R. Khanna 《Journal of the Indian Society of Remote Sensing》2013,41(2):223-235
The present paper discusses the impact of topography on accuracy for land cover classification and “from-to class change using improved spectral change vector analysis suggested by Chen et al. (2003). Two AWiFS sensor images of different dates are used. Double Window Flexible Pace Search (DFPS) is used to estimate threshold of change magnitude for change/no change classes. The topographic corrections show accuracy of 90% (Kappa coefficient 0.7811) for change/no change area as compared to 82% (Kappa coefficient 0.6512) in uncorrected satellite data. Direction cosines of change vector for determining change direction in n-dimensional spectral space is used for image classification with a minimum distance categorizing technique. The results of change detection are compared (i) Improved CVA with conventional two bands CVA and (ii) Improved CVA before and after topographic corrections. The improved CVA with topographic correction consideration using slope match show maximum accuracy of 90% (Kappa coefficient 0.83) as compared to conventional CVA which show maximum accuracy of 82% (Kappa coefficient 0.6624). The overall accuracy of ”from- to class using improved CVA increases from 86% (Kappa coefficient 0.7817) to 90% (Kappa coefficient 0.83) after topographic corrections. The improved CVA with proper topographic corrections is found to be effective for change detection analysis in the rugged Western Himalayan terrain. 相似文献
14.
通过多源数据融合,将目前GIS中包含的地理信息作为辅助数据,引入到RS图像分析过程中。GIS数据在空间信息、目标属性和目标范围等方面对RS图像分析具有重要的引导作用。 相似文献
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《International Journal of Digital Earth》2013,6(3):219-236
Abstract This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001–2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multi-temporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p=0.01. 相似文献
17.
The Shuttle Radar Topography Mission (SRTM), the first relatively high spatial resolution near‐global digital elevation dataset, possesses great utility for a wide array of environmental applications worldwide. This article concerns the accuracy of SRTM in low‐relief areas with heterogeneous vegetation cover. Three questions were addressed about low‐relief SRTM topographic representation: to what extent are errors spatially autocorrelated, and how should this influence sample design? Is spatial resolution or production method more important for explaining elevation differences? How dominant is the association of vegetation cover with SRTM elevation error? Two low‐relief sites in Louisiana, USA, were analyzed to determine the nature and impact of SRTM error in such areas. Light detection and ranging (LiDAR) data were employed as reference, and SRTM elevations were contrasted with the US National Elevation Dataset (NED). Spatial autocorrelation of errors persisted hundreds of meters spatially in low‐relief topography; production method was more critical than spatial resolution, and elevation error due to vegetation canopy effects could actually dominate the SRTM representation of the landscape. Indeed, low‐lying, forested, riparian areas may be represented as substantially higher than surrounding agricultural areas, leading to an inverted terrain model. 相似文献
18.
The Effect of DEM Raster Resolution on First Order, Second Order and Compound Terrain Derivatives 总被引:8,自引:0,他引:8
Stefan Kienzle 《Transactions in GIS》2004,8(1):83-111
It is well known that the grid cell size of a raster digital elevation model has significant effects on derived terrain variables such as slope, aspect, plan and profile curvature or the wetness index. In this paper the quality of DEMs derived from the interpolation of photogrammetrically derived elevation points in Alberta, Canada, is tested. DEMs with grid cell sizes ranging from 100 to 5 m were interpolated from 100 m regularly spaced elevation points and numerous surface‐specific point elevations using the ANUDEM interpolation method. In order to identify the grid resolution that matches the information content of the source data, three approaches were applied: density analysis of point elevations, an analysis of cumulative frequency distributions using the Kolmogorov‐Smirnov test and the root mean square slope measure. Results reveal that the optimum grid cell size is between 5 and 20 m, depending on terrain com‐plexity and terrain derivative. Terrain variables based on 100 m regularly sampled elevation points are compared to an independent high‐resolution DEM used as a benchmark. Subsequent correlation analysis reveals that only elevation and local slope have a strong positive relationship while all other terrain derivatives are not represented realistically when derived from a coarse DEM. Calculations of root mean square errors and relative root mean square errors further quantify the quality of terrain derivatives. 相似文献
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In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features). 相似文献