Extraction of fluvial networks from SPOT panchromatic data in a low relief,arid basin

Abstract

Euclidean distance classification of SPOT panchromatic data was used to delineate ephemeral, fluvial networks on low-relief, alluvial fan surfaces in an arid basin in the southwestern United States. The SPOT classified channel network was registered to a Universal Transverse Mercator (UTM) projection and compared to areas of channelized flow and a Strahler-ordered channel network extracted from U.S. Geological Survey 7½ minute topographic maps and black-and-white aerial photographs. One-pixel and two-pixel proximity searches of the SPOT classified image correctly classified over 80 per cent of the areas of channelized flow, 77 per cent of the second order and greater channel network, and 98 per cent of the fourth order and greater channel network. The SPOT channel network was simplified by elimination of lower-order channel segments to produce a network closely resembling (in terms of number of channel segments and total channel length) a network created for hydrological modelling from topographic maps. This suggests that SPOT panchromatic data can be used to delineate fluvial network for hydrological investigations in similar physiographic settings without existing topographic map or aerial photographic coverage.     

Mapping Arctic tundra vegetation types using digital SPOT/HRV-XS data A preliminary assessment

Abstract

Multispectral (XS) image data recorded by the High Resolution Visible (HRV) sensor aboard the SPOT-1 satellite are being evaluated for the mapping of Arctic tundra vegetation in the Arctic Foothill Province of Alaska. This research is part of a current ecosystems study that requires an efficient means for mapping vegetation types over large areas. Conventional spectral-based image classification techniques were applied to SPOT/HRV-XS data from a single date. The unique characteristics of the vegetation cover (mainly tussock tundra) and illumination conditions of the location necessitated a detailed examination of classification approaches that have generally been applied in mid-latitude studies. Preliminary results suggest that areal estimates of Arctic tundra vegetation types can be made accurately (±2·5 per cent per category), but maps generated by classifying spectral features of SPOT/HRV-XS data alone arc unsuitably accurate (56 per cent). This is partly due to the high occurrence of relatively small vegetation parcels, determined by measuring the characteristic lengths of vegetation parcels from a ‘ground reference’ map covering the same area as the SPOT/HRV-XS subscene.     

Multi-point matching along vertical lines in SPOT images

Abstract

Digital elevation models(DEMs) are measured in SPOT images with a digital matching technique. A multi-point matching method developed by the author in earlier work is reformulated from epipolar geometry with the stereoparallaxes in one of the images used as unknowns, to SPOT geometry with the elevations as unknowns. Constraints minimizingthe curvature and the slope of the Computed DEM are investigated, as well as the use of data snooping, low-pass filtering and an alternative logarithmic grey level function. The investigatedimage pair was a stereo SPOT pair from Stockholm, with a rather poor base-to-height ratio of 0·29. All 2073 investigated points were compared with heights obtained from the official Swedish maps, scale 1:10000, with a 5 m contour interval. The results point towards a r.m.s. matching accuracy around 0·15-0·20 pixels in this image material.     

A comparison of SPOT and Landsat-TM data for use in conducting inventories of forest resources

Abstract

SPOT multispectral (XS) and Landsat Thematic Mapper (TM) digital data were studied in an attempt to evaluate the use of this data in detailed assessments of forest conditions. Forest type, basal area, and age class information were collected from 256 sample sites within an intensively managed 80000acre experimental forest in North Carolina, U.S.A. A comparison of the SPOT and TM data with the sample site information showed that XS3, the near-infrared waveband, and TM bands 2, 3, 4, 5, and 7 were significantly correlated with basal area. Age class was not found to be significantly correlated with any of the three SPOT XS wavebands. TM bands 2, 3, 4, 5, and 7 were, however, shown to be significantly correlated with age class. Although significant, the correlation coefficients between the TM or SPOT waveband data and basal area or age class were low (<0.65).

Six forest cover types, and an additional water category, were selected as the basis of a land cover classification system for use with the TM and SPOT data. Verification of the classification of the seven cover types using the SPOT XS waveband data resulted in an estimated accuracy of 74.4 per cent. Classification accuracy was slightly reduced (70.8 per cent) when the TM wavebands corresponding to the SPOT XS bands were used as inputs to the classifier. When each of the six visible and reflective infrared TM wavebands were included in the classification process overall accuracy increased to 885 per cent.     

Predictive models for remotely-sensed data acquisition in Indonesia

Abstract

Indonesian spatio-temporal cloud cover distribution was quantified with the aid of Geostationary Meteorological Satellite (GMS) and Landsat data. For all land areas iterative interactive factorial analyses grouped GMS-derived pixels with similar cloud cover profiles into 18 classes. Statistics of Landsat and SPOT images, grouped by class, were used to quantify temporal profiles of probability of acquiring remotely-sensed data with 10 per cent, 20 per cent and 30 per cent cloud cover for any Indonesian land area. Analysis of the patio-temporal characteristics of local climatic conditions permitted one to explain these profiles and to verify the validity of their seasonal variations for long periods. These profiles were fitted with a seventh-order polynomial for use in computer simulation of predictive models of remotely-sensed data acquisition.     

A drainage system analysis evaluation of,and comparison between,Landsat-3 RBV,Landsat-5 TM and SPOT PA imageries covering the Central Macedonia district,Greece

Abstract

Landsat-3 RBV, Landsat-5 TM imageries and SPOT PA stereopair diapositives were visually interpreted for the purpose of finding the accuracy of certain morphometric variables of three drainage basin sample areas in Central Macedonia, North Greece, drawn separately from each of the above three types of satellite imageries and comparisons were made between the efficiency of drainage systems drawn from each of the above imageries and the drainage systems extracted from the available topographic maps of 1:50000 scale.

The main findings were the following: (1) SPOT PA stereopair diapositives of 1:200000 scale can be used to map drainage systems to an order of magnitude slightly more than TM imagery of 1:125000 scale, but significantly more than RBV imagery of 1:125000 scale. This slight superiority of SPOT imagery over TM imagery implies that the greater spectral range of TM, compared with the shorter range of SPOT imageries, vastly outweighs the advantage of SPOT'S superior resolution, but not the superiority of stereoscopic view; (2) TM imagery can be used to map drainage systems to an order of magnitude significantly more than RBV imagery; and (3) RBV imagery can be used to map drainage systems to an order of magnitude less than topographic maps of 1:50000 scale but better than topographic maps of 1:100000 scale.     

The use of SPOT-simulated imagery in hydrological mapping

Abstract

The fine spatial resolution offered by the SPOT system promises great improvements in the mapping of surface water and drainage networks. Simulated SPOT imagery of sites covering a variety of landscape units in Wales were examined. Identification of streams on the basis of spatial characteristics alone was found to be inefficient, and the use of thematic information was found beneficial. Strategies for interactive image enhancement are discussed, and the results of a visual interpretation of standard photoproducts are presented. The results of the interpretation of simulated SPOT data are compared with results of an interpretation of LANDSAT MSS data.     

A graphical approach for the evaluation of land-cover classification procedures

Abstract

A method for evaluating the effectiveness of different feature combinations and training strategies is described. Preliminary tests have been made using two groups of feature combinations derived from SPOT High Resolution Visible (HRV) data and two sets of training samples. The method is objective, and needs no ground confirmation or interaction from the image analyst. It is recommended as a surrogate for detailed accuracy assessment when attempting to find an optimum set of training pixels or feature combinations for image classification.     

Vector choice in the texture spectrum approach

Recently the texture spectrum approach has been proposed as a statistical method for texture analysis, and applied to remotely sensed images. In the present study, this method is generalized to a space of M vectors and N grey level intervals for the elements of texture units, instead of M = 8 and N = 3 in the earlier studies of the texture spectrum. In this way, the texture unit set can be defined in a neighbourhood of 3 pixels by 3 pixels, 5 pixels by 5 pixels, 7 pixels by 7 pixels or other forms and sizes, and the co-occurrence matrix approach is unified to the texture spectrum method with the extreme case of M = 1. Several combinations of M and N have been evaluated to classify an imagery composed of six natural textures. The results show maximum discrimination for M = 5, followed by M = 4. In this way we minimize the calculation lime needed to maximize the accuracy of the classification.     

Use of cartographic data and Landsat TM images to determine land use change in the vicinity of Mexico City

Evaluation of change in land use is important for planning further development in populated areas. Here we attempt to determine the growth of urban areas in the vicinity of Mexico City, using a 1993 Landsat Thematic Mapper (TM) image and cartographic data contained in maps published by the Instituto Nacional de Estadistica Geografia e Informatica (INEGI 1975, 1983). The area occupied by urban areas in 1975 and 1983 was quantified using raster images generated by scanning the maps. Supervised classification processes were applied to a 1993 Landsat TM image in bands 1, 2, 3, 4, 5 and 7, of the area of Chalco. The image was pre-processed and then processed to enhance the spectral response of the surface materials. The different land cover types that characterise distinct land uses in the study area were identified in the image and an overall classification accuracy of 82% was estimated using aerial photographs from the Chalco area. The resulting evaluation of the land use changes in the Chalco urban area was plotted, and a growth greater than 14% per year was estimated.     

Per-field classification: an example using SPOT HRV imagery

Abstract

The classification of land cover on remotely sensed imagery is usually undertaken in a per-pixel format within an image file or in a per-field format within a non-image file. The latter is more accurate but does not produce an image output and is not readily input to a vector-based geographical information system. We propose setting the pixels in each field to a representative statistic for that field and then using a per-pixel classifier to perform a per-field classification in an image file. This procedure was evaluated using SPOT high resolution visible (HRV) imagery. The highest classification accuracy of 62.1 per cent (12 class) was achieved using measures of prior probabilities and image texture within the proposed per-field format.     

Cloud cover distribution in Indonesia

Abstract

Indonesian spatio-temporal cloud cover distribution was quantified to allow planners to forecast probabilities for remote sensing data acquisition. The original data consisted of four randomly chosen Geostationary Meteorological Satellite displays per month from 1981 to 1985, which were analysed with a microcomputer after a 1 °15'pixel size digitization. Iterative interactive factorial analyses combined with a parallelepiped classifier clustered data by grouping pixels with similar profiles. For all classes, pixel profiles were linearly combined to provide a small number of classes with high pixel-class correlations. A 0–7 correlation threshold led to 18 classes for all land areas. Statistics of Landsat and SPOT images, grouped by class, were used to verify, calibrate and improve class profiles. This led to quantified temporal profiles of probability of acquiring remotely-sensed data with a given cloud cover percentage for any Indonesian land and marine area.     

Noise removal for SPOT HRV imagery

Abstract

SPOT HRV imagery acquired in ‘double’ mode displays near vertical striping occurring every seven or eight pixels in the down-track scan direction. Six digital image restoration procedures have been examined for suppressing or removing this noise. Image restoration by filtering in the spatial frequency domain was found to be the most effective procedure. However, although less effective at noise removal, a simple median filtering procedure could be used with greatly reduced computational cost.     

Detection of surface soil variation using high-resolution satellite data: results from the U.K. SPOT-simulation investigation

Abstract

A problem in using multispectral scanner(MSS) data for soil and landsystem analysis in north-west Europe is the poor spatial resolution which is insufficient to provide adequate within-field data. The SPOT satellite system will provide MSS data at 20m resolution and panchromatic data at 10m resolution. For any given ground feature the SPOT MSS mode will provide considerably more sample areas than LANDSAT 80m data. The object of this study is to determine how far variation in surface soil parameters can be detected and quantified on the basis of SPOT data.     

Classification of SPOT HRV imagery and texture features

Abstract

Spatial co-occurrence matrices were computed for a SPOT HRV multispectral image for a moderate-relief environment in eastern Canada. The texture features entropy and inverse difference moment were used with the spectral data in landcover classification, and substantive increases in accuracy were noted. These range from 10 per cent for exposed bedrock to over 40 per cent in forest and wetland classes. The average classification accuracies were increased from 511 per cent (spectral data alone) to 86.7 per cent (spectral data plus entropy measured in band 2 and inverse difference moment in band 3). Classes that are homogeneous on the ground were characterized adequately by spectral tone alone, but classes containing mixed vegetation patterns or strongly related to structure were characterized more accurately by using a mixture of spectral tone and texture.     

结合区域协方差分析的图像显著性检测

目的 图像显著性检测的目的是为了获得高质量的能够反映图像不同区域显著性程度的显著图,利用图像显著图可以快速有效地处理图像中的视觉显著区域。图像的区域协方差分析将图像块的多维特征信息表述为一个协方差矩阵,并用协方差距离来度量两个图像块特征信息的差异大小。结合区域协方差分析,提出一种新的图像显著性检测方法。方法 该方法首先将输入的图像进行超像素分割预处理;然后基于像素块的区域协方差距离计算像素块的显著度;最后对像素块进行上采样用以计算图像像素点的显著度。结果 利用本文显著性检测方法对THUS10000数据集上随机选取的200幅图像进行了显著性检测并与4种不同方法进行了对比,本文方法估计得到的显著性检测结果更接近人工标定效果,尤其是对具有复杂背景的图像以及前背景颜色接近的图像均能达到较好的检测效果。结论 本文方法将图像像素点信息和像素块信息相结合,避免了单个噪声像素点引起图像显著性检测的不准确性,提高了检测精确度;同时,利用协方差矩阵来表示图像特征信息,避免了特征点的数量、顺序、光照等对显著性检测的影响。该方法可以很好地应用到显著目标提取和图像分割应用中。     

Technical note GPS location data An aid to satellite image analyses of poorly mapped regions

Abstract

In inaccessible and highly heterogeneous areas where aerial photography and accurate up-to-date topographic maps are unavailable and where suitable features for ground-based navigation by triangulation are absent, accurately locating ground truth sites and then integrating the field data with digital imagery is often extremely difficult. The advent of the satellite global positioning system (GPS) offers a solution to this problem. Over a 6 month period a battery powered, solar recharged, backpack mounted GPS was used to collect the precise location data essential to the ground truth component of a Landsat Thematic Mapper (TM) landcover classification of the Ituri rain forest of northeastern Zaire. Three-dimensional locations were readily obtained in forest openings > 0·125 ha where the angle to the horizon did not exceed 50^° and canopy closure was less than 30 per cent. GPS location data are presently being used to reference a TM scene geographically and to assign pixels to appropriate landcover classes accurately.     

Multitemporal land-cover classification of Mexico using Landsat MSS imagery

A complete land-cover classification of Mexico was performed using Landsat Multi-Spectral Scanner (MSS) imagery corresponding to years 1974, 1986 and 1990 ( - 1 y). The categorization of the approximately 2 M km 2 geographical region included the classification of approximately 300 equivalent scene images. Vegetation experts throughout the country provided an initial 250-class inventory of major vegetation associations by applying an unsupervised classification approach. A final regrouping was performed to produce a generalized thematic product containing 12 classes to provide a consistent national scale product. Classification accuracies were evaluated for each scene by means of cartographic comparison using two independently developed reference datasets corresponding to the 1970s and 1990s. An automated evaluation procedure was developed that incorporated decision rules to duplicate the results obtained using a manual accuracy assessment procedure. Overlaying both the image and the digital cartographic information allowed for the comparison of randomly selected pixels within each image scene. An overall accuracy for the three epochs of 62% was obtained for the 300 image scenes. Study results have provided a historical baseline documenting vegetation extent and distribution across Mexico over the two-decade period. This study serves as a possible model for subsequent North American land-cover characterization efforts.     

Requirements for labelling forest polygons in an object-based image analysis classification

The ability to spatially quantify changes in the landscape and create land-cover maps is one of the most powerful uses of remote sensing. Recent advances in object-based image analysis (OBIA) have also improved classification techniques for developing land-cover maps. However, when using an OBIA technique, collecting ground data to label reference units may not be straightforward, since these segments generally contain a variable number of pixels as well as a variety of pixel values, which may reflect variation in land-cover composition. Accurate classification of reference units can be particularly difficult in forested land-cover types, since these classes can be quite variable on the ground. This study evaluates how many prism sample locations are needed to attain an acceptable level of accuracy within forested reference units in southeastern New Hampshire (NH). Typical forest inventory guidelines suggest at least 10 prism samples per stand, depending on the stand area and stand type. However, because OBIA segments group pixels based on the variance of the pixels, fewer prism samples may be necessary in a segment to properly estimate the stand composition. A bootstrapping statistical technique was used to find the necessary number of prism samples to limit the variance associated with estimating the species composition of a segment. Allowing for the lowest acceptable variance, a maximum of only six prism samples was necessary to label forested reference units. All polygons needed at least two prism samples for classification.     

Landsat-comparable land cover maps using ASTER and SPOT images: a case study for large-area mapping programmes

The long-term record of global Landsat data is an important resource for studying Earth's system. Given the identified gaps in Landsat data and the undetermined future status of Landsat data availability, alternatives to Landsat imagery need to be tested in an operational environment. In this study, forest land cover and crown closure maps generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and System Pour 1'Observation de la Terre (SPOT) data were compared to Landsat-based map products currently in use by the grizzly bear habitat-mapping program. Overall accuracies greater than 85% were obtained for both ASTER- and SPOT-based land cover maps. The ASTER and SPOT classification accuracies were higher than that achieved by Landsat. Crown closure maps derived from ASTER and SPOT data show a small increase in accuracy when compared to the Landsat products. Overall, these results demonstrate that ASTER and SPOT could provide alternative data sources for producing maps in the event of a gap in the Landsat data.