Spectral unmixing of Landsat Thematic Mapper data

The observed spectral signature of pixels in remote sensing imagery in most cases is the result of the reflecting properties of a number of surface materials constituting the area of a pixel. Despite this knowledge most image classification techniques aim at labelling a pixel according to a singular surface category. An alternative product can be generated using spectral unmixing: a technique that strives to find the surface abundances of a number of spectral components together causing the observed spectral reflectance at a pixel. A stepwise approach to implement spectral unmixing in Landsat Thematic Mapper image analysis is proposed: (1) atmospheric calibration of the image data, (2) preselection of a large number of ‘candidate’ endmembers, (3) reduction to the most important spectral endmembers using spectral angle mapping, (4) finding the relative abundances of the endmembers through spectral unmixing analysis, (5) combining the abundance estimates into a final product comparable to a classified image, and (6) accuracy assessment. A Landsat Thematic Mapper image from southern Spain covering a large peridotite body with adjacent limestone and low-grade metamorphic rocks is used as an example to demonstrate the usefulness of unmixing.     

Processing and accuracy of Landsat Thematic Mapper data for lake surface temperature measurement

Processing of Landsat-5 TM thermal images for lake surface temperature determination is addressed. A specific preprocessing algorithm to reduce sensor noise is presented and calibration and atmospheric correction is discussed. The atmospheric impact on thermal radiation measurements is modelled using Lowtran-7 utilizing radiosonde data. Comparing ground truth measurements acquired for 21 images between 1987 and 1994 with satellite derived temperatures yielded a mean square error of 0.53 deg K. A systematic overestimation or underestimation of Landsat derived temperatures was not found. The emissivity effect upon the accuracy of the derived surface temperature is discussed as well as effects of using alternate atmospheric profile data.     

Assessment of the distribution of aerosols in the area of Athens with the use of Landsat Thematic Mapper data

Landsat high resolution images, at the spectral interval 0.45-0.52 mu m, are used in an effort to qualitatively assess distribution of aerosols in the greater Athens area. It is found that the spatial distribution of aerosols in Athens can be described by calculating the differences in optical density between two images, the one referring a pollution-free day and the second reflecting a day with an atmospheric pollution episode. Results are supported by a positive correlation between the differences in optical densities for the 'polluted' and 'pollution free' images, and the measured levels of sulphur dioxide and smoke in various city areas. Finally according to the above methodology, aerosols exhibit their highest concentration mostly in the industrialized region of Athens, to the west of the city's centre, as well as in a limited area in the centre of Athens.     

ETM+数据绝对反射率反演方法分析

将传感器原始记录信号反演为地面反射率对于遥感定量化应用非常重要。为了寻找一种简单、精度高的绝对反射率反演方法，本文以新疆哈密为试验区，使用经验线方法和基于模拟大气状况的辐射传输模型方法对1999年8月的ETM 数据进行了反演研究。通过将各反演方法得到的反射率值与地面实测值及星上反射率进行对比来验证反演效果，发现经验线方法精度最高，中误差仅为0．063，而基于模拟大气状况的辐射传输模型方法精度差，甚至不如未经大气校正的数据。这是由于星上定标系数误差、辐射传输方程自身误差以及模拟大气误差综合的结果，本研究也说明今后在使用辐射传输模型进行大气校正时要慎重考虑。     

Variability of Landsat Thematic Mapper data in boreal deciduous and mixed-wood stands with conifer understory

In this study, we examine Landsat TM satellite multispectral imagery and several image processing strategies to determine the most accurate method to detect and map white spruce understories in deciduous and mixed-wood stands in Alberta. These stands may be considered as part of the conifer land base that is defined as stands which contain or are projected to contain a minimum conifer volume at rotation. Images acquired in late April (leaf-off) and late July (leaf-on) were used to generate signatures for three levels of understory (heavy, light, nil) in five overstory classes. Separability statistics indicate that a reasonable degree of success can be obtained in mapping some of the understory classes with conventional classification tools. Linear discriminant functions using different classification schema and discriminating variables are presented to indicate the level of accuracy that may be obtained in a supervised classification mapping exercise.     

Application of Landsat Thematic Mapper data in sabkha studies at the Red Sea coast

Salt-enriched flat areas are known as sabkhas in the Red Sea coasts. Previously classified sabkhas north of Jeddah city, Saudi Arabia were examined by remote sensing techniques. Confirmation of ground studies was reached with the synoptic coverage and spectral characteristics of the Landsat digital data. New maps were produced utilizing satellite data; this may include geomorphological and sabkha zonation maps. False colour, ratio and principal components composites were used in this study by image analysis system. Three main parameters, i.e., moisture content and its salinity, salt content and water-table level are exhibiting themselves as different colours in the coloured images. The approach described herein is recommended to be applied along the Red Sea coasts and other similar areas in order to present a preliminary but precisely identification of sabkha environments utilizing remote sensing techniques.     

An evaluation of Landsat Thematic Mapper (TM) digital data for discriminating coral reef zonation: Heron Reef (GBR)

Coral reefs exhibit patterns of zonation. In this study we have evaluated the usefulness of Landsat-TM digital data as a tool for discrimination and mapping of reef zones. Classification, on bands 1, 2 and 3, and grouping of classes into reef zones was carried out with the aid of canonical variate analysis and minimum spanning trees. Thirteen reef zones can be identified and mapped, at a spatial scale relevant to their dimensions, with confidence. These zones can be further subdivided and mapped as spatially coherent subzones, in order to provide detailed information regarding the density of coral cover on the reef flat. In addition, the canonical variate analysis provides the basis for the aggregation of classes into sub-zones on interpreted primary productivity gradients, which is of relevance to coral reef management, monitoring and research.     

Geological applications of Landsat Enhanced Thematic Mapper (ETM) data and Geographic Information System (GIS): Mapping and structural interpretation in south-west Iran,Zagros Structural Belt

Arabia-Eurasia convergence is achieved in north-west Zagros by a combination of shortening on NW-SE-trending folds and thrusts, mainly in the Zagros Structural Belt, and by right-lateral strike-slip on the NW-SE-trending Main Recent Fault. Structural patterns are often used as indicators of major features on the surface. In the present study, Landsat Enhanced Thematic Mapper (ETM) data from 9 November 2002 was digitally processed to enhance the structural patterns. The contrast stretching of ETM band 4 was found to be the best in displaying lineaments and folds for the area, hence was further processed by using directional filters and generating different combinations of false colour composites (FCCs). Finally, a structural map of the study area (Dezful-Brojerd) was prepared using the processed ETM images. This study indicates that the Landsat ETM data are useful for locating and interpretation of the structural features in the Zagros Structural Belt. Examination of the Landsat ETM 2002 images combined with field visits revealed that the area is highly active and structurally is in a dynamic state due to compressional forces. However the density of the structural features on the imagery calibrated to several field visit data shows that the south and south-east of the study area are not as tectonically active. This may be due to presence of thick evaporite sequence of Gachsaran Formation on the Landsat image.     

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R² of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.     

Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data

The derivation of leaf area index (LAI) from satellite optical data has been the subject of a large amount of work. In contrast, few papers have addressed the effective model inversion of high resolution satellite images for a complete series of data for the various crop species in a given region. The present study is focused on the assessment of a LAI model inversion approach applied to multitemporal optical data, over an agricultural region having various crop types with different crop calendars. Both the inversion approach and data sources are chosen because of their wide use. Crops in the study region (Barrax, Castilla-La Mancha, Spain) include: cereal, corn, alfalfa, sugar beet, onion, garlic, papaver. Some of the crop types (onion, garlic, papaver) have not been addressed in previous studies. We use in-situ measurement sets and literature values as a priori data in the PROSPECT + SAIL models to produce Look Up Tables (LUTs). Those LUTs are subsequently used to invert Landsat-TM and Landsat-ETM+ image series (12 dates from March to September 2003). The Look Up Tables are adapted to different crop types, identified on the images by ground survey and by Landsat classification. The retrieved LAI values are compared to in-situ measurements available from the campaign conducted in mid July-2003. Very good agreement (a high linear correlation) is obtained for LAI values from 0.1 to 6.0. LAI maps are then produced for each of the 12 dates. The LAI temporal variation shows consistency with the crop phenological stages. The inversion method is favourably compared to a method relying on the empirical relationship between LAI and NDVI from Landsat data. This offers perspectives for future optical satellite data that will ensure high resolution and high temporal frequency.     

Integration of Corona and Landsat Thematic Mapper data for bedrock geological studies in the high Arctic

The Severnaya Zemlya Archipelago near the continental edge in the Russian high Arctic is one of few land areas along the Eurasian Arctic margin. It is of particular interest for investigating the Arctic's tectonic history. This study focuses on the Palaeozoic bedrock of October Revolution Island. In the Russian high Arctic detailed topographic maps and aerial photography often are not available. The potential of low-cost satellite imagery as a substitute is shown in this study. High-resolution Corona KH-4A panchromatic satellite imagery and Landsat Thematic Mapper (TM) multispectral data have been integrated. In combination with field investigations in key areas, these data provide the basis for new interpretations of the geology. Corona images were digitized and georeferenced to provide a basis for conventional and digital geological mapping. Merging Corona and Landsat TM data resulted in a high-resolution multispectral image of enhanced interpretability. Lithological contacts have been traced, supported by a bedrock image extracted from the Landsat TM data. Stereoscopic coverage of the Corona KH-4A photographic sensor allowed a structural interpretation. All results were integrated into a geological interpretation of southern October Revolution Island which provides an encouraging platform for further work in the high Arctic.     

Logistic regression modelling of multitemporal Thematic Mapper data for burned area mapping

This study focused on the development of a logistic regression model for burned area mapping using two Landsat-5 Thematic Mapper (TM) images. Logistic regression models were structured using the spectral channels of the two images as explanatory variables. The overall accuracy of the results and other statistical indications denote that logisticregression modelling can be usedsuccessfully for burned area mapping. The model that consisted of the spectral channels TM4, TM7 and TM1 and had an overall accuracy of 97.62%, proved to be the most suitable. Moreover, the study concluded that the spectral channel TM4 was the most sensitive to alterations of the spectral response of the burned category pixels, followed by TM7.     

Interpretation and geometrical aspects of Thematic Mapper data

Abstract

For a visual analysis assisted by digital imageenhancement procedures of a Thematic Mapper scene of the German Bight, a combination of the spectral channels 1-4 is suitable. Because of the extremely high correlation between channels 2 and 3 a principal component transformation is performed. This procedure enhances the interpretation possibilities especially for the recognition of structures like surface material in water. Incorporation of digitized contour lines from a marine chart and a syntheticchannel derived from the linescan support the interpretation. For that reason it is necessary to rectify the scene to the Mercator projection of the marine chart. The rectification performed by a second dxegree polynomial produces subpixel accuracies.     

Detectability of land systems by classification from Landsat Thematic Mapper data. Virunga National Park Zaire

Abstract

Abstract. A regional survey (1978-1982) in the Virunga National Park (Zaire) led to the mapping of land systems with the help of aerial photographs and fieldwork, following the morphopedological approach. The use of classification techniques on Landsat Thematic Mapper data (1987 scene) enabled us to assess the detectability of the morphopedological limits.

The statistical clustering by an unsupervised classification did not give results which were readily interpretable in terms of morphopedological and vegetation units. A supervised classification gave better control in the attribution of the classes. The reasons for the relative discrimination of the morphopedological units are analysed on the basis of ground information.     

A preliminary comparison of Landsat Thematic Mapper and SPOT-l HRV multispectral data for estimating coniferous forest volume

Abstract

Digital Landsat Thematic Mapper (TM) and Satellite Probatoire d'Observation de la Terre (SPOT) High Resolution Visible (HRV) images of coniferous forest canopies were compared in their relationsbip to forest wood volume using correlation and regression analyses. Significant inverse relationships were found between softwood volume and the spectral bands from both sensors (P <0·01). The highest correlations were between the log of softwood volume and the near-infared bands (HRV band 3, r = -0·89; TM band 4, r = -0.83).     

Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data

Population density is usually calculated from the census data, but it is dynamic over time and updating population data is often challenging because it is time-consuming and costly. Another problem is that population data for public use are often too coarse, such as at the county scale in China. Previous research on population estimation mainly focused on megacities due to their importance in socio-economic conditions, but has not paid much attention to the township or village scale because of the sparse population density and less importance in economic conditions. In reality, population density in townships and villages plays an important role in land-use/cover change and environmental conditions. It is an urgent task to timely update population density at the township and cell-size scales. Therefore, this article aims to develop an approach to estimate population density at the township scale and at a cell size of 1 km by 1 km through downscaling the population density from county to township and then to cell size. We estimated population density using Landsat Thematic Mapper (TM) and census data in Zhejiang Province, China. Landsat TM images in 2010 were used to map impervious surface area (ISA) distribution using a hybrid approach, in which a decision tree classifier was used to extract ISA data and cluster analysis was used to further modify the ISA results. A population density estimation model was developed at the county scale, and this model was then transferred to the township scale. The population density was finally redistributed to cell-size scale based on the assumption that population only occupied the sites having ISA. This research indicates that most townships have residuals within ±50 persons/km² with a root mean squared error (RMSE) of 71.56 persons/km², and a relative RMSE of 27.6%. The spatial patterns of population density distribution at the 1 km² cell size are much improved compared to the township and county scales. This research indicates the importance of using the ISA for population density estimation, where ISA can be accurately extracted from remotely sensed data.     

An autologistic regression model for increasing the accuracy of burned surface mapping using Landsat Thematic Mapper data

An autologistic regression model, which takes into account neighbouring associations, was developed and applied for burned land mapping using Landsat-5 Thematic Mapper data. The integration of the autocovariate component (estimated using a moving window of 3 @ 3 pixels) into the ordinary logistic regression model increased significantly the overall accuracy from 88.18% to 92.44%. In contrast, the accuracy derived with application of post-classification majority filters, which follow the same principles, were not significantly different to that derived with ordinary logistic regression.     

A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images

A hybrid method that incorporates the advantages of supervised and unsupervised approaches as well as hard and soft classifications was proposed for mapping the land use/cover of the Atlanta metropolitan area using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The unsupervised ISODATA clustering method was initially used to segment the image into a large number of clusters of pixels. With reference to ground data based on 1?:?40?000 colour infrared aerial photographs in the form of Digital Orthophoto Quarter Quadrangle (DOQQ), homogeneous clusters were labelled. Clusters that could not be labelled because of mixed pixels were clipped out and subjected to a supervised fuzzy classification. A final land use/cover map was obtained by a union overlay of the two partial land use/cover maps. This map was evaluated by comparing with maps produced using unsupervised ISODATA clustering, supervised fuzzy and supervised maximum likelihood classification methods. It was found that the hybrid approach was slightly better than the unsupervised ISODATA clustering in land use/cover classification accuracy, most probably because of the supervised fuzzy classification, which effectively dealt with the mixed pixel problem in the low-density urban use category of land use/cover. It was suggested that this hybrid approach can be economically implemented in a standard image processing software package to produce land use/cover maps with higher accuracy from satellite images of moderate spatial resolution in a complex urban environment, where both discrete and continuous land cover elements occur side by side.     

A dynamic Bayesian network data fusion algorithm for estimating leaf area index using time-series data from in situ measurement to remote sensing observations

Leaf area index (LAI) products retrieved from remote sensing observations have been widely used in the fields of ecosphere, atmosphere etc. However, because satellite-observed images are captured instantaneously and sometimes screened by cloud, some current LAI products are inherently discontinuous in time and their accuracy may not meet the needs of users well. To solve these problems, we proposed a dynamic Bayesian network (DBN)-based data fusion algorithm that integrates dynamic crop growth information, a canopy reflectance (CR) model and remote sensing observations from the perspective of Bayesian probability. Using the proposed algorithm, LAI was estimated using data sets from both field measurements for winter wheat in Beijing, China, and MODIS reflectance data at two American flux tower sites. Results showed good agreement between the LAI estimated by the DBN-based data fusion method and the true ground LAI, with a correlation coefficient of (R) 0.95 and 0.96, respectively, and a corresponding root mean square error (RMSE) of 0.35 and 0.49, respectively. In addition, the LAI estimated by the DBN-based data fusion method formed a continuous time series and was consistent with the variety law of vegetation growth at both plot and flux tower site scales. It has been demonstrated that the proposed DBN-based data fusion algorithm has the potential to be used to accurately estimate LAI and to fill the temporal gap by integrating information from multiple sources.     

Multi-polarization Envisat-ASAR images as a function of leaf area index (LAI) of White Poplar and Desert Date plantations

The relationship between leaf area index (LAI) of plantations and multi-polarization Synthetic Aperture Radar (SAR) data (Envisat-ASAR) was investigated for White Poplar (Populus tomentosa Carr) and Desert Date (Elaeagnus angustifolius) in Heihe district, northwest china. The study showed that, for homogeneous White Poplar plantations, HH and HV polarization data (where H and V represent horizontal and vertical polarizations, respectively, and the first of the two letters refers to the transmission polarization and the second to the received polarization) were sensitive to LAI and the r² (logistic relationship fits) values between HH polarization and LAI, 0.56 and 0.58 on 25 and 28 June images respectively, was much higher than that for the other polarizations of VV, VH and HV. For Desert Date plantations, the heterogeneity of the forests results in a more complex backscattering than that for White Poplar. Incidence angle also plays an important role in SAR backscattering, so a suitable SAR mode should be chosen to avoid scattering saturation when the LAI and incidence angle exceed certain values. The logistic polarization ratios of HH/HV and VV/VH showed varying correlation with LAI over White Poplar plantations, probably due to incidence angle.