An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites

The Statewide Landcover and Trees Study (SLATS) use both Landsat-7 ETM+ and Landsat-5 TM imagery to monitor short-term woody vegetation changes throughout Queensland, Australia. In order to analyse more subtle long-term vegetation change, time-based trends resulting from artefacts introduced by the sensor system must be removed. In this study, a reflectance-based vicarious calibration approach using high-reflectance, pseudo-invariant targets in western Queensland was developed. This calibration procedure was used to test the existing calibration models for ETM+ and TM, and develop a consistent operational calibration procedure which provides calibration information for the MSS sensors. Ground based data, sensor spectral response functions and atmospheric variables were used as input to MODTRAN radiative transfer code to estimate top-of-atmosphere radiance. The estimated gains for Landsat-7 ETM+ (1999-2003), -5 TM (1987-2004), -5 MSS (1984-1993) and -2 MSS (1979-1982) are presented. Results confirm the stability and accuracy of the ETM+ calibration, and the suitability of this data as a radiometric standard for cross-calibration with TM. Vicarious data support the use of the existing TM calibration model for the red and two shortwave-infrared bands. However, alternative models for blue, green and near-infrared bands are presented. The models proposed differ most noticeably at dates prior to 1995, with differences in estimated gains of up to 9.7%, 10.8% and 6.9% for the blue, green and near-infrared bands respectively. Vicarious gains for Landsat-2 MSS and Landsat-5 MSS are presented and are compared with those applied by the on-board calibration system. Updated calibration coefficients to scale MSS data to the SLATS vicarious measurements are given. The removal of time based calibration trends in the SLATS data archive will enable the measurement of vegetation changes over the 26 year period covered by Landsat -2, -5 and -7.     

Spectral characterization of the LANDSAT Thematic Mapper sensors

A summary of the spectral characteristics of the LANDSAT-4 and LANDSAT-5 Thematic Mapper instruments, the protoflight (TM/PF) and flight (TM/F) models, respectively, is presented. Data collected by the Hughes/Santa Barbara Research Center on the instruments and their components to determine compliance with the spectral coverage and spectral matching specifications served as the basis for the characterization. Compliance with the spectral coverage specifications (e.g. band locations) was determined by deriving band-by-band relative spectral response (RSR) curves from spectral measurements on the individual components contributing to the overall spectral response: filters, detectors and optical surfaces. The integrated system RSRs were not measured. The derived RSRs for the reflective bands were similar between TM/PF and the TM/F. The bandpasses between 50 per cent RSR points varied by only 2nm at most between the sensors and were: band 1, 452-518 nm; band 2, 528-609 nm; band 3, 625-693 nm; band 4, 776-904 nm; band 5, 1568-1784 nm and band 7, 2097-2348 nm. The upper-band edge of band 5 was outside the desired and specified range of 1750 ±20 nm; this implies that there will be more contribution from variable atmospheric water vapour absorption. In the emissive thermal band 6, the TM/PF and TM/F showed fundamentally different spectral responses. Though the lower band edges were both at approximately 104/mi, the TM/PF upper-band edge was detector limited at a temperature-dependent value of about 11·7μm, whereas the TM/F upper-band edge was filter limited at 12·4μm. Despite the TM/PF's band 6 narrow bandwidth, its radiometric performance exceeded requirements, so the band's narrowness was not a serious concern. Spectral matching measures the spectral differences between channels within a band by calibrating them all with one source and comparing their outputs to a spectrally different source. Satisfactory TM/PF spectral matching data were never obtained. TM/F channels were shown to have comparable or better spectral matching than past and existing MSS sensors.     

Extension of retrospective datasets using multiple sensors. An approach to radiometric intercalibration of Landsat TM and MSS data

The establishment of monitoring frameworks for environmental problems is frequently based on retrospective, multi-temporal series of satellite images. The derivation of concise conclusions from these series largely depends on their quantitative consistency; hence, a completely standardized analysis of multi-temporal time series is mandatory. In the context of the required radiometric rectification, sensor calibration is an essential component, the accuracy of which largely determines the overall result. While for newer sensor systems extensive documentation on the development of the sensor's sensitivity exists, older systems often lack the corresponding information.A methodology has been developed, tested and validated to radiometrically intercalibrate different sensor systems based on the precondition of simultaneous image acquisition, which is for example provided by the Landsat TM and MSS sensor systems hosted on Landsat 5. Different interpolation and processing steps within the procedure have been analyzed, and an intercalibration scheme is proposed to derive Landsat MSS calibration factors from Landsat TM, which is used as a calibrated reference. It supports a full radiometric rectification of Landsat MSS data and enables their incorporation into satellite image time series, which can thus be significantly extended by a maximum of 12 years.     

The utility of multi-sensor data for mapping eroded lands

The availability of remote sensing data with improved spatial, spectral and radiometric resolution is now available to fully exploit their potential for a specific application subject to the relative merits and the limitations of each sensor's data. Presented here is a case study where Landsat MSS and TM; and SPOT MLA data for part of the Bijapur district, southern India, which were acquired on the same day, have been evaluated for mapping eroded lands. The approach involves the geometric registration of all three data to a common map grid using tie points and third order polynomial transform; and resampling the MSS and TM data to a 20m by 20 m pixel dimension and radiometric normalization. Thematic maps showing eroded lands were generated on a micro-VAXbased DIPIX system using a maximum likelihood classifier. Accuracy estimates were made for the thematic maps following stratified unaligned random sampling technique, and subsequently, computing overall accuracy and Kappa coefficient. Spectral separability and classification accuracy was maximum from SPOT-MLA data followed by a combination of Landsat MSS band 1, SPOT-MLA band 2 and Landsat TM band 4; Landsat TM, a combination of Landsat MSS, TM and SPOT MLA; and Landsat MSS data.     

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds.

In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM?+?data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R ² of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.     

LANDSAT-8卫星影像长条带数据处理

目的 LANDSAT-8卫星发射以来,美国地质调查局(USGS)向全球发布WRS(world reference system)分幅体系下的标准景产品,该产品覆盖区域较小。针对面向区域遥感应用需要较大覆盖范围长条带卫星影像的问题,提出一种长条带数据处理方法。方法 长条带处理分为预处理、辐射校正和几何校正3个部分,包含了预处理、长条带数据辐射一致性纠正和姿轨数据精化等过程,解决了长条带影像处理的关键技术。结果 利用3组LANDSAT-8数据进行实验,本文方法处理得到的长条带影像,辐射均一性得到提高,整体精度同单幅标准景产品的精度相当,且效率较传统的分景处理再镶嵌的方式提高了45.9%。结论 本文LANDSAT-8长条带处理方法能够得到高质量的长条带影像,且处理效率较高,能够有效满足大区域遥感应用的数据需求。     

The effective resolution element of Landsat Thematic Mapper

Abstract

As part of the U.K.'s contribution to NASA's Landsat [mage Data Quality Analysis (LIDQA) programme the system performance of the Thematic Mapper (TM) scanner has been studied by the determination of the Effective Resolution Element (ERE), a measure of its spatial resolution. Two methods have been used, the first by a simulation of the TM's spatial responses in an analytical model and the second by measurements of water bodies on a selected Landsat-5 TM scene. Bands 1-4 of the Thematic Mappers of both Landsat-4 and Landsal-5 have an effective resolution element by the former method of 52 m. This compares with a value of 122 m for band 4, near-infrared, of the Multispectral Scanner (MSS) by the same method. An image-derived value, by the latter method, for band 4 of the Landsat-5 TM scene is approximately 75 m. This increase in the ERE over the sensor-only value is attributed to additional factors within the imagery such as the pixel sampling of the scene, ground segment processing and the contribution of atmospheric effects. The image-derived measure of the ERE will give a useful indication of the size of targets that can be recorded to a given radiometric accuracy and can thus determine the type of informational classes that may be utilized from present methods of automated classifications of remotely-sensed data.     

Forestry information content of Thematic Mapper data

This paper provides an initial investigation of the spectroradiometric data structure and information content of Thematic Mapper (TM) data for some Canadian forest-cover types. The Dryden-Lac Seul region in western Ontario is an important commercial and tourist area containing mainly boreal forest. A LANDSAT-4 TM scene of this region was analysed in conjunction with a considerable amount of ancillary data. The data were reduced to a manageable volume by selectingsubscenes;a preliminary attempt at atmospheric correction and radiometric calibration was then carried out; polygons representing a wide variety of cover classes were defined and, finally, the spectroradiometric information available for class discrimination was analysed using several techniques. Principal component analysis and feature selection reveal that the spectral data can be reduced to three eigenvectors with a loss of less than 10 per cent of the scene variance, and that the best three TM bands perform almost as wellas the first three principal components; for general cover-type discrimination, these bands areTM 3,4 and 5.TM bands 1,4 and 5 are marginally better for separating a set of softwood classes, although TM I has a very small dynamic range. The shortwave infrared (SWIR) spectral region, represented byTM bands 5and 7, seems to be particularly sensitive to forest vegetation density, especially in the early stages of clearcut regeneration. Shadowing is suggested as a factor at least as important as leaf moisture content in influencingthe spectral reflectanceof forests in this region. The first three principal components are related to the scene brightness (PC I), greenness (PC 2) and the contrast between the SWIR and the visible and near-infrared regions. We propose the name ‘swirness’ for the third component until a more complete understanding of its properties is achieved.     

Classification automatique assistée par une analyse de texture,des paysages de la Pointe d'Arçay d'après des données Thematic Mapper

Abstract

Cet article présente une classification automatique des unités de paysages de la Pointe d'Arçay d'aprés des données Thematic Mapper (TM), avec un algorithme de classification non supervisée d'agrégation autour de centres mobiles, en utilisant non seulement des informations radiométriques du TM mais aussi un certain nombre de caractéristiques texturales des paysages. La qualité de la classification multispectrale est améliorée par l'introduction d'une analyse de texture

An approach to automatic classification of landscape using LANDSAT-5 Thematic Mapper (TM) data is presented with an example of application for the Arçay spit (Véndee, France). First of all, using unsupervised classification of aggregation round mobile centres (the dynamic-cluster algorithm), a multispectral classification is realized withTM bands 1,2,3,4, 5 and 7, and then a texture analysis is introduced to eliminate classification confusion between certain classes. The classification quality is improved owing to the texture analysis.     

Land-use inventory and mapping in a mountainous area: the Ardèche experiment†

Abstract

The paper is a review and discussion of the work done on the Ardèche test site by using TM data. The Ardèche is a mountainous and strongly dissected area with very small fields, and multitemporal processing is essential in order to reach sufficient classification accuracy. This necessitated the following developments: high-quality image-registration integrating altitude information, image-based radiometric calibration, spatial zoning before classification, and post-classification sorting. Monotemporal results are shown for the complete area together with multi-temporal results for a part of the area. A multiband multi-temporal segmentation method is presented as preparation for segment-wise classification.     

On-board radiometric calibration for thermal emission band of FY-3C/MERSI

On-board radiometric calibration is the most efficient method to improve the measurement accuracy of satellite-borne sensors. Chinese Medium Resolution Spectral Imager (MERSI), loaded on the Fengyun-3C (FY-3C) satellite, uses one satellite-borne fixed-temperature blackbody (BB) for its thermal emission band’s (TEB) radiometric calibration, but its optimal calibration algorithm is not determined. By using MERSI’s on-board calibration data taken on November 2013, this article investigates two algorithms of linear and semi-nonlinear calibration for MERSI TEB’s on-board radiometric calibration and finds that the linear calibration is more reasonable than semi-nonlinear calibration because linear coefficients’ variation tendency can reflect MERSI’s inherent systematic properties better. The relative difference between linear properties and inherent properties for pixel variability being 9.5%, mirror-side variability being 21.5% and scan variability being 17.8% are all smaller than those between semi-nonlinear case and inherent case. All of those suggest that the linear calibration is coincident with the inherent systematic properties. By using MERSI’s calibration data at June 2014, the performance of those two algorithms is validated by comparing the difference between inferred BB radiance L_I and standard BB radiance L_S (0.01%), and between inferred BB brightness temperature T_I and standard BB temperature T_S (0.25 K).     

Effet de la reésolution spatiale sur des proprieeacute;teés statistiques des images satellites: une eétude de cas

Abstract

The instruments on such satellites as Landsat or SPOT present the advantage provided by high spatial resolution. This advantage is tempered by their low time resolution. Therefore, it is not always possible to monitor seasonal variations of parameters such as the normalized vegetation index. The AVHRR instrument on board the NOAA satellites has a very high repetitivity but a very low spatial resolution. In our research we proposed to monitor the normalized vegetation index with this low-resolution instrument. It is therefore of interest to examine the relations between high- and low-resolution images for using the AVHRR data as a means of interpolation between two MSS images. This problem is addressed here using satellite images of an important agricultural region in France. In terms of the transformation of the mean radiometric values, it is shown that a linear transformation exists to calculate the AVHRR data from those of the MSS. but the relation presents a strong dependency on the observed scene. The effect on the higher-order statistical properties is studied through the transformation of the images textures by progressively degrading the MSS images. It is shown that a threshold, which depends on the scene, exists on the resolution below which all statistical information disappears.     

Validation of the Geocoding and Compositing System (GEOCOMP) using contextual analysis for AVHRR images

Mapping of remotely-sensed data to a common projection can potentially degrade the quality of the data. The Canada Centre for Remote Sensing (CCRS) has developed the Geocoding and Compositing System (GEOCOMP) to map Advanced Very High Resolution Radiometer (AVHRR) data to a 1 km2 equal area projection. Using data from the Boreal Ecosystem-Atmosphere Study (BOREAS), we tested GEOCOMP's processing of individual pixels and its ability to preserve the spatial integrity of the original AVHRR observations. Our study shows that GEOCOMP processing does not significantly degrade the ability to derive environmental variables and indices using a contextual approach. Errors produced by the GEOCOMP system are largest for scenes with high radiometric contrast and the parts of scenes with large satellite view angles.     

Spectral texture for improved class discrimination in complex terrain

Abstract

A spatial co-occurrence algorithm has been used to derive image texture from Landsat Multispectral Scanner (MSS) data to increase classification accuracy in a moderate relief, boreal environment in eastern Canada. The aim was to investigate ‘data-driven improvements’, including those available through digital elevation modelling. Overall classification accuracy using MSS data alone was 59·1 per cent when compared to a biophysical inventory of the area compiled primarily by aerial photointerpretation. This increased to 66·2 per cent with MSS plus texture and to 89·8 per cent when MSS data were analysed with geomorphometry extracted from a digital elevation model (DEM). The introduction of MSS texture resulted in statistically significant increases in individual class accuracies in classes that were also well defined using the geomorphometric and integrated data sets. This suggested that some of the additional information provided by geomorphometry was also contained in spectral texture. It was also noted that individual texture orientations resulted in higher class accuracies than average texture measures; this is probably related to structural (slope/aspect) characteristics of specific vegetation communities.     

Agricultural land-cover discrimination using thematic mapper spectral bands

Multispectral scanner system data simulating the thematic mapper (TM) of LANDSAT-4 were analysed for an area near Gedney Hill, Lincolnshire, U.K. The data were found to have a three-dimensional statistical structure similar to that for the LANDSAT-4 TM of parts of the United States. Divergence analysis indicates that the optimal choice of bands for cover discrimination should include one band from the visible, one from the near-IR (infrared) and one from the middle- or far-IR. It was further shown, primarily from consideration of principal-component images, that significant discriminatory power may be lost if all bands are not used. Comparisons with LANDSAT-4 TM principal-component images are made. The role of noise factors in obscuring information, especially from highly correlated bands, is shown to be of considerable importance.     

A cloud detection algorithm for AATSR data, optimized for daytime observations in Canada

To extract information about the Earth's surface from Earth Observation data, a key processing step is the separation of pixels representing clear-sky observations of land or water surfaces from observations substantially influenced by clouds. This paper presents an algorithm used for this purpose specifically for data from the AATSR sensor on ENVISAT. The algorithm is based on the structure of the SPARC cloud detection scheme developed at CCRS for AVHRR data, then modified, calibrated and validated for AATSR data. It uses a series of weighted tests to calculate per-pixel cloud presence probability, and also produces an estimate of cloud top height and a cloud shadow flag. Algorithm parameters have been optimized for daytime use in Canada, and evaluation shows good performance with a mean daytime kappa coefficient of 0.76 for the ‘cloud’/‘clear’ classification when compared to independent validation data. Performance is independent of season, and is a dramatic improvement over the existing AATSR L1B cloud flag for Canada. The algorithm will be used at CCRS for processing AATSR data, and will form the basis of similar processing for data from the SLSTR sensors on Sentinel-3.     

Synergistic effects of combined Landsat-TM and SIR-B data for forest resources assessment

Abstract

Three different incidence angle data sets, obtained by the Shuttle Imaging Radar-B in October 1984 over a forested area in northern Florida, were combined with Landsat-5 Thematic Mapper data, to create a digitally registered 10 channel optical/microwave data set. The work discussed in this paper involved the analysis of the data obtained by the two sensors separately and in combination, to determine if there are synergistic effects obtained through the simultaneous use of data obtained from both the optical and microwave portion of the spectrum. The radar data were filtered with a low-pass filter to eliminate the speckle noise. Classifications of the TM, SIR-B and combined TM +SIR-B data sets were performed with both per-point and contextual classifiers. The results showed that filtered radar data can be used to classify accurately major cover types (i.e., pine forest, swamplands and radar smooth targets) and that the contextual classifier provided better classification performance. The combined TM and SIR-B data provided statistically improved classification performances compared to classifications from the three incidence angle SIR-B data, or the TM data alone. A four band subset (TM-2, TM-4, TM-5, and SIR-B 28°) of the 10 channels of the combined TM and SIR-B data set provided higher classification performances (91 per cent overall performance) than the 10 channel data set (86 per cent overall performance).     

Suspended sediment dynamics from repetitive Landsat data

Abstract

Repetitive Landsat CCT data were used to monitor the motion of non-uniformly distributed suspended sediments in a New Zealand tidal basin. The movement of such suspended sediment was followed using all four MSS bands with non-uniformly distributed suspended sediment being differentiated from bottom features by the data from two satellite overpasses. As pan of this study penetration depths of 45-50±5 cm and 10-15 ±5 cm were found for channels MSS 5 and MSS 6 over the sediment-laden tidal inlet. The difference in penetration depths within each MSS band was ascribed to differing concentrations of uniformly distributed suspended sediments between the two overpasses.     

Haze removal for new generation optical sensors

Haze and cloud contamination is a common problem in optical remote-sensing imagery, as it can lead to the inaccurate estimation of physical properties of the surface derived from remote sensing and reduced accuracy of land cover classification and change detection. Haze optimized transform (HOT) is a methodology applicable to radiometric compensation of additive haze effects in visible bands that exhibits a spatially complex distribution over an image. The generic approach of HOT allows for the use of older satellite imaging sensors that include at least two visible bands (e.g. Landsat Thematic Mapper (TM) and Landsat Multispectral Scanner (MSS) sensors). This study proposes modifications to extend HOT applicability to new sensors. The improvements and extended functionality adapt the method to the higher radiometric resolution specifications of newer generation sensors and use percentile-based minimum in the correction procedure to avoid causing fake minimum. Alternative filters are also evaluated to smooth raw HOT output and the cloud mask is generated as an additional output. A Landsat 8 scene of Los Angeles is used to demonstrate the improved methodology. The methodology is applicable to sensors such as QuickBird, Worldview 2/3. More than 20 additional scenes were used to evaluate the effectiveness of the methodology.