International Journal of Remote Sensing

Tan, K.; Wan, Y.; Zhou, X.; Song, D.; Duan, Q.

doi: 10.1080/01431160600554389pmid: N/A

A key project within the National High Technology Research and Development Program of China (863 Program) ‘Synthetic Archaeological Studies with Remote Sensing and Geophysics Survey’ was a comprehensive research carried out on the Mausoleum of the Emperor Qinshihuang (MEQ) (259–210 bc). The project was finished by September 2003. We utilized remote sensing and geophysical survey to probe the characteristics of the surface nature and underground features of the MEQ. We were particularly interested in the underground palace, and hoped to find more information on the historical relic remains at the same time. The remote sensing survey team utilized many kinds of remote sensing methods, such as panchromatic remote sensing, colour infrared remote sensing and hyperspectral remote sensing (including one thermal infrared band), combined with testing the ground soil spectrum, temperature, humidity, soil composition, digital mapping and three‐dimensional observations. This paper presents the principal achievements of the remote sensing techniques.

Anderson, K.; Milton, E. J.

doi: 10.1080/01431160500444780pmid: N/A

Ground calibration targets (GCT) fulfil an essential role in vicarious calibration and atmospheric correction methodologies. However, assumptions are often made about the temporal stability of GCT reflectance. This letter presents results from a multi‐year study aimed at testing the temporal stability of a typical weathered concrete GCT in southern England. Very accurate measurements of hemispherical‐directional reflectance factors in the 400–1000 nm range were collected using a mobile dual‐beam spectroradiometer. Results demonstrated that the calibration surface was subject to seasonal growth of a biological material, which caused the reflectance factor to vary by a factor of two during the year (range = 16.4% reflectance at 670 nm). The spectral effect of this was most noticeable in field spectra collected in April. As environmental conditions became drier throughout the summer, concrete reflectance factors increased. Over multiple seasons the same patterns in reflectance factors repeated, indicating the predictable nature of the biological signature. The research also suggested that the biological material was affected to a small but measurable extent on a daily basis by changes in relative humidity occurring after onset of a local sea breeze. The research highlights the dynamic nature of weathered GCTs, and has wider implications for those using similar sites for vicarious calibration or atmospheric correction purposes.

Carvalho, Julia; Soares, Amilcar; Bio, Ana

doi: 10.1080/01431160600658099pmid: N/A

A methodology is proposed, to assess land surface cover classification using a geostatistical methodology of stochastic simulation, direct sequential cosimulation, to combine field observations with remotely sensed data classified with the classical algorithm of maximum likelihood classification. This procedure has two main advantages: (1) incorporation of a spatial continuity statistics; and (2) integration of different scales of information, contained in polygons (training areas) and point information (field observations), which also involves different qualities of information that is less reliable and more reliable, respectively. Moreover, this methodology allows production not only of a classified map, but also of maps of occupation proportions and of uncertainty for each thematic class. Local co‐regionalization models are applied to account for local differences in both field data availability and distribution, and the correlation between these hard data and the classified satellite images as soft data. The methodology is based on two criteria: the influence of the hard data dependent on their availability and proportional to their proximity; and the influence of the soft data dependent on their local correlation to the hard data. The method is applied to a study of four economically important forest tree species on the Setúbal Peninsula (south of Lisbon, Portugal). The results show more contiguous forest covers, i.e. more spatial contiguity, than the classical classification. In comparison to a contemporary field inventory, the proposed method improved forest cover estimations, showing a difference of only 3%.

Linhai Jing, Qiuming Cheng; Panahi, Alireza

doi: 10.1080/01431160600606882pmid: N/A

Principal component analysis (PCA) has been commonly used and has played an important role in remote sensing for information extraction. However, the ordinary PCA based on second‐order covariance or correlation is capable of forming components on the basis of the statistical properties of a majority of pixel values – pixel values around mean values. For many applications, principal components should be constructed on the basis of optimum correlation coefficients so that the components can represent low or high values of minority pixels of interest. A new version of the PCA has been proposed on the basis of an optimum order sample correlation coefficient for enhancing the contribution of the image bands including the low or high minority pixel values that can assist in extracting weak information for image classification and pattern recognition. The ordinary PCA becomes the special case of the new version of the PCA introduced in this paper. The new method was validated with a case study of identification of Au/Cu‐associated alteration zones from a Landsat Thematic Mapper (TM) image in the Mitchell‐Sulphurets district, Canada.

Lee, J. Y.; Warner, T. A.

doi: 10.1080/01431160600606866pmid: N/A

Five aspatial and spatial classification methods were compared in this study: standard per‐pixel maximum likelihood classification; Kettig and Landgrebe's ECHO classification; maximum likelihood classification using the segment mean; classification using the segment divergence index; and maximum likelihood classification using the segment probability density function (PDF). The five classification methods were compared using test data from digital aerial imagery with a nominal 1‐m pixel size, and four multispectral bands, acquired over Morgantown, West Virginia, USA. Classification using the segment divergence index produced the lowest accuracy, followed by ECHO, standard maximum likelihood classification and classification with segment mean. The highest accuracy was obtained from classification using the segment PDF.

Yang, Jian; Xiong, Tao; Peng, Ying‐Ning

doi: 10.1080/01431160600589161pmid: N/A

In this letter, a generalized optimization of polarimetric contrast enhancement (GOPCE) is employed for supervised polarimetric synthetic aperture radar (SAR) image classification. The GOPCE is the extension of optimization of polarimetric contrast enhancement (OPCE), and it includes three optimal coefficients associated with the Cloude entropy and two special similarity parameters in addition to the optimal polarization states. Using the GOPCE, the authors propose an approach to supervised classification. For comparison, the authors also use the maximum likelihood (ML) classifier for classification, based on the complex Wishart distribution. The classification results of a NASA/JPL AIRSAR L‐band image over San Francisco demonstrate the effectiveness of the proposed approach.

Santoro, M.; Eriksson, L.; Askne, J.; Schmullius, C.

doi: 10.1080/01431160600646037pmid: N/A

JERS‐1 L‐band SAR backscatter from test sites in Sweden, Finland and Siberia has been investigated to determine the accuracy level achievable in the boreal zone for stand‐wise forest stem volume retrieval using a model‐based approach. The extensive ground‐data and SAR imagery datasets available allowed analysis of the backscatter temporal dynamics. In dense forests the backscatter primarily depended on the frozen/unfrozen state of the canopy, showing a ∼4 dB difference. In sparse forests, the backscatter depended primarily on the dielectric properties of the forest floor, showing smaller differences throughout the year. Backscatter modelling as a function of stem volume was carried out by means of a simple L‐band Water Cloud related scattering model. At each test site, the model fitted the measurements used for training irrespective of the weather conditions. Of the three a priori unknown model parameters, the forest transmissivity coefficient was most affected by seasonal conditions and test site specific features (stand structure, forest management, etc.). Several factors determined the coefficient's estimate, namely weather conditions at acquisition, structural heterogeneities of the forest stands within a test site, forest management practice and ground data accuracy. Stem volume retrieval was strongly influenced by these factors. It performed best under unfrozen conditions and results were temporally consistent. Multi‐temporal combination of single‐image estimates eliminated outliers and slightly decreased the estimation error. Retrieved and measured stem volumes were in good agreement up to maximum levels in Sweden and Finland. For the intensively managed test site in Sweden a 25% relative rms error was obtained. Higher errors were achieved in the larger and more heterogeneous forest test sites in Siberia. Hence, L‐band backscatter can be considered a good candidate for stand‐wise stem volume retrieval in boreal forest, although the forest site conditions play a fundamental role for the final accuracy. When the article was submitted L. Eriksson was at the Department of Geoinformatics and Remote Sensing, Friedrich‐Schiller University, D‐07743 Jena, Germany.

Wagenseil, H.; Samimi, C.

doi: 10.1080/01431160600639743pmid: N/A

Time series of Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) were used to capture plant phenology in Etosha National Park, a dry savannah environment in Namibia. Data from two consecutive growing periods with different precipitation conditions were included to study impacts of inter‐seasonal rainfall variations on a highly water‐limited ecosystem. Additionally, a contemporary reference map with four major vegetation units was used to compare phenology between plant formations. Phenological attributes were acquired for both seasons using Fourier analysis. Parameters were calculated for the entire study area and further stratified with respect to the mapping units of the reference. Vegetation growth was found to vary significantly between the two periods in accordance with available rainfall data. Additionally, separability of vegetation entities based on Fourier parameters was weak due to within‐class scattering and was commonly outranged by inter‐seasonal variations. Finally, discrimination of cover types was tested by combining selected Fourier parameters in a clustering procedure. Spatial class distribution was compared to the reference statistically and only a moderate correspondence was discovered. We conclude that Fourier‐based NDVI attributes are limited for cover‐type discrimination across space and time, as they only quantify certain aspects of plant phenology and seem to be largely altered by the actual rainfall situation.

Ma, Mingguo; Frank, Veroustraete

doi: 10.1080/01431160600593031pmid: N/A

A long time series (20 years) of Advanced Very High Resolution Radiometer (AVHRR) data with 8 km resolution are used to monitor vegetation cover change in the Heihe river basin. Linear regression is used to characterize the trends in vegetation cover change. The yearly Maximum Normalized Difference Vegetation Index (MNDVI) in the Heihe river basin elicits an explicit inter‐annual change in the period 1982–2001. An increase in MNDVI occurs in mid‐basin oasis mainly, while a decrease in MNDVI is mainly observed in the mountainous and Alxa's natural oasis regions of the Heihe river basin. Precipitation is the major climate driving force for vegetation cover changes in the Heihe River Basin. The MNDVI is sensitive to precipitation and its sensitivity decreases with increasing precipitation. Precipitation has a higher effect on the mountainous regions' vegetation cover than on the oasis regions' vegetation cover. Precipitation also elicits a lag effect on the MNDVI with a phase of one year at a yearly scale mainly in the mountainous regions of the Heihe River Basin. In the Heihe River Basin, the temperature has a slight and positive effect on the oasis regions' vegetation cover and a slight and negative effect on the mountainous regions' vegetation cover.

Li, Zhengquan; Yu, Guirui; Li, Qingkang; Fu, Yuling; Li, Yingnian

doi: 10.1080/01431160600647241pmid: N/A

Quantification of areal evapotranspiration from remote sensing data requires the determination of surface energy balance components with support of field observations. Much attention should be given to spatial resolution sensitivity to the physics of surface heterogeneity. Using the Priestley–Taylor model, we generated evapotranspiration maps at several spatial resolutions for a heterogeneous area at Haibei, and validated the evapotranspiration maps with the flux tower data. The results suggested that the mean values for all evapotranspiration maps were quite similar but their standard deviations decreased with the coarsening of spatial resolution. When the resolution transcended about 480 m, the standard deviations drastically decreased, indicating a loss of spatial structure information of the original resolution evapotranspiration map. The absolute values of relative errors of the points for evapotranspiration maps showed a fluctuant trend as spatial resolution of input parameter data layers coarsening, and the absolute value of relative errors reached minimum when pixel size of map matched up to measuring scale of eddy covariance system. Finally, based on the analyses of the semi‐variogram of the original resolution evapotranspiration map and the shapes of spatial autocorrelation indices of Moran and Geary for evapotranspiration maps at different resolutions, an appropriate resolution was suggested for the areal evapotranspiration simulation in this study area.