International Journal of Remote Sensing

Boyd, D. S.; Entwistle, J. A.; Flowers, A. G.; Armitage, R. P.; Goldsmith, P. C.

doi: 10.1080/01431160500328355pmid: N/A

The 1986 Chernobyl accident resulted in radionuclide contamination (dominated by 137Cs) across large areas of Belarus. Consequences of this accident continue to affect Belarus long after initial contamination, which in turn has placed strain upon social, economic and political infrastructures. One method to reduce this strain and remediate contamination is to return areas of land no longer posing a risk, back to an appropriate use. As a method of remediation, this requires regular and accurate monitoring of the landscape at which existing ground based techniques have not been entirely well‐suited. Remote sensing, specifically the use of imaging spectrometry offers the potential to monitor the Belarusian landscape at opportune spatial and temporal resolutions. Vegetation has been shown to be an important agent in the cycling of radioactive isotopes in the environment and therefore a useful indicator of radionuclide contamination. This pilot research has focused on assessing the spectral response from Pinus sylvestris (dominant on the Belarusian landscape) at differing ages and with varying levels of 137Cs contamination. Continuum removal was applied to the spectra showing that for older forests (c. 35 years) significant spectral differences between low and high contaminated sites exist at wavelengths that are causally related to foliar biochemicals. This was not the case for young forests (c. 15 years) where no significant differences were found. The results signify the potential to infer contamination levels from spectra of forests, partitioned by age, thus indicating the possibility of using imaging spectrometry to monitor radionuclide contamination, a possibility warranting further investigation.

Liu, Jian Guo; Mason, P. J.; Ma, Jiming

doi: 10.1080/01431160500292023pmid: N/A

An imageodesy study has been carried out, using pre‐ and post‐event Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) images, to reveal regional co‐seismic displacement caused by the Ms 8.1 Kunlun earthquake in November 2001. The two Landsat scenes, Kusai Lake and Buka Daban, cover an area of some 57 600 km2 (320 km W–E and about 180 km N–S), which includes most of the fault rupture zone. The co‐seismic displacement measured in the Kusai Lake scene shows that the average left‐lateral shift along the Kunlun fault is 4.8 m (ranging from 1.5 to 8.1 m) and the maximum shift appears west of the Kusai Lake. The splayed nature of the fault to the west of Buka Daban, where the fault splits into three branches, causes the displacement pattern to become complicated. Here the average left‐lateral shift, between the south side of the southern branch and the north side of the northern branch, is 4.6 m (ranging from 1.0 to 8.2 m). Our results also illustrate that the south side of the fault is the ‘active’ block, moving significantly in an east–south‐easterly direction, relative to the largely ‘stable’ northern block.

Noferini, L.; Pieraccini, M.; Mecatti, D.; Macaluso, G.; Luzi, G.; Atzeni, C.

doi: 10.1080/01431160500353908pmid: N/A

A ground‐based synthetic aperture radar (GB SAR) interferometer was employed to detect small terrain movements which occurred over long periods of time on a landslide hazard zone in the Citrin Valley in northern Italy. Three measurement campaigns were carried out from September 2003 to September 2004. The radar instrumentation was carefully re‐installed at each campaign on the same observation point in order to avoid geometric decorrelation. Because of the global loss of coherence due to the long temporal separation between the observations, the retrieved displacements are limited to a number of very coherent points throughout the surveyed scenario. Radar data are locally compared with data provided by a global positioning system (GPS) network installed on the area interested by the landslide. Digital elevation model, photographic images, interferometric and GPS data are integrated within GIS environment providing a very effective geophysical knowledge tool.

Baghdadi, N.; Holah, N.; Zribi, M.

doi: 10.1080/01431160500239032pmid: N/A

The potential of Advanced Synthetic Aperture Radar (ASAR) for the retrieval of surface soil moisture over bare soils was evaluated for several ASAR acquisition configurations: (1) one date/single channel (one incidence and one polarization); (2) one date/two channels (one incidence and two polarizations); (3) two dates/two channels (two incidences and one polarization); and (4) two dates/four channels (two incidences and two polarizations). The retrieval of soil moisture from backscattering measurements is discussed, using empirical inversion approaches. When compared with the results obtained with a single polarization (HH or HV), the use of two polarizations (HH and HV) does not enable a significant improvement in estimating soil moisture. For the best estimates of soil moisture, ASAR data should be acquired at both low and high incidence angles. ASAR proves to be a good remote sensing tool for measuring surface soil moisture, with accuracy for the retrieved soil moisture that can reach 3.5% (RMSE).

Henry, J.‐B.; Chastanet, P.; Fellah, K.; Desnos, Y.‐L.

doi: 10.1080/01431160500486724pmid: N/A

During the August 2002 Elbe river flood, different satellite sensor data were acquired, and especially Envisat Advanced Synthetic Aperture Radar (ASAR) data. The ASAR instrument was activated in Alternating Polarization (AP) and Image (IM) modes, providing high resolution datasets. Thus, the comparison with a quasi‐simultaneous ERS‐2 scene enables the evaluation of the contribution of polarization configurations to flood boundary delineation. This study highlights the increased capabilities of the Envisat ASAR instrument in flood mapping, especially the benefit of combining like‐ and cross‐polarizations for rapid mapping within a crisis context.

Palenzuela, J. M. Torres; Vilas, L. González; Cuadrado, M. Sacau

doi: 10.1080/01431160512331314038pmid: N/A

Space‐borne synthetic aperture radar has been proven to be a useful tool for ocean oil spill monitoring due to its large coverage, independence of the day–night cycle and all‐weather capability. In this paper, a method for oil spill detection based on a visual interpretation was applied to two consecutive Advanced Synthetic Aperture Radar (ASAR) images acquired during the Prestige oil spill off the Spanish coast. The obtained oil spill information was integrated into a Geographical Information System (GIS) database in order to study the spatial distribution and the evolution of the slicks between both days, in addition to carrying out a comparison with field observations. The results show the great capability of monitoring and forecasting marine oil spills caused by large oil tanker accidents by means of the use of radar imagery jointly with other information, such as wind data or in situ observations.

Cayuela, L.; Golicher, J. D.; Rey, J. Salas; Benayas, J. M. Rey

doi: 10.1080/01431160500181788pmid: N/A

The landscape of the Highlands of Chiapas, southern Mexico, is covered by a highly complex mosaic of anthropogenic, natural and semi‐natural vegetation. This complexity challenges land cover classification based on remotely sensed data alone. Spectral signatures do not always provide the basis for an unambiguous separation of pixels into classes. Expert knowledge does, however, provide additional lines of evidence that can be employed to modify the belief that a pixel belongs to a certain coverage class. We used Dempster–Shafer (DS) weight of evidence modelling to incorporate this information into the classification process in a formal manner. Expert knowledge‐based variables were related to: (1) altitude, (2) slope, (3) distance to known human settlements and (4) landscape perceptions regarding dominance of vegetation types. The results showed an improvement of classification results compared with traditional classifiers (maximum likelihood) and context operators (modal filters), leading to better discrimination between categories and (i) a decrease in errors of omission and commission for almost all classes and (ii) a decrease in total error of around 7.5%. The DS approach led not only to a more accurate classification but also to a richer description of the inherent uncertainty surrounding it.

Lindström, Johan; Eklundh, Lars; Holst, Jan; Holst, Ulla

doi: 10.1080/01431160500380539pmid: N/A

The strong systematic change in solar zenith angles (SZA) due to annual orbital drift of the NOAA satellites has raised the suspicion of the influence of residual illumination on the calibrated normalized difference vegetation index (NDVI) derived from the Pathfinder AVHRR Land (PAL) database. The aim of this work is to analyse if trends in AVHRR NDVI from 1982 to 2000 over the Sahel region in Africa depend on variations in SZA. The analysis uses both ordinary least squares regression and cointegration to analyse possible linear dependencies between NDVI and SZA on a per satellite basis. Tests for integration and cointegration fail to find any significant evidence for either. This, together with the ability of simple deterministic models to explain primarily SZA constitutes evidence against integration and cointegration, indicating that linear relationships can be examined using ordinary linear regression. Regression gives no consistent relationship between NDVI and SZA and the explanatory power (R 2) of the regression is low (on average 0.08). However there is some evidence for downward bias in NDVI due to nonlinear interactions between NDVI and SZA when SZA is large (⩾80°) leading to the conclusion that PAL data from the year 2000 should not be used for analyses in these environments.

Yunhao, Chen; Xiaobing, Li; Peijun, Shi; Adu, Gong; Wen, Dou

doi: 10.1080/01431160500127450pmid: N/A

A remote sensing approach towards modelling the land cover pattern for controlling local soil erosion based on the universal soil loss equation (USLE) was developed. The approach was used to analyse the possible impact of land cover changes on local ecological security. Then, land cover pattern optimization aiming at the reduction of local soil erosion was taken. Results show that this method can be applied to local ecological security management.

Dransfeld, S.; Tatnall, A. R.; Robinson, I. S.; Mobley, C. D.

doi: 10.1080/01431160500245658pmid: N/A

A bio‐optical model coupled with the radiative transfer model Hydrolight was used to create 18,000 synthetic ocean colour spectra corresponding to open ocean and coastal waters. The bio‐optical model took into account the optical properties of the three oceanic constituents, chlorophyll‐a, suspended non‐chlorophyllous particles and coloured dissolved organic matter (CDOM) as well as of normal seawater. The resulting spectra were input into multilayer perceptron neural network algorithms with the aim of computing the original concentrations of chlorophyll‐a, non‐chlorophyllous particles and CDOM initially input into the bio‐optical model. The process of training the neural networks is essential for the accuracy of the inversion the neural net performs on the coupled bio‐optical and radiative transfer models. The objective of this paper is to investigate the performance difference of a neural network trained with untransformed as opposed to logarithmically transformed data.