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BORZELLI, G.; CIAPPA, A.; ULIVIERI, C.; ANTONELLI, G.; LANEVE, G.
doi: 10.1080/01431169608948703pmid: N/A
Abstract In this paper an extension of the Split Window Technique algorithm, to account for small surface emissivity variations, is presented. This algorithm has been used, along with an adaptive filtering pattern recognition approach, in order to detect oil spills on the sea surface under the assumptions of thermal equilibrium between the oil polluted areas and the surrounding water, of weak horizontal sea surface temperature gradients (i.e., <1°C) in the area of interest and of a horizontal uniform atmospheric water vapour distribution over the discharged area. AVHRR/2 data acquired both on the Gulf of Genoa in April 1991 during an oil pollution episode following the wreck of the Haven tanker and on the Persian Gulf during war operations in January-February 1991 were considered. Comparing satellite retrieved polluted areas with in situ observations available in literature and high spatial resolution satellite observations (Landsat and SPOT), the algorithm has proved to supply satisfactory results in detecting oil contaminated areas.
doi: 10.1080/01431169608948704pmid: N/A
Abstract By referring to the sea surface temperature profiler buoy (SSTPB) data observed in Mutsu Bay, this study showed under calm and strong sunshine conditions, the vertical water temperature profile near the sea surface bends abruptly, and sea surface temperature detected by satellite remote sensing is not necessarily coincident to the bulk sea surface temperature. Besides the atmospheric effect, this effect causes another error in the estimation of sea surface temperature by remote sensing data known as the sea surface effect (SSE). As a sequel to a former paper, this paper is concerned with the investigation of the conditions which occur in the apparent SSE. Statistical analyses were directed to the total data set of SSTPB. The amount of SSE was evaluated by the water temperature difference between the uppermost surface and 1 m depth, and apparent SSE was identified to when the absolute value of the difference is larger than 0·5 °C. Apparent SSE was observed in the season from May to September. Its occurrence rate in May and June was about 40 per cent of the total days, and about 20 per cent in July. SSE grew when wind speed was less than 2 m s−1 and the solar zenith angle is smaller than 30°. If either of those two conditions were violated, SSE easily transferred into reducing phase.
LIANG, S.; TOWNSHEND, J. R. G.
doi: 10.1080/01431169608948705pmid: N/A
Abstract A new simplified radiative transfer model of the soil bidirectional reflectance is developed that approximates multiple-scattering radiance by a four-stream formulation and calculates single-scattering radiance and double-scattering radiance exactly. This model also takes any arbitrary anisotropic sky radiance into consideration. The comparisons with the numerical code based on the discrete-ordinate algorithm (DISORT) indicate that the present analytical model has high accuracy much better than the widely used Hapke model, especially when soil particles scatter very strongly or anisotropically. Evaluation of the inversion accuracy of the present model is also discussed.
doi: 10.1080/01431169608948706pmid: N/A
Abstract Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the ‘hard’ classifications. Moreover, the outputs derived from the artificial neural network and the fuzzy c-means algorithm in particular were strongly related to the land cover on the ground and provided the most accurate land cover representations. The ability to appropriately represent fuzzy land cover and evaluate the accuracy of the representation should facilitate the use of remote sensing as a source of land cover data.
FOODY, G. M.; BOYD, D. S.; CURRAN, P. J.
doi: 10.1080/01431169608948707pmid: N/A
Abstract The remote sensing of biophysical properties has generally relied on the use of data acquired in red and near-infrared channels only, often combined in a vegetation index such as the Normalized Difference Vegetation Index (NDVI). This is wasteful of information acquired in other channels and may prevent the accurate estimation of biophysical properties. The use of vegetation indices based on red and near-infrared radiation to estimate biophysical properties of tropical forests has met with little success and this may be due to the asymptotic nature of the relation between the indices and biophysical properties, the variable sensitivity of vegetation indices to vegetation biophysical properties in different environments, the low radiation reflected in red and near-infrared channels, and severe attenuation by atmospheric water and aerosols. For tropical forests the only feasible way to estimate biophysical properties at regional to global scales is through the use of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) which operates in three channels in addition to the red and near-infrared. The potential of the data acquired in all five AVHRR channels for the estimation of tropical forest biophysical properties was investigated. Radiometrically calibrated AVHRR imagery of Ghana were related to ground data on tree density and mean basal area. Radiation measured in the middle-and thermal-infrared channels were more strongly correlated with the forest biophysical properties than radiation acquired in red and near-infrared channels. Moreover, vegetation indices containing data acquired in the middle- and thermal-infrared were also more strongly correlated with biophysical properties than the widely used NDVI. Correlation coefficients of 0·71 and 0·23 were derived between the NDVI and tree density and basal area respectively, while the corresponding correlation coefficients for indices based on data acquired in AVHRR channels 3–5 were up to −0·88 and −0·52 respectively. Since current and proposed sensors operate in visible to thermal-infrared channels users should consider the potential of data acquired all channels and not just a subset.
ALPARONE, L.; BARONTI, S.; CARLÀ, R.; PUGLISI, C.
doi: 10.1080/01431169608948708pmid: N/A
Abstract An algorithm based on local order statistics is proposed for adaptive reduction of speckle noise in synthetic aperture radar (SAR) images. A selective smoothing is obtained by replacing a pixel value belonging to either of the tails of the local histogram by its percentile, whose area is adaptively defined by a Gaussian function of the Local Variation Coefficient. The filter can fit the actual noise level and preserves structures, textures, and point targets, as well as the local mean without introducing any blur on the edges, mostly due to its closure property. Comparisons with algorithms suitable for speckle smoothing are performed on true SAR images and show selective signal-to-noise ratio (SNR) enhancements.
COOPER, A. P. R.; HINTON, J. C.
doi: 10.1080/01431169608948709pmid: N/A
Abstract The problem of correcting satellite altimeter measurements for errors introduced by topographic surfaces was chosen to test the capabilities of a prototype integrated geographical information system. A system to calculate and apply corrections to altimeter data has been implemented and tested using Geosat data from the Wilkins Ice Shelf, to the west of Alexander Island, West Antarctica. The method used is presented here, with particular discussion of the suitability of the GIS as a platform for performing such corrections.
doi: 10.1080/01431169608948710pmid: N/A
Abstract Accurate information on the extent and characteristics of rainfed rice-growing environments is lacking because of the high degree of temporal variability and spatial heterogeneity of the ecosystem/subecosystem-differentiating parameters. Although the repeated classification and mapping capture the temporal variability, the information generated reflects post facto conditions and the same geographical area gets classified under different ecozones over years; hence, the delineated ecosystems/subecosystems seem to be transitional. This limits the utility of such an enormous effort. To overcome these problems, this study developed a remote sensing and GIS-based methodology which captures the temporal variability as well as spatial heterogeneity of the ecozone-differentiating parameters and their effect on vegetation (crop) conditions, and prepared criteria for accurate delineation and characterization of rainfed rice ecosystems/subecosystems. The results showed vegetation condition to be a comprehensive reflection of the crop-growing situations and the vegetation index as an excellent criterion for ecosystem/subecosystem classification when combined with the existing classification criteria. These methods have been successfully applied in pilot areas and show greater promise for applicability in similar situations, as well as in other environments with some modifications.
doi: 10.1080/01431169608948711pmid: N/A
Abstract Multi-temporal satellite data along with other collateral information have been used to assess and monitor the environmental changes experienced by the three severely degraded watersheds of the Chamoli district (Central Himalaya) after the 1970s Alaknanda flood. We find that the popular Chipko movement launched in 1973 by Chandi Prasad Bhatt helped significantly in regenerating the denuded forest cover in these watersheds. Monitoring studies carried out from 1972 to 1991 suggest that there is a steady growth of the forests on the denuded slopes which show a quantum jump from 1980 onwards. The 1991 IRS-LISS-II satellite data suggest that the forest loss of pre-1972 has nearly been regained indicating for the first time that a deforested area will require at least 20 years to rejuvenate in the Central Himalaya provided the area is protected from outside interference. There is a linear correlation with the magnitude of landslides and the forest cover. However, the stabilization process of active landslide zones seems to be quite slow due to the presence of sheared carbonate rocks and the proximity of the watersheds to the Main Central Thrust. There is a marginal 0-8 per cent improvement of the area prone to active landslides in the past two decades.
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