During the northern burning season, biomass burning is found north of the equator, while satellite estimates from the residual-type method such as the CCD method show higher ozone south of the equator. This discrepancy is called the tropical Atlantic paradox (Thompson et ai., 2000). We use satellite and ground-based measurements to investigate the paradox. When the background tropospheric ozone over the Pacific Ocean from TOMS measurements is subtracted from the latitudinal total ozone distribution (e.g. TOMS-Pacific method), the results show remarkable agreement with the latitudinal stratospheric ozone distribution using the CCD method. The latitudinal tropospheric ozone distribution using the CCD method, with a persistent maximum over the southern tropical Atlantic, is also seen in the latitudinal tropospheric ozone distribution using the TOMS-Pacific method. It suggests that the complicated CCD method can be replaced by the simple TOMS-Pacific method. However, the tropical Atlantic paradox exists in the results of both the CCD and TOMS-Pacific methods during the northern buming season. In order to investigate this paradox, we compare the latitudinal ozone distributions using the CCD and TOMS-Pacific methods by using the SAGE measurements (e.g. TOMS-SAGE method) and the SHADOZ ozonesoundings (e.g. TOMS-Sonde method) assuming zonally invariant stratospheric ozone, which is the same assumption as of the CCD method. During the northern burning season, the latitudinal distributions in the tropospheric ozone derived from the TOMS-SAGE and TOMS-Sonde methods show higher tropospheric ozone over the northern tropical Atlantic than the southern Atlantic due to a stronger gradient in stratospheric ozone relative to that from the CCD and TOMS-Pacific methods. This indicates that the latitudinal tropospheric ozone distribution can be changed depending on the data that is used to determine the latitudinal stratospheric ozone distribution. Therefore, there is a possibility that the north-south gradient in stratospheric ozone over the Atlantic can be a solution of the paradox.

Coastal sea surface temperature (CSST) and meteorological data from January through December 1995 are used to estimate the net surface heat flux and heat content for Sendai Bay. The average annual surface heat flux in the area north of the bay is estimated to be $+35Wm^{-2}$, whereas the southwestern area is estimated to be $+56Wm^{-2}$. Therefore, the net surface heat flux shows a net gain of heat over the whole bay. The largest heat gain occurs near Matsukawaura, where the strong Kuroshio/Oyashio interaction produces anomalously cold SST and wind is more moderate than in other regions of Sendai Bay over most of the year. The lowest heat gain occurs around Tashiro Island, where the temperature difference between air and sea surface is lower and wind is stronger. The heat budget shows that both surface forcing and horizontal advection are potentially important contributors to the seasonal evolution of CSST in the bay. From the A VHRR and SeaWiFS data, it is found that offshore conditions between the bay and Eno Island are different due to the presence of the Ojika Peninsula. It is also shown that the temporal behaviors of SSTs in the bay are closely connected with the air-sea heat flux and offshore conditions.

Synthetic aperture radar (SAR) from satellites provides the opportunity to regularly incorporate microwave information into forest classification. Radar backscatter can improve classification accuracy, and SAR interferometry could provide improved thematic information through the use of coherence. This research examined the potential of using multi-temporal JERS-l SAR (L band) backscatter information and interferometry in distinguishing forest classes of mountainous areas in the Northeastern U.S. for future forest mapping and monitoring. Raw image data from a pair of images were processed to produce coherence and backscatter data. To improve the geometric characteristics of both the coherence and the backscatter images, this study used the interferometric techniques. It was necessary to radiometrically correct radar backscatter to account for the effect of topography. This study developed a simplified method of radiometric correction for SAR imagery over the hilly terrain, and compared the forest-type discriminatory powers of the radar backscatter, the multi-temporal backscatter, the coherence, and the backscatter combined with the coherence. Statistical analysis showed that the method of radiometric correction has a substantial potential in separating forest types, and the coherence produced from an interferometric pair of images also showed a potential for distinguishing forest classes even though heavily forested conditions and long time separation of the images had limitations in the ability to get a high quality coherence. The method of combining the backscatter images from two different dates and the coherence in a multivariate approach in identifying forest types showed some potential. However, multi-temporal analysis of the backscatter was inconclusive because leaves were not the primary scatterers of a forest canopy at the L-band wavelengths. Further research in forest classification is suggested using diverse band width SAR imagery and fusing with other imagery source.

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

The lifting scheme has been found to be a flexible method for constructing scalar wavelets with desirable properties. In this paper, it is extended to the UDAR data compression. A newly developed data compression approach to approximate the UDAR surface with a series of non-overlapping triangles has been presented. Generally a Triangulated Irregular Networks (TIN) are the most common form of digital surface model that consists of elevation values with x, y coordinates that make up triangles. But over the years the TIN data representation has become an important research topic for many researchers due its large data size. Compression of TIN is needed for efficient management of large data and good surface visualization. This approach covers following steps: First, by using a Delaunay triangulation, an efficient algorithm is developed to generate TIN, which forms the terrain from an arbitrary set of data. A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub-triangles and the elevation step has been used to 'modify' the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets. The quality of geographical surface representation after using proposed technique is compared with the original UDAR data. The results show that this method can be used for significant reduction of data set.

Currently, in order to achieve accurate geolocation of satellite images we need to generate control points from individual scenes. This requirement increases the cost and processing time of satellite mapping greatly. In this paper we investigate the feasibility of modeling entire image strips that has been acquired from the same orbital segments. We tested sensor models based on satellite orbit and attitude with different sets of unknowns. We checked the accuracy of orbit modeling by establishing sensor models of one scene using control points extracted from the scene and by applying the models to adjacent scenes within the same orbital segments. Results indicated that modeling of individual scenes with $2^{nd}$ order unknowns was recommended. In this case, unknown parameters were position biases, drifts, accelerations and attitude biases. Results also indicated that modeling of orbital segments with zero-degree unknowns was recommended. In this case, unknown parameters were attitude biases.

This paper has dealt with a data fusion for the problem of land-cover classification using multisensor imagery. Dempster-Shafer evidence theory has been employed to combine the information extracted from the multiple data of same site. The Dempster-Shafer's approach has two important advantages for remote sensing application: one is that it enables to consider a compound class which consists of several land-cover types and the other is that the incompleteness of each sensor data due to cloud-cover can be modeled for the fusion process. The image classification based on the Dempster-Shafer theory usually assumes that each sensor is represented by a single channel. The evidential approach to image classification, which utilizes a mass function obtained under the assumption of class-independent beta distribution, has been discussed for the multiple sets of mutichannel data acquired from different sensors. The proposed method has applied to the KOMPSAT-1 EOC panchromatic imagery and LANDSAT ETM+ data, which were acquired over Yongin/Nuengpyung area of Korean peninsula. The experiment has shown that it is greatly effective on the applications in which it is hard to find homogeneous regions represented by a single land-cover type in training process.