Ms. Elena-Corina Alecu Profile

Elena-Corina Alecu

Remote Sensing and GIS Specialist at Administratia Nationala de Meteorologie

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Publications (3)

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Proceedings Article | 24 October 2007 Paper

The georeferencing errors of satellite data in remote sensing applications

Corina Alecu, Nektarios Chrysoulakis, Simona Oancea, Gheorghe Stancalie

Proceedings Volume 6748, 67481Q (2007) https://doi.org/10.1117/12.747001

KEYWORDS: Error analysis, Computer simulations, Satellites, Remote sensing, Georeferencing, Sensors, Short wave infrared radiation, Distortion, Infrared imaging, Thermography

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The change detection and analysis, as remote sensing application, is based on the multi-temporal and/or multi-sensors approaches. However, the accuracy of such change detection activities can be limited by several factors. A key variable that may reduce the accuracy of change detection is the misregistration error between the used images. By accounting for the spatial variation in geometric and misregistration errors there is the potential to reduce their effects during change detection, increasing the accuracy of land cover change mapping. The effect of misregistration on land cover mapping and change detection could be more accurately predicted and ultimately removed if this spatial variation in error was modeled. In this study, the estimation of the effect of misregistration on ASTER derived land cover types was attempt. The proposed methodology is based on the comparison of the regression correlation coefficients between two images derived either from one single band or from two bands. To check the level of correlation, a procedure of modifying the geometric position of one single band or of two different bands, using the same resolution or different resolutions was applied. In order to obtain this artificial degradation, a transformation on three directions: on x axis, on y axis and on both x and y axis of one image comparing with itself or with another was applied. The study was performed for different scales, different land cover types and different complexity to evaluate the most influencing factors. This approach allowed quantification of the inappropriate image georeferencing, as well as the quantitative estimation of the size of distortion of the final results, in case of comparison of images from different dates and different sensors.

Proceedings Article | 17 January 2006 Paper

MODIS versus ASTER water classification

Corina Alecu, Simona Oancea, Emily Bryant

Proceedings Volume 6062, 606206 (2006) https://doi.org/10.1117/12.642865

KEYWORDS: MODIS, Satellites, Image classification, Image resolution, Earth observing sensors, Satellite imaging, Floods, Sensors, Data acquisition, Spatial resolution

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Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) are multi-spectral sensors embarked on the EOS AM-1 (TERRA) satellite platform. Both sensors opperate in different spectral bands, but also with different pixel resolutions. The overall goal of this paper is to classify MODIS data to get an estimation of water surface area, very useful in the post-crisis periods for the decision makers at all levels. To develop the classification technique, the strategy was to obtain MODIS and ASTER data acquired at the same time over the same location, and use the ASTER data as "ground truth". Two lakes in the Bihor County of Romania were chosen and satellite data from October 31, 2002 were utilized. From the ASTER data we created a detailed water mask to be used as ground truth for the MODIS water classification. The percent water image derived from ASTER was superimposed on the MODIS image. A supervised classification for water was performed on the 3-band MODIS image using the feature space algorithm. The water surface area as measured from the MODIS classification was about 16% more than the ASTER ground truth-value. Due to the constraint that high spatial resolution satellite images are low temporal resolution, there exists a need for a reliable method to obtain accurate information from medium resolution data. This approach provided useful information concerning the water classification from different resolution data that could help in the estimation of water surface area from MODIS imagery.

Proceedings Article | 31 October 2005 Paper

Multi-resolution analysis of MODIS and ASTER satellite data for water classification

Corina Alecu, Simona Oancea, Emily Bryant

Proceedings Volume 5983, 59831Z (2005) https://doi.org/10.1117/12.649848

KEYWORDS: MODIS, Satellites, Image classification, Earth observing sensors, Floods, Data acquisition, Spatial resolution, Image resolution, Satellite imaging, Temporal resolution

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In Romania there are many areas flooded every year. The estimation of the surfaces covered by water in the post-crisis periods is of real use for the decision makers at all levels. Due to the constraint that high spatial resolution satellite images are low temporal resolution, there exists a need for a reliable method to obtain accurate information from medium resolution data, for example, MODIS satellite images. The overall goal of this paper is to classify MODIS data to get an estimate of water surface area. To develop the classification technique, the strategy was to obtain MODIS and ASTER data acquired at the same time over the same location, and use the ASTER data as "ground truth". For this study, two lakes in the Bihor County of Romania were chosen and MODIS and ASTER data from October 31, 2002 were utilized. The ASTER data were used to create a detailed water mask to be used as ground truth for the MODIS water classification. The percent water image derived from ASTER was superimposed on the MODIS image. A supervised classification for water was performed on the 3-band MODIS image using the feature space algorithm. The water surface area as measured from the MODIS classification was about 16% more than the ASTER ground truth-value. This approach provided useful information concerning the water classification from different resolution data that could help in the estimation of water surface area from MODIS imagery.

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