alireza bazrgar; morteza tayebi
Abstract
Land surface temperature (LST) monitoring has been widely used as one of the most important environmental parameters by the high temporal resolution sensors such as the MODIS sensor (daily temporal resolution capability and spatial resolution of one kilometer). One of the main problems of these sensors ...
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Land surface temperature (LST) monitoring has been widely used as one of the most important environmental parameters by the high temporal resolution sensors such as the MODIS sensor (daily temporal resolution capability and spatial resolution of one kilometer). One of the main problems of these sensors is their low spatial resolution, which limits the performance of these sensors for applications such as fire detection in forest areas and the study of urban thermal islands. In contrast, high spatial resolution sensors such as the ASTER sensor (90 meter spatial resolution and 16-day temporal resolution at the land surface temperature product), they have low temporal resolution, which results in application such as rapid change monitoring. In fact, due to technical limitations, there is no sensor that has a high resolution in spatial and temporal dimensions. To solve this problem, low-cost and efficient spatial-temporal fusion methods have been developed. The most important methods for fusion spatial-temporal methods are enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Spatial and Temporal Data Fusion Approach (STDFA). This work uses the ESTARFM and STDFA algorithms and a new method (SWT-STDFA) based on the STDFA method and the two-dimensional stationary wavelet transformation to fuse LST data spatially and temporally. The LST products of ASTER and MODIS sensors were fused for a part of Tehran city and finally, a virtual image was obtained with a spatial resolution equal to that of the ASTER sensor and a temporal resolution equal to that of the MODIS sensor. Also, based on the existence of a classification map prepared on the basis of normalized vegetation difference index (NDVI) in STDFA and SWT-STDFA algorithms, the effect of using normalized Green Difference Vegetation Indices (GNDVI) and soil adjusted vegetation Index (SAVI) on the accuracy of the synthetic image of the output is discussed. The results of the research indicate the high accuracy of the proposed method with the root mean square error of 3.03 Kelvin, standard deviation of 2. 21 Kelvin, mean absolute difference 1.72 Kelvin and correlation coefficient of 0.92 between the image of the actual land surface temperature and the predicted synthetic image Compared to the other two methods. Also, the increase of vegetation’s indices GNDVI and SAVI in the classification of both STDFA and SWT-STDFA methods did not have much effect on the accuracy of the synthetic image of the output.
