جستجوی مقالات مرتبط با کلیدواژه « land cover » در نشریات گروه « برق »

The accuracy of retrieved LST from satellites is of great importance. Among different LST validation methods, a cross-calibration procedure is highly cost-effective and applicable. The Indian National Satellite-3D series (INSAT-3D) and Meteosat Second Generation (MSG) are two geostationary satellites that which provide LST products with high temporal resolution. Considering MODIS as the reference (polar orbit that is onboard Aqua and Terra satellites), the comparison of the LST products of these geostationary satellites was evaluated from 4th March to 1st September 2015. For this purpose mean LST ratios were calculated for both MODIS-Imager (from INSAT-D) and MODIS-SEVIRI. Then the behavior of their mean LST ratio was analyzed for the exciting four major land covers and five elevation classes in the study area. The results showed that Imager data underestimated and overestimated the LST in comparison to MODIS data during the day and night time respectively. The SEVIRI LSTs underestimated the LST in both day and night time in comparison with MODIS products. In order to model the discrepancies between MODIS-Imager and MODIS-SEVIRI, for each land cover a multilinear regression model was fitted based on slope, aspect, azimuth, and View Zenith Angle (VZA). The results showed that barren, Shrub, grass, and cereal crops had low RMSEs in model fitting, respectively.

The accuracy of retrieved LST from satellites is of great importance. Among different LST validation methods, a cross-calibration procedure is highly cost-effective and applicable. The Indian National Satellite-3D series (INSAT-3D) and Meteosat Second Generation (MSG) are two geostationary satellites that which provide LST products with high temporal resolution. Considering MODIS as the reference (polar orbit that is onboard Aqua and Terra satellites), the comparison of the LST products of these geostationary satellites was evaluated from 4th March to 1st September 2015. For this purpose mean LST ratios were calculated for both MODIS-Imager (from INSAT-D) and MODIS-SEVIRI. Then the behavior of their mean LST ratio was analyzed for the exciting four major land covers and five elevation classes in the study area. The results showed that Imager data underestimated and overestimated the LST in comparison to MODIS data during the day and night time respectively. The SEVIRI LSTs underestimated the LST in both day and night time in comparison with MODIS products. In order to model the discrepancies between MODIS-Imager and MODIS-SEVIRI, for each land cover a multilinear regression model was fitted based on slope, aspect, azimuth, and View Zenith Angle (VZA). The results showed that barren, Shrub, grass, and cereal crops had low RMSEs in model fitting, respectively.

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to remote sensing imagery (MODIS & OLI images), and the resultant maps provided an accurate and improved representation of the land covers. Low RMSE, high accuracy. By using a Hopfield neural network, more accurate measures of land cover targets can be obtained, The Hopfield neural network used in this way represents a simple, robust, and efficient technique, and results suggest that it is a useful tool for identifying land cover targets from remotely sensed imagery at the sub-pixel scale. The present research purpose was evaluation of HNN algorithm efficiency for different land covers (Land, Water, Agriculture land and Vegetation) through Area Error Proportion, RMSE and Correlation coefficient parameters on MODIS & OLI images and related ranking, results of present super resolution algorithm has shown that according to precedence, most improvement in feature’s recognition happened for Water, Land, Agriculture land and ad last Vegetation with RMSEs 0.044, 0.072, 0.1 and 0.108.