Nahid Haghshenas; Ali Shamsoddini
Abstract
Normally, images with a high resolution (temporal or spatial) are available, while there is a limitation in accessing images which are simultaneously high spatial and temporal resolution. While, in some applications, access to images with high spatial and temporal resolution is necessary. Therefore, ...
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Normally, images with a high resolution (temporal or spatial) are available, while there is a limitation in accessing images which are simultaneously high spatial and temporal resolution. While, in some applications, access to images with high spatial and temporal resolution is necessary. Therefore, this study was conducted to downscaling MODIS images to Sentinel- 2 spatial resolution by STARFM, ESTARFM and FSDAF spatio-temporal downscaling algorithms in different land cover classes including urban, garden, pasture, agricultural and water classes. The study area was selected with a variety of land covers around the city of Mahabad, Iran. First, the corresponding visible and near-infrared bands in Sentinel- 2 and MODIS were selected and necessary pre-processes such as geometric correction were done on these images. Then, Sentinel- 2 images were simulated using downscaling algorithms. The results indicated the accuracy of downscaling in the urban, garden and pasture classes compared to the agricultural and water classes. So that the ESTARFM, FSDAF and STARFM algorithms averagely showed the coefficient of determination of 88.25, 87.25 and 86.5 for the urban class, the coefficient of determination of 83.75, 83.25 and 80.5 for the garden class and the coefficient of determination of 90.75, 70.5 and 87.5 for the pasture class in all bands
Nahid Haghshenas; Ali shamsoddini; Hossein Aghighi
Abstract
It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted ...
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It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted on the images, digitally. There are several parameters which must be optimized prior to use of object oriented classification. One of these parameters is Scale affecting the segmentation results, significantly. Scale is usually set by trial and error which is an experimental approach. One of the aims of this study is to optimize Scale parameter, automatically. In addition, after segmentation process based on a proper Scale, it is required to classify the identified segments based on the attributes which are extracted from these segments. In this stage, the selection of suitable classification method fed by the proper attributes is critical. In this research, LiDAR data and aerial image acquired on Vaihingen, Germany, were utilized for segmenting the urban area. In order to identify suitable attributes, random forest feature selection was applied on the attributes derived from the identified segments. Machine learning methods including support vector machine, random forest, and decision tree were compared for classifying the segments based on their suitable attributes into two classes including tree canopy cover and others. The results indicated that Scale of 25 is the best one to segment this area. Also, the tree canopy cover map derived from support vector machine with quality index of 79.90 showed the best performance among different classifiers used in this study.
