Kazem Aliabadi; omid baghani
Abstract
This study aims to provide a computational-approximate algorithm based on Rationalized Haar (RH) to estimate the vegetation of the Landsat image using reflecting this phenomenon in the near-infrared band. This band is in the RGB color combination and located in the R section.This algorithm, using Digital ...
Read More
This study aims to provide a computational-approximate algorithm based on Rationalized Haar (RH) to estimate the vegetation of the Landsat image using reflecting this phenomenon in the near-infrared band. This band is in the RGB color combination and located in the R section.This algorithm, using Digital Number (DN) vegetation in 200 selected pixels of R band (infrared band) from the study area, tries to extract the features and vegetation of the whole study area. The number of selected pixels is distributed uniformly and only covers the vegetation.Due to using the matrix format in the input data, first vegetation reflection matrices for 4 and 8 wavelets are constructed using the assumed 200 pixels. Then, these matrices are extended to 16 and 64 parts respectively, through blocking the Landsat image of the region.Each matrix element represents the average vegetation of the area in its corresponding block. Then, by introducing an efficient mathematical equation, the vegetation of the entire study area is extracted. In addition, each pixel is reconstructed. Due to matrix calculations, speed and accuracy of calculations at the pixel scale will be listed as advantage of this approach.In this study, vegetation extraction with 4 and 8 RH Wavelets was performed with 75 and 87.5% accuracy, respectively. As the number of wavelets increases, the accuracy of the RH wavelet algorithm increases. However, rounding error and the increase in computational cost in high number of wavelet can be listed as disadvantage of this method. Such that, time and space memory will be increased exponentially. In remote sensing, extraction techniques such as classification have been proposed by remote sensing software. The accuracy of vegetation pixel extracted using this approach will be as advantage in comparison with those common methods. In processing and analytical techniques (for vegetation extraction and classification) in remote sensing, many pixels contain vegetation depicted as single or clustered (but in small numbers) while, in other classes such as barren or Urban land will be merged, which RH wavelet overcomes this shortcoming.
K Aliabadi; H Soltanifard
Volume 8, Issue 1 , November 2016, , Pages 95-108
Abstract
Knowledge of temporal and spatial distribution of LST to determine the amount of earth energy is much applicable for climatology studies, examination of vegetation and also determination of urban structure. With respect to deriving LST from the studied area and its relationship with urban structure and ...
Read More
Knowledge of temporal and spatial distribution of LST to determine the amount of earth energy is much applicable for climatology studies, examination of vegetation and also determination of urban structure. With respect to deriving LST from the studied area and its relationship with urban structure and vegetation, the present study illustrates that climate conditions specially wind, urban structure and vegetation are some of the effective factors on LST. According to the importance of heat islands at pixel scale in this study, and the ability of Newton Interpolation Polynomial in this respect, urban construction and vegetation are derived by the stated polynomial and their relationship with LST is examined and the areas concluding heat island are known. In this study, Newton Interpolation Polynomials have presented two equations of grade 7 by received DN from 200 points of image including vegetation and the areas with urban structure. The produced error rate from deriving vegetation by using Newton Interpolation Polynomial in 100 locations of the studied area and in urban construction are calculated as 10.1 and 12.02 respectively. It should be stated that no research with similar method has been done yet. The use of mathematical techniques in remote sensing and the amount of accuracy and ability of them are considered some of the main purposes in this research
