Abstract
Abstract. Quality of life in urban environments is closely related to vegetation cover. The Urban growth and its related environmental problems, planners are forced to implement policies to improve the quality of urban environment. Thus, vegetation mapping for planning and managing urban is critical. Given the spectral complexity of the urban environment and the sparse vegetation in these areas, to generate a reliable map of coverage Vegetation in these areas requires the use of high spatial resolution images. But given the size of cities and the rapid changes in vegetation status, Mapping of vegetation using these images will have cost much. In this study, using a moderate spatial resolution image with the help of a small part of high spatial resolution image vegetation cover in a Metropolitan area is obtained. We make use of Ikonos image to get Fractional vegetation cover (FVC) and used as a vicarious validation of FVC. Then using linear and nonlinear regression and neural network between the FVC derived from the Ikonos image and vegetation indices on Landsat image, the relationship was established. A number of pixels were randomly selected from the images for the model validation. The results show that the neural network, nonlinear regression and linear regression models are more accurate for the estimation of FVC respectively.
Highlights
Fractional vegetation cover (FVC) is generally defined as the ratio of the vertical projection area of vegetation on the ground to the total vegetation area
In this study we derive moderate resolution estimates of aggregate vegetation fraction from Landsat imagery and quantify the correspondence between these estimates and integrated vegetation fraction measurements derived from high resolution Ikonos imagery (Figure 1.). cloud-free Landsat ETM+ image (Path 163, Row 39) acquired on January 19, 2001 and a Ikonos image of the area with a bit of time difference it was prepared
Several regression methods can be used to determine the relationship between the FVC and the normalized difference vegetation index (NDVI) value
Summary
FVC is generally defined as the ratio of the vertical projection area of vegetation (including leaves, stalks, and branches) on the ground to the total vegetation area. In order to get regional-scale estimation of vegetation fraction, we can utilize remote sensing data. Xiao and Moody, through the linear regression of 60 points selected from a Landsat ETM NDVI image and FVC (considered as the actual surface FVC), extracted a highresolution (0.3 m) true-color orthoimage and found a strong linear relationship between NDVI and FVC They applied this formula to estimate the FVC of all of the pixels in the Landsat ETM image (Xiao and Moody, 2005). The results indicated that NDVI is the most commonly applied method, it did not have the strongest correlation with the FVC of trees (Choudhury et al, 1994). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013 SMPR 2013, 5 – 8 October 2013, Tehran, Iran a b
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