Analysis of relationship between geomorphology and vegetation with emphasis on soil coefficient

  Over the past few decades, various vegetation indices derived from the reflection of various satellite wavelengths (generally a combination of near infrared and infrared bands) have been used to estimate biophysical characteristics of vegetation such as leaf area index (LAI), biomass, Plant growth and percentage of coverage, each of which, depending on the conditions in the study area, has shown good results (Qi et al.,1994; Rondeaux et al., 1996; Huete et al., 1997; Rouse et al., 1974) Generally, vegetation density is affected by a variety of environmental conditions such as climate, soil, geology and geomorphology (Abbate et al., 2006)

Determination of geomorphologic units, types and facies Providing vegetation map To prepare a vegetation map, Landsat 8 satellite imagery was prepared from Google Earth in 2017 and previewed images containing geometric and radiometric corrections. The NDVI index was extracted from 4 and 5 bands of Landsat 8 satellite images of Zilberchay watershed and classified into 12 classes. In the next step, the canopy measurements in the studied area were carried out within a representative area along the transect line. The purpose of vegetation is the shading level of any one. For this purpose, 1 × 1 m plot was used for vegetation diversity and vegetation form.


Calculation of soil gradient in each geomorphologic unit
In this study, in order to calculate the soil line equation, the geomorphic units were matched with satellite imagery. In each geomorphologic unit 60 pixels and in the amount of 720 pixels of soil were extracted using the position of geomorphic units and by plotting the reflection values of these pixels in the range The red and infrared bands near the soil line coefficients were calculated for each unit of geomorphology.

Calculation of correlation coefficient
To study the type and severity of relationships between geomorphic units and vegetation of the region, as well as the slope of the correlation coefficient line between them. Pearson correlation coefficient was calculated between vegetation values and gradient of soil line and geomorphic units (after encoding them).

Since there is a reverse relationship between vegetation and slope of the soil, geomorphology of Zilberchay watershed has a positive correlation with the vegetation cover and showed a negative correlation with the gradient of the soil coefficient. After calculating the correlation coefficients for each geomorphology unit, Qt had the highest negative correlation with the slope of the soil line to -0.988 and positive correlation with vegetation was 0.18 and the mic unit with soil gradient correlation There was no significant difference in the level of 0.01, while the vegetation showed positive correlation of 0.39. Also, the hio unit with the gradient of soil and vegetation cover was Pearson correlation coefficient of -0.45 and 0.62, respectively. The hio unit has more levels of rocky and vegetation-free extinction and a weaker correlation with other units.

Due to changes in soil characteristics, vegetation cover vegetation indices, which are presented in remote sensing sciences, often have errors. According to studies conducted by some researchers, the NDVI index can not quite accurately indicate the percentage of vegetation in arid regions, and the indicators that consider soil reflection can more accurately determine the percentage of vegetation in the study estimate (Darvishade et al., 2008). The findings of the research also show that each of the geomorphologic facies that have better vegetation cover have a more negative correlation with the soil line coefficient. Regular domain facies, in comparison with irregular domain facies that are better off of vegetation, have the same negative correlation with soil line factor. The soil factor coefficient is a Therefore, for the assessment of vegetation using remote sensing, it is better to use the modified indicators that have been applied to the land line, such as MSAVI, MCARI2, MTVI2 (Alavi Panah, 1390).for reducing the effects of spectral properties of soil on spectral reflections of the crown