GIS and satellite images have widely been used in natural resources assessment and management. Vegetation cover can be measured using remote sensing indices. The objective of present study was to investigate the application of AWiFS satellite images to measure vegetation cover percentage in Semirom- Isfahan province. Vegetation cover percentage was measured in 150 plots with size of 100 m2 each in 2009. Linear regression between field data and remote sensing vegetation indices was used to produce vegetation cover in the study area. Vegetation cover models were produced using different indices. According to the results, SAVI index had the highest correlation with field data (r = 0.74) and it was used to produce vegetation cover percentage map. Study area vegetation cover was classified in four classes: <20%, 20-30%, 30-40% and >40% using SAVI index. The produced vegetation map indicates that most of the region had low vegetation cover (<20% and 20-30%). The correlations of other measured vegetation indices including NDVI, PVI, RVI, TSAVI and MSAVI with vegetation cover percentage were 0.64, 0.69, 0.71, 0.72 and 0.70 respectively.

The presence of dry and green vegetation in pixels containing spectral information is essential in geological and mineralogical studies. Thus, retrieving sub-pixel information, including estimation of a mineral’s quantity in a single hyperspectral RS image pixel is very important. In this study, the vegetation corrected continuum depth (VCCD) method was trained and its results were validated using spectrometry, laboratory mineralogy, and Hyperion image to reduce the effect of vegetation on the estimation of minerals. The study was conducted in Oghlansar region located in northwestern Iran. SAVI and absorption depth (2102 μm) were used for the estimation of the green and dry vegetation, respectively. Meanwhile, the trained models do not have a high sensitivity to the presence of noise in the spectrum and vegetation type changes. The correction of continuum removed band depth (CRBD) analysis was possible up to 60% for maximum green vegetation cover threshold, 56-60% for dry vegetation, and 72-76% for both dry and green vegetation. Effect of noise and different vegetation types on model capability was examined and the result shows that VCCD is not highly sensitive to random noise and changes in vegetation types. After correction of the coefficients and confirmation of its efficiency, the model was used to correct CRBD and reduce the effect of vegetation on Hyperion image. In the estimation of kaolinite and muscovite, the presence of green and dry vegetation led to the underestimation of the minerals present in the study area. The results showed that VCCD was able to increase the prediction accuracy (R2) by 0.25 and 0.13 and reduce RMSE by 0.0108 and 0.125 for kaolinite and muscovite, respectively.

The present study discusses a method used to produce updated information about vegetation cover in arid and semi-arid zones, using RS data and GIS technique. In this method, Landsat ETM+ data in 2002 was collected in an area of about 60000 ha in Nodoushan basin, Yazd, Iran. To collect the necessary ground data, 50 sites of different vegetation types were selected and the percentage of vegetation cover in each one was determined. Also, different vegetation and soil indices were derived and crossed with located sampling points using ILWIS software capabilities. To get the best fitted curve, the relationship between vegetation cover, as a dependent variable, and satellite data bands, vegetation indices and environmental factors, as independent variables were assessed. Therefore, a multiple linear regression model was established for the prediction of vegetation cover percentage in the studied area. Finally, a vegetation cover map with high a precision was produced. As a conclusion, it can be said that mapping of vegetation cover via remote sensing is possible even if its vegetation cover is sparse.

Given the importance of the geometry of hillslopes in producing runoff and sediment and the influence of soil surface cover on these two components, the present study was conducted to investigate the effect of Vetiver vegetation cover and surface rock fragment cover on a convex-parallel hillslope. After preparing the plot based on the geometry of the convex-parallel hillslope and transferring Vetiver vegetation cover and surface rock fragment cover with 30% coverage, rainfall simulation was performed, and the results showed that in this type of hillslope, surface rock fragment cover reduced the average volume of runoff by 13.6% compared to the control and reduced soil loss by 33.37%. In addition, Vetiver vegetation cover led to a 65.76% reduction in runoff volume and a 69.4% reduction in soil loss. Furthermore, the statistical results showed that the volume of runoff and soil loss in the convex-parallel hillslope in protective treatments were significantly different from the control (Pvalue <0.05), indicating that the addition of surface rock fragment cover and Vetiver vegetation cover reduces the volume and soil loss. These results demonstrate the positive effect of both covers on reducing runoff and sediment. Therefore, depending on the goal and the conditions of the study area, appropriate cover can be used.

IntroductionRangelands encompass about 55% of Iran's total area. These lands are the habitat of Iranian nomads and their animals. The extension, variations in climate, soil and vegetation cover require specific management. Having knowledge about the vegetation parameters, especially the coverage, is highly significant for efficient utilization and erosion control of the rangelands. Vegetation cover is defined as the percentage of ground covered by live vegetation within a given reference area such as a plot. Normally vegetation cover is measured using a reference frame, a ring, a square or a circle. The reference frame allows the researcher to focus on a small area (depending on the size of the frame) and estimate vegetation cover versus bare rangeland areas. Point-intercept and line-intercept methods were suggested by some researchers. Vegetation cover is an indicator for some environmental indices. For instance, Amaral, et al. (2007) applied normalized difference vegetation index (NDVI) along with climatic and topographical factors for the probability distribution of five plant species. They reported that the application of NDVI had increased the efficiency of models. Firmino, et al. (2009) studied the relationship between NDVI and precipitation. They concluded that NDVI is a spatial function of precipitation. Using remote sensing techniques for the monitoring of vegetation cover in rangelands of Iran is inevitable due to the extensive and dynamic behavior of these land covers. Normally, using remote sensing for vegetation monitoring corresponds to the application of vegetation indices, which are suggested to minimize the external impacts on spectral data and determining vegetation cover and leaf area index. Taylor et al. (1985) reported the existence of significant correlation coefficients between biomass and grass-prosopis cover, and NDVI and ratio vegetation index (RVI), calculated from NOAA-7 data. Rondeaux, et al. (1996) compared the correlation coefficients of six vegetation indices of SAVI, OSAVI, TSAVI, GEMI, NDVI and MSAVI with vegetation cover and reported that MSAVI is the most sensitive index to vegetation cover. Zhaa, et al. (2003) studied the vegetation cover of grasslands in Western China using 68 plots, and reported that NDVI had the highest correlation coefficient with vegetation cover percentage. Madoganda et al. (2008) assessed biomass and leaf area index of deciduous forests of Westchatz in India, by using IRS-P6 LISS-IV. They reported existence of positive correlation coefficients between satellite data and leaf area index and biomass. Tang e Sayyad National Park is the habitat of precious wild life in the semi arid zone of Charmahal-o-Bakhtiari Province. Assessing the vegetation cover of this national park contributes to management, indirect estimation of animal capacity, and soil conservation of the park. This study was carried out at the early season of drought of 2008.

Vegetation is considered as an important measuring indicator in rangelands, which play a significant role in ecological processes. In this regard and due to the lack of models in estimating green vegetation canopy, this research aimed to evaluate temporal changes of canopy cover of vegetation in Marjan rangelands of Broujen. As the completion of the past investigated studies in other areas, it is tried to present the growth stages and time effect on destroyed green canopy cover through a model which has an appropriate estimation power throughout the growing season and for a time such that the education sample do not exist. To this end, the canopy of green vegetation was measured in 19 sampling points over a length of 10 km transects. In each of the points, there are about five quadrats as one quadrat in center and four quadrats at the four directions around the central quadrat (a total of 95 quadrats in a period). Sampling points were placed with distance of 400-1000 meter apart from each other. Measurements were repeated during 4 periods of field operations (in total 380 quadrats). Thus regarding to the index type (Atmospheric Resistant Vegetation Index and the moderating effect of soil line) 12 vegetation indices due to the use of multi-temporal images and the effect of soil reflectance were calculated in this arid region. In the next stage, the model of estimating green plant canopy was prepared using the regression equations between obtained canopy cover from five sampling periods and derived vegetation indices of Landsat 8 image data. Thus this model was also generated to prepare the vegetation cover map to other 4 periods which have not education data. Finally canopy cover map of vegetation was prepared at the 8 periods during the growth season (April to mid-September). Using image differencing technique, the changes of the vegetation coverage of area was estimated. Validation of cover estimating model represents no significant difference between estimated cover and land cover data. It is concluded that achieving to the growth model which can be used for all seasons are possible by using Landsat 8 images. The results showed that the indicators of ARVI, SARVI and EVI (with coefficients of determination about 8.0) have the best correlation relationship with canopy cover. So not only there is a significant relationship between vegetation indices and vegetation in arid areas, also it is possible to prepare only a unique data model for estimating all different time of growing seasons. According to the results of this research, the vegetation is in its peak on May among the eight growth times in the Mrjan rangelands and the canopy cover has heterogeneous distribution. In terms of coverage changes during the growing season, most of area is categorized in small variations class. In the period from April to mid-May we are witnessed for increased class in the region, but after this period, reduction in area were the dominant phenomena, as well as amplification of increased class area from 3 to 19 September is related to the emergence of new autumn species in the highlands region.

There are very factors that effect on plant species distribution and vegetation cover in dry lands. Geomorphology effect on vegetation is one of the most prominent factors on plant survival and establishment in arid lands. In this study we surveyed relationship between vegetation cover percentage and geomorphology units. We measured weathering degree and amount of vegetation cover percentage, type and life forms in different geomorphology units, slope degrees and aspects on different lithology. Our results show that there are significant differences between vegetation cover percentage in different geomorphology units, aspects and slopes. Also tree species only there are on limestone lithology geomorphology units. It is clearly that different lithology and geomorphology units have different properties in regard to soil formation, therefore there are different plant communities in these units. It was concluded that the first prominent factor is lithology and second prominent factor is geomorphology unit on plant cover percentage in this study area.

Land Surface Temperature (LST), a significant variable of micro climate and radiation transfer within the atmosphere, is one of the most important criteria in zonal and regional planning because it is a major factor in controlling the Earth’s biological, chemical and physical processes. Natural and man-made activities, especially land use and land cover, by changing the physical and biological conditions of a region are an important parameter in the amount of land surface temperature.
Material and In this study, the relationship between land surface temperature and vegetation cover associated with land use and the land cover patterns of Dena County in 2016 were investigated using a Single Window algorithm and Landsat-8 data. The split-window algorithm is a dynamic mathematical tool which estimates land surface temperature (LST) using ground information, brightness temperature of thermal bands of the TIRS sensor, the land surface emissivity (LSE) factor and fractional vegetation cover (FVC) obtained from a multiband OLI sensor.
Based on classification of images of the Landsat-8OLI sensor in 2016 with an accuracy of about 80% and the kappa coefficient 0.90, rangeland and residential areas with 50.67 and 0.3 percent, respectively, were allocated the highest and the lowest areas of Dena county. The mean of land surface temperature in Dena County is about 32 ° C and the mean of the land cover index is about 0.14. In analyzing the relationship between LST and the vegetation index (NDVI) in Dena County and in each category of land use and land cover, results showed a different trend so that there is a positive and significant relationship between NDVI and LST in the whole of Dena County and rangeland in the event that there is no significant relationship in other land uses such as forest, farm and garden and residential area.
Various factors affect the type and shape of the relationship between NDVI and LST such as land use and land cover, vegetation cover, season, time of day, type of ecosystem, latitude and factors in triggering the growth of vegetation such as water and solar energy. The main cause of the ineffectiveness of vegetation cover in reducing the land surface temperature of Dena County is the lack of a sufficient amount of vegetation cover. However, the determining factor of temperature in Dena County is not increases or decreases in vegetative cover but is rather a change in the height above sea level. In other words, the effect of altitude on temperature is more important than the effects of vegetation on the Earth's surface temperature. At the lower altitude of Dena County where the temperature is relatively high and there is enough vegetation to grow, the vegetation cover is denser and more abundant and therefore there is a positive relationship between land surface temperature (LST) and vegetation cover index (NDVI).

Vegetation can be divided into two groups in terms of height and water depth as the submerged vegetation and the emergent vegetation. This paper investigates the emergent vegetation, using reedy rods. The presence of vegetation cover in rivers and open channels calls for better understanding the effect of this significant factor in application of the boundary-layer laws, including the logarithmic law and the Coles’ law. The results show that the logarithmic law can be applied in the presence of vegetation cover with different densities as well as the gravel-bed streams. However, this law deviates in the outer layer where the empirical Coles’ law is not valid in the presence of vegetation cover. The more vegetation density, the more deviation of data from the Coles’ law. The reason for invalidity of Coles’ law in the outer layer is due to irregular velocity distribution and turbulence intensity in the presence of vegetation, leading to augmentation of the deviation with vegetation density.

Vegetation cover is one of the main indexes in environmental ecological life that is directly in relation with region humidity conditions. Comparing vegetation cover between two periods with different humidity conditions in a region can show the rate of stress nicely in a drought period on the green vegetation cover. The aim of this study was investigating the changing in birjand plain vegetation cover during the recent drought. This study was done whit the help of GIS and remote sensing technology and SPI drought index. For doing this research, these data were used: percentage vegetation cover data that obtained from the field visiting, rainfall data, satellite images from Landsat TM 1998 And 2010. All of the satellite images preprocessing and processing, making vegetation index and change detection procedure were done in Erdas 9.2 and Idrisi Kilimanjaro 14 software and for establish regression relation between vegetation cover percentage and vegetation index, the Spss software was used. In among of vegetation index, msavi2 showed the highest relation that this index was used to make ready vegetation cover percentage map for the years which these studies were going on. This with post classification changes detection procedure, the changes that occurred in vegetation cover percentage in the region were detected the result showed vegetation cover severely affected from drought. So that in occurrences of drought from 1998 until 2010, we observed decreasing in vegetation cover percentage on %29 area of the region.