Showing 8 results for Vegetation Index
N. Zahedifard, S. J. Khajeddin, A. Jalalian,
Volume 8, Issue 2 (7-2004)
Abstract
Satellite data use is finding global applications because they provide repeated cover, broad information, high electromagnetic spectral resolution, and software-hardware compatibilities. This study aims to evaluate of the Landsat TM data capabilities in land-use mapping of Bazoft River basin (Chahar Mahale Bakhtiary Province). Six spectral bands of the Landsate TM were employed to produce land-use map of the Region. The date of image acquisition was May 5th, 1998. Performance of the geometric correction completed with RMSE= 1.008 pixels. Various image enhacement methods (e.g. FCC, filtering and Vegetation Indices) were used to study the different land-covers. Field investigations were carried out using a GPS, 1:50000 scale topographic map and false color composites images. Heterogeneous land-use units were studied in 62 sample sites estimating percentage of vegetation cover. A regression analysis was performed between percentage vegetation covers and vegetation indices values of NDVI, RVI, SAVI, DVI, TSAVI1, NRVI and MSAVI2. Results show that NDVI, SAVI, TSAVI1, NRVI and MSAVI2 have high correlation coefficients. But RVI, DVI and PVI have low correlation coefficients. The resulting values of vegetation cover were density sliced to produce the land-cover map. After supervised classifications and density slicing of Vegetation Indices, classifacation accuracy was assessed and, finally, land-use map of the study area was produced with Hybrid classification method. Supervised classification with maximum likelihood method was the best technique for land-use mapping in the study area the total Kappa index was %87. In general, detection of some land-use classes through single date TM data is not feasible, these include: scattered forest trees with cultivated understory, annual grasses, and fallow lands. Also TM digital data are incapable of distinguishing small and separated rural constructions or soil-covered routes.
K. Solaimani, R. Tamartash, F. Alavi, S. Lotfi,
Volume 11, Issue 40 (7-2007)
Abstract
In order to manage the rangeland resources, remote sensing data is able to provide a sensible role of different cases in flora community such as biomass. The study area in SefidAb subbasin of the Lar Dam basin is located in central Alborz, where the climatic condition is semihumid and near to moderate. For the assessment of the sattelite data and their capability in estimation of the range production, Landsat-TM data with different bands was used. In this research, the field data was collected using random-systematic method in 20 sampling units of 200 plots. For geographic coordinates of the sampling units and related pixels in digital data, GPS and also existing benchmark data of the nearest points were used. Then correlation between ground data and vegetation index from different band combination was investigated and the reasonble vegetation indices were obtained. Finally, the best models were extracted for this purpose, which showed sensible relation between the field data and vegetation index. Therefor, it is possible to estimate range production using Landsat TM data related to ground control.
L. Parviz , M. Kholghi, Kh. Valizadeh,
Volume 15, Issue 56 (7-2011)
Abstract
The determination of air temperature is important in the energy balance calculation, hydrology and meteorological studies. In this regard, the limited number of meteorological stations is one of the serious problems for air temperature determination on a large spatial scale. The remote sensing technique by covering large areas and using updated satellite images might be appropriate for estimation of this parameter. In this research, the negative correlation between land surface temperature and vegetation index (NDVI) has been used for air temperature estimation through TVX method in which the inference of air temperature is based on the hypothesis that the temperature of the dense vegetation canopy is close to air temperature. For investigation the performance of TVX method, images of MODIS sensor have been applied for the Sefidrod River basin in the years 1381- 1382-1384. The spilt window technique which was developed by Price has been used for land surface temperature calculation. The mean difference between observed and estimated land surface temperature using Price algorithm was about 6.2Co. This error can affect the air temperature values. Because of using NDVI index in TVX method, this method has the sensitivity to the vegetation density, though in the parts with sparse vegetation, the value of error increases. 4 percent variation of air temperature against the 0.05 increasing of maximum NDVI indicates the high performance of TVX method for air temperature estimation in large areas.
L. Khodakarami , A. Soffianian,
Volume 16, Issue 59 (4-2012)
Abstract
Precision farming aims to optimize field-level management by providing information on production rate, crop needs, nutrients, pest/disease control, environmental contamination, timing of field practices, soil organic matter and irrigation. Remote sensing and GIS have made huge impacts on agricultural industry by monitoring and managing agricultural lands. Using vegetation indices have been widely used for quantifying net annual production on different scales. The aim of this study was to find a rapid method with acceptable precision for the identification and classification of agricultural lands under cultivation (wheat and barley, alfalfa and potatoes). We used multi-temporal AWiFS data and applied Boolean logic and unsupervised classification.
Results indicated that Boolean logic approach had a higher accuracy and precision in comparison to unsupervised classification, although it is more complicated and time consuming.
V. Habibi Arbatani, M. Akbari, Z. Moghaddam, A.m. Bayat,
Volume 26, Issue 4 (3-2023)
Abstract
In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R2, MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.
H. Jafari,
Volume 27, Issue 2 (9-2023)
Abstract
The ability of remote sensing (RS) in irrigation scheduling has been accepted in the world due to the collection of data on a large scale and the determination of water stress indicators with greater speed and less cost. Crop Water Stress Index (CWSI) and Water Deficit Index (WDI) are components of the most recognized water stress indices. Despite the accuracy and precision of the CWSI index that has been proven in plant irrigation scheduling, the lack of complete density of vegetation, especially in the early stages of growth, is one of the most important defects of using this method in crop irrigation scheduling. While estimating the water deficit index using remote sensing technology does not have these limitations. An experiment was performed in the crop year 98-99 in the city of Karaj to check the accuracy of this index. The amount of WDI and CWSI in a wheat field with optimized irrigation management was determined and compared and evaluated using statistical parameters. The results showed that the coefficient of explanation between these two indicators in the months of April, May, and June is 0.77, 0.85, and 0.71, respectively.
S. Afshari, H. Yazdian, A. Rezaei,
Volume 27, Issue 3 (12-2023)
Abstract
Awareness of the types of vegetation changes and human activities in different parts has particular importance as basic information for different planning. It is very difficult and expensive to collect information about the continuous changes in vegetation cover by conventional methods. Therefore, the use of new technologies such as remote sensing is very beneficial. The objective of the present research was to introduce the appropriate vegetation index and determine the vegetation cover of the Abshar network. NDVI, EVI, SAVI, and MSAVI vegetation indices were calculated from 2000 to 2021 every year and monthly in the Google Earth Engine system using Landsat 7 satellite images of the ETM+ sensor. Also, the SPI drought index was calculated using the precipitation statistics of Kohrang station in Excel software. The results of the comparison of four indices showed the superiority and higher performance of NDVI compared to the other three indices for detecting vegetation changes. Then, vegetation changes were calculated. The results showed that the trend of agricultural development in the Abshar network is downward and has a direct relationship with precipitation and the SPI drought index. Also, the results indicated that the SPI drought index was equal to -1.73in 2008, which showed a severe drought in the region. Comparing these results with the vegetation area showed that the vegetation area was 35721 hectares in this year and the year after the drought (2009), the vegetation area was 22950 hectares. Therefore, there was a decrease in precipitation and a sharp decrease in the SPI index in 2008, which led to a sharp decrease of 35% in the vegetation area in 2009.
M. Tajsaeid, M. Gheysari, E. Fazel Najafabadi, R. Jafari, E. Seyfipurnaghneh,
Volume 28, Issue 3 (10-2024)
Abstract
and water management in the field. Therefore, its measurement has special importance. The surface soil has a great diversity in soil moisture and different methods were used to measure this property. Due to the problems of contact methods of soil moisture measurement, remote sensing has gained attention because of the possibility of analyzing and monitoring soil moisture on a large and global scale. In this research, satellite data and moisture measured in selected fields located in Hormoaz Abad Plain have been analyzed and compared. Sentinel-2 satellite data have been analyzed using the Google Earth Engine system. The results of this research showed that the use of triple indices in the OPTRAM model to estimate moisture is not very accurate, but the use of the EVI plant index has provided better results than the other two indices.
