Ali Sadeghi; saham Mirzaei; Saghar Chakherlou; Mehdi Gholamnia; Hossein Ali Bahrami
Abstract
Leaf chlorophyll and nitrogen, due to their important role in photosynthesis are among the major biological parameters of plant physiological status. The ability to quantify chlorophyll and nitrogen can provide important information for precision agricultural activities, plant and agricultural resource ...
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Leaf chlorophyll and nitrogen, due to their important role in photosynthesis are among the major biological parameters of plant physiological status. The ability to quantify chlorophyll and nitrogen can provide important information for precision agricultural activities, plant and agricultural resource management planning, and modeling ecosystem services and production capabilities. This study aimed to assess the capability of indices for estimating the amount of chlorophyll and nitrogen in wheat using spectral data at the canopy level and also determine the most suitable spectral regions and absorption features for this purpose. This research was carried out in a greenhouse environment and the spectroscopic measurements were performed using ASD Fieldspec-3 full-range spectral spectroradiometer. Four plant band indices were classified into two groups of ratio- (NDVI, RVI, and DVI) and soil-based indices (SAVI2) for the raw spectrum and the first derivative of the spectrum for the total samples, and the results were compared. The parameters of position, depth, area, asymmetry and width were calculated for seven absorption features extracted from continuum-removed spectra, and the correlation of these indices with chlorophyll and nitrogen content of wheat was examined. The results showed that SAVI2 had a stronger correlation (RMSE = 0.12, R2 = 0.85) with the chlorophyll content NDVI (RMSE=0.30, R2=0.69) had a higher correlation with the nitrogen content, while using the first derivative with NDVI provided better results. Moreover, area and depth parameters of 430-760 nm absorption spectrum were the best indicators for estimating the amount of chlorophyll and nitrogen in wheat, respectively.
Alireza Zahirnia; Hamid Reza Matinfar; Hossianali Bahrami
Abstract
Organic carbon plays a activate role in environmental sustainability, soil quality and health index, so identifying the spatial distribution of carbon sequestration is a requirement of environmental planning and soil management. The purpose of this study is to investigate the amount of carbon sequestration ...
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Organic carbon plays a activate role in environmental sustainability, soil quality and health index, so identifying the spatial distribution of carbon sequestration is a requirement of environmental planning and soil management. The purpose of this study is to investigate the amount of carbon sequestration in sugarcane and traditional uses of sugarcane, traditional agriculture and barren. In each land use, 60 soil samples were taken and organic carbon, salinity, lime, soil reaction and solution sodium were measured. Using Landsat 8 satellite OLI and TIRS spectral data, the variable of soil and vegetation indices including: NDVI, SAVI, TSAVI, OSAVI, MSAVI, SOCI, WDVI, PVI, RVI and BI in the sample points was obtained and the relationship between them and the amount of soil organic matter was calculated. The results show that in agro-industrial use, SOCI index with 50.30% and band 3 with 53.82% have the highest correlation, in traditional agriculture, PVI index with a correlation of 60.35% and band 7 with 60.63% and in Barren lands ,RVI index with a correlation of 34.27% and band 2 with 36.67% have the highest correlation with the amount of soil organic matter. The results of statistical analysis by partial least squares fitting method showed that the average of calibration and validation results are 43.48 and 39.08%, respectively. The results of estimating soil organic matter by kriging method and M5 tree model show that the correlation between measured and predicted organic matter was 66.20% and 82.00%, respectively. The results show that there is a significant correlation between soil organic matter and Landsat 8 satellite indices and bands, and it is possible to estimate the soil organic matter levels of the study area and other areas with similar conditions with acceptable probability.
M Danesh; R Darvishzadeh; A.A Noroozi
Volume 8, Issue 1 , November 2016, , Pages 71-94
Abstract
Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the ...
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Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.
H.A Bahrami; S Mirzaei; A Darvishi Boloorani
Volume 7, Issue 4 , November 2015, , Pages 13-26
Abstract
In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important ...
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In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important agricultural species, reflectance and best band for selected narrow band indices to discriminating wheat canopies which are under dust stress in different growing stages. Two wheat (Triticum aestivum L.) varieties, Aflak and Pishtaz, were grown in pots under controlled conditions. The treated samples were exposed to simulated dust storm, in the wind tunnel, at two growth stages including Tillering and Heading stages. In each stage the treatments were exposed in 2, 4 and 6 days. Field spectroscopy measurements were carried out at canopy level using a full range spectro-radiometer Fieldspec-3-ASD. New narrow-band vegetation indices from NDVI, RVI, PVI and SAVI2 indices were computed from the all measured canopy spectra, Tillering and Heading stageseparately. To assess the performance of the indices, the RMSE, R2 and cross-validation method were used. For most indices, the selected optimum narrow bands are very close to one another and located in visible and NIR spectral domains. The result showed that the PVI index performed the best for considering the dust effect on wheat crops. The result also show that the selected indices have better performance in the Tillering stage ( 0.77; 0.63 0.80)for estimating the dusty days, compared with Heading stage ( 0.91; 0.62 0.71). Therefore, determining the dusty days by narrow band indices could be done precisely in the early stage of wheat growing.
