Hamid Reza Matinfar; Aliakbar Shamsipor; Hadis Sadeghi
Abstract
Vegetation plays an important role in protecting water and soil resources, stabilizing carbon and improving air quality. In Middle Zagros, forest and pasture vegetation is very important in terms of protecting soil and water resources and sustaining economic activities. In this research, using the Google ...
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Vegetation plays an important role in protecting water and soil resources, stabilizing carbon and improving air quality. In Middle Zagros, forest and pasture vegetation is very important in terms of protecting soil and water resources and sustaining economic activities. In this research, using the Google Earth Engine platform and Landsat 7 satellite images, the drought of Middle Zagros (Lorestan province) was monitored with vegetation indices NDVI, SAVI and VCI, as well as meteorological drought index SPI for the statistical period of 2020-2000. To calculate the SPI index, the precipitation data of 9 synoptic meteorological stations with appropriate spatial distribution and the length of the statistical period (2020-2000) were used, and the processing was done in DPI software. In order to calculate the plant indices, first, all the geometrically corrected satellite images of the ETM+ sensor of the Landsat satellite were called for Lorestan province for each year. At this stage, an average of 52 images were called for each year. Then the images with less than 5% cloud cover were selected and processed. The results of the VCI index showed that mainly the studied area was affected by mild drought during the statistical period of 2020-2000. The year 2008 had the highest amount of drought related to the middle class with 5880.6 hectares among the studied years. The results of the SPI index showed that there was a moderate drought in 2010, a severe drought in 2008 and 2017, a mild drought in 2006, and a severe drought in 2019. The results of NDVI and SAVI indices also show the increase of thin vegetation classes and areas without vegetation by 1.331679 and 115164 hectares, respectively, and the decrease of normal and dense vegetation by 446160.7 and 682.4 hectares respectively per year. 2008 was compared to 2006 and 2007. Based on the results of all three investigated indicators, the favorable conditions of vegetation cover and ecological threat were obtained in 2016, 2019 and 2020. The highest level of this coordination between SPI meteorological drought and vegetation indices was observed in 2008 and 2010 and to some extent in 2019. In general, the results show that the increase or decrease of vegetation can be caused by the occurrence or absence of drought, while other factors such as land use changes should also be considered.
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.
Hamidreza Matinfar; foziyeh kohani; Ali Akbar Asilian mahabadi
Abstract
Soil salinity is one of the most important environmental problems, and the identification and zoningof saline soils is difficult due to the need for sampling and laboratory analysis, as well as havingtemporal and spatial variability. In recent years, the use of satellite imagery has always been ofinterest ...
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Soil salinity is one of the most important environmental problems, and the identification and zoningof saline soils is difficult due to the need for sampling and laboratory analysis, as well as havingtemporal and spatial variability. In recent years, the use of satellite imagery has always been ofinterest to experts due to its ease of use and ability to detect phenomena. Remote sensing informationgreatly aids the study of soil salinity and can be helpful in identifying salinity values. In this study,220 soil samples were collected from Meymeh area of Dehloran, south of Ilam province, according tothe type of study and physiographic types and soil units. Then, pH and EC values were measuredusing standard methods. Soil salinity values were evaluated using correlations between EC electricalconductivity values obtained from ground data and variables obtained from Landsat 8 satellite imagesincluding bands, salinity indices, vegetation indices and soil indices. Finally, the soil surface salinityestimation model was obtained using stepwise regression method. This method involves the automaticselection of independent variables, and with the availability of statistical software packages, it ispossible to do so even in models with hundreds of variables. In previous studies, indicators and bandshave been used separately and in a limited way, but in this study, an attempt has been made to use acombination of different indicators more widely, and finally to achieve the best relationship byeliminating the indicators that have the least impact on soil salinity estimation. Using significant levelanalysis and correlation between the output of models and ground data, the best model with a value of(R2 = 0.882) was selected and a soil salinity map was prepared based on it. In the study area, thehighest area belonged to non-saline class which comprises 75% of the total study area and about 1%of the soils belong to the saline class.
Elham Mehrabi Gohari; Hamid Reza Matinfar; Azam Jafari; Ruhollah Taghizadeh-Mehrjardi; F khayamim
Abstract
Visible and Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) reflectance spectroscopy (400-2450nm), which are at least as costly and time-consuming, are widely used in the estimation of physical and chemical properties of the soil. The purpose of this study was to investigate the ability of this method ...
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Visible and Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) reflectance spectroscopy (400-2450nm), which are at least as costly and time-consuming, are widely used in the estimation of physical and chemical properties of the soil. The purpose of this study was to investigate the ability of this method to estimate the amount of organic matter, carbonates and gypsum content of soil surface. In the present study, 115 profiles were identified based on the Hypercube technique, and the horizons were sampled and the amount of organic matter, carbonates and gypsum content were measured by standard methods. Reflectance spectra of all samples were measured using an ASD field-portable spectrometer in the laboratory. Soil samples were divided into two random groups (80% and 20%) for calibration and validation of models. PLSR and PCR models and different pre-processing methods i.e. First (FD) and Second Derivatives (SD), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) were applied and compared to estimate texture elements. The highest RPD of calibration and validation were obtained for PLSR with First derivative of reflectance+ Savitzky_Golay filter pre-processing technique which was classified as a good for the amount of organic matter and gypsum and was classified as a poorly for the amount of carbonates.
Hamidreza Matinfar; H Mahmodzadeh; A Fariabi
Volume 10, Issue 2 , September 2018, , Pages 15-32
Abstract
Soil organic matter is one of the most important Physical and chemical properties of soil that it iscritical in determining the quality and management of soils. Quantify of soil organic carbon due to thehigh spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is ...
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Soil organic matter is one of the most important Physical and chemical properties of soil that it iscritical in determining the quality and management of soils. Quantify of soil organic carbon due to thehigh spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is afeasible method to reduce the time and cost to check the organic carbon. The aim of this study was toevaluate soil organic carbon through near-infrared and visible spectroscopy with the statistical modelsPLSR and PCR. For this purpose, 40 soil samples from depths of 0 to 30 cm were collected bysystematic random method based on previous studies and determination of different classes of soils inthe region. Chemical analysis of soils was performed according to standard methods. Spectralreflectance of soil samples in the range of 350 to 2500 nm was measured then after applying thepreprocessing methods such as Savitzky and Golay filter, Soil organic carbon were calculated byprincipal component analysis (PCA), regression partial least squares (PLSR) and principal componentregression (PCR) models. The results of this study showed that the Savitzky and Golay filter was thestrongest preprocessing method for spectral data. Coefficients of determination (R2), root mean squareerror of Prediction (RMSE) and ratio of prediction to deviation (RPD) in the calibration andvalidation to predict organic matter, respectively, 0.97, 0.05, 5.09 and 0.85, 0.14, 2.78 respectively.Therefore, for dry and semi-arid soils of the PLSR model, it is more efficient to predict the organiccarbon of the soil. The results showed that the PLSR model has better performance than the PCRmodel in soil organic carbon estimation.
