Neamat Karimi
Volume 28, Issue 4 , November 2021, , Pages 652-671
Abstract
The main objective of the present study is to delineate all-natural resources of Iran with the priority of desert and semi-desert areas using indicators and criteria extracted from remote sensing data and new satellite image classification techniques. Accordingly, desert, semi-desert, and salinity areas ...
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The main objective of the present study is to delineate all-natural resources of Iran with the priority of desert and semi-desert areas using indicators and criteria extracted from remote sensing data and new satellite image classification techniques. Accordingly, desert, semi-desert, and salinity areas of Iran in conjunction with other natural resources areas (such as forests, rangelands, water bodies, and farmlands) were studied using time-series MODIS satellite images and different indices and parameters such as Albedo, NDVI, and surface temperature during day and night along with the temperature difference between day and night. Here, unlike the classical classification methods, based on using one-single satellite image and features such as vegetation density or surface temperature, the behavior of different natural resources over time, extracted from satellite images, was analyzed. Accordingly, the temporal behavior of each of the mentioned natural resource areas during 2019 was studied and analyzed using the remote sensing criteria. The basic object classification method was used to classify the country using the mentioned indicators using the least distance technique based on fuzzy logic. Based on results, about 41.2%, 14.8%, and 3.9% of Iran (totally about 60% of Iran) are classified as desert, semi-desert, and salty areas, respectively. By considering the percentage of rocky mountainous areas (11.3%), about 71.2% of Iran has no biological conditions (unsuitable for agricultural activities). As expected, desert and semi-desert areas are concentrated in central, eastern, southeastern, and southern regions of Iran, and no signs of such areas are found in the northern, northwestern, and western of the country.
Mahshid Souri; Tayebeh Alibeygy; Mehdi Erfanian; Javad Motamedi; Rostam Khalifezadeh
Volume 28, Issue 1 , April 2021, , Pages 21-33
Abstract
Gross primary production is one of the key factors for understanding growing grassland conditions and rangeland monitoring. The present study aims to introduce an improved index based on the primary GDP and NDVI vegetation index of MODIS. In this regard, field operations were carried out ...
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Gross primary production is one of the key factors for understanding growing grassland conditions and rangeland monitoring. The present study aims to introduce an improved index based on the primary GDP and NDVI vegetation index of MODIS. In this regard, field operations were carried out in May, simultaneously with the growth of vegetation in the Rasin basin of Kermanshah province. In each of the types, reference areas were determined, and six (100-meter) transects were deployed in each of the representative points. Along each transect, five plots were placed at a distance of 20 meters, and a total of 84 transects and 420 plots were used in the field. Information such as rangeland type and actual fresh forage (AFY, kg/ha) were determined in the Rasin basin. In general, three types were identified in the field. Modified primary GDP data were calculated and validated with the measured data in the Rasin basin of Kermanshah province. The results showed that, in type 1, with R2 equal to 0.77 Ss index of gross primary production of NDVI as an improved index, in type 2, Ss index of primary production of NDVI with R2 0.73, in type 3, Ss index of gross primary production NDVI with R2 equal to 0.71, and finally, for the whole rangeland type, Ss index of gross primary production of NDVI with R2 equal to 0.51 was determined as the improved index. The results also showed that the modified primary GDP data is an acceptable indicator for monitoring grasslands. The accuracy of estimating rangeland production based on the Ss index of gross primary production of NDVI was 80%. The statistical results of comparing the estimated values with field observations indicate the acceptable accuracy of statistical models in estimating production. Also, since MODIS data is available twice a day, the improved index can supply the real need for rangeland monitoring on a regional scale.
Maryam Mirdailamy; Mohammad Rahimi; Shima Nikoo; Ali Akbar Damavandi
Volume 27, Issue 1 , April 2020, , Pages 58-74
Abstract
In the past four decades, land use changes in Iran, have become more frequent as a human desertification factor, which has led to an intensification of land degradation in all types of land uses. In this research, due to the wide range of these changes, we used remote sensing technology to assess land ...
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In the past four decades, land use changes in Iran, have become more frequent as a human desertification factor, which has led to an intensification of land degradation in all types of land uses. In this research, due to the wide range of these changes, we used remote sensing technology to assess land use and vegetation changes in Damghan plain. The area of the study region was classified into three classes including unutilized land, lands with vegetation cover (agriculture and pasture) and urban areas using supervised classification strategy and the changes of the land uses were compared over four periods. The results indicated an increase of 184% and 1.07% respectively in urban land use and unutilized land use and a 15.7% reduction in lands with vegetation (agriculture and pasture). Using the Normalized Difference Vegetation Index(NDVI), the results of classification of vegetation in the region were such that class 1 (index less than zero), from 93.3 to 99.5 percent increase, class II( Between 0 and 0.2), from 5.6 to 0.38 percent, and the third class (index ranging from 0.2 to 0.5), from 1.1 to 0.01 percent, and class four (index ranging from 0.5 to 1) has changed from 0.03 to 0%, which indicates the validity of the findings from the review of the changes in land use, both of which indicate an increase in dry land and a decrease in vegetation. Also by using the Iranian model's climatic indices, the potential of desertification, the results of the 17-year calculations during the period of 1997 to 2013 indicated that desertification had decreasing trend in three classes: weak, severe and very severe, and increasing trend in the middle class. Finally, by combining the findings from the Iranian model and the results of remote sensing method, a weak trend in desertification based on climate criteria in the region was confirmed.
Morteza Hoseini |Tavasol; Hosein arzani; manoocher farajzadeh asl; Mohamad Jafari; sasan babayee kafaki; asghar kohandel
Volume 22, Issue 4 , March 2015, , Pages 615-624
Abstract
This research was aimed to monitor the vegetation changes in the rangelands of Alborz province during 2000-2011 using satellite images as well as determining its relationship with climatic factors including average rainfall, temperature, and relative humidity. According to the results, the highest NDVI ...
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This research was aimed to monitor the vegetation changes in the rangelands of Alborz province during 2000-2011 using satellite images as well as determining its relationship with climatic factors including average rainfall, temperature, and relative humidity. According to the results, the highest NDVI was recorded for Taleghan followed by Savojbolagh and Karaj, and then Eshtehard and Karaj with less significant difference. Based on the obtained model, the rainfall of November, December, January, February, and average annual rainfall had the most positive impact on the growth of range plants, while other factors including temperature and relative humidity had no significant relationship with the vegetation of the study period. According to the trend of vegetation changes, the amount of vegetation has been reduced after year 2000 and it has increased again in 2011.
Manuchehr Farajzadeh; Aman alah Fathnia; Bohlul Alijani; Parviz Zeaiean
Volume 18, Issue 1 , May 2011, , Pages 107-123
Abstract
The aim of this research was to evaluate the effect of climatic factors on vegetation in rangelands of Zagross with normalized difference vegetation index (NDVI) derived from Advanced Very High Resolution Radiometer (AVHRR) sensor, and climatic data. The study area was rangelands of Zagross with ...
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The aim of this research was to evaluate the effect of climatic factors on vegetation in rangelands of Zagross with normalized difference vegetation index (NDVI) derived from Advanced Very High Resolution Radiometer (AVHRR) sensor, and climatic data. The study area was rangelands of Zagross with 51-75, 26-50 and 10-25 density. Satellite images and climatic factors were respectively studied from January to October 2006 and from September 2005 to October 2006. Effects of seven monthly climatic variables (precipitation, temperature and relative humidity (maximum, mean and minimum) were studied on monthly NDVI. Zoning was performed by geostatistical method and Multivariate Ordinary Least Squares regression (MOLS) was applied to study the effect of climatic factors on vegetation changes. According to the Results CO-Kriging was identified as the best method for zoning, and Inverse Distance Weighting (IDW) only in mean temperature showed a better distance. Vegetation responses to precipitation of last two months, and temperature and relative humidity of last one month. Results of MOLS showed higher correlation in rangelands with a density of 51-75 %, but generally, correlation was low in rangelands with a density of 10-25 %, that can be due to the low altitude from sea level, effect of soil background and agricultural field margins. Also, low height in rangelands with a density of 10-25 % increased the temperature and transferred the start of leaf greenness to March, while in rangelands with a density of 51-75 % greenness started in May. The highest and lowest R2 values were calculated as 0.6478 for dense rangelands in May and 0.136 for low density rangelands in August.
Khosro Mirakhorlo; Zein ... Hosseini
Volume 13, Issue 2 , February 2006, , Pages 127-138
Abstract
Estimating rangelandsۥ production is one of the range management tools. This requires detailed information about the present available forage of the rangelands. The assessment of the parameters is difficult and cost-intensive using clipping method. Therefore, new estimating methods are required. We ...
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Estimating rangelandsۥ production is one of the range management tools. This requires detailed information about the present available forage of the rangelands. The assessment of the parameters is difficult and cost-intensive using clipping method. Therefore, new estimating methods are required. We estimated available forage using remote sensing data in the production model that it extracted from ecological parameters and remote sensing data. For quantifying evaluation of vegetation cover stratified random sampling and transect sampling methods were selected. Plot size calculated from “minimal area and species curve” method. Overall, 28 transects (50m) one transect in each site that contain 280(1m2) sample plots were measured. Modeling performed using NDVI index, animal density and effective ecological factors (altitude, slope, aspect, precipitation, temperature and evaporation) on rangelandsۥ yield. After analyzing of ecological factors of Damavand region, some ineffective factors omitted. Finally three factors namely slope, precipitation and NDVI index were entered in the model for calculating the available forage in the study area. The calculated amount of average standard predict value of forage model is 38% and its standard deviation value is 97%. They show that the validation of model for predicting of forage is fairly acceptable.