Hasan Ghelichnia; Hamidreza Mirdavoodi; Ali Cherati Araie
Volume 30, Issue 2 , September 2023, , Pages 264-285
Abstract
Background and objective Today, predictive models of plant species distribution play a crucial role in assessing, restoring, protecting, and developing rangeland ecosystems. It is one of the most important tools to learn about species distribution and habitat suitability. This research determined ...
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Background and objective Today, predictive models of plant species distribution play a crucial role in assessing, restoring, protecting, and developing rangeland ecosystems. It is one of the most important tools to learn about species distribution and habitat suitability. This research determined ecological factors affecting plant composition, species response to environmental factors changes, and the potential of the target species in the study area using multivariate analysis. Based on this, with a better understanding of the ecological niche of this plant, the necessary recommendations can be made for using the species in rangeland improvement programs in similar habitats. Methodology Vegetation sampling was done by a systematic-random method during 2016-2018.Vegetation studies were carried out, including the percentage of canopy cover of species and the density of species inside the plots. Also, the percentage of litter, bare soil, stones, and pebbles was determined in each plot. For this purpose, five transects with the same distance were used. Then, six plots (with 2 x 2 meters dimensions) were established on each with the same distance. To investigate the effect of environmental factors on the distribution of the studied species, three soil samples were taken from each plot at a depth of 0 to 30 cm, and their physical and chemical properties were measured in the laboratory. Climatic factors such as average rainfall and annual temperature were collected using data from weather stations near the study area. To investigate the relationship between effective and significant environmental variables and vegetation and to choose the appropriate linear and non-linear method, DCA was performed on the vegetation data, and the gradient length was determined. A generalized additive model predicted plant species' response to environmental changes. Canoco software version 4.5 was used to analyze the data in this section. Results The results of conventional comparative analysis showed that environmental factors such as clay percentage, geographical direction, acidity, sand, saturated moisture percentage, organic matter percentage, average annual temperature and soil lime percentage in the studied habitats, respectively 10.3, 4.4, 3.3, 3.3, 1.6, 1.4, 1.4 and 1.3 percent of the variance in plant composition, play an important role in changes in vegetation in the habitats.It showed that A.specigera species respond to the amount of phosphorus, percentage of organic matter, electrical conductivity, percentage of nitrogen, percentage of clay, percentage of organic carbon, and altitude. This is following the monotonic decrease model. The response pattern of this species to the percentage of silt, percentage of sand, average annual temperature, average annual rainfall, the direction of slope, acidity, potassium, the apparent specific gravity of soil, percentage of soil saturation and percentage of slope follows the bell model (Unimodal) and limit its growth optimum for each of these factors is 21%, 60%, 16°C, 400mm, for eastern and southern slopes, 8, 650mg/liter, 1.4g/cm3, 39% and 40-50%. Conclusion The generalized incremental model provides valuable information to determine species' ecological needs. This information can be used in vegetation management and rangeland improvement operations in similar areas using the data from this research. Forage production is high in the studied species, suggesting its potential for increasing rangeland vegetation cover.
Seyedeh Habibeh Hoseini; Gholamali Heshmati; Mehdi Mirza; Parviz Karami
Volume 26, Issue 2 , July 2019, , Pages 447-458
Abstract
This research was conducted to evaluate the environmental factors affecting functional characteristics (biomass, density, regeneration, cover%, basal area and species richness) of Ferula haussknechtii in Saral rangelands of Kurdistan. After determining three altitude classes (1850, ...
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This research was conducted to evaluate the environmental factors affecting functional characteristics (biomass, density, regeneration, cover%, basal area and species richness) of Ferula haussknechtii in Saral rangelands of Kurdistan. After determining three altitude classes (1850, 2250 and 2650 m), a systematic-random sampling was performed to measure aboveground biomass, cover percentage, height, basal area, and density of Ferula haussknechtii with 1.5-m2 plots along transects. In each plot, a soil sample was taken from 0-30 cm soil depth. Soil texture, CaCo3, acidity, electrical conductivity, nitrogen, potassium, phosphorus, and sodium were measured in the laboratory. Analysis of variance of data was done using a randomized complete design with R software, and also CCA was used to compare the relationship between species and environmental factors. The results showed that aboveground biomass, vegetation percentage, height and density of Ferula haussknechtii increased with increasing altitude, while the basal area of this species decreased. The green biomass, cover percentage, basal area and height of this species were positively correlated with nitrogen, carbon, potassium, silt and clay and negatively correlated with acidity, phosphorus and sand. The results also showed that due to the impact of this valuable species on the soil stabilization, forage supply and production of active ingredients, altitude, nitrogen, carbon, clay and silt were the most effective factors in establishing this species.
Mohsen Kazemi; Sadat Feiznia; Hasan Khosravi; Hamid Mesbah; Reza Shahbazi
Volume 24, Issue 4 , January 2018, , Pages 815-828
Abstract
Understanding the physical and chemical characteristics of sediments of lakes and wetlands is important for sedimentology and erosion studies. Maharloo Lake is one of the most important lakes of Fars Province. Surface sediments of this lake consist of evaporitic and muddy sediments. For recognition of ...
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Understanding the physical and chemical characteristics of sediments of lakes and wetlands is important for sedimentology and erosion studies. Maharloo Lake is one of the most important lakes of Fars Province. Surface sediments of this lake consist of evaporitic and muddy sediments. For recognition of the nature of the sediments, samples were taken from lake bed and carbonate and evaporitic compositions including calcium-carbonate, calcium-solfate and sodium-chloride were measured. After omitting carbonate and evaporitic compositions, granolumetric analysis of terrigenous fraction was performed using hydrometry method and sediment classification was performed using Pettijohn (1975) Method. The result of the percentage of terrigenous and chemical fractions of surface sediments showed that most of the sediments were fine-grained terrigenous sediment, containing chemical salts. Terrigenous sediments are poorly sorted, skewed toward coarser sizes and have slightly gravelly, sandy mud texture. More than 90 % of sediment minerals in the lake is clay and less than10 % is gypsum, quartz, quartz sandstone, and limestone. Depending on the type of sediment, minerals, salts and frequency of deposits, the sediments of Maharloo Lake are prone to wind erosion and dust generation in the region.
Hasan Fathi Zad; Rashid Fallah Shamsi; Ali Mahdavi; Saleh Arekhi
Volume 22, Issue 1 , June 2015, , Pages 59-72
Abstract
Rangelands are one of the most important renewable resources and because of their extent and economic, social and distinctive environmental impacts are of very special importance. Unfortunately, in our country, like most developing countries, rangelands have been exposed to degradation for various reasons ...
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Rangelands are one of the most important renewable resources and because of their extent and economic, social and distinctive environmental impacts are of very special importance. Unfortunately, in our country, like most developing countries, rangelands have been exposed to degradation for various reasons including the non-systematic management of these resources. Remote sensing technology and satellite data are useful tools in the studies of rangeland and vegetation sciences. One of the applications of satellite data is mapping range land use. The aim of this study was to compare two methods of maximum probability and fuzzy for rangeland zonation. For this purpose, Landsat ETM+ was used; then, after final geometric and radiometric corrections, the final classification map was prepared. According to the results of accuracy of these two methods using the kappa coefficient, the artificial neural network algorithm of fuzzy Artmap with a coefficient of 0.9614 was more accurate than the maximum probability algorithm with a coefficient of 0.8058. Results of this study also indicated that the traditional algorithms of classification such as statistical methods due to their low flexibility, and parametric types such as maximum probability method because of the dependence on the Gaussian statistics model, could not provide optimal results, when the samples were not normal. In this study, ENVI 4.5, Idrisi Andes 15 and Arc GIS9.3 software were used
Hosein Arzani; Khosro Mirakhorlou; Zeinalabedin Hosseini
Volume 16, Issue 2 , December 2009, , Pages 150-160
Abstract
Range management needs to accessing data by fast and suitable methods for planning. Satellite data and geographic information systems (GIS) can be used for planning and integrating field and remotely sensed data. Landuse map is one of the most important information in range management plans. This requires ...
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Range management needs to accessing data by fast and suitable methods for planning. Satellite data and geographic information systems (GIS) can be used for planning and integrating field and remotely sensed data. Landuse map is one of the most important information in range management plans. This requires overlaying, retrieve and analysis detailed information about the rangelands in GIS. Land use map provided using Landsat7 ETM data (15 Apr. 2004) for the study area (middle catchment’s of Taleghan) in the Tehran province. Based on histogram of bands and statistical analysis, bands 4, 3, 2 were selected for color composite in unsupervised classification method. We identified 4 classes land use type of study area in the fieldwork. Ground data was collected using systematic with random start point, from 86 plots (250*250m), using unsupervised map as a primary map. Overall, with combining the ETM data and the field data using supervised classification method, boundary of the landuse types were put in four classes (Rangeland, Farm land, Dry farming and Bare soil). The classification accuracy assessment showed that the overall accuracy 70.64 percent and accuracy rates of the rangeland, farm land, rain fed carping and bare soil were 81, 54, 61, and 81 percent, respectively. So it is possible to use Landsat 7 ETM+ data for landuse mapping which is essential in range management and range suitability classification.
Seyed Alireza Mousavi; Mahdi farahpour; Maryam Shokri; Karim Solaimani; Mahmood Godarzi
Volume 13, Issue 3 , February 2006, , Pages 186-200
Abstract
Landsat 7 ETM+ satellite data of 2002 versus vegetation cover map of 1976 were used to: 1- assess the capability of satellite data to prepare vegetation cover classes map and 2- study the vegetation changes trend in an area of about 26858.6 ha in Lar Dam Basin. Field Sample spots were defined after accomplishing ...
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Landsat 7 ETM+ satellite data of 2002 versus vegetation cover map of 1976 were used to: 1- assess the capability of satellite data to prepare vegetation cover classes map and 2- study the vegetation changes trend in an area of about 26858.6 ha in Lar Dam Basin. Field Sample spots were defined after accomplishing necessary corrections of satellite images. Suitable band compositions were selected by considering the Optimum Index Factor (OIF), correlation matrix, Principal Components Analysis (PCA) and 2-dimensional diagram analysis. These compositions were classified using Maximum Likelihood, Minimum Distance and Box Classifier algorithms and then Majority Filter was used. Accuracy of resulted maps was evaluated by pixel to pixel method. Then Overall Accuracy Coefficient and Kappa Index were calculated. The map resulted from classification of band composition 123457 through Maximum Likelihood and Majority Filter was selected as the vegetation cover map of 2002. Vegetation cover map of 1976 prepared by Asgari-khah (1977) via field survey was used as "vegetation cover classes" map of that year. Then the changes happened in each class were assessed by operation of cross function on the mentioned maps. Due to complexity of initial classes, more homogenous classes were merged resorting to more detectable maps having only four classes. Overall Accuracy Coefficient of final map was promoted by using these classes. Then the changes happened during 1976-2002 were detected. The results showed that 28.55% of total area which was covered by rangeland vegetation types in 1976, is unchanged, 14.03% is dropped into lower and 57.42% into higher classes. The map of vegetation cover changes was produced, finally.