جستجوی مقالات مرتبط با کلیدواژه « جنگل های زاگرس » در نشریات گروه « منابع طبیعی »

Fire is an ecological factor in the forest, during which most of the growing plants are damaged. By burning the vegetation on the soil surface, fire can cause negative or even positive effects on the physical, chemical, biological, and vegetation properties of the soil. Due to the fact that so far many studies have been done on the effect of natural fires on the characteristics of soil and vegetation, but researches on the impact of fire at different times of the growing season and the effect of those characteristics on soil and vegetation in Iran and other parts of the world are few. There are minimal resources in this field, so this research can provide appropriate information about the condition of the soil after the fire, as well as the ability to restore and restore valuable plant species and preserve the Zagros forests at the discretion of researchers. In this research, the effect of fire in the three seasons of spring, autumn, and winter on the chemical and biological characteristics of soil was investigated.

 The studied area was a part of Zagros forests in Kermanshah province. In this research, a control area was also considered in addition to the burned forests. For this purpose, the desired points (9 points for under the crown and 9 points for outside the crown in both control and burned areas) were determined by measuring with a meter on the transects (6 transects) and regularly and with time intervals. Specified (once every 2 months), they were sampled. The distance of the samples from each other in each row was 20 m and the distance between the rows was 50 m so that 3 rows were identified in the burned area and 3 rows in the control area. Soil properties were measured by conventional laboratory methods.

The results of the investigations show that the fire that occurred in the studied area (under the canopy and outside the canopy of trees in the Zagros region) was superficial and was capable of causing small changes in the chemical and biological factors of the soil. Only soil respiration rate and absorbable phosphorus were affected in this surface fire. The amount of phosphorus in the soil increased and then decreased to its initial level in the first month of sampling.

Considering that the fire in the study area was superficial and caused slight changes in soil characteristics in all sampling months. Probably, severe fires have the ability to cause extensive changes in the chemical, physical and biological characteristics of the soil, and the sampling time in different months did not have much effect on the results.

Since there are different statistics on the area, distribution, and density of Zagros forests, continuous monitoring and evaluation of these forests are facing implementation problems. The available statistics and information from Zagros forests are not effective in the management decisions of these forests due to many reasons such as their preparation in the traditional way or their oldness. In this research, a high-accuracy map of canopy density and distribution of Zagros forests as well as the determination of other land uses have been prepared using Sentinel-2 images of 2019 in the south of West Azarbaijan province. The image classifications were performed with the supervised method and ML and SVM algorithms in the ENVI 5.3 software environment. The Bing and Google Earth images were used to prepare the ground truth map to evaluate the accuracy of the output maps. In this research, the five non-forest land uses including ranges, gardens, agriculture, water sources, and barren and residential lands were classified. The results showed that the maximum likelihood algorithm with an overall accuracy of 87.3% and a kappa coefficient of 0.74 was the most accurate in preparing the canopy density map. The available statistics showed that the state of forest cover in the province is not in a favorable condition and the area of Zagros forest in the province is equal to 60200.55 ha, which is equivalent to about 90% of the updated statistics of the forests, rangelands, and watershed management organization of the country (67235,91 ha) in 2020. It can be stated that Sentinel-2 data has an acceptable efficiency in terms of accuracy and cost for preparing the canopy density and distribution map of forest areas and preparing the land use map.

The implementation of the forest protection plan was started in 2003 in the form of a national plan due to the unfavorable situation of the Zagros forests and with the aim of protecting and developing forests in this region of the country. Considering the national nature of the multi-purpose forestry plan and the allocation of the national budget to it, the present study was conducted with the aim of quantitative and qualitative evaluation of the implementation of the plan in Khuzestan province. In this study, some parts of the forests under the management of the plan in Izeh, Andimeshk, Baghmalek and Indika were selected. At first, the multi-purpose forestry plan was defined based on the law on the protection and utilization of forests in the form of three goals of protection, restoration, and development and, of course, increasing the income of foresters, and all executive projects were included in these goals. To analyze the quantitative data, a one-way analysis of variance and Duncan's multiple-range mean comparison test were used to compare the means of the variables. Qualitative data and data differences between different studied areas were also analyzed using the non-parametric Kruskal-Wallis test. Based on the results of this study, it was found that in Khuzestan province in terms of quantity and quality of the plan implementation, Izeh area had the best performance, and Indika, Baghmalek and Andimeshk areas were in the next rank. The results also showed that the goals in the form of protection of Zagros forests are at the highest quantitative and qualitative levels. Forest development and rehabilitation are the second priority and the least planning to increase the income of forest dwellers and reduce their dependence on forests. Therefore, it cannot be expected that the executive projects within multi-use forestry plan of Zagros forests will succeed without changing the income level of foresters and the natives of the region will have maximum cooperation in advancing the goals of the plan with the government.

Quantitative ethnobotany of medicinal plants is one of the most important branches of traditional knowledge that deals with the quantitative discovery and analysis of human relationships with medicinal plants. Since the forests of Zagros are the cradle of various local communities that have a long-standing relationship with the forest, in this study, the traditional relationship between the local communities of the common-law forest area of Harabalout in Nourabad Mamasani city, Fars province with woody species (trees, shrubs and bushe) was studied. The data of this study were collected by snowball sampling method, using key informants and purposively selective. An attempt was made to use a combination of interview methods, direct observation and participatory observation. Data were analyzed using quantitative indicators of Relative Importance Index (RI) and Cultural Importance Index (CI). According to the results, 12 medicinal woody species were identified in the region including 7 trees, 3 shrubs and 2 bushes. In general, the values of the indices were higher for tree species than other forms. The highest index of RI (1) and CI (5.92) were calculated for Brant`s oak (Quercus brantii Lindl.) and the lowest of them with values of 0.31 and 0.48 belonged to Teucrium polium L., respectively. The results of this study, in addition to confirming the richness of traditional knowledge related to woody medicinal plants in the study area, emphasized the usefulness of quantitative indicators in ethnobotany studies.

Considering the increasing importance of forest ecosystems in climate change mitigation projects, reliable and cost-effective methods are required to estimate the aboveground biomass (AGB). Common  methods used to estimate the aboveground biomass (AGB) include in-situ measurement, the biomass calculation using aalometric equations and using remote sensing techniques. Remote sensing has been widely used to estimate the biomass of forests in recent decades.The used statistical modeling method is one of the most important factors to use remotely-sensed data for estimation of the aboveground biomass. A large number of researches have been carried out about using the modeling methods. However, these studies face the following different challenges: 1) no modeling method has been recommended as the best method 2) the performence of these modeling methods is affected by forest type, the forest structure, and the present disturbance intensity 3) the performance evaluation and the comparion of the results of these methods were done by using goodness-of-fit test and cross-validation methods. The purpose of this study is to considering the role of choosing statistical modeling methods to estimate remotely-sensed aboveground biomass, the current study was conducted to investigate nine statistical modeling methods including linear regression (LR), generalized additive model (GAM), random forest (RF), support vector machine (SVM), boosted regression tree (BRT), k-nearest neighbor (kNN), cubist regression (CR), Gaussian process model (GPR), multivariate adaptive regression spline (MARS) using bootstrap process and 1000-repeated 10-fold cross-validation approach to estimate the aboveground biomass of Zagros forests using Landsat 8 images.

 The cuurent study was conducted in Kermanshah forests which is mostly dominated by oak species trees (Quercus spp.) and is located in western Iran on the Zagros Mountains. Zagros forests are generally sparse and open  and comprise approximately 20% of Iran’s area and 40% forest regions of Iran. In order to conduct this study, two forest regions with different levels of human disturbances were chosen; SarfiruzAbad region with highly degraded (HD) forests, and Gahvareh forest region with minor degradation (MD). Geographical coordinates of SarfiruzAbad and Gahvareh regions are 33º57′-34º04′N / 47º03′-47º17′E & 34º21′- 34º24′N / 46º16′-46º23′ E respectively. The Leaf area index (LAI) map derived from the Landsat images based on a global model was used to collect field-based sample plots  in both regions of the study. Both regions were divided into three  low, moderate and high  Leaf area index (LAI) strata, and the locations of the sample plots were located by using a systematic inventory at the intersections of a 200m×200 m grid in each stratum. 124 georeferenced square plots of field-based sample plots (63 plots in Gahvareh region and 61 plots in SarfiruzAbad region) with 30m×30m dimensions the same size as a Landsat 8 image’s pixel were collected. Allometric equation developed for oak tree in Zagros forests was used to calculate the amount of  the aboveground biomass of each individual tree or sprout-clump. The allometric equation used in this study uses  two vertical tree crown diameters to estimate the amount of the biomass of each individual tree or sprout-clump. The sum of the amount of the biomass  of each individual tree in sample plot was used to calculate the amount of the biomass plot in sample plot level at a ton per hectare. Our study regions were located in a frame of Landsat 8 images (path/row:167/36). A cloud-free Landsat image relating to 19th Mordad 1394 (10th August 2015) relating to the time when the tree canopies are completely closed and near to the date of land inventory was downloaded from earthexplorer.usgs.gov site. Based on the previous studies, the pre-processing of the used image comprising the radiometric and topographic corrections was done.using C method. To estimate the aboveground biomass in the study areas by using remote sensing, 38 spectral variables including band values, simple band ratios, vegetation indices and common linear transformations like tasseled cap and principle component analysis  were extracted from the used Landsat 8 image. Generally, the efficiency of nine different statistical modeling methods including parametric methods (Linear Regression, LR), semiparametric (Generalized Additive Model, GAM), and nonparametric Random Forest (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Boosted regression trees (BRT), multivariate additive regression splines, cubist regression (CR), and Gaussian processes regression/model) were compared in order to estimate aboveground biomass. To assess the models, two common quality statistics: (i) determination coefficient and (2) root mean square error via 10 fold cross validation repeated 1000 times approach were calculated. This number of repeats helps to ensure an acceptable assessment of robustness of the results.

 The measuredstatistical characteristics of the field sample plots showed that the mean aboveground biomass of SarfiruzAbad and Gahvareh regions were 12.6 ton/ha and 20.5 ton/ha respectively. ANOVA indicated significant differences between modelling methods (treatment effect: p< 0.001) for both R2 and RMSPE calculated in 1000-time repeats using 10-fold cross- validation.The Cubist modeling method with the mean determination coefficient of 0.61 outperformed other methods in SarfiruzAbad region.These resultsfor Gahvareh region showed better efficiency of linear regression (LR), generalized additive model (GAM), and k-nearest neighbor (KNN) with the mean determination coeffieient of 0.87.The multiple comparisons of different models by using Tukey test concerning RMSE showed that in SarfiruzAbad region, cubist method  with the mean of RMSE 3.3 ton/ha and kNN and RF methods with the mean of RMSE 5.8 ton/ha had a significant difference in comparison to the other methods. Totally, the results of the research revealed the suitable efficiency of Landsat 8 image for AGB estimation in Zagros forests. The acceptable results are due to the low AGB in our study regions that did not reached the saturation point as one of challenges of using optical images like Landsat. The other results of this research is the assessment of the effiecieny of modeling method in order to increase the accuracy of the estimation of remotely-sensed aboveground biomass.Unlike the results of the previous studies, linear regression yielded better results compared to nonparametric methods that can be due to the presence of the linear relationship between aboveground biomass and spectral variables derived from Landsat images. Among the used various spectral variables, red, near infrared, and  shortwave infrared 1 and 2  band ratios were selected as the final variable in most modeling methods.

In this study, we evaluated the effieincy of different statistical modeling methods to estimate AGB in Zagros forests by using Landsat images. The biomass estimations were compared by using nine parametric, semi-parametric, and non-parametric methods and using 1000-repeated 10-fold cross-validation. The results illustrated the acceptable potentiality of Landsat images for cost-efficient AGB estimating in Zagros oak forests. The accuracy of AGB estimation in Gahvareh region with low-degraded forest stands was higher than SarfiruzAbad region with highly degraded stands.

The aim of this study, was to evaluate the effectiveness of different preprocessing methods and modeling techniques on the accuracy of aboveground carbon stock estimates in two forest stands with different degradation levels (Gahvareh forest and SarfiruzAbad), in Zagros forests in Kurdistan province. Comparison of different digital pre-processing methods on Landsat 8 images was carried out in different scenarios of radiometric, atmospheric, topographic and their combination. In each scenario, we used four modeling methods included linear regression, generalized additive model, random forest, and support vector machine. In most cases, radiometric correction with improved correction coefficient was 0.71 (R2adj =0.71) and the root means square error of 30% (RMSe% =0.30) was outperformed. Comparison of four modeling methods indicates the lower accuracy of estimates in the SarfiruzAbad area with more degradation severity (R2adj =0.58) compared to the less damaged Gahvareh area (RMSe% =0.74). The random forest method for Gahvareh area and linear regression and a generalized additive model for SarfiruzAbad provides better results, respectively. However, our findings showed that selection of suitable preprocessing and modeling method have a noticeable effect on the accuracies of characteristics estimates in forest ecosystems by Landsat imagery.

The main aim of this study was to investigate cluster sampling method and random sampling method with one hundred percent of the estimated inventory features of density and canopy cover in protected forest occupied by Chartagh Ardal forest area for this purpose, 50 hectares of forest of the study area 100% survey with rectangular plots (20×50 m) were measured. In each clustering methods20 clusters were measured with 100 × 250 m demotions. In each cluster, characteristics the number of trees and canopy trees (both large and small diameter of the crown) were measured. In random systematic sampling methods 100 plots were selected in grid inventory with 50×100 m Dimension. The results this study showed a significant difference between the mean estimates of sampling methods with the actual 100% survey. The results of statistical error showed the cluster sampling method with inequality sampling units (5.59% and 9.64%)had more accurate than the cluster sampling method with equality units (6.46% and 10.69% ) and random systematic sampling (8.9 and 11.02% ). The basis of the criterion of authenticity and accuracy of cluster sampling with inequality members had more accurate than the cluster sampling method with equality units. Therefore, the cluster sampling method with inequality sampling units was suitable sampling method in the Chartagh Ardal forest area.

Persian oak (Quercus brantii Lindl.), the family of Fagaceae, is the most important tree species in central Zagros forests. The present study aimed to evaluate the biometrical properties (fiber length, fiber diameter, lumen and cell wall thickness) and physical properties (density and volumetric shrinkage and swelling) of Persian oak in central Zagros forests. For this propose, three normal trees with the diameter between 20to 30 cm were selected and the mentioned properties were evaluated. Disks were cut at breast height from each tree. Testing samples were prepared in radial direction (pith, middle and bark) and then were investigated. The results indicated that the average length, diameter, lumen and cell wall thickness of fiber were 5/971, 8/18, 22/4 and 88/6 μm, respectively. Results also showed that the average density was 0.99±0.14g/cm3 and the volumetric shrinkage and swelling were 11/87 and 13/84 percent, respectively.

The study and determination of vegetative elemens with similar ecological needs are important steps to identify the plant communities and to study the edaphic conditions of habitat in different plant communities. Accordingly, the present study deals with the plant communities and their relationships with edaphic conditions in Manesht forests of Ilam province. To this end, the Braunblanquet method was used and the vegetation data were analyzed with Pc- Ordwin software. After recognizing the plant communities and determining indicator and preferable species with the use of TWINSPAN and DCA analysis based on the maps of land form, land type and vegetations and overlaying of the above-mentioned maps, the locations of digging profiles were determined and the soil was sampled. Soil samples were analyzed chemically and physically and soil data were also analyzed. The results showed that there are three recognizable communities for this region, namely Amygdalo- Daphnetum Mucronata, Pistacio-Acertum Cinerascentis and Quercetum Brantii. With regards to edaphic conditions of the habitat in different plant communities, it can be concluded that Pistacio-Acertum cinerascentis and Quercetum Brantii communities are on the insepthisol soils and Amygdalo- Daphnetum Mucronata community exists on the enthisol soils. Based on the PCA analysis, the soils of Pistacio-Acertum Cinerascentis and Quercetum Brantii communities are more fertile than the soil of Amygdalo- Daphnetum Mucronata community. Also, in Quercetum Brantii community in Iran, the soil is lighter, more drained, and its depth is more.