امیراسلام بنیاد

Prticipatory forest management, as a model for sustainable forest development, prepares local people to play an effective role in their fate and helps them  participate in the process of sustainable forest development. The aim of this research is to identify the strengths, weaknesses, opportunities, and threats of forest resource management in Qazvin Province from an economic and social points of view to provide and prioritize suitable management strategies.

This research was carried out in the Tarem-Sofla section of Qazvin city. The social fabric of the region is rural-nomadic and the main occupation of the residents of this area is agriculture and animal husbandry. Required data was collected by field studies and questionnaires. In order to determine SWOT elements and identify strategies, 50 questionnaires were completed and reviewed by experts. In the next step, AHP technique was used to prioritize strategies.

Results indicated that six strengths, six weaknesses, seven opportunities and seven threats were identified for managing these forests. According to on the results of the SWOT matrix, the most important of strengths of forest management in the region include soil conservation and prevention of erosion, the most important of opportunities include economic stability, improving and diversifying the livelihood of dependent communities, the most important of weaknesses include weak enforcement of laws, policies, and strategies, and the most important  threats include  reducing the level of forests. The final prioritization of SWOT factors showed that opportunities have priority in this analysis, with economic stability, improvement, and diversification of the livelihoods of dependent communities, and participatory forestry having final weights of 0.166 and 0.109, respectively. The prioritization of strategies based on the AHP showed that program-based management with stakeholder participation, with a relative weight of 0.252, has priority over other strategies.

As a result, managing with an attitude toward planning with the participation of stakeholders was introduced as a solution to achieve the sustainable forest management considering  the current conditions of forest management in these areas.

Wetlands play an important ecological role in the balance of ecosystems. The excessive increase of aquatic plants causes important changes such as eutrophication, increase of water turbidity, swamping and finally drying of the wetland. Using the time series of satellite data in the Google Earth Engine is an efficient and useful way to investigate the ecological conditions of wetlands. Due to the importance of Anzali Wetland and its complex and strategic conditions, temporal and spatial changes of the vegetation and water body of the wetland were studied using Landsat satellite (TM), (ETM+) and (OLI) sensor data from 1990 to 2020 in Google Earth Engine. Vegetation changes were evaluated using the (NDVI) index and (mNDWI) index was used to study water body changes. The results show the surface of the wetland water body has decreased by 5520 hectares (20%) during the three decades of study, dense vegetation covers have decreased from 7756 to 5350 hectares and semi-dense vegetation has increased from 6967 to 14100 hectares, according to the sampling, these changes were confirmed. By examining the annual precipitation in Guilan province, based on TRMM satellite rain data, it was found that the precipitation factor has a nonsignificant role in reducing the water body of the wetland. And due to human interventions through the introduction of river sediments and the pollution load resulting from the entry of agricultural, urban and industrial effluents into Anzali Wetland, as well as the reduction of the amount of water in the rivers leading to the Wetland due to their excessive use in the upstream of the basin, the water body of the Wetland has decreased and semi-dense vegetation has increased.

Earth's surface reflection is significantly affected by atmospheric conditions such as water vapor and particulate matter; therefore, atmospheric correction is needed to minimize these effects and convert digital number values to surface reflection. Therefore, as a remote sensing approach, atmospheric correction is required to minimize these effects and convert digital number (DN) values ​​to surface reflectance. The main objective of this research was to study of four atmospheric correction models, including (1) dark object subtraction (DOS), (2) fast line-of-sight atmospheric analysis of spectral hypercube (FLAASH), (3) the second simulation of the satellite signal in solar spectrum (6SV), (4) atmospheric topographic correction (ATCOR) model and their comparison with the OLI original image for estimation of the aboveground biomass (AGB) in the forests of Avardim of the Shafarood watershed, Guilan province.

In order to estimate of biomass, 246 plots (3600 m2) were established as systematic-random sampling pattern with 300m×300m dimensions and DBH, height of trees and shrubs were measured. A handheld GPS device (Garmin GPS MAP 64s with an accuracy of ±3 meters) was used for sampling and finding the sample pieces, and for this purpose, before starting the statistics, the latitude and longitude of the points (all sample pieces) were entered into the GPS device. And then, using the above device, the sample parts were determined in the field and the characteristics of the diameter at the chest, the height of the existing trees and shrubs (diameter more than 7.5 cm) were measured and then recorded in the relevant forms. The OLI sensor images of Landsat 8 satellite were extracted from the USGS global site. The selection of images was done according to the season, the amount of minimum cloud cover and also in the growing season close to the time of maximum greenness. These images are presented at L1T level and fully compatible with digital maps. In this research, 7 OLI sensor bands of Landsat 8 satellite related to pass/row number 166/34 have been used. Before the Landsat 8 satellite passes over Iran, Terra satellite prepares images with a time difference of about half an hour to local time. Due to the stability of the atmospheric conditions, it is possible to use MODIS information in step with Landsat 8. Also, in this research, three daily MODIS products were used for each of the Landsat 8 images with a spatial resolution of 500 meters, including: MOD04 (optical thickness of suspended particles in the range of 550 micrometers), MOD05 (water vapor) and MOD07 (total ozone). The DEM obtained from ASTER with a spatial resolution of 30 meters was obtained from the USGS global site. The DEM model was directly used in the SV6 atmospheric correction method. Also, DEM model was used for the atmospheric effect correction method of ATCOR to prepare the map of slope, direction, sky visibility.

The results showed that the atmospheric correction model based on 6SV radiative transfer code had a good performance in most of the plant indices obtained from the OLI sensor data of Landsat 8 satellite. The ARVI index obtained from the 6SV atmospheric correction model has the highest correlation analysis results with a correlation coefficient of 0.801. Also, in the case of using the FLAASH method, ARVI (0.779) and RVI (0.586) indices have the highest and lowest correlations, respectively. In the DOS or dark object atmospheric correction method, the highest and lowest correlations are related to GARI (0.762) and EVI (0.518) indices, respectively, and finally, in the ATCOR method, the highest and lowest correlations are respectively related to NDVI indices (732.0) and GNDVI (0.454). In general, in estimating forest biomass, 6SV atmospheric correction model showed the best performance with the lowest RMSE percentage (15.04%), followed by FLAASH, ATCOR and DOS models.

Estimating and monitoring the amount of biomass on land is necessary for climate change studies, carbon cycle production, food allocation and fuel accumulation, fire behavior studies, etc. in the ecosystem. Also, applying atmospheric corrections on the main bands of the images in the pre-processing process before classifying and extracting plant indices is necessary and unavoidable to remove the unwanted effects of the atmosphere and it improves the accuracy of the results. From the results obtained in the present study, it can be suggested that the 6SV atmospheric correction model, with the integration of water vapor and aerosol optical depth obtained from MODIS products, is more suitable for the estimation of terrestrial zinc based on remote sensing data, especially when using the data in are obtained in summer, when water vapor and temperature are both high and the forest canopy is in full development. Finally, it is suggested to use the 6SV atmospheric correction model to estimate of aboveground biomass based on remote sensing data.

The objective of this study was to estimate the recreational value of Chitgar forest park and the willingness to pay (WTP) of visitors to this park using the contingent valuation method (CVM). The questionnaire was used for data collection. The Cochran formula was applied to determine the required sample size for the survey. The random sampling method was used to survey. The double-bounded dichotomous choice approach was used to design the questionnaire. A logit regression model was used to estimate the relationship between WTP and explanatory variables. The results of the logit model showed that WTP had a significant negative relationship with the explanatory variables such as the respondent's age, household size, education level, and membership in non-governmental organizations (NGOs). In addition, results indicated that WTP had a positive relation with respondents' gender, marital status, and monthly household revenue as the explanatory variables. Results showed that the maximum expected WTP of visitors was 79630 IRRials. Furthermore, results showed that the total annual economic value of the recreation in Chitgar forest park was 38.9 billion IRRials. The results of this study can help decision-makers to enhance the quality of Chitgar forest park, develop tourism, increase the number of visitors, and generate revenue for the park.

Wetland habitats are one of the most important natural ecosystems in the world. Evaluating and managing these valuable ecosystems require accurate and up-to-date data that remote sensing makes it possible. In this study, the changes of Anzali International Wetland in Gilan province, Iran, were investigated using Landsat satellite images and the Modified Nomalized Diffrence water index (MNDWI) in Google Earth Engine (GEE) platform during the years 1986 to 2020. To monitor waterbodies changes, two classes of water and non-water area were classifeied by Support Vector Machine (SVM) algorythm and MNDWI index was used to distinct the water surface areas. On the other hand, climate data including TRMM satellite data and PDSI index from TerraClimate data and Caspian Sea water level data were used to determine their effects on water level fluctuation of the wetland. The maps of SVM classification had overall accuracy more than 87% and Kappa coefficient was more than 88%. The wetland water body loss has decreased by 20% in its area according to MNDWI index maps, it has reached from 5926 hectares to 954 hectares, so that initially (until 2000) there was an upward trend and then a downward trend in the wetland water level. Also, the water level of Anzali wetland have been affected more by the sea level than the climatic factors. The results show that water indices and Google Earth Engine are efficient tools to identify the trends of water level changes of wetlands, and could provide more detailed scientific guidance to protect and manage natural resources in the studied areas.

The distribution of canopy classes is the most important structural characteristics of Zagros forest stands. Determining the fitting pattern of canopy classes in Zagros forests shows that, the overall status of these forests from the destruction process and ecological sequence points of view at different times. The aim of this research was to evaluate of probability-statistical functions in order to fit canopy classes of trees in Chartagh forests of Ardal city, Chaharmahal ve Bakhtiari, Iran.

For this purpose, an area of 50 hectares in Ardal forests was selected (3157 trees) were fully callipered and statistically analyzed. Exponential, Gamma, Normal, Beta, Weibull and Log-normal probability distribution patterns were fitted to crown canopy classes. Characteristics of distribution patterns were estimated using maximum likelihood method. Kolmogorov-Smirnov, Anderson-Darling and Chi-square tests were used for comparing of actual probability and probability which derived from functions.

The results of fitting tests showed that log-normal probability distribution was suitable for canopy classes modelling in Chartagh forests.  

Log-normal probability distributions can be used for those who want to simulate changes of forests

The purpose of this study was to measure the relative efficiency of forest harvesting processing companies in Guilan province by considering pollution as an undesirable output. For this purpose, data envelopment analysis (DEA) method was used to evaluate the efficiency. In order to conduct this research, data from 12 forest harvesting companies in Guilan province were collected during 10-year period (2007-2016). First, the variable reduction method was used to reduce the indicators and then the units were ranked using the super-efficiency method. The results show that due to the importance of undesirable output, a number of companies have good ranks in terms of efficiency despite their high adverse undesirable output (air pollution) are in a good position in terms of efficiency (Iran Wood Coating Company and Layl Forest Cooperative). Due to the existence of undesirable output (air pollution) in forest exploitation companies, it is possible to improve the performance of companies with high efficiency along with undesirable output by reducing the amount of pollution.

Trees are one of the most important and largest carbon reservoirs in forest ecosystems and measuring and estimating carbon reserves in different organs of trees is one of the most important research topics for ecologists. The aim of this study was to estimate the monetary value of aboveground carbon sequestration of tree species in Chitgar forest park. Based on a systematic random sampling method, a total of 100 circular sample plots were collected in a grid with dimensions of 100×100 m. In each plot, species type, diameter at the breast height, and the height of trees were measured. Allometric equations were used to estimate the carbon sequestration of different tree species. The analysis of variance and Duncan tests were used to compare carbon sequestration between different species. The results showed that the mean carbon sequestration in Robinia pseudoacacia, Malus sp., Pinus taeda, Fraxinus sp. and Juniperus sp. were 4.58, 1.92, 58.31, 2.08 and 0.86 ton/ha, respectively. The results of the analysis of variance showed that there was a significant difference between the mean carbon sequestrations between different species. Besides, the results also showed that the above ground sequestered carbon was 67.76 tons per hectare and in the whole studied area with 800 hectares, the sequestered carbon was 54212 tons. The economic value of carbon sequestration in Chitgar forest park was calculated at 1.37 million EUR.

Increasing carbon storage in forests is the goal of sustainable management, in recent years, it has been recognized as one of the important tools in mitigating the impact of climate change. Carbon trading is one of the market-based tools as a cost-effective and convenient way to climate change and environmental issues. The aim of this study is to determine the economic value of carbon in poplar plantation. Carbon content was measured in the plantation of Populus deltoides and adjusted carbon price was determined using the non-stationary regression model. Net present value of carbon was calculated and finally sensitivity analysis of price and carbon interest rate were performed. Results show the plantation stock was 180.2 m3/ha (57.6 t/ha) and carbon content was 32.2 t/ha at age 36 years. Net Present Value calculated at different ages and the sensitivity analysis showed that the net present value increased with increasing price and interest rate.

Background and Objective:

Sisangan forest park is one of the important habitats of Buxus Hyrcana in Iran. Unfortunately, the park has suffered from dieback in recent years, and many Box trees have been destroyed. Monitoring and management of this zone can be effective in controlling, protecting, and supporting it. However, due to the destruction of Box trees, on a large scale, it is not possible to accurately estimate the area using the available data. On the other hand, manual measurements are also very time-consuming and tedious. Therefore, a way must be found to do this process accurately and automatically. Unmanned aerial vehicles (UAV) have made this possible by using highly accurate sensors (spatial resolution). Another solution that can be used to automatically separate dieback trees from green trees is to use different classification methods. The aim of this study is to prove the ability of low-cost UAV data with conventional sensors to detect and zoning areas that have suffered Dieback. Since the cost of UAVs with multispectral sensors (red edge band and near infrared) is very high, it should be possible to reduce this cost. Since the cost of UAV with multispectral sensors (red-edge and near-infrared band) is very high, it should be possible to reduce this cost.

Materials and Methods :

Sisangan Forest Park has located 30km to the east of Nowshahr County, Mazandaran province, at latitude 36º33′30″ to 36º35′30″ N, and longitude 51º47′ to 51º49′30″E. This park is both a tourist destination and many important plant species of the country grow in it. One of the most important of these species is the Buxus Hyrcana. But unfortunately, in recent years they have become snag due to pests and insect infestations. Multirotor UAVs have been used in this research. The camera installed on this device is capable of capturing 20 megapixel images. Imaging operations were performed on December 28, 2017, at 10:00 AM, which lasted 45 minutes. The study area was visited for field sampling and its different points were identified in terms of density of snags and preserved Buxus Hyrcana. Then, three circular pieces with a radius of 60 meters and an area of 1.13 hectares were designed in the zone and the density of snag stands and preserved Buxus Hyrcana stands were determined in these three samples. In each plot, 50 training points were recorded in the places where the Buxus Hyrcana stands were located and also 50 points were recorded in the places where the preserved Buxus Hyrcana stands, floor grass cover, and blackberry was located. In this study, in order to evaluate the accuracy of UAV images in identifying and classifying zones covered with Dieback, the smallest Dieback stands with the smallest canopy width were also recorded. Because UAV images require geometric corrections, they were first corrected geometrically and geographically. They were classified with ENVI software. According to the above explanations, 100 points were recorded in each sample plot, 75 of which were monitored for the classification process and 25 of which were used to evaluate the classification accuracy. Three monitored artificial neural network classification algorithms, maximum likelihood and minimum distance were used to classify these images. Finally, after performing each of the classification steps, a low-pass filter with a size of 3 by 3 pixels was used for smoothing the images. Kappa coefficients and overall accuracy indices were also used to evaluate the results.

Results and Discussion:

 In this number of sample plots, 579 stands were measured. Buxus Hyrcana was by far the most frequent in the zone. European hornbeam, Parrotia persica, and Oak were in the next ranks, respectively. The results showed that the artificial neural network algorithm had the best results compared to the other two algorithms. But the results of the artificial neural network also fluctuate according to the condition of the sample piece. This algorithm with an overall accuracy of 97.47% and a kappa coefficient of 0.94 had the best results in the separation and detection of the Buxus Hyrcana snags in the sample plot with the dominance of Buxus Hyrcana snags. After the artificial neural network algorithm, the maximum likelihood algorithm showed more favorable results in separating the Buxus Hyrcana snag stands. The minimum distance algorithm showed good results, but it was not as accurate as of the previous two algorithms. All three algorithms showed poorer results in separating the bases in the sample plot with the dominance of live bases in the sample than the other two sample plots. The sample piece with the predominance of live and green bases compared to the other two sample pieces has more phenomena and effects and in terms of image texture, there are many significant differences compared to the other two sample pieces. All three algorithms showed poorer results in separating the stands in the sample plot by dominance the preserved stands in the sample than the other two sample plots. The sample plot with the predominance of preserved stands compared to the other two sample plots has more phenomena and in terms of image texture compared to the other two sample plots has a lot of significant differences. In this sample plot, in addition to the presence of preserved and snag stands, grass cover and blackberry accessions can also be seen. In this study, the results of classification and detection of Buxus Hyrcana snags using an artificial neural network algorithm were much better than the maximum likelihood and minimum distance algorithms. One of the reasons for the better results of the artificial neural network algorithm is its nonlinearity and non-parametricity. But in classification by traditional algorithms such as statistical methods, they have lower accuracy because they have less flexibility. Parametric types of traditional methods, such as the maximum likelihood algorithm, due to depending on Gaussian statistics, if the data are not normal, cannot have the desired accuracy in classifying and separating classes from each other. In traditional algorithms such as maximum likelihood and minimum distance algorithms, training data play a vital role. In these methods, it is assumed that the distribution within the training samples should be normal so that if this condition cannot be met, the classification accuracy will be greatly reduced. While artificial neural network methods operate based on the characteristics and structure of the data itself.

Conclusion:

 The results of this study showed that using the data and ordinary images of a low-cost UAV, it is possible to study the condition of Dieback after the outbreak of the disease and determine its area. Despite the high cost of purchasing expensive sensors to monitor vegetation status, these methods presented in this article can be done at a much lower cost. This method can be of great help to the relevant institutions in determining the area of snag coatings.

In recent years, a considerable part of the western oak forests of the country has been faced with drought and decline. Initial studies have identified one of the factors affecting oak forest dryness as increasing frequency and severity of dust phenomenon. In this study, the MODIS sensors with a resolution of 1 km and 250 m and meteorological data of Kermanshah Province synoptic stations over a 15-years period (2014-2000) was used to investigate the dust phenomenon and the process of forest vegetation changes in the region. Dust intensity was investigated using visibility data and unsupervised image classification method. Also, in this study, the relationship between the occurrence of dust and vegetation changes in oak forests was investigated. Survey of dust events showed that Kermanshah Province had the highest number of dust events in 2008 and 2009, respectively. The highest incidence of severe dust was in 2012. In addition, satellite imagery and spectral indices also showed that the highest reduction of vegetation in dense forests (>50% canopy cover) by 67% and with an overall accuracy of 62% in the south and southeast of Kermanshah Province and at the border of Lorestan and Ilam provinces has happened. Results also showed that the frequency of dust phenomenon is highly correlated (70%) with the phenomenon of oak forest degradation; and dust has the effect of reducing the greenness of oak forests in Kermanshah province.

There was a possibility to study earth coverage on a large scale using remote sensing data. The support vector machines (SVM), artificial neural network ‏)ANN( and maximum likelihood )ML‏( algorithms were used to Land cover classification on OLI sensors data and 4 kernels in Guilan province.

Classifications were based on training samples of 10 different covers in the entire Guilan province. To improve the classification accuracy on OLI image data, the MODIS atmospheric products used in 6SV atmospheric correction model. The OLI atmospheric corrected image segmented to 219000 polygons based on homogeneity. In this study 2% of polygons were used to test and training samples by the random statistical method. Polygons labeled to classes by field survey.

Applying ANN, SVM and ML algorithms on the OLI images after atmospheric corrected by 6SV model, the overall accuracy of classification improved 0.11%, 0.8%, and 1.9% respectively. The results indicated that the land cover map by RBF-SVM had overall accuracy and kappa coefficient with 75.6% and 0.72 respectively. In this algorithm accuracy of agriculture, range‏ shrub land and water body classes were ‏93.16%, 72.55% and‏ 96.57% respectively. The results of this study indicated that SVM algorithm improved overall accuracy 1.67% compared to the ML algorithm.

This research indicated that in land cover classification and mapping of Guilan province, the nonparametric SVM algorithm had more accurate than the ML parametric algorithm. According to the results of this research, it is suggested that atmospheric correction models should be used especially on the large and local images.

This research considers the optimal rotation age regarding to the carbon sequestration value in populous deltoides plantation in Choobar located in Guilan Province, north of Iran. First, the volume increment is determined by stem analysis method and carbon sequestration is determined by combustion in an electric oven. Then, Net present value (NPV) of carbon is calculated by using volume increment, carbon amount, interest rate, adjusted price of carbon and net revenue in order to determine the optimal rotation age. Faustmann model was used in order to determine the optimal rotation age. Data such as volume increment, carbon content, revenue, timber and carbon prices are used to estimate the NPV. The study also evaluates the effects of different cost of plantation and discount rates on the optimal rotation ages using NPV approach. Results showed that when economic value of carbon sequestration is considered, optimal rotation ages is 27 years. Sensitivity analysis is done for interest rate, cost of plantation, and carbon price. As cost of plantation increases, the optimal rotation age also increases and the net present value decreases. With the increase in carbon prices, the age of optimal rotation age decreases and the net present value increases.

Today, modeling and determination of allometric equations of forest trees, especially Junipers trees, are very important for determination of biological status and carbon storage capacity of forest species. The aim of this study was to determine the most suitable allometric equations for estimating the biomass of leaf, sub branch, main branch, trunk, and biomass of total Juniperus excelsa trees in Chaharbagh region of Golestan province. For modeling, at least 3 trees were selected in each diameter class for harvesting leaf and branch samples. A total of 35 sample trees were harvested and in the laboratory after weighing and drying samples, the biomass of leaf, branch, trunk and total was calculated. Allometric equations were obtained based on power, exponential and polynomial regression models. For validating the models, the criterion of determination coefficient, standard deviation of fitted model, regression analysis, and compliance with the distribution of residual values from normal distribution were used. The results showed that independent variables, diameter at beast height, crown surface and mean diameter of the crown produced equations with better modeling indices. Also, the power and polynomial models of the exponential model were more appropriate. The correlation coefficient obtained from regression models showed that these allometric equations are suitable for estimating the biomass of Junipers trees in the study area.

The increase in greenhouse gases results in climate changes and global warming and adversely affects the life on earth, while forests play a prominent role in carbon storage. The purpose of this study was to determine the most suitable allometric equations for estimating carbon storage in juniper species. For modeling, after examining the statistical results of quantitative characteristics (diameter at beast height, height and canopy area) of juniper trees and determination of amplitude of changes in breast height diameter, samples of leaf, branch, and trunk were collected from at least 3 trees in each class (total of 35 trees). The amount of carbon storage of leaf, branch, trunk and total trees was calculated in the laboratory. Allometric equations were obtained based on power, exponential and polynomial regression models. Coefficient of determination, standard deviation of fitted model, regression analysis, and normal distribution of residual values were used to validate the models. The results showed that the independent variables, diameter at beast height, crown surface and mean diameter of the crown produced equations with better modeling indices. Also, the power model (R2 = 0.96) and polynomials (R2 = 0.95) was more appropriate than the exponential model. The coefficient of explanation obtained from the regression models showed that these allometric equations are suitable for estimating carbon storage of juniper trees in the study area. The results also showed that each hectare of juniper forest stands was 4.42 tonnes and 13260 tonnes of carbon was stored by underground part of the juniper species in the whole study area. The highest amount of carbon storage is in the trunk and the lowest is in the leaf. The economic value of carbon storage by species was calculated as 25034880 Rials per hectare.

One of the most important factors for determining the volume of trees is selection of an appropriate form factor. This study determined the most appropriate form factor formula and modeling the actual form factor in the Caspian region of northern Iran, Guilan’s forests. Totally, 150 beech (Fagus orientalis) trees were measured. Three coefficients of natural, artificial, and Hohenadel were compared with the actual form factor using pair-wise comparison test. Several nonlinear equations including logistics, Chapman-Richard, and exponential functions were used for modeling the form factor of beech trees. These functions were evaluated in terms of coefficient of determination (R2), paired t test, and accuracy statistics of ME, Root Mean Square Error (RMSE), MEr and RMSEr. The result showed that there were significant differences between the actual form factor and   the each mentioned coefficients. None of the models presented high R2 values. Accuracy statistics showed that exponential and logistic models can be usefully applied for estimation of the actual form factor coefficient. Also the results of t-test showed that there was no significant difference between of actual and estimated means of form factor derived from the logistic model. Therefore, logistic model was selected as the best for the estimating form factor of beech trees in Guilan’s forests of the Caspian region.

In addition to investigate effects of physiography factors including of slope (3 classes), aspect (4 classes), height above sea level (3 classes) on decline of oak trees, the aim of this study was decline modelling of Quercus brantii under effects of physiographic factors in Dalab forests of Ilam province. For this purpose, 509 hectares of Dalab forests of Ilam province were selected. Based on systematic-random sampling method with 200 × 250m dimensions, 100 circular plots (2000 m2) were measured. In each sample, physiography factors and percentage of decline were measured. ANOVA and Duncan tests were used to compare of oak decline in different physiography factors. Logistic regression model was used for modelling of oak decline. Omnibus test, log-likelihood and pseudo r-square coefficients were used to evaluate the logistic regression model. The results of ANOVA test showed that physiography factors had a significant effects on the oak decline. Also the results showed that with increasing slope and altitude, decline of oak trees was increased. The results showed that the lowest and highest of tree decline were belong to east and south aspects respectively. The results of this study can conserved areas that have oak decline, to be effective.

The present study aimed to investigate the accuracy and precision estimation of the number, basal area and volume of the standing trees by methods of random and systematic random sampling in the forests of West Guilan. The cost or inventory time was determined using the criteria (E%2 × T). Inventory was carried out by complete sampling (census) in an area of 52 hectares. The study area (section 11, district 2, Nav forests, Asalem) was divided into 520 rectangular plots (50m×20m) and each plot was measured separately. Measured characteristics were the kind of tree species, diameter at breast height and height. After inventory operation, the study area was stratified based on forest density. In each stratum, sampling was carried out by simple random sampling and systematic random sampling. The results showed that implementation of stratified sampling has reduced the sampling error and increased the sampling time for estimating the characteristics of abundance, basal area and standing volume of trees per ha in the study area. Amount of criterion (E%2 × T) in stratified sampling was less than sampling without stratification and between stratified samplings; stratified random systematic sampling had the lowest value. Therefore, among the studied methods, stratified random systematic sampling, due to high precision and minimum criteria of (E%2 × T), was a suitable method for evaluation of the number, Basal area and volume of standing trees in the study area.

Crown ratio is the ratio of live crown length to the tree height. Crown ratio is a useful indicator for tree vigor, wood quality, wind firmness, stand density. It is also useful in managing many non-timber resources including wildlife habitat and recreation. The objective of the study was to model the crown ratio of oak trees and evaluate the obtained models in natural forests of Ilam province based on nonlinear regression method. For this purpose, 509 hectares of Dalab forests was selected in which 100 circular plots (1000 m2) were established as systematic-random sampling pattern with 200 m × 250m dimension. Within each sample plots, such variables as collar diameter, DBH, stem height, total height, crown area, crown length, crown diameter, basal area, tree Slenderness coefficient and crown ratio were measured. In total, 1113 oak trees were measured. Several nonlinear equations including logistics, Chapman Richard and exponential functions were used for crown ratio modelling of oak trees. These functions were evaluated in terms of coefficient of determination (R2), paired t-test, ME, RMSE, MEr and RMSEr. The results of accuracy statistics showed that all of the models are suitable. But results of R2 showed that exponential model perform better than other models. Also the results of t-test showed that there is no significant difference between of actual mean and estimated mean of crown ratio derived from exponential models. Therefore, exponential model was selected as the best one to estimate the crown ratio of oak trees in Dalab forests.

The aim of this research was analysis of the effect of harvest intensity on the volume growth of valuable beech forests in the North of Iran. For this reason, volume growth of stand trees were estimated by permanent sample plots during a 10 year period in three parcels with different harvest intensities of Low, medium, and high (6, 13.1 and 18 m3/ha, respectively), and in a protected parcel (control) in Asalem Nav mixed beech forests. Results indicated the harvest intensity had significant effect on the stand volume growth (P

Proper forest management needs quantitative and precise estimates of forest stands characteristics. Remotely sensed imageries, due to accurate and broad spatial information, has become a cost-effective tool in forest management. Classification of forest attributes and generation of thematic maps are among the common applications of remote sensing. The objective of this study was to optimize Random Forest algorithm for classification of quantitative attributes of Manesht forest in Ilam Province. Two parameters including mtry= 8, 8, 6 and ntree =300, 800, 200 were used as the optimum numbers to classify basal area, canopy cover and density, respectively. The results showed the more accurate classification in canopy cover (overall accuracy=83%, Kappa coefficient=0.73), basal area (overall accuracy=78%, Kappa coefficient=0.72) and density (overall accuracy=75%, Kappa coefficient=0.69), respectively. Furthermore, variable importance index indicated distance-based vegetation indices are more important for basal area and density classification. It is concluded that the Random Forest algorithm as a non-parametric method could classify basal area, canopy cover and density properly.

In recent years, one of the most important occurred events in Zagros is dieback of forest trees; which every day become more critical. To estimate the dieback severity of trees and determination of the relation between physiographic factors including: slope, direction and elevation from sea level with the dieback severity of forest trees, an area of 400 ha were selected and surveyed in the Ilam forests. Inventory was done by a systematic random sampling, and 80 rectangular sample plots with area of 0.1 ha (40 m × 25 m) were measured. Evaluated parameters into each sample plot were including: species name, diameter at breast height (DBH), severity of dieback, and topographic factors. The results showed that there is a significant relation between severity of dieback and slope (p = 0.008) and direction (p = 0.00001). Dieback of trees was increased with increasing of slope gradients. Severity of dieback was observed in west and south directions more than others. On the base of results, dieback percent of coppice trees was more than high trees. That's means; coppice trees relative to their abundance were damaged more than high trees. Also, there was a significant relation between DBH and severity of dieback (P=0.042) and dieback of trees was increased with increasing of DBH. The most number of withered trees were observed in diametric classes of equal & higher than 40 cm (DBH ≥ 40 cm). About 50% of the total trees were wizen or have had a signs of dieback.

The aim of this study was to estimate the biomas of loblolly pine (Pinus taeda L.) using allometry method in compartment 10 of Chobar-Shaft plantation with an area of 61 ha. For this purpose, 30 circular sample plots, each 500 m2 were established. In each sample plot, the diameter of the stump, diameter at breast height (D.B.H), crown width and height of all trees were measured. Totally, 12 trees in different diameter classes were selected and cut about 10 cm above the ground. Fresh weight of different sections of trees was measured. Samples of each section were transferred to the laboratory and get dried in oven and percentage of dry weight was calculated. Modeling of biomass and independent variables was performed using non-linear regression. Normal distribution of data was assessed by Kolmogorov-Smirnov test. For accessing the best result, several models were tested. The results showed that the model of E(YT) = 0/045D2 H0/864 was the best model for estimation of total tree biomass (R2 = 0.952). Results of our research confirmed the value of allometric equations for estimating of stand biomass.

Quantitative and qualitative information of forest structure is the most important for forest management in Zagros forests. This study was done in order to investigate of effect forest stratification approach on the precision estimation of the quantitative features of trees in N- tree sampling methods (one, two, four, six and eight trees) in Dorood forest, Lorestan province. For this purpose, 141 2500 m2 (50 × 50 m) sample plots were selected in an area ca. 42 hectares and full-calipering inventory was done. Within each plot, the number of trees, diameter at breast height and canopy of each tree (largest/smallest crown diameter) were recorded for all trees with DBH ≥ 7.5 cm. The N-trees sampling methods were then designed in GIS software and according to the scale of the map were implemented in the forest. The forest was then stratified into three types ( 250 trees per hectare) by using the density (number per hectare). The statistical parameters for the number of trees per hectare, basal area per hectare and canopy cover percentage per hectare were calculated before and after the stratification. Results showed that with the implementation of the stratification in the N-tree sampling method, the estimated mean of the characteristics was closer to the real mean. It also eliminates the heterogeneity in the study area and reduces the standard deviation and the coefficient of variation. As well as stratification improved the heterogeneity in the study area and standard deviation and coefficient of variation were reduced. This increases the precision of the N-tree sampling method (one, two, four, six and eight trees) in Dorood forest. Therefore implementation of stratification in N-tree sampling method was positively assessed.

Single tree selection method needs to an accurate estimation of logging damage to residual stand for ecological and sustainable forest management. In this research accuracy and required time for estimation of frequency of damaged trees due to logging operation were assessed through plot and line samplings in a harvest unit of 30 ha area in Asalem Nav forest, northern Iran. In both samplings with same sampling intensity (10% of total area in plot sampling and equal total line lengths in line sampling) five samples size (plot and line sample numbers) were applied with different grid dimensions of systematic sampling method. The results showed that the sample size had significant effect (P 0.618) with actual damage (100% inventory). Adequate grid dimensions were determined 100 m × 100 m with plot areas of each 0.1 ha for plot sampling and 77.5 m × 77.5 m with sample line lengths of each 60 m for line sampling. Required time for line samplings were about 90% lower than plot samplings. These results indicate the line sampling can be used for accurate and rapid estimation of frequency of damaged trees due to selection cutting and ground skidding.