shaban shataee

Leaf area index (LAI) is one of the most important structural characteristics of forest ecosystems, which has a great impact on changes in energy, weather, atmospheric and gas functional models. This index provides managers and planners with a lot of information related to the amount of photosynthesis, evaporation and transpiration and fertility of different habitats. Physiographic factors also (elevation above sea level, aspect and slope) play an important role in the global LAI. In this research, the changes of the LAI in different classes of elevation, aspect and slope are compared and evaluated.

n this study, 230 square-shaped sample plots with an area of 1000 square meters were used to collect the ground information of the leaf surface index using a systematic sampling method with a sampling grid of 100 x 100 meters in five sites (Kordkoy, Shast Kalateh, Zarrin Gol, Sokhdari and Loveh). In the center of each plot, a leaf harvesting trap with dimensions of 60 x 60 cm was used to measure the LAI. First, height maps from elevation, aspect and slope were also prepared and their values were extracted. Then the LAI was compared and analyzed in different classes of elevation, aspect and slope using Duncan's and independent t tests.

The results of LAI showed that the elevation of LAI in Kordkoy vegetation was measured with 8.91 and the lowest level of LAI was measured with 5.10 in Zarrin Gol vegetation. The results of analysis of variance showed that there is a significant difference between the LAI in different classes of elevation above sea level in the entire study area. And with the increase in elevation above sea level to about 1300 meters, the LAI increases and after that, the LAI has little changes. The significant results of Duncan's test and independent t-test showed that there is no significant difference between the LAI of each site in different classes of elevation above sea level, aspect and slope, except for Kurdkoy site.

In general, in this study, the elevation compared to the slope and aspect was raised as a very important factor for investigating LAI. Investigating the effect of physiographic factors such as elevation above sea level, slope and aspect on the LAI and monitoring its changes in order to know the formulation of appropriate policies to reduce the effects of climate change and global warming and in sustainable management is of great importance.

Natural disturbances caused by the canopy gap an important role in forest dynamics. Analyzing the dynamics of the canopy gap provides an accurate understanding of the process of forest stands dynamics as well as natural disturbances that play an important role in the future management of the forest ecosystem. The present study examines the dynamics of the canopy gap in the Arab Dagh forestry in Golestan province.

The identification and preparation of the gaps map was done by the basic object classification method. The classification of the basic object can be done in three general stages, which include segmentation, classification and evaluation of classification accuracy.In the next step, the 1345 canopy gap in 2011 and the 1058 canopy gap in 2019 were extracted from the method that had the best performance compared to other methods of preparing gap maps. The characteristics of the gaps were obtained at each time period. Also, with the intersection of these two maps, the dynamics of the gaps was analyzed by calculating the characteristics Gap formation rate, Gap closure rate, Gap expansion rate, Gap reduction rate, and Gap number increase rate.

The results of the canopy gap dynamics during the 9 years showed that the number and density of the canopy gap decreased. But the average area canopy gap and the share of the area of the canopy gap in the total area increased.In both periods, 65% of the canopy gap have an area of less than 150 m2, and a large canopy gap (larger than 300 m2) covered the lowest frequency and share of the total area of the canopy gap. The highest rate of changes in the canopy gap source was related to the Gap expansion rate (1.09% per year). The Gap closure rate is almost equal to the Gap reduction rate. The lowest rate of changes in the light is the Gap formation rate (0.77% per year). The logarithmic Gap number increase rate (GNIR) was negative (-2.6) that in all canopy gap classes, the number of closed canopy gaps per year is more than It is the number of new canopy gap that is created.

With the passage of 9 years, the density of gaps and the number of gaps have decreased and the total surface of gaps has increased. This increase in the total level of gap can be attributed to the increase in the Gap expansion during the period. Because the Gap expansion rate is higher than the Gap formation rate, the Gap closure rate and Gap reduction rate.In both periods of time, the most abundant size was related to small holes. which indicates the predominance of small lights in the studied area. which shows the predominance of small gaps in the studied area, small gaps make up most of the dynamics of gaps and close faster than big gaps.Keywords: Gap, UAV, dynamic indicators, coniferous forestry, remote sensingps.

Deforestation and forest degradation have led to the formation of poor landscapes all over the world. Although in the past conservation strategies have emphasized intact natural systems, today the strategies have focused on the conservation and restoration of degraded ecosystems. The forest landscape restoration approach (FLR) is considered an important approach for the restoration of ecological and socio-economic services of forest ecosystems in landscape scales. Identification and prioritization of efficient and effective criteria and indicators in the process of prioritizing areas to restore the forest landscape are a necessity that has not been done in Iran so far. Therefore, the purpose of this research is to introduce effective criteria and indicators for prioritizing appropriate areas for forest landscape restoration in the selected area. In this study, a total of 26 criteria and 68 indicators from ecological and socio-economic points of view were identified with an emphasis on directing restoration efforts towards areas that guarantee maximum profits from two perspectives of necessity (the need for restoration) and opportunity (feasibility of a restoration) and then, they were prioritized using the Delphi method. The results showed that in the section on the need for restoration, the process of landscape degradation from ecological criteria and poverty from socio-economic criteria were the first priorities. Besides, the ecological criteria of destruction and natural regeneration potential with equal priority and socio-economic criterion of the budget in terms of feasibility of reconstruction were the first priority. The results of this study, in addition to helping decision-makers and planners dealing with protecting nature protection and its use, can lead to the attraction of active participation of stakeholders in the forest landscape restoration programs with a balanced attention to both aspects of the necessity and the opportunity of restoration.

Forest canopy gaps play an important role in forest dynamics. Unmanned aerial vehicle (UAV) data provide demonstrated capacity to systematically and accurately detect and map canopy gaps and have been considered as an alternative way to describe the forest stands. This study aims to extract canopy gaps using UAV data and compare the performance of different canopy gap extraction methods in a part of the replanted forest in the Arab Dagh Region, Golestan Province, Iran.

After the acquisition of UAV images and initial preprocessing, the digital terrain model (DTM), digital surface model (DSM), Canopy height model (CHM), and orthophoto mosaic were produced. CHM classification performs to extract forest gaps by different methods of height thresholding on CHM, CHM slope thresholding, and object-based classification. For performance evaluation of used methods and accuracy assessment of the canopy gap maps, the central position and boundary of some gaps were measured by DGPS. Finally, the point and polygon base accuracy of delineated gaps were assessed for each of the methods..

The results of the point accuracy assessment showed that the canopy gap map obtained by object-based classification method with applying the support vector machine (SVM) algorithm with 99% overall accuracy and 0.98 kappa coefficient had the best performance compared to other algorithms and methods. About area accuracy assessment, the best match between delineated gaps and ground truth polygons was achieved by using 3 m height thresholding.

The results showed that with aerial images of the UAV and its outputs, as well as the use of automated methods, the map of the canopy gap can be accurately extracted. Of course, the degree of accuracy depends on several factors such as the type of drone and cameras used, flight parameters and so on. Given the results, it is hoped that this approach will gradually be used as a cheap and accurate method in forest surveying.

Forests are subject to a variety of disturbances that adversely affect their health. There are a number of disturbances that cause trees to become weak and dry in the long run. One of them is the stress caused by burning leaves, which typically takes place during very hot summers. The identifying and monitoring of the expansion of its level is therefore very important. As one of the important information sources, remote sensing data can be used to identify it. It is possible to capture UAV images quickly and at desired times, but they have limitations such as the captured surface and the sensor's spectral range. In identifying and preparing a map of leaf burns, tree crowns, and other damages, the combined use of UAV images and satellite images with a high resolution can be helpful. The purpose of this research is to investigate and identify leaf burn of the canopy of trees using UAV images in a part of Deland Forest Park in Golestan province and combining it with Sentinel 2 images to prepare a classification map of the percentage of leaf burn in the tree canopy of the area in a wider area.

RGB UAV images were taken in Deland Forest Park and processed appropriately. Also, Sentinel 2 images were prepared and analyzed geometrically and radiometrically. Ground truth of trees with leaf burn and healthy trees was done in September 1400 selectively and with high precision differential global positioning device. The support vector machine algorithm was used to classify UAV images into three categories: trees with leaf burn, healthy trees, and clear trees. The classification of the main UAV images along with vegetation indices obtained from UAV spectral bands and with selected spectral indices was also repeated. Finally, the prepared maps were evaluated for accuracy. In the second step, for preparing a map of the classes of leaf burn percentage of the canopy cover on a wider level and in the whole park, using the image of Sentinel 2, based on the classified map of the UAV, which has the highest accuracy, as a training example in the regression model of the random forest and calibration. Finally, a map of leaf burn intensity classes of the entire Deland Forest Park area was prepared using the prepared model.

UAV image classification results indicated that UAV visible spectrum bands are able to distinguish healthy trees from trees with leaf burn with an accuracy of 89.62% and a Kappa coefficient of 0.84 for the classification of UAV images. By combining the main bands and 9 vegetation indices, the results of the research decreased slightly (overall accuracy 87.40% and kappa coefficient 0.81); however, when the main UAV bands were combined with the best vegetation indices (EGMRI, EGI and

A basic critical phase in watershed monitoring is choosing the most suitable observation points which are followed by monitoring the natural resources with a wide range of view using visibility spatial analysis. Moitoring towers are crucial assets in detecting forest fires, in addition to other technological advancements. For this study 30 villages, located in Chehelchai watershed, were selected as the observation points along with 26 convenient and accessible perspective points as the observer points. For this purpose, visibility analysis lookout towers were determined using QGIS software and the percentages of visible and unvisitable areas were determined for the study area. Four scenarios were employed aimed at looking into the good visibility of the points for indirect watershed monitoring using Viewshed analysis in QGIS software; First scenario: Offset A = 1.6M, Radius 2 = 1200M; Second scenario: Offset A = 11.6M, Radius 2 = 1200M, Third scenario: Offset A = 1.6M, Radius 2 = 10000 and Fourth scenario: Offset A = 1.6M, Radius 2 = 10000M. The results shwed that the Viewshed side program makes it possible to locate visible and invisible areas from the observer points according to different scenarios. Moreover, it was concluded that visibility analysis is a very useful and practical method for evaluating monitoring  lookout towers and determining the potential location of the towers.

The purpose of this study is to investigate the effectiveness of the early warning system of the Canadian Forest Fire weather Index in the Golestan Province. To estimate the weather index of Canadian forest fires, the input variables to the system were four climatic variables including temperature, humidity, wind speed at a height of ten meters, and rainfall for a period of 24 years (1997-2021) during the fire season (April to January) were collected from synoptic and evaporative stations in the province. First, the Canadian forest fire climate index system was calculated daily during the study period at each climate station. Then, the obtained 24-year average of fire risk potential rate for each station was estimated using the conventional kriging interpolation method with high accuracy and a continuous raster map of the fire risk potential of the Golestan province was prepared. Then, the continuous climatic potential of fire risk was zoned into three categories of medium, high risk, and dangerous using the Canadian Climate Index system classification. 1452 occurred fire points in the past years were used to evaluate the performance of the Canadian forest fire weather index system in the Golestan Province. The results showed that 68% of the fire points, which cover 80% of the burned area, are in the high-risk category. Based on obtained results, it could be concluded that the Canadian Forest Fire Climate Indicator Early Warning System can predict fires in susceptible areas in the Golestan Province with higher accuracy.

Early warning systems in natural areas are one of the ways to prevent and manage fires. Many factors that affect fire occurrence can be divided into two categories climatic factors and human factors. Among the most important climatic factors; increasing the temperature, decreasing the rainfall, decreasing the humidity and increasing the wind speed can be mentioned. Thus,forest fire risk assessment and early warning systems have become an important component of land management in recent decades. The Canadian Forest Fire Danger Rating System is one of the most important early forest fire warning systems in the world. The Fire Weather Index subsystem provides relative numerical ratings of various aspects of wildfire potential based on four weather observations. The purpose of this study is to identify the most effective input variables of the system in predicting the probability of forest fire in Golestan Province.

In order to estimate the Canadian Forest Fire Weather Index System, the input variables to the system include four climatic variables: daily observations of maximum temperature, relative humidity, wind speed, and 24-hour precipitation for a period of 21 years (1997-2018) during the fire seasons (April to December). Were collected daily from synoptic and evaporative stations in the province. First, the Canadian Forest Fire Weather Index modeling system was calculated daily during the study period at each station. Then, the correlation test and Multi-Dimensional Scaling analysis between input Weather variables and system output index were performed using Spearman, Pearson, and Kendall correlation coefficients.

The results showed that the variable of maximum daily temperature with a correlation coefficient of 0.911 in the study period has the highest effect on the weather index of Canadian Forest Fires compared to other variables. After temperature, daily relative humidity, daily rainfall and daily wind speed with correlation coefficients -0.89, -0.79, 0.29 have the greatest impact on the output of the system of forest fires in predicting the probability of forest fires in this province. Also, in this study, it was found that Spearman’s correlation coefficient is better than other correlation coefficients.

Forest managers can use this system to predict the likelihood of fire if other variables are not available, using the order of importance of each of the climatic variables and rate the risk in different parts of the province.

In this study, spatio-temporal forest canopy cover density changes in the Golestan National Park (Iran) were assessed from 1967 to 2016 using aerial photos and satellite images in a Geographical Information Systems environment. The digital aerial orthophoto mosaics were created after georeferencing photos of the years 1967 and 1994. A special canopy interpretation key was used for interpreting and determining the forest canopy cover classes on the digital orthophoto mosaics and Google earth images and for providing the canopy cover density maps. The results indicated that throughout the period from 1967 to 1994, the non-forest areas in some places were converted into forest due to physical and technical management measures. Results also showed that between 1967 and 2016, the canopy cover class 10-25% had the highest increase (16.6%) followed by the 25-50% class that increased by 21.17%. Our research exhibited that applying the physical preservation and protection management plans are the major factors in conversion of non-forest to forest areas and increase of the canopy cover density. These results emphasize that active physical protection and proper implementation of management methods are required for forest restoration in the study area.

The impact of logging and tree harvesting on the ecological function and the future of Hyrcanian forests structure is a continuing debate topic. The information is currently available does not support firm conclusions about the impact of forest harvest intensity on forest structure and biomass. The present study aims to evaluate the effect of single-tree selection harvest intensity on biomass of hornbeam-Persian ironwood stand. For this purpose, information on all trees and dead trees were collected in 70 half-hectare plots. The 35 sample plots were in single-tree selection stands with three harvest intensities and the same sample plots were in control stand. Trees litter fall were measures from September to February and the storage of soil organic carbon was determined through organic carbon and bulk density. Then, tree biomass, litter biomass, dead tree biomass and total biomass were finally calculated. The results showed with increasing the rate of harvest intensity up to 13.5% the total biomass had rising trends, although harvest intensity had no significant effect on aboveground and belowground biomass and soil organic carbon. The aboveground biomass was higher in single-tree selection stand than control stand. The average of total biomass was 147 and 144 ton/ha in harvest and control hornbeam-Persian ironwood stands respectively and harvesting increased two percent of mean total biomass. The medium harvest intensity (3.6-9.5% of stand volume) was finally determined as appropriate selective logging intensity. The correspondence of litter biomass and dead tree biomass as bio indicator indicated that tree marking in the single-tree selection system was advisable.

SAR data have been widely used for estimation of forest variables. These data have a high potential in flat terrain, however, there are some problems such as the effect of topography on backscatters in mountainous regions. The goal of this study was to evaluate the effect of oriented and non-oriented aspect slopes and polarization in the estimation of stand Lorey’s mean tree height, volume, and basal area variables using dual-polarization (HH, HV) data of ALOS-2 PALSAR-2 in a part of Shastkalateh forest of Gorgan. Modeling was performed in three modes including without considering the aspect slope, in the slope-aspect orientated with the transmitted waves and non-orientated with waves, and also in the polarizations as separately using linear regression and support vector machine methods. The results of correlation between the backscattering of the PALSAR-2 and studied variables showed that in the slope-aspect orientated with the transmittance, the correlations were significant for basal area and volume, but it was not significant for Lorey's height. While in the non-orientated slopes, the correlation rate was very low for all variables and no significant. Also, the results showed that the HV polarization and the support vector machine method were very sensitive to the Lorey’s mean tree height, basal area, and volume variables with r and relative RMSE (-0.14, 12.44), (-0.48, 29.35) and (-0.44, 36.40), in the area in the direction of alignment with the waves, respectively. While the lower sensitivity is (-0.18, 14.14), (-0.05, 35.85), and (-0.04, 38.40), respectively, in the non-orientated with waves.

Knowledge on the relationship between tree height (H) and diameter at breast height (D) is crucial for forest planning, monitoring, biomass estimation, and forest stands dynamics description. In keeping with, in this study, the researchers evaluated 20 different height-diameter models to estimate accurately the height of the trees of Pinus brutia and Cupressus sempervirens var. horizontalis species in Arabdagh region (northeast of Golestan province). Required data for cypress and pine species were collected from 114 and 98 circular sampling plots, respectively, using a systematic cluster survey network with cluster spacing of 400 * 600 m. Species characteristics such as diameter at the breast height of all trees (greater than 7.5 cm) and trees height were measured and recorded in all sample plots. Overall, 389 and 299 pairs of height-diameter observations were collected for cypress and pine, respectively. The 75 percent of the data were used in the modeling process and the rest data were used to evaluate the models. The fitness of the models was evaluated using statistical criteria of% RMSE and R2. The results showed that for cypress and pine, the R2 ranging from 0.55 to 0.59 and 0.50 to 0.51 and the RMSE% varied from 15.32 to 22.87 and 11.09 to 16, respectively. At the end, the models of Sibbesen, Michaelis-Menten, Power, Prodan, and Chapman-Richards for the Cypress, and the Gomperz, Korf, Weibull, Michailoff, and Ratkowsky models for the pine species were better able to estimate trees height.

Background and the importance of litter thickness in the regulation of microclimate and the physical, chemical and biological characteristics of the soil, the regulation of soil input and output exchanges and evolution and continuity of forest stands and the study of its nature in natural endemic hardwood and planted forests and different environmental conditions is an essential issue. In this study, different techniques of interpolation were analyzed and compared to estimate the spatial variability of litter thickness in the Arabdagh area located in Northeast Golestan province. Litter thickness data were collected in 422 sample plots with an area of 400m2 using a systematic cluster network (400×600m, each cluster includes 5 plots with a distance of 100 meters) in different hardwood, softwood tree stands and shrub&herb lands including Pinus brutia, Cupressus sempervirens var. horizontalis, Pinus pinea, Pinus sylvestries and broad-leaved species such as Zelkova carpinifolia, Carpinus betulus, Acer velutinum, Parrotia persica and mixed broadleave. Then, after an initial analysis of the data in statistical software, their spatial and descriptive database was prepared in GIS environment . In order to produce litter thickness thematic map, the efficiency of different interpolation methods of EBK, OK, RBF, LPT, LDW and Co-kriging were compared. Cross-validation was performed to evaluate the accuracy of different interpolation techniques by the coefficient of determination (R2), mean relative error (MRE), mean error (MBE), mean absolute error (MAE) and root mean square error (RMSE). The results of this study presented that the highest thickness of the litter was in the natural broad-leaved stands and the lowest thickness in the Cupressus arizonica type in the region. Also, in the broad-leaved group, the pure type of Zelkova carpinifolia had the most changes (1.09 cm). While, in the needle-leaved group, the Pinus brutia type showed the most variation in the thickness of the litter. The results of this study showed that the Co-kriging interpolation method with the exponential (0.783), spherical (0.789) and Gaussian (0.791) models using the auxiliary data of Basel area per hectare and with least-squares error and the highest coefficient of determination had high capability Compared to kriging (0.8 to 0.817) and deterministic models (0.875 to 1.05) in spatial distribution interpolation of litter thickness in Arabdagh region. Due to the effect of litter thickness on some stand and habitat factors such as regeneration , soil quality and Permeability and The intensity of some natural disturbances in the area such as wild fire, it is recommended to take an effective step in managing plan and forestry projects by evaluating it. Due to the effect of litter thickness on some factors of habitat and habitat such as regeneration, soil quality and permeability and the severity of some common natural disturbances in the region such as fire and the results of this study, the Co-kriging interpolation method with the exponential and Gaussian models compared to other methods, due to the accuracy of the results can be more effective in determining the litter thickness of forest stands as well as managing similar stands and afforestation plans.

Knowledge of forest inventory and estimation the exact volume of trees is one of the important features in planning for forest resources. The form factor is one of the most important factors in determining the exact volume of trees. To estimate the actual volume of trees, the form factor must be calculated. The aim of this study, determination of form factor for three species (Pinus brutia, Pinus pinea and Cupressus sempervirens) in the Arabdagh reforests, and comparison of the real form factor with the natural, artificial, and Hohnadl factors.

In this research four types of form factors including real, natural, artificial, and Hohnadl factors for three species (Pinus brutia, Pinus pinea and Cupressus sempervirens) were evaluated in the Arabdagh reforests, Golestan province. For this purpose, 39 trees )13 trees for each species) randomly selected in different diameter at breast height classes from 7.5 to 42.5 cm (2 trees of each diameter class). In each tree, height, diameter at breast height (D.B.H) and diameter at 0.1, 0.3, 0.5, 0.7 and 0.9 of tree height were measured, then 2-meter length logs from every tree were cut and separated and their diameter at two tops of 2 m length logs were measured. In addition, the height and diameter of the clog and the length of the tree was also measured. To calculate the exact volume of the trunk from the sum of 2 m long logs volumes and log using the Smalian formula. Then the form factors (real, natural, artificial, and Hohnadl) were calculated for every 3 species.

The results showed that true, natural, artificial, and Hohnadl form factor value were 0.49, 0.49, 0.41 and 0.45 (Pinus brutia) respectively. In addition, there was a significant statistical difference between artificial with true form factor (α = 0.05), but There was no significant difference between natural and Hohnadl form factor with the true form factor. The form factors (Pinus pinea) using real, natural, artificial and Hohnadl formulas was obtained 0.51, 0.47, 0.41 and 0.45 respectively. There was a significant difference between the real form factor with artificial and Hohnadl form factors. However, there was no significant difference between natural form factor with the real form factor. The form factors (Cupressus sempervirens) using real, natural, artificial and Hohnadl formulas were 0.55, 0.53, 0.48, and 0.48 respectively. There was a significant difference between the Hohnadl and real form factor. However, there was no significant difference between the real, natural and artificial form factors

According to the results of this research, we can conclude that the natural form factor can be used as an appropriate form factor for Pinus brutia, Pinus pinea, and Cupressus sempervirens, and Hohnadl form factor for Pinus brutia and artificial form factor for Cupressus sempervirens can ability replace the real form factor.

Fire severity mapping is very important for managing the fires in forest ecosystems. The extraction of spectral indices from optical sensors is recognized as one of the most effective bands for the classification of vegetation classes. In this study, the ability and sensitivity of some spectral indices extracted from Sentinel-2 and Landsat 8-OLI images with different spatial resolutions have been investigated for fire severity mapping using the Random Forest algorithm in a burned area located in the reforested area of Arabdagh, Golestan province. After necessary preprocessing on the bands, the appropriate mono and bi-temporal spectral vegetation indices were created. The optimal index values for bands in the bi-spectral spaces pre/post-fire were calculated to evaluate the sensitivity of bands to the changes occurring within the fire classes. The best results were obtained for the NIR-SWIR2 bands with an optimal index value of 0.77 for Sentinel-2 and 0.67 for Landsat8-OLI. The best indices were selected based on values of optimality index. The values of these indices were calculated after the fire as well as the differential (pre/post-fire) ones. The ground truth of fire severity classes map was prepared by a selective sampling method through field surveying. The classification was done with different indices by random forest (RF) algorithm and the results were assessed by the ground truth points. The result showed that the best results were obtained for a combination of many differential indices from all bi-bands of Landsat 8-OLI with kappa coefficient (0.96).

In the most parts of the country, the number of meteorological stations is inadequate and don't cover the appropriate statistical period. The conventional method of yield assessment in Iran is based on those experiments which require numerous field measurements, that makes it expensive, difficult and sometimes impossible. On the other hand; the final data is available much later than when field managers need them. Remote sensing data can be used as an alternative or complementary data for meteorological data to estimate the yield, drought and crop vegetation. To evaluate the soybean yield and its relationship with drought risk potential in western parts of Golestan province (AQ-Qala, Aliabad Katoul, Gorgan, Bandar-e-Gaz, Bandar-e-Torkaman and Kordkouy), the meteorological (SPI) and Landsat satellite imagery-based vegetation indices (including NDVI, VCI and DSI) were used.

In this study Landsat satellite images from 2000 to 2016 were used. After appropriate pre-processing and processing, the vegetation indices were prepared. Also the meteorological drought index (SPI) was calculated using the weather stations data of the study area. To calculate the relationship between the yield and all aforementioned studied indices (NDVI, DSI, VCI and SPI indices), the averaged-DN average for each index in each city was calculated. Then, the yield of each city was regressed against the meteorological (SPI) and satellite-imagery based indicators (NDVI, DSI, VCI). The linear function with the highest signifsignificant determination coefficient was selected and the soybean yield map was provided for the study area. The final drought risk model was adopted using drought frequency maps for SPI, DSI and VCI indices. The yield change in all drought risk categories was assessed through investigating the compatibility of the Boolean-classified yield maps and drought risk maps of the studied region. The

Coefficient of determination for yield prediction in different years ranged from 0.13 to 0.52, also the most of the predicted values were put in confidence level of 15% range of discrepancy which proved the accuracy of used equations in predicting yield maps. It can be concluded that yield can be predicted in a precise and accurate manner at the peak of soybean vegetation growth by applying meteorological and satellite-imagery indicators. The results showed that the yield of 43% to 50% of the soybean fields was higher than reported mean yield. Results showed that soybean fields in the AQ-Qala, Aliabad Katoul, Gorgan, Bandar-e-Gaz, Bandar-e-Torkaman and Kordkouy counties were not classified as very severe drought risk areas. The compliance of the yield and drought risk maps indicated that the possibility of yield reduction in facing with drought is higher in those areas with higher drought risk.

Totally, the results showed that Bandar-e-Torkaman, Gorgan and the central and southern parts of Aliabad Katoul are facing a lower risk of drought. The intersected map of yield and drought risk can be used as a predictive tool to provide strategies to manage drought risks as well as coping with drought effects on the yield.

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.

Drought is not a characteristic of a region and may occur in any weather regime. Therefore, reasonable management is required to deal with this natural disaster which can be creating a management system and providing accurate information at different time scales. Meteorological, agricultural, hydrological and socioeconomic drought are defined based on lack of rainfall, lack of soil moisture, shortage of rivers stream and the shortage of some commodities affected by the drought trend. One of the effective methods for analyzing various kinds of drought is the evaluation of drought indices, which aims to provide a simple and quantitative assessment of drought characteristics. The purpose of this research is to investigate the Spatial and temporal monitoring of agricultural drought in the Wheat cultivated area using standard precipitation index in west of Golestan province.

In this research, in order to monitor the spatial and temporal distribution of drought in the years of 2000 to 2016 in AQ-Qala, Ali Abad, Gorgan, Bandar-e- Gaz, Bandar-e- Torkaman and Kordkouy counties, 15 meteorological stations with common statistical database (1984 to 2016) were used to estimate the rainfall. After calculating the standard precipitation index (hereafter SPI) and more reliable correlation of May’s three-month SPI index (hereafter SPI3) with the mean wheat yield, this index was zoned with ordinary Kriging interpolation method and then drought was assessed. In order to prepare a drought risk map, SPI3 rasters were reclassified as binary (BOOLEAN) for a time span of 17 years (2000 to 2016). Then, a map of the drought frequency was provided by the overlaying of Boolean maps. Using drought frequency maps of the SPI index, a final agricultural drought risk map was generated.

In 2000, 88% of the total area were affected by moderate drought and another 12% (the majority of which was in Aliabad) was affected by moderate classes of drought. But in 2007 and 2016 there was no drought in the investigated counties. In this research, by studying the drought severity and drought risk maps, it was clarified that the SPI index was an effective index in evaluating the drought patterns in different temporal and spatial scales and during drought periods. Also, the results showed that six studied counties in Golestan Province were classified into two risky drought areas (including severe and extreme classes). Risk map showed that in the applied statistical time period (2000 to 2016), an extensive part of the study area (about 98%) is at extreme risk class. Also, a small northern part of the Bandar-e-Torkaman (about 2% of the total area) has experienced an extreme drought. In fact, the magnitude of the drought range in this region is greater due to coexistence with the Turkmen desert and its dry and semiarid weather conditions, which leads to a potential for drought sensitivity.  

The results revealed a positive correlation between wheat yield and SPI3 in AQ-Qala, Ali Abad, Gorgan, Bandar-e- Gaz, and Kordkouy counties. The effect of meteorological drought on the crop production can be a concern (especially in drought-prone areas) and suggests that SPI3 can be helpful for predicting drought-induced yield loss. These results indicate that the western region of Golestan province is one of the areas where droughts will increase in the future. Therefore, rain-fed cropping systems will be in danger in the future, and some of the agroecosystems currently dedicated to the cultivation of rain-fed products will be excluded from the production process. rainfall reduction in recent years, has been led to retreat the Caspian Sea in Bandar-e-Torkaman area, as well as the eliminating of many crops in recent years, which reveals that the consequences of these droughts have been emerged.  Therefore, agricultural policy makers need to consider the replacement of crops with high efficient water usage instead of current ones in cropping patterns in the coming years, as well as be concerned more about land salinization.

In this study we evaluated the potential of ALS data in estimation of height, volume, basal area and DBH and canopy cover area of individualtrees for the part of Shast kalate of Gorgan.

In this study 125 tree that located in dominant story and without overlay with adjacent trees, were selected. Tree species, tree diameter at breast height (DBH) and tree crown diameter were measured. The height of trees was measured using a Vertex VL 402. Center coordinates of sample trees were determined using Digital Golobal Position System. After separating the crown border of a single tree and providing a polygon of the boundaries of them using aerial digital images, all of height and density metrics were created.Then, we explored the possibility of defining relationships between combination of airborne laser scanning data and height, volume, basal area, DBH and canopy cover area of individual trees using machine learning algorithms (random forest (RF), support vector machine (SVM), k-nearest neighbor (k-NN) and artificial neural network (ANN)).

The best RMSE% on an independent validation data for height, volume, canopy basal area, diameter, and basal area were 13.39, 56.88, 33.17, 22.34 and 25.88%. Also, the results demonstrate that the ANN algorithm can be useful for modeling biophysical properties of individual tree in the North of Iran.

Overall, the results showed the ALS data has the ability to estimate of tree height, basal area, diameter at breasts, and canopy cover but this data hasn’t the ability to estimate the volume very accurate. Also, the results showed that between all algorithms, the ANN algorithm have a better performance than other algorithms.

Estimation of quantitative forest attributes is important for its applications in order to understand the forest condition and performance. The aim of this study was the estimation of some quantitative forest attributes (stand volume, basal area, and tree stem density) using the RapidEye satellite data (2011) and non-parametric algorithms in the part of Hyrcanian forests in the Gorgan region. For this purpose, 418 plots each with an area of 1000m2 were established using a simple random sampling method. In each plot, information including a position of plot center, diameter at breast height of all trees and height of selected trees were recorded. Based on which the standing volume and basal area per ha were derived. A RapidEye image was processed by different synthetic bands derived from rationing, principal component analysis, texture analysis, and Tasseledcap, and the pixel gray values corresponding to the ground samples were extracted from spectral bands. These were further considered as the independent variables to predict the Quantitative characteristics. Modeling was carried out based on 75% of sample plots as training set using K-Nearest Neighbor, support vector machine, and random forest methods. The predictions were cross-validated using the left-out 25% samples. The results showed Random forest comparatively returned the best estimates for stand volume, basal area and tree stem density with root mean square error of 39.83%, 29.71%, and 50.11% and relative bias of 0.01, 1.69 and 2.11 as well, respectively. The results of this study also showed that due to the heterogeneity and density of Caspian forests, RapidEye satellite spectral data have a moderate ability to estimate the quantitative forest attributes.

Estimation of cultivated area is controlled on the basis of some features such as seed rate, fertilizer and other delivered chemical inputs which are costly andtime consuming. In this way, using satellite data as a new solution not only reduces the drawbacks of conventional estimation methods of cultivated area , but also can be helpful for various programming aims in the agriculture scope. Land cover mapping is important for much planning and management activities. Today, satellite images and remote sensing techniques are extensively used in all sectors, including agriculture and natural resources because they provide updated data and high analyzing abilities. It is one of the fastest and most cost-effective methods to map those lands are available for researchers. In recent years, sattelite imagery and different detection methods have helped researches to detect the features at a lower cost and spend less time. Different methods are avialbale for this purpose.. Each method has some advantages and disadvantages. Artificial neural network, fuzzy logic, support vector machine, decision tree, object-oriented classification and intelligent systems can be considered as advanced classification methods (Guo et al., 2012). In this study, we want to detect wheat and soybean-grown fields with two advanced classification methods (i.e. support vector machine (SVM) and artificial neural network (ANN)) in west of Golestan Province, Iran. Cultivation areas were determined from those maps extracted from satellite images to use them as the base layer for other research goals.

The current research was conducted with the aim of estimating the wheat- and soybean-grown areas during a 16-year period from 2000 to 2016 using Landsat satellite imagery. To this purpose, two classification methods, support vector machine and artificial neural network, were used. In order to classify and detect aforementioned two crops, ground control points (GCPs), the normalized difference vegetation index (NDVI) for agricultural lands, and spectral behavior of wheat and soybean training GCPs were involved. In order to validate the results of classification, the generated maps were checked by GCPs (coordinated with GPS).

Wheat and soybeans were at the maximum vegetative growth in May and September, and were well-detected from other crops. So the images of these two months were used for detection in all studied years. According to the results of previous studies, support vector machine and artificial neural network could be used as two reliable image classification methods for detecting the crops vegetation cover (Rahimzadegan & Pourgholam ,2017; Mokhtari & Najafi, 2015). The accuracy of image classification was assessed using kappa coefficient and overall accuracy. Kappa coefficient and overall accuracy showed the support vector machine method was superior than artificial neural network method in classifying agricultural lands and detecting studied wheat- and soybean-grown fields. In the all images, the calculated overall accuracy coefficient was more than 80% (0.84 to 0.92 for wheat; and 0.84 to 0.90 for soybean) and Kappa coefficient was more than 0.8, indicating the reliability of the classification outputs. According to comparisons of satellite image-based estimations and real recorded ststistics, about 93% of the estimated areas of wheat - and soybean grown fields in the 16 consecutive years of study were within the range of 15% confidence level, which indicates that this method is a reliable method for detecting these two crops using images of April (for wheat) and September (for soybean).

The support vector machine method of classification was identified as the superior method. wheat- and soybean-grown fields maps extracted from satellite images can be used as a base layer for regional modeling, providing yield gap layer, calculating the water requirement, designing crop pattern and etc

The use of remote sensing techniques as a suitable solution to estimate the levels of species diversity is of high importance for the sustainable management of forests. In order to investigate the potential of using sensor data from Landsat 7 ETM+ to estimate species diversity in the Zagros forests, digital data related to the August 7, 2002 from forests in the Qalajeh Kermanshah Province were analyzed. To this end, 114 sample plots were created using a systematic method. The plots had the dimensions 60 × 60 m and were positioned using a GPS device. The number of features, species, DBH and crown diameter in two directions, North-South and West-East harvest, were recorded. The Shannon-Wiener species diversity index per sample plot was calculated. After testing for normalization using the Kolmogorov–Smironov test, the Shannon-Wiener index was used as the dependent variable, and spectral values from original and synthetic bands from different processing on the ETM + data were used as the independent variables. The Pearson correlation was used to select the best bands among 40 major and artificial bands, and three-bands (ETM4, NDVI and MSAVI2) were selected. To analyze the relationship between species diversity and these bands, the best subset regression was used. The results showed that the combination of linear regression, in which ETM4, NDVI and MSAVI2 were set as the independent variables, compared to other bands and compounds that were used for species, are better able to estimate the tree and shrub diversities (Radj2=0.327). The results of this study indicate that ETM+ sensor data has a relatively low ability to estimate species diversity of trees and shrubs in the study area that was analyzed.

Soil microorganisms are the essential part of forest ecosystems which play a key role on soil nutrient changes. The biological activity in soil is largely concentrated in topsoil. Despite the small volume of microorganisms in soil, they have a key role on nitrogen, sulphur and phosphorous cycles and the decomposition of organic residues. Soil microorganisms have been identified as the sensitive indicators for soil quality. The composition of microorganisms and their fractional activities in soils significantly affect biochemical cycles, carbon sequestration and soil fertility. As soil microbial communities respond differently respected to environmental conditions, it seems that variation in forest ecosystem could significantly affect microbial community. Plants are one of the important variables for assessing soil microbial communities which their effect is related to root secretions and litter decomposition. The phospholipid fatty acid (PLFA) analysis is one of the methods that can overcome the problem of selective growth of microorganisms on culture media which is a major defect in the identification of microbial diversity. The objective of this study was to investigate the effects of different tree compositions and soil properties on soil microbial community using PLFA analysis approach.
This study was conducted in ShastKalate forest, an experimental forest station of Gorgan University, located at eastern Caspian region, North of Iran (36° 43′ 27″ N ,54°24′ 57″ E). Eleven different tree compositions were selected and the surface soils collected from 0-10 cm depth of 33 plots. Soil samples were air dried and passed through a 2mm sieve. Then one portion of the sieved samples was used for physical and chemical analyses. The other portion was rewetted to 65% of field capacity and incubated at 37 °C for 3 days to analyses PLFA. Soil particle size distribution (clay, silt and sand) was determined using the hydrometer method. Soil pH in 1/ 2.5 soil to water suspension and electrical conductivity (EC) in the same extract were measured.. Calcium carbonate equivalent (CCE),soil organic carbon (OC) and total nitrogen (TN) was determined, too. Biological analyzes including soil microbial respiration determination and PLFA analysis were carried out. The PLFA detection and quantification were performed with a Hewlett-Packard 5890 Series II gas chromatograph (GC) equipped with an HP Ultra 2 capillary column and a flame ionization detector. The normalized data were employed for Pearson's correlation analysis and ANOVA to determine the effects of soil properties and different tree compositions on soil microbial community.
Gram and Gram- bacteria were the most microorganisms and protozoa were the least microorganisms in soil samples. The results of the correlation between soil properties and microorganisms showed that OC and TN had significant positive effects on microorganism’s communities. EC was significantly correlated with Arbuscular Mycorrhizal Fungi (AMF), actionbacterial, protozoa and total PLFA. In addition, soil microorganisms and total PLFA were significantly correlated with soil respiration. However, there was no significant correlation between TN and OC with protozoa. The correlations between pH, EC, CCE and sand with protozoa were significantly negative, but in the case of silt, this correlation was significantly positive. Different studies showed that soil organic matter is the main nutrient source for soil microorganisms and soil microorganisms are also the essential part of C and N cycles. The effects of tree compositions on 16:0 10-methyl, 18:2 w6c, 20:2 w6c, 20:3 w6c and 20:4 w6c were significant(p

The present study eas aimed to evaluate the potential of ALS data in estimation of individual canopy cover area and tree heights for the part of Shast Kalate of Gorgan. In this study 117 tree that located in dominant forest story and without overlay with adjacent trees, were selected. Center coordinates of sample trees were determined using DGPS system. Individual canopy area and tree heights were extracted using in Fusion software. UltraCam-D images were used to separating the canopy border of a single tree and produce a tree polygon. The result of linear regression between individual canopy area and tree heights in the field measurement and ALS data were yielded coefficients of determination , (0.974 and 0.997); RMSE% (6.13 and 7.24) respectively. Mean differences between individual canopy area and tree heights of field measurement and ALS data were achieved 0.72 m and 12.8 . Therefore the results of this study showed that ALS data are capable of estimating of individual canopy and tree heights with high accuracy.

Investigation on spatial distribution of tree growth characteristics in different forest stands, has a fundamental role in assessing possible harvest planning considering potential of the stands. The aim of this study is mapping the spatial distribution of forest characteristics such as stand volume growth, diameter, growth, ingrowth) and determining the amount of tree mortality in the district three of Sangdeh region within a period of 5 years. Two methods of Ordinary Kriging (OK) and weighted Inverse Distance (IDW) interpolation were applied for mapping. For this purpose, we calculated the increment using direct measurement in 130 permanent sample plots. The results of this study showed that the mean volume increment, diameter increment, ingrowth and annual mortality were 5.65 cubic meters per hectare per year, 0.48 cm, 3.5 and 4.2 stems per hectare per year, respectively. For volume increment, the IDW method by power one and with a root mean square error 0.29 cubic meters per hectare per year, for diameter increment, ingrowth and annual mortality the ordinary Kriging method with a root mean square error 0.219 cm per year, 1.4 and 2.8 nha-1y-1 showed better results, respectively. Overall the results showed that geostatistical methods are efficient methods for mapping forest growth characteristics.