hormoz sohrabi

Detailed information on forest combination is required for many environmental, monitoring, and forest protection purposes. The link between ecology and remote sensing provides valuable information for the study of forest trees to facilitate the study of ecosystem performance and to measure the spatial distribution of vegetation. In recent years, the use of modern remote sensing methods and techniques based on UAVs have been used for regular updating of forest inventory. In this research, different data sources including multi-spectral and RGB images with very high spatial resolution, were used for tree species recognition in plain forests of Noor City located in Mazandaran province. Also, taking images was performed in the growing season to prepare a time series of UAV-RGB images for investigating the effect of tree crown phonological changes on classification accuracy.  Following orthomosaic generation, RGB (NGB, NRB) and multi-spectral (NDVI, CIgreen) indices were calculated and the random forest classification method was used for forest species classification. Based on single-time images, late April images provided the highest overall accuracy (75%). However, the results of the time series obtained from RGB images showed an increase in accuracy of up to 86%. Species identification based on multispectral images obtained from the Sequoia sensor also provided 85% accuracy. The results showed that the single-time image at the appropriate time using a UAV-RGB, compared to taking a time series and using a UAV equipped with multispectral sensors, has acceptable and less expensive results for tree recognition in the study area.

Mangrove forests are known as important sea carbon ecosystems because they play an important role in carbon sequestration among coastal ecosystems. This coastal ecosystem has 10 to 50 times more carbon sequestration capacity compared to terrestrial ecosystems, and among the most productive systems, they can effectively reduce climate change. Therefore, an accurate estimation of the biomass of mangrove forests is a necessity. Meanwhile, the evaluation of the terrestrial carbon storage in mangrove forests relies on the accurate measurement of tree biomass, which is traditionally time-consuming and expensive. In this study, height and crown diameter was estimated by using UAV equipped with an RGB sensor; following sampling and measuring soil carbon in three forest sites of Sirik, Qeshm, and Khamir, the carbon storage in trees and soil was investigated. Orthophoto mosaic and dense point cloud were created based on structure from motion algorithm. Crown diameters were extracted from orthophotos. The canopy height model was extracted by subtracting the digital surface model and digital terrain model which were derived from point cloud. Tree heights were extracted from the canopy height model following imaging in November 2021. Considering that there was no significant difference between the measured variables on the ground and the extracted variables from the UAV images, the data obtained from the UAV images and allometric equations were used to estimate the aboveground carbon storage.  After estimating the biomass according to the two variables of crown diameter and tree height, the carbon storage on land obtained from the information extracted from UAV images in the three sites of Sirik, Khamir, and Qeshm was obtained at 11.63, 7.97, and 9.87 t/ha respectively. The soil carbon was also measured at two depths of 0 to 15 cm and 15 to 30 cm using the Walkley-Black method, and the values were shown as 67.98, 81.9, 85 t/ha, and 187.2, 133.53, and 113.7 for Sirik, Khamir, and Qeshm sites. This research shows that UAV data has a high ability to estimate the variables related to individual trees in forest areas with difficult traffic conditions, and subsequently to estimate the height and crown diameter variables, estimate the forest stock and carbon storage based on the mentioned variables. It can be achieved in relatively homogeneous mangrove forests. Especially because these ecosystems are environments that are often inaccessible or difficult to work in.

Advances in remote sensing enable fast mangrove mapping the less need for intensive fieldwork, complex and heavy processing, and skill-based classification techniques. In this research, mangrove forest mapping was performed using Sentinel-2 satellite images in Google Earth Engine (GEE) in Hormozgan province in three ecosystems of Qeshm, Khamir, and Sirik. For this purpose, all steps of mapping these forests, including pre-processing and classification were performed in the GEE. The Modular Mangrove Recognition Index (MMRI) and classic spectral indices were also used to highlight the spectral differentiation of mangrove cover from the surroundings. To classify the image of the study area, three land cover classes were used: mangrove, non-mangrove, and sea (water). The classification was performed based on the random forest algorithm and accuracy assessment was evaluated in R software based on the K-fold validation method. The Qeshm site was demonstrated the highest accuracy among the three ecosystems with an overall accuracy of 98% and a kappa of 0.73. Khamir and Sirik sites were shown an overall accuracy of 97% and a kappa value of 0.71 and 0.70, respectively. The MMRI index was the most important variable in the RF classification in Qeshm and Khamir, while in Sirik, the SAVI index was the most important spectral index in mangrove map providing. The overall accuracy of over 95% at all three sites indicates that combining Sentinel-2 data using appropriate indices in the GEE is an effective approach to mangrove forest mapping

Estimating aboveground carbon (AGC) of forest is a fundamental task for sustainable management of forest ecosystems; therefore, there is a critical need for appropriate approaches for quantifying of AGC. The most commonly used approaches for estimating include global regression models that estimate the target variable over a wide range using cost-effective auxiliary data. Traditional regression models with fixed regression coefficients at all locations do not consider heterogeneity and spatial structure in modeling. The objective of this study is estimating the AGC using Regression Kriging, Geographically Weighted Regression Kriging and Landsat 8 data and compare methods.

The study was carried out in part of Zagros Forest, in Kohgiluyeh and Boyer-Ahmad Province. Totally, 184 plots (30×30 meters) surveyed and AGC were calculated by allometric equations. 32 variables were extracted from Landsat 8 as auxiliary data in the modeling process. The assessment of accuracies of methods was evaluated by K-fold cross validation via criteria such as coefficient of variation (R2), root mean square error (RMSE).

The results showed that Geographically Weighted Regression Kriging (R 2 = 0.66, RMSE= 21) had a better performance compared to Regression Kriging.

Hybrid methods with heterogeneity and spatial correlation can be a good alternative to early regression methods for estimating aboveground carbon (AGC).

One of the requirements for achieving efficiency and maximum productivity is continuous monitoring and identifying of horticultural and agricultural products. Traditional plant monitoring and evaluation methods are time-consuming, labor-intensive, and costly. Unmanned aerial vehicles (UAVs) using real color imaging (red/green/blue) is a game-changer in horticultural and agriculture and an economically viable option for recognizing stress and disease. In this paper, the ability of UAV images was evaluated for identifying citrus trees and determining their health using a simple method. For this purpose, In June 2019, the study area was photographed and surveyed. The region growing algorithm was tested for a series of CHMs generated from point clouds, across a range of spatial resolutions. The highest overall accuracy for the individual tree crown delineation was achieved for a spatial resolution of 50 cm (F-score =0.63). In the next step, the orthomosaic was generated with a pixel size of 2.5 cm was generated by structure from motion algorithm. Then vegetation indices and bands obtained from orthomosaic and CIE L* a* b* color space were used as input data in a random forest classification algorithm. We classified the trees into 2 classes: health trees and unhealthy trees; then the random forest algorithm was applied using R software. The classification accuracy for identified trees was performed using 10-fold cross-validation. The classification resulted in overall accuracies of 69%; that display the effectiveness of UAVs to inform stakeholders.

 One of the most important active remotely sensed data for quantification of different attributes of forest stands is LiDAR data. A research hotspot in this subject is to estimate forest biomass using different LiDAR derived metrics.

Forest biomass was measured in 127 systematically designed 900-meter square rectangular plots in two different sites (dense and sparse sites). LiDAR data was inspected for any possible error. DTM, DSM, and CHM were extracted from LiDAR data and different metrics at plot level were calculated. For modeling, stepwise regression was applied. 

 Result showed a moderate precision for biomass estimation using LiDAR data in a way that the coefficient of determination and root mean square error (Ton/ha) for biomass estimation of leaves, twigs, branches, bole, and whole tree were 0.58-28, 0.54-23, 0.68-1.35, 0.68-1.53, .65-3.69, respectively. 

 Due to high error of determination of tree tips in broadleaves forests especially in coppice stands which have low tree height with irregular shapes; the error of the estimation of tree height using LiDar data is high. Better results demand further researches.

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.

Despite the low area coverage, riparian vegetation presents several ecosystem services. But there is no precise spatial information on these ecosystems in Iran. Considering the lack of such information, mapping and providing a spatial database is essential. Due to the mixture of these vegetation types and other land covers, the detection of these types of vegetation is challenging, and choosing an appropriate classification method is of great significance. This study examines the performance of pixel-based and object-based classification approaches for the detection of these vegetation types using freely available Sentinel-2 imagery. Five different riparian areas in Chaharmahal-va-Bkhtiari province were selected and used for training the classification models. We used random forest algorithm and multi-temporal Sentinel-2 data to perform the classification models. The validation of classification models was based on independent validation points spread across the province. Our results showed that multi-temporal Sentinel-2 imagery has a high capability to map riparian vegetation in the Zagros Mountains. Moreover, the pixel-based classification approach (overall accuracy of 83.9%) represents more accurate results compared to the object-based classification approach (overall accuracy of 76.7%). Overall, this study recommends a pixel-based classification approach to map these vegetation types using freely available multi-temporal Sentinel-2 imagery. Note that it is important to use pure pixels for training the classification models.

This paper aims to conduct a model-based analysis of the spatial patterns of three tree species in a Hyrcanian forest and investigate their associations. There are many known and unknown mechanisms that influence the spatial forest structure and species associations. These complex and mainly unobservable mechanisms can be modeled by hidden Gaussian random fields and log-Gaussian Cox process models are appropriate for linking them to the spatial patterns of tree species. We consider a multivariate log-Gaussian Cox process model that can take into account the overall mixed effects of all influential factors on spatial distributions of species and quantify species associations in terms of some parameters. This construction provides a suitable framework for modeling and analyzing spatial patterns of several species. We also discuss modeling tree diameters, parameter estimation and goodness of fit methods and apply them to the data. Results from fitting the model to the data show that there is a significant negative association between two light-demanding species. Finally, a Gamma intensity-dependent model is considered to model spatial correlation in tree diameters of one of the species.

 Estimation of forest Carbon stocks plays an important role in assessing the quantity of carbon exchange between the forest ecosystem and the atmosphere. Direct methods of measuring carbon stock are not economically efficient. Optical remote sensing methodsalso have limited capability in predicting forest biomass, because the spectral response of optical images is related to the interaction between solar radiation and canopy, especially in mature forests. These obstacles limit the efficiency of optical sensors for forest biomass estimation. Recently, airborne data has received a great deal of scientific and operational attention for estimation of forest features. LiDAR data also faces challengessuch as limited efficiency in large areas, high costs and large data volumes. In contrast to the optical and LiDAR systems, SAR systems have some advantages, such as the possibility of data collection in any weather condition, penetration through clouds and canopy, and easy access. The potential of SAR images with quad polarization for the estimation of Iranian Hyrcanian forests biomass will be investigated. The main purpose of this study was to investigate the efficiency of ALOS-2 /PALSAR-2 backscattering coefficients andpolarimetric features in leaf-on and Leaf-off crown conditions, evaluate the linear regression model and select the most appropriate variables for biomass estimation.  

The study area is located in a part of forests of Mazandaran province. The region forms a part of the deciduous broadleaf temperate plain forests. The forestsunder study was classified into 4 major types: (1) Forest reserve, (2) Natural forest, (3) Degraded forest and (4) Mixed species forest plantations. 115 circular sample plots (each including 0.1 hectares)were collected from the 4 different sites with various forest structures and biomasses. In each sample, tree species and diameter at breast height (DBH) of all trees with DBH > 7.5 cm were recorded. Allometric equations were used to convert tree diameter to biomass. The present study is based on polarimetric L-band PALSAR-2 data collected in spring and winter. Backscattering matrix was generated using the PALSAR data which consists of amplitude and phase information. Speckle noise filtering was performed using the Refined Lee adaptive filter. Following the filtering, all polarimetric features were extracted. After converting the SAR products to NRCS, geometric correction and georeferencingwere performed and the average backscattering coefficient (sigma naught value)was extracted for each sample plot by overlaying the AOI layers on corresponding SAR images. Finally, the relationship between forest biomass and backscattering intensity was investigated. 

The resultsvary regarding to the forest type, the range of biomass and forest canopy cover percentage.Forest type and biomass range as well as canopy cover percentage affect the scattering mechanism and correlations between biomass and SAR backscattering coefficient. Canopy cover percentageofthe 1stand 4thsites were over 90% and consequently, the sensitivity of HV backscatter value to biomass was higher than HH and VV. In the 2nd and especially 3rd sites, the correlation between HH backscattervalueand AGB was better than its correlation with HV backscatter. This is mainly because of the canopy structure in these sites which is not complete and the fact that the sensitivity of HH backscatter value to biomass is higher than HV. Results indicate in the 1st and 4th sites, the correlation between volume scatter component of decomposition methods with AGB was better than its correlation with double-bounce scatter component. In contrast, the double-bounce decomposition componentsexhibited the best results in the 2nd and 3rd sites. These findings are in agreement with the results obtained from the T3 matrix components. The least correlation value was observed between Freeman decomposition components and AGB. The volume scatter component of Cloude and also double-bounce component of Freeman did not provide suitable results. Results also indicate higher efficiency of images collected in spring as compared to those collected in winter.Linear regression results show that in the best possible situation, RMSE of the first forest habitat was 34.68 t/ha, and 30.09, 27.07 and 23.69 t/ha were estimated for the 2nd, 3rd, and 4th forest habitats, respectively. Therefore, it seems that classification of forests is necessary before biomass estimation.  

The potential of PALSAR-2 data for Hyrcanian forest biomass estimation was assessed in this study. We demonstrated that L-band data are sensitive to the above-ground biomass (AGB) of Hyrcanianforestsand can be used to provide accurate estimates of biomass. Findings confirmed that decomposition methods are more efficient than backscattering coefficients for biomass mapping.

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

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

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

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

Recent advances in unmanned aerial vehicles (UAVs) technology, as well as the development of lightweight sensors, offers a great possibility for the measurement of different tree features with relatively low costs compared to traditional methods. In this research, the precision and accuracy of tree height measurement and estimation using imagery by a low-cost UAV were studied. For this aim, 854 images with an altitude of 100 m above the ground were taken and the images were processed and dense point cloud was extracted by applying Structure from Motion (SFM) algorithm. The study was conducted in 34.79 ha of Sisangan forest park and 28 sample plots (30 × 30 m) were located in the field and tree heights were measured. Also, tree height was measured using the canopy height model. Linear regression was applied to estimate the actual tree heights based on CHM derived tree eights. The accuracy and precision of the estimates were assessed using relative bias and relative root mean square error. The differences between the field measured and CHM derived tree heights were statistically significant. Based on the results, the relative root means the square error of the height estimation of Buxus hyrcana, Carpinus betulus, Parrotia persica, and other species was 20.39, 20.39, 20.57 and 20.52 percent, respectively. The results showed that tree height measurement based on UAV images and methods that were applied in this research, is biased and the estimations are highly uncertain.

This study was carried out to understand the dynamics of Lebanon oak coppice regeneration. In this regard, the effect of thinning on both individual sprouts and stools as well as estimation of sprout biomass using allometric equation were investigated. 45 Lebanon oak trees with different sizes (i.e. DBH from 25 to 65< cm) in five sites were cut and individual stumps were fenced to monitor the sprouting behavior. In the second year after the cutting, light (six sprouts reserved in each stool) and heavy thinning (three sprouts reserved in each stool) were conducted in two third of stools equally and the rest un-thinned ones were considered as control. Data analysis, five years after thinning, showed that the most portion of individual biomass allocated to main stem (57.4%), main branches, leafs and fine branches, respectively. On average 42% of total fresh mass of sprouts were reduced due to water loss in drying process. Analysis of variance indicated that stump size has no significant effect on biomass of individual sprout either stools while it was significantly different among sites. Comparison of thinning treatments and control one showed that light thinning resulted in sprouts with the most biomass while no significant difference was observed between heavy thinning and un-thinned. Using a basic power model, allometric equation was fitted to estimate the sprout biomass. Based on regression analysis as well as derived indicators (R2, STDEV, RMSE, rRMSE and nRMSE) and models validation by leave one-out method, the collar diameter was found as the best explanatory variable in young sprouts biomass.

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

In the present study the impact of silviculture activities on soil organic carbon (SOC) and soil organic carbon pool (SOCP) in the mixed beech-carpinus forest located in district one of Shastkolate forest, Golestan province, was investigated.
In the first month of growth season in 2014, from 4 one-hectare treatments, 80 soil samples were randomly collected (up to depth of 20 cm) for organic carbon and coarse fragments estimation and 80 soil sample cylinders were collected for bulk density measurement. Treatments were 3 managed forests (selection system) in time periods of 1, 7 and 10 after the last disturbance and 1 unmanaged forest (control). Amounts of SOC and SOCP in different treatments were compared by one-way variance analysis.
Findings: Result showed that the amounts of SOC and SOCP in 4 treatments were different significantly (p

The aim of the present study is to assess the effect of pure and mixed plantations of Eastern Cottonwood (Populus deltoides) and Alder (Alnus subcordata) on quantitative stand characteristics and soil properties. The experiment was established in 1994 using randomized block design with five treatments (1- P100%, 2- P67%-A33%, 3- P50%-A50%, 4- P33%-A67%, and 5- A100%) and four replicates in Chamestan Forest Research Center, Mazandaran province. All trees were measured and mixed soil samples were taken from 0-20 cm depth in all treatments in 2014. Result of ANOVA showed that after 20 years, there are significant differences in tree attributes including diameter at breast height, height, crown width, and leaf area index as well as stand attributes including basal area, volume and leaf area per hectare. The maximum growths of poplar were recorded in P33%:A67% treatments with an average of 35.0 cm DBH, and the maximum growth of Alder were recorded in 100%A treatment with an average of 25 cm. No significant difference was observed in soil properties between different treatments. Based on the results, P33%:A67% treatment is the best treatment for future plantations.

Road construction is one of the effective factors for ecological changes which affects diversity and species composition in forest roads edges. This research was performed in Kheirud forest to evaluate herbaceous diversity and tree regeneration establishment in different distances from road edge. For this purpose, we sampled herbaceous and tree species regeneration in 0,5,10,25, 50 and 100 m distances from road in down and up-hill sides. Biodiversity indices (Shannon and Simpson), richness index (species number )s)) and evenness index (Pielou) were calculated to determine the degree and magnitude of road effects on herbaceous and regeneration diversity. The results showed that, the species diversity, richness and evenness indices were not significantly related to down and up slope while these attributes were significant to distance from the road. Distance from road edge was a significant factor which affect composition and distribution of herbaceous plants and tree regenerations. In road edges, the first plots (0, 5, 10 m distance) had significantly greater number of light demanding species like Rubus hirtus, Carex remota ,Sumbucus ebulus , Acer velutinum and Alnus glutinosa than interior plots. Based on results, among the environmental factors, light condition was major determinant of species distribution and abundance of road to forest interior gradient. These results are expected to provide critical information for decision makers to design carefully and appropriate forest roads to minimum damages of habitats to establishment of regeneration and also help to select the proper species bioengineering

Soil pollution caused by cadmium has recently become a major concern. Phytoremediation is a convenient, cost-effective and eco-friendly method for remediation of polluted environments. In this study, the effect of soil texture (clay, clay-loam and silty- clay- loam) on the cadmium accumulation by leaves, stems, and roots of black locust (Robinia pseoudoacacia L.), round-leaf ash (Fraxinus rotundifolia Miller) and Arizona cypress (Cupressus arizonica Greene) was surveyed. The study was carried out in a complete random-based factorial design with four factors (plant species, plant organs, soil texture and cadmium concentration level). At the end of the growing season, the cadmium concentration in the organs was measured. Analysis of variance showed significant deference (P

The length-weight relationships, (LWRs) were calculated for 14 fish species belonging to five families (Cyprinidae, Cobitidae, Nemacheilidae, Salmonidae, and Gobiidae) collected from the Tajan River, north of Iran. Significant length-weight relationships with high correlation coefficients were found for all species.

A variety of new methods have been proposed by ecologists and biologists for data collection on different attributes of coarse woody debris (CWD). Amongst others methods based on probability proportional to size are the newest techniques. These approaches are based on selecting individual logs into the sample unite using some forms of unequal probability sampling. In the present study, we aimed to introduce “Distance Limited Sampling” (DLS) and “Sausage protocol” of “Fixed Area Method” as well as assessing their precision and accuracy for estimating volume of CWD. These methods select individual logs into the samples using probability proportional to length of the logs. To collect the data, a set of attributes including location, top and bottom diameter and length of all logs in a 250×250 m plot in an un-harvested temperate forest were fully inventoried, and the location of logs were mapped. Then different methods with different sample unit sizes were simulated in R programming language environment using SampSurf package. Results indicated that both methods are unbiased. Considering precision of the estimates, Sausage protocol of fixed area sampling led to more precise estimates of volume of the loges than those estimated by DLS. Consequently, Sausage protocol is concluded for further usage as a promising method in the field due to its unbiasedness and production of highly-precise estimates.

Today، measurement of the forest biomass is important from different aspects. The biomass estimation of small diameter trees as missing part of measurements is significant. The main objective of this study is to develop allometric equations for estimating the stem، branches، leaf، bark and total biomass of small diameter trees including Carpinus betulus، Fagus orientalis and Parrotio persica. To achieve this purpose، 10 intact trees from each species that have 2-8 cm diameter were randomly selected and collar diameter، diameter at breast height، diameter where crown started، crown diameter and height were measeured. Then trees were felled down and different parts were weighted. One sample from each part was transferred to lab and percent of dry weight was calculated. The most appropriate equations for estimating different parts of the trees biomass were selected by using power regression model. The results show that among the variables، diameter at breast height is the most appropriate estimator for estimating woody parts biomass of small diameter trees in all three species and equations can be produced by heigh R2. But، regression models for non-woody parts of small diameter trees can not be produced by sufficient accuracy.

South Khorasan province with vast rangelands with an arid and semi-arid climate and specific species is one of the most suitable areas for camel breeding. Despite the importance of camel in the province, no research has been conducted on species selection by camel as well as its relation to chemical factors of plants. Therefore this research was aimed to investigate the relationship between palatability and nutritive value of range species in south of Birjand. For this purpose, the selection of range species by camel was investigated in three phenological stages of vegetative, flowering and seeding using chronometer. For each stage, the study was conducted during three days in the morning and evening. In this technique, each camel was followed for 30 minutes during the feeding. In addition, plant samples were collected for chemical analysis. Data analysis was performed using SPSS 15 software. Results indicated that there were no significant differences between preference value and chemical composition of species. The highest values of DMD, ME and CP as well as the lowest CF, ADF and NDF were recorded for Seidlitzia rosmarinus. According to the obtained results, the effects of chemical composition on species palatability varied from one species to another one. However, these effects depend on different factors including vegetation composition, available forage, species distribution, livestock taste and so forth.

Estimates of tree biomass are useful in assessing forest structure and evaluating ecological and economic processes such as nutrient cycle، forest productivity and fuel inventories. The information on tree biomass is required to assess the amount of carbon held in trees. This research was conducted in order to measure biomass and Carbon stocks of two vegetation forms of Brant''s oak (Quercus brantii) in Chaharmahal & Bakhtiari Forests in west of Iran. Therefore، sample trees were selected to be felled using 30 randomized systematic plots. Thirty trees including 16 individuals with single stem and 14 sprout-clumps were randomly selected. Quantitative and qualitative traits were measured before felling. The felled trees were separated into six different components and weighed on a portable hanging scale in the field in order to obtain the fresh weight. Samples were taken from each tree component. The samples were weighed on a scale with 0. 01 gr accuracy and transported to a laboratory. According to results، biomass of single stem trees was 2. 5 times more than coppice forms. Moreover، biomass allocation of single stem trees and coppice trees were identified.

Throughfall (TF) has a large spatial variability due to the heterogeneous canopy structure and variable rainfall patterns. In this study، the aim was to estimate the optimum number of collectors needed to obtain a mean cumulative TF value within certain error limits for five individual Brant’s oak trees (Quercus brantii) in the Zagros forests of Ilam in western Iran. Sixteen TF manual gauges were placed beneath the five selected tree canopies in eight geographic directions and the gross rainfall (GR) was measured by the mean of six homemade gauges for a period of 14 months in leaf-on condition. During this period، 23 rainfall events with cumulative depth of 257. 4 mm were collected، of which an average TF rate of 68. 9% reached the forest floor. An average of 70 (range between 41-138)، 18 (range between 10-35)، and 8 (range between 5-15) gauges would be required to estimate mean cumulative TF to within ±5، ±10، and ±15% percent errors at the 95% confidence level، respectively. Based on the results of this study، 16 gauges are sufficient to estimate mean cumulative TF with an error of 15% and a confidence interval of 95%. However، the number of gauges should be increased if a lower error rate for the mean cumulative TF estimations is required.