Department of Forestry (2011 - Present)
Forestry - Forestry and Forest Sciences
Forest science, Tarbiat Modares University, Tehran, Iran
Natural Resources Engineering, Forestry
Forest science, Tarbiat Modares University, Tehran, Iran
Natural Resources Engineering, Forestry
Forest science, University of Kurdistan, Sanandaj, Iran
Dr. Hormoz Sohrabi studied forest science at the University of Kurdistan and earned his doctoral degree at the Department of Forestry of TMU in 2008. He started his carrier in the University of Shahre-Kurd, he returned to the TMU Department of Forestry in 2010.
In view of the important role played by roots against shallow landslides, root tensile force was evaluated for two widespread temperate tree species within the Caspian Hyrcanian Ecoregion, ie, Fagus orientalis L. and Carpinus betulus L. Fine roots (0.02 to 7.99 mm) were collected from five trees of each species at three different elevations (400, 950, and 1350 m asl), across three diameter at breast height (DBH) classes (small= 7.5–32.5 cm, medium= 32.6–57.5 cm, and large= 57.6–82.5 cm), and at two slope positions relative to the tree stem (up-and down-slope). In the laboratory, maximum tensile force (N) required to break the root was determined for 2016 roots (56 roots per each of two species x three sites x three DBH classes x two s
Aboveground biomass (AGB) of forests is an essential component of the global carbon cycle. Mapping above-ground biomass is important for estimating CO2 emissions, and planning and monitoring of forests and ecosystem productivity. Remote sensing provides wide observations to monitor forest coverage, the Landsat 8 mission provides valuable opportunities for quantifying the distribution of above-ground biomass at moderate spatial resolution across the globe. The combination of the sample plots and image data has been widely used to map forest above-ground biomass at local, regional, national, and global scales. Many predictive methods have been suggested to estimate forest aboveground biomass from sparse sampling points into continuous surface
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
Accurate spatial modelling of forest characteristics is one of the most important challenges in remote sensing applications. In this study, we compared the ability of Multiple Linear Regression (MLR), Geographically weighted regression (GWR), and Random Forest (RF) to estimate different forest attributes based on field sample data and Landsat 8 image. CA was modelled with the highest accuracy compared to other variables using GWR. GWR outperformed other methods. The highest and the lowest values of RMSE were for BA using RF (31.0%) and CA using GWR (12.0%), respectively.
The use of freely accessible Sentinel-1 synthetic aperture radar (S-1 SAR) and Sentinel-2 multispectral instrument (S-2 MSI) data are currently a feasible way of mapping forest aboveground biomass (AGB) over large areas. Despite the extensive mapping of forest AGB by remote sensing, how to effectively combine different sensors data, selecting the proper statistical modelling method, and variable screening are still poorly understood. This paper presents a framework for Sentinel-based AGB estimation through the use of four variable screening techniques, namely, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), boruta, and removal-based; and three statistical modelling methods including multiple lin
Sparse vegetation such as riparian forests and trees outside forests (TOF) cover only small areas but present various ecological advantages. The detection of these vegetation types in semi-arid mountainous areas is challenging as trees are heavily mixed with other land cover types. Their mapping requires therefore high-resolution imagery. We propose to leverage the advantages and synergies of freely available Sentinel-2 data and a light-weight consumer-grade unmanned aerial vehicle (UAV) with a simple red–greenblue (RGB) camera to detect these vegetation types. In our approach, an object-based random forest land cover classification is first developed over smaller sites using very high-resolution UAV data. The resulting maps are afterward
Knowing the tree species combination of forests provides valuable information for studying the forest’s economic value, fire risk assessment, biodiversity monitoring, and wildlife habitat improvement. Fieldwork is often time-consuming and labor-required, free satellite data are available in coarse resolution and the use of manned aircraft is relatively costly. Recently, unmanned aerial vehicles (UAV) have been attended to be an easy-to-use, cost-effective tool for the classification of trees. In fact, given the cost-efficient nature of UAV derived SfM, coupled with its ease of application, it became a popular choice. The type of imagery is an important factor in classification analysis because the spatial and spectral resolution can influ
AbstractBackground and objectives: 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 estimate the AGC using Regression Kriging, Geographically Weighted Regression Kriging and Landsat 8 data and compare methods.Method: The study was carried out in
Abstract Background and Objective 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
Extended Abstract Introduction 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
Investigating a tree’s biomass can provide basic information about forest carbon stock. The Biomass Expansion Factor (BEF) is a variable for estimating carbon stock of forests. The aim of this study was to analyse the Above Ground Biomass (AGB) allocation, developing the BEF and carbon stock for two vegetation forms of Brant’s Oak (Quercus brantii Lindl.) based on forest inventory data. BEF is defined as the ratio of AGB to crown volume variables. The study data were taken from 30 trees that include 16 individual trees with single stem and 14 coppice shoots located in West-Iran. The trees selected were felled and separated into different components including: bole, main branches, lateral branches, twigs and leaves. The fresh weight of t
This study was carried out to understand the dynamics of Lebanon oak coppice coppice Subject Category: Miscellaneous
Predicting fire hazards and simulating fire intensity require knowledge of fuel conditions. Many aspects of wildfire behavior including the rate of spread and intensity are influenced by the amount of vegetation that fuels the fire. Coppice Oak Forests (COF) are strongly influenced by wildfires. In the present study, we examined the ability of airborne LiDAR data to retrieve available canopy fuel (ACF) of coppice Oak forest in Zagros Mountains, Iran. Two different oak-dominated stands were selected based on the stand density including sparse and dense forests. Systematically, 127 plots were established in the field and ACF was calculated using species-specific allometric equations. An outlier filter was used to remove any out
Forest carbon stocks are a time-integrated manifestation of various phenomena and processes ranging from tree growth and mortality to natural and human disturbances. Understanding the effects of environmental and human activities is critically important in vulnerable ecosystems like arid and semi-arid forests, given climate variability coupled with historical human activities. Zagros forests are one of the largest vegetation communities in the Middle East. This region is highly affected by dust storms which are mainly a result of the loss of vegetation. This current study is an exploration of changes to aboveground carbon (AGC) density as affected by climate change (CC) and local management from 1987 to 2015 at 5-year intervals based on L
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 squa
Advances in Unmanned Aerial Vehicle (UAV) technology made it possible to collect very high resolution images with affordable cost. From the other hand, data processing capabilities have made it feasible to obtain three dimensional (3D) data which can be used for measurement and estimation of different forest structural properties. In this research, we examined the accuracy of tree height measurement derived from photogrammetric processing of UAV images. Using structure from motion (SfM) algorithm, point cloud was generated and canopy height model (CHM) was derived. The study area is 2.50 hectares in which the height of all the trees were measured. Images were taken at five flight heights including 60, 80, 100, 120, and 140 meters above sea
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 me
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