ali asghar darvishsefat

Mapping forest types is essential for sustainable management, however, field methods are time-consuming and costly. Therefore, Modern remote sensing, especially hyperspectral imagery like PRISMA with 243 spectral bands, is valuable for mapping Hyrcanian mixed forests. However, its accuracy and capabilities must be assessed. In this study, the capability of PRISMA data was assessed using SVM, SAM, and RF classification approaches to create forest type maps for the Gorazbon district of Kheyrud Forest. A ground truth map consisting of 131 field sample plots, each measuring 45 × 45 m, was generated using Kuchler's abundance method based on the 100 trees with the largest diameters. Five forest types were identified: pure beech, mixed-beech, beech-hornbeam, hornbeam-beech, and mixed hornbeam. Geometric correction, smoothing and noise filtering was applied, then, Illumination correction was performed. Anomalies were detected using the RX algorithm. The minimum noise fraction (MNF) transformation and pixel purity index (PPI) were applied to the remaining 103 spectral bands and then used in the classification process. Additionally, indices such as NDVI, ReNDVI, MTCI, NDIred, and RTVI were incorporated into the classification process. The results showed that the SVM and RF algorithms achieved overall accuracies of 53.09% and 43.19%, with kappa coefficients of 0.38 and 0.25, respectively. The best combination of input data was derived from the spectral bands obtained through the MNF transformation. Based on the findings, high noise in PRISMA imagery and the similar spectral behavior of forest types in this region hindered the species discrimination and classification performance.

Knowing the forest canopy height is essential for evaluating the health and dynamics of forest ecosystems, as well as for monitoring and modeling the carbon cycle and biodiversity. However, measuring canopy height through ground surveys is costly and time-consuming. Since 2018, the ICESat-2 satellite, equipped with the ATLAS laser sensor, has enabled the direct measurement of tree height. Although ICESat-2 is specifically designed to estimate ice height, it also provides significant data on vegetation height on the Earth's surface. This study aims to investigate the ability of the ATLAS sensor to accurately estimate forest canopy height in northern Iran. For this purpose, the vegetation height data from the ATLAS sensor (ATL08) was evaluated in the Kheyroud experimental forest. To validate the accuracy of the estimated forest canopy height by ATLAS, the forest height data obtained from the satellite were compared with the maximum tree height measured in 121 plots collocated with LiDAR footprints. The estimated forest canopy height by ATLAS and the maximum measured height of trees were compared using a t-test. The RMSE, rRMSE, and R2 values were 0.87 m, 2.7%, and 0.98, respectively, indicating the high accuracy of the ATLAS sensor in forest canopy height estimation. The results of the t-test showed that the mean difference between the measured maximum height of trees in the plots and the corresponding values extracted from ICESat-2 satellite data is not statistically significant (P > 0.05). This study demonstrates that the satellite estimates forest canopy height with very good accuracy in the forests of northern Iran, with a slight overestimation in areas with low-height trees.

Canopy cover is an important forest structure parameter with many applications in ecology, hydrology and forest management. This study aimed at investigating the capability of Landsat 8 satellite OLI data for modeling and estimation of forest canopy in a part of the Zagros forests. First the images were evaluated in terms of geometrical and radiometric errors. Required processing such as Vegetation Indices, Principle Component Analysis (PCA), and Tasseled Cap transformation were performed on the images of the study area. To measure aboveground canopy cover using hemispherical photography method, 60 sample plots were designed with a square dimension of 45 × 45 m. The spectral values of the corresponding sample plots were extracted using a polygon map of sample plots. Forest canopy cover was modeled using stepwise multiple linear regression and the accuracy of the model was evaluated via the k-fold cross validation technique. The results of multiple linear regression between canopy cover with main and computational bands showed that the model obtained from SR vegetation index and band 8 with R2 = 0.662 and RMSE (%) = 15.24 was the best model. Overall, this study demonstrated that the estimation of forest canopy cover is cost-effective and requires low computation power using Landsat 8 satellite.

Estimating the biomass values in forests stands through remote sensing is important. It has been reported that the major reasons of uncertainty are the lack of concurrency in satellite data and field information as well as the use of global allometric equations for estimating the weight of biomass of forest trees inside the country. Minimizing the above problems and the investigation of data performance in developing appropriate model for estimating the forest biomass in the Bankoll region of Karazan District of Sirvan County in Ilam province using Sentinel-1 satellite data in 27th of June, 2017 was the main goal of this study. Average size of the trees crown in 53 rectangular plots related to the coppice growth form with dimensions of 30×30 mwhich during 23 may 2017 to 10 June 2017 through applying DGPS by RTK method have been implemented on the ground were entered in the process of estimation the value of biomass. The average harvested field biomass was 10.63 Mg ha-1. After extraction of radar features, those features which had the greatest correlation with the values of biomass were selected using genetic algorithm by two models including K-Nearest Neighbor (K-NN) regression and Support-Vector Regression (SVR), then the most appropriate combination was identified and the biomass values were modelled. Models were validated using 26 test plots. Correlation of features obtained from radar data and the value of biomass indicated that features of VH، Mean VV، Mean VV GLCM (Correlation) and Mean VH GLCM (Dissimilarity) had the greatest sensitivity towards the value of biomass. Using regression models indicated that SVR model (Relative RMSE of 0.08) was more precise compared with K-NN regression (relative RMSE of 0.10). The best combination in the use of K-NN regression model with a relative RMSE of almost 0.99 Mg ha-1 (equal to 10%) and the coefficient of determination (R2) of 0.22 and the best combination when using SVR model was a relative RMSE of 0.87 Mg ha-1 (equal to 8%) and the R2 of 0.14. The results indicated that the final models, obtained from the optimal features extracted from radar data in the wavelength of C band and used parametric and non-parametric regressional methods in this research, were not abled to improve the saturated effect in data for estimation of biomass in the studied forests and it was not resulted in presenting an estimating model with an acceptable accuracy.

Wildfires have proven to cause considerable damage to natural environments in Ardabil in the last years, and the prevalence of such events is anticipated to increase in the future. Fine scale wildfire exposure and risk maps are fundamental to landscape managers and policy makers for prevention, mitigation and monitoring strategies. In this paper, we provided 100 m resolution wildfire risk and exposure metric raster grids for the fire-prone municipalities in South Ardabil province corresponding to a fire simulation modeling and a geospatial analysis with a geographic information system, along with complementary historic ignition and fire area data (2005-2018). Fire risk parameters (burn probability (BP), conditional flame length (CFL) and fire size (FS)) were generated with FlamMap Minimum Travel Time (MTT) algorithm considering fire weather conditions during the last 14 wildfire seasons. Moreover, we estimated fire potential index (FPI) to spatially analyze where large fires likely initiate. Average BP, CFL and FS ranged from 0.00007 to 0.0025, 0.05 to 1.6 m, and 54.7 to 360.3 ha, respectively, that highlighted a large variation in the fire exposure factors in the study area. The calculated FPI showed two major areas with the highest values, where historic ignitions were high, and where large areas of faster burning fuels were present. The results of this study can be useful for analyzing potential wildfire risk and effects at landscape scale, evaluating historical changes and future trends in wildfire exposure, as well as for determining fuel treatment strategies to mitigate wildland fire risk.

Hyrcanian forests play an important role in absorbing carbon dioxide and reducing the severity of climate change and global warming. The accurate estimation of actual evapotranspiration can help to plan for conservation and management of these forests and their water resources. Direct measurement methods for evapotranspiration have limitations, one of the most important of them is the pointed measurements. Methods for distance measuring have been developed to address these constraints. The purpose of this study was to prepare and estimate actual instantaneous () and daily () evapotranspiration maps from Landsat 8 satellite imagery and SEBAL model in Hyrcanian forests. For this purpose, Landsat 8 satellite images were prepared in two different dates, i.e. 2014/7/26 and 2016/5/28 in 200 thousand hectares of Hyrcanian forests in the cities of Pol-Sefid and Kiasar, and after processing and calculations, latent heat flux was estimated from images and from that basis,  and  maps were prepared. To evaluate these maps,  and land values were calculated using weather data of Pol-Sefid and Kiasar stations and the FAO Penman Monteith method. The estimated  and  means at the Pol-Sefid station were 0.53 and 5.39 and at Kiasar station, 0.48 and 4.98 respectively. The calculated mean of these two parameters, respectively, were 0.60 and 6.16, at the Pol-sefid station and 0.55 and 5.82, at the Kiasar station. The results showed that the percentage of estimated relative differences for  and is 0.068 and 0.809 respectively, and mean percentage of estimated relative differences for  and is 11.99% and 13.56%, respectively, which in total shows the high ability of the used approach for evapotranspiration maps.

Orthphoto mosaic, and digital elevation models (DEMs) that created from UAV imagery can be used to delineate tree crowns. The goal of this research is to compare two segmentation techniques in the estimation of crown diameter. In this regard, two successful flights were carried out in two different seasons (leaf-off and leaf-on). Then we generated accurate CHM through a photogrammetric workflow using DTM and DSM. We used invert watershed (IWS) and multiresolution segmentation (MRS) to detect tree crowns on CHM and orthophoto mosaic, respectively. To compare the estimates of mean crown diameter from UAV images with actual values, 95 trees were measured. The results of comparing the estimated and field measured values showed that all two algorithms effectively delineate tree crowns. The results of linear regression showed there is a high agreement between estimated and measured values, which were (R2=0.88) for MRS and (R2=0.92) for IWS. The best result was obtained using IWS techniques (RMSE= 7.02 % and MAE = 5.97 %). T-test results showed that there are no significant differences between the field measurement and IWS estimated values. Although the t-test result showed there are significant differences between the means of MRS estimated and measured crown diameters, but based on RMSE (8.74 %) and MAE (8.11 %) of the MRS technique, the error of crown diameter estimation was small and therefore acceptable. Finally, the results showed that this methodology, as an accurate and low-cost process, could be used to estimate mean tree crown diameter.

Over time, environmental and human factors have caused positive and negative changes in the quantity and quality of vegetation, and these changes will continue. Vegetation greenness trend may be either increasing (Greening) or decreasing (Browning). Understanding the factors creating these changes and detection of trends in the past and future, would be useful for managers and decision makers. This study investigates the long-term changes in Kurdistan vegetation greenness by using the 16-day composite MODIS NDVI time series for 2000–2017, and 414 images with 250-m pixel size. The coordinate system of MODIS data was converted from the sinusoidal to the geographic map projection and a time series of data was created. After seasonality removal in the time series data, the long-term trend analyses were performed using parametric ordinary least squares (OLS) regression and non-parametric Theil-Sen and Mann-Kendall methods at pixel by pixel basis. Both OLS and Theil-Sen represented the similar result and indicated a slight increase in NDVI i.e., the poor greenness during the study period. According to the Mann-Kendall trend analysis, about 97 % of the province experienced a slight increasing and 2.46 % showed decreasing trend. However, based on the Kendall significance test, only 12 % of the province had a significant increasing or decreasing trend at 1 % confidence level and the rest of the area showed a slight and negligible increasing trend.

In this research, the possibility of application of LiDAR-derived characteristics has been studied for discrimination amongst common hornbeam (Carpinus betulus) and Persian maple (Acer velutinum) trees. LiDAR data of individual sample trees were separated in laser point clouds using their measured center coordinates and crown diameter in the field. In district 1 of Shast-Kolate Education and Research Forest in Gorgan, 80 individual A. velutinum and C. betulus tree samples were selected. The trees were either located in dominant storey or were not overlaid by adjacent tree crowns. Tree heights were measured using Vertex 1V GPS device. Crown diameter was measured in four cardinal directions using Leica Disto lasermeter. Center coordinates of the sample trees were determined using both DGPS and distance/azimuth measurement by Total Station device. Different geometrical and statistical metrics of sample trees were extracted from LiDAR data. The results of discriminant analysis suggested the height standard deviation of laser points over 80% of tree height and crown slope as the selected metrics to differentiate the tree species (accuracy=80.3%). The mean of those two metrics showed larger values for hornbeam than maple. The selected LiDAR metrics were ascribed to represent the shape and height variation of returned crown laser points. It is therefore concluded that geometrical LiDAR-derived attributes could successfully helo by differentiating between the two tested tree species.

Tourism as a growing industry needs special attention in developing countries. During last decades، nature based tourism has been growing faster than other tourism fields and it seems to grow even faster. Ever growing demand for nature based tourism emphasizes the necessity of a holistic planning for preserving environment against degradation. Objective of this study is to present a nature based tourism planning in arid and semi-arid areas. Khatam township in south of Yazd province was selected as case study. First، criteria for nature based planning were chosen from foreign and domestic literature. Then Delphi method was implemented for screening and prioritizing these criteria resulting in 11 criteria and 29 sub-criteria ordered based on percentage and degree of importance. Each criterion was measured or mapped by a number of one to four sub-criteria and each sub-criterion by one indicator. Sub-criteria referred to climate were used to determine tourism climate index and time constraints. Subjective maps were developed by spatial sub-criteria. Using Spatial Multi Criteria Evaluation (SMCE) to find proper fields for nature based tourism; four zones were chosen with a total area of 17278 (ha) which is equal to 5% of study area. Employing qualitative sub-criteria in TOPSIS method and distance to ideal solution was used for prioritizing zones. Results showed that the smallest zone has the highest priority. Constraints on tourism activities were developed on sustainable nature based tourism framework basis in arid and semi-arid areas using tourists demands projected in questionnaires.