فهرست مطالب علی اصغر تراهی

This study used satellite imagery to evaluate the effect of oak trees’ decline on carbon storage and its economic value in Zagros forests of Kuhdasht, Lorestan Province, Iran. To this goal, the study used the Sentinel-2 Satellite MSI sensor data recorded on June 15, 2021. In order to measure the ground biomass, we randomly selected 250 square samples with dimensions of 30 × 30. The Diameter at Breast Height [DBH] of each tree was measured and the amount of ground biomass for each sample plot was also obtained. General multivariate regression, Artificial Neural Networks, and K-nearest neighbors were used for modeling the ground biomass of the selected regions. In order to value the amount of stored carbon, the average rate was used for each ton of absorbed carbon dioxide gas (54 euros). The plot for the monetary valuation of carbon (in terms of carbon dioxide absorption) was determined by using the relationship between the amount of ground biomass and stored carbon, as well as absorbed carbon dioxide gas. The results showed that linear regression obtained by the vegetation index of NDVI with R2=0.73 and RMSE (%) = 21.88 was the best model for the studied area. The findings confirm that Sentinel-2 imagery data were considerably efficient for estimating the ground biomass of the Zagros declined forests.  Moreover, the results of the economic valuation of the carbon inventory in terms of carbon dioxide absorption service showed that each hectare of forests in the region has a value equivalent to 2547.12 Million Rial in carbon dioxide absorption function. The economic valuation of declined areas indicated that the more the degradation [decline] of these areas escalates, the more their economic value decreases. Accordingly, the highest value was obtained in the healthy areas and the lowest amount was calculated in the most declined regions. The findings suggest that Sentinel satellite images could be used to determine the accurate biomass map for the degraded areas. This map can be employed as a base map for decision-making, forestation/protection operations, and the economic valuation of these invaluable resources.

Vegetation is considered to be one of the most important elements in all major ecosystems on the Earth. Thus, a proper understanding of vegetation and its growth trends and other environmental factors has always been of particular importance for environmental research. Estimating vegetation phenology parameters (VPPs) requires continuous NDVI data collection over a specific period of time. However, soil moisture, cloud cover, and particulate matter may affect the energy reflected from the vegetation cover and result in noisy images or erroneous data. Vegetation phenology parameters cannot be extracted from raw data due to the presence of random errors. These errors do not follow the phenological process and thus, overestimate or underestimate NDVI and fail to produce accurate results. Smoothing functions and especially the TIMESAT model are used to resolve this issue and eliminate errors in the NDVI time series. There is still no general consensus on which function acts more efficient and accurate in the TIMESAT model especially regarding the highlands. Naturally, each method yields different results in different regions, and thus it is necessary to compare and evaluate different functions used in the TIMESAT model and determine their accuracy in producing a continuous time series. The present study aimed to evaluate the performance of various functions such as asymmetric Gaussian (AG), double-logistic (DL), and Savitzky–Golay (SG) used to extract VPPs especially in mountainous regions.

TIMESAT model is a time-series analysis model based on remote sensing (RS) vegetation indices. It includes three functions: Savitzky–Golay, asymmetric Gaussian, and double-logistic, which are used to smooth collected data and identify outliers. Savitzky–Golay is an adaptive-degree polynomial filter (ADPF). The other two functions fit the information using nonlinear functions. These functions use unmodified NDVI data as input to produce modified and smoothed NDVI output. Four wheat farms in cold and warm regions of Khorramabad were used in the present study to investigate plant phenological behaviors and extract VPPs. The northern and eastern parts of Khorramabad have a cold climate, while the southern and western parts have a warm climate. One-year time series (2020) data of MODIS sensor was used in the present study. Using the infrared and near-infrared spectral reflectance values, NDVI was calculated in the Google Earth Engine environment. Errors of the NDVI time series were first corrected and a phenology curve was produced for wheat in both warm and cold farms. Asymmetric Gaussian, double-logistic, and Savitzky–Golay filter functions were also used to adapt the NDVI data. Following the reconstruction of growth curves in the time series of vegetation indices and smoothing the curve, various VPPs such as start of the season (SOS), end of the season (EOS), middle of the season (MOS), length of the growing season (LOS), base limit and value, maximum NDVI, vegetation growth season range, large seasonal integral, and small seasonal integral were extracted.

 Results and Discussion:

The model indicated that on average, beginning of the wheat growing season (SOS) in the warm regions of Khoramabad coincided with the 31.5th day of the year in the Gregorian calendar, whereas it happened on the 90th day of the year in the cold regions, thus indicating a 1.5-2 month difference between the beginning of the wheat growing season in cold and warm regions. The wheat growing season ended (EOS) on the 163rd day of year in the warm regions and on the 193rd day in the cold regions. In addition, in order to analyze the effect of climate on VPPs such as SOS and EOS, a comparison was made between the parameters obtained from farms in warm and cold regions. On average, the peak of vegetation growth has occurred in late March (Mar. 28, 2020) in farms of warm regions while cold regions experienced the peak of growth on May 20, 2020. In other words, warm regions have experienced peak growth approximately two months earlier than cold regions. Finally, the models were assessed and obtained values were compared with ground-based data collected in field surveys. Validation results showed that with an average RMSE of 2, Savitzky–Golay smoothing model reconstruct data more accurately as compared to asymmetric Gaussian, and double logistic function with an RMSE of 4 and 11, respectively. In other words, Savitzky–Golay estimates SOS and EOS with a higher accuracy and lower errors.

Findings indicate that Savitzky–Golay filter outperformed asymmetric Gaussian and double logistic functions in extracting VPPs in mountainous areas. Accordingly, it is suggested to use Savitzky–Golay in future studies aiming to investigate the phenological behavior of different vegetation covers in other Iranian highlands. The study has also showed that different climatic conditions within the study area affect plant phenological behaviors, which can lead to differences in SOS, peak of growing season, and EOS in different cold and warm regions of the province. Growing season of plants in cold regions of the province has occurred with an approximately two-month delay compared to the warm regions of the province.

Forest roads are essential for forest management, forest harvesting, wood transportation, recreation, education, research, and forest protection. To meet these needs, forest road networks have been constructed in the northern forests of Iran. Forest road mapping especially over large and mountainous areas is time-consuming and expensive. Today, remote sensing data can be considered as an important tool for forest roads extraction. Therefore, in this research, LiDAR data and UltraCam images were applied in order to extract hidden forest roads. At the first step, noise points in the point cloud data were removed. Then, according to the Central Limit Theorem (CLT), the third statistical moment (amount of skewness) of the data was calculated and the non-ground points were eliminated. At this next step, a number of non-ground points were identified as ground points. In order to eliminate these errors, slope-based algorithm with a radius of 10 meters and a slope of 22 degrees was applied on the points obtained from the first step, these points were eventually removed and the ground points were extracted. Then, extracted ground points were converted to grid. Then the grid was converted to polygon based on the pixel density, by using the DTM as well as UltraCam aerial images, polyglots that were not related to the road were removed. Until this stage, the output was the roads that were not hidden by the forest canopy. Therefore, the hidden parts of the roads were extracted by applying slope-based algorithm with the radius of 10 meters and 65 degrees slope on the whole LiDAR points and interpolating the results by spline interpolation method. By connecting and modifying the polygons, 3m wide dirt roads and 2m wide skidding roads were extracted. The results are evaluated by comparing to manually acquired road data. The quality measures completeness, correctness and quality were 82%, 86% and 72%, respectively.

Land use change due to human activities is one of the important issues in regional and development planning. Given the advantages and capabilities of satellite data, this technology can be of great help in identifying and detecting these changes. The purpose of this study is to detect land use changes in Kamyaran city using satellite images over a period of 35 years. In this study, data from 1984 TM sensor, 2000 ETM + sensor and 2019 Landsat OLI sensor were used.  Initially, preliminary preprocessions including radiometric, atmospheric and geometric corrections were performed on the raw data. Land control points were used for training, accreditation and to prepare land use map. Land use class was determined by field survey and using Google Earth images in 9 land use classes of agricultural lands, forests, gardens, rich and wooded pastures, medium rangelands, residential areas, water area, barren lands and rock outcrops. Next, the neural network method was used to monitor the images in ENVI 5.3 software. The evaluation results showed that the overall accuracy and kappa coefficient of OLI classified images are 94.3 and 0.92%, ETM + 92.6 and 0.91% and TM 90.3 and 0.87%, respectively. The results showed that forest lands and rich and wooded pastures decreased significantly during three time periods, which decreased by 11.64 and 19.12 percent, respectively. So that rich and wooded pastures have an increasing trend until 2000 and in the next period until 2019 has a decreasing trend. Residential lands, water areas and gardens increased by 2.27%, 0.57% and 3.98%, respectively. Due to the growing trend of population and urbanization, the results of this study provide the necessary information to make basic decisions in the development of management policies for planners and regional managers for the sustainability and evaluation of natural resources.

Examining the effects of climate change on the oak spatial distribution, as the main species of Zagros forests and its ecological and economic values is of significant importance. Here, we used species distribution models for simulating current climatic suitability of oak and its potential changes in 2050 and 2070. For this purpose, five regression-based and machine learning approaches, four climatic variables related to temperature and precipitation and two optimistic (RCP 2.6) and pessimistic (RCP 8.5)  greenhouse-gas scenarios were used. The results of measuring the accuracy of models by AUC indicated the good performance of all algorithms and Random Forest achieved the highest accuracy (AUC = 0.95) among other methods. The results showed that in both time periods and under both scenarios, changes will occur in oak spatial distribution and the most severe one would be a 42.9 percent loss in the oak climatic suitability in 2070 under pessimistic scenario (RCP 8.5).

Image fusion is the process of integrating the information from a set of images in an image, as the fused image contains more useful information than any input data. The aim of remotely sensed image fusion is integration of information that obtained from sensors with different spatial, spectral and temporal resolution in order to get an image with more detail than any individual data. In the fusion process, output image is a combination of important features of two or more input data. The aim of this study was to evaluate the performance of discrete wavelet transform in fusion of hyperspectral and high resolution images. For this purpose, a window of images of Hyperion, ALI and OrbView3 sensors was selected. First, the Hyperion image was corrected for unusable bands and strip noise. Panchromatic band of ALI sensor was used for geometric correction and registration of hyperion image. The hyperion image transformed into a 10 m pixel image using the sampling operation and fused with the ALI image using the Gram-Schmit algorithm. Using the OrbView3 image, the results was captured on the fused image, then both images were converted to 4-pixel pixel size using the resampling operation. The OrbView3 image was decomposed into four levels using a HAAR wavelet and used for fusion procedure. The results showed that with increased level of image decomposition, the accuracy and precision of the integration increases.

Fires in Zagros Rangelands and Forests Given the protective role of these forests in preventing water and soil erosion, it is a serious environmental threat to these forests and rangelands. The purpose of this study was to analyze the spatial distribution of fires in rangelands and forests of Kamyaran city and identify areas prone to fire. According to reported fires, the major fires were identified as having the highest frequency and frequency, and the file was compiled using the Global Positioning System (GPS) of the entire area in the past few years and after converting it to soft format. The Arc Map was transferred and a map of the scattered areas of forests and rangelands was prepared. Then, by analyzing the studies in this area and asking the experts of natural resources, Jihad of Agriculture and Meteorology, six factors influencing the fire in rangelands and forests of this region were analyzed. Influential factors include precipitation, temperature, river distance, distance from road, elevation and slope of the area. Then, using the Analytical Hierarchy Process (AHP), the information layers in the Expert Choise software were weighted and then compared by pairwise comparison. Finally, after incorporating layers in Arc Map software, the final map of fire hazard zoning of forests and rangelands of the city was classified into five classes of areas with high potential to very low potential. The results showed that the ground truth layers were in full agreement with the final map and most of the large-scale wildfires were located in high potential and very high critical areas and from the total forest area and rangelands of the high potential areas. The fire covered an area of 45218.14 hectares, comprising 22% of the total area.

Identification is a mission to learn about the activities, resources, abilities and position of the enemy. Military targets detection can provide commanders with a variety of information on the status of activities, deployment of forces, military arrangement of targets, and many other information from a military area. In recent years, the advancement of remote sensing technology has made it possible to produce different images with high resolution Spectral and spatial. fusion of hyperspectral and high resolution imagery can help effectively identify, extract, and produce maps from the constituent elements of an environment. The purpose of this research is to target detection (military) using fusion of hyperspectral and high resolution imagery. For this reason the Hyperion, ALI and OrbView3 data was acquired. Firstly, Hyperion Data Preprocessing was used in terms of unused bands, bad straights, atmospheric correction and geometric correction. The image of Hyperion in a two-step process with panchromatic bands combined ALI and OrbView3 images using gram-schmidt, Pc Spectral, and IHS algorithms. The combined results showed that Gram schmidt had the best spectral and spatial performance. In the next research, the MNF conversion was used to reduce the image size and reduce the noise, and the PPI algorithm of the purest pixels was used to extract the spectral profile in a visual and precise manner compared with the reference spectra. In the following, algorithms, BANDMAX, spectral angle mapper and divergence spectral information were used to identify the targets. The results of the identification of the objectives showed that the BANDMAX method with a Overall accuracy of 89.25 and Kappa coefficient of 0.723 was better than the other two algorithms.

It is important to have new land use plans in many areas, including natural resource management and land planning. Remote sensing data has a high potential for preparing up-to-date land use maps and land cover. The purpose of this study is to evaluate the methods of preparing land use maps of Kamyaran city using satellite images. In this study, OLI Landsat 8 satellite sensor data for June 2018 were used. Initially, preliminary processing, including radiometric, atmospheric, and geometric corrections, was performed on raw data. ground control points were used for training, accreditation, and land use mapping. The Landuse class was identified at each point by field survey and using Google Earth images in 9 user classes of agricultural lands, forest, garden, rich pastures, medium pastures, residential areas, water area, barren lands and rocky outcrops. In the following, maximum probability, minimum distance, support vector machine and Mahanalubi distance were used for the supervised classification in ENVI 5.3 software. To evaluate the accuracy of classification methods, two criteria of general accuracy and capa coefficient were used with ground control data. The results showed that the support vector machine method was 91.4% more accurate and the Kappa coefficient was 0.88% more accurate than other methods.

Classification of high-dimensional hyperspectral data with many spectral bands for the derivation of good accuracy is an important problem in hyperspectral remote sensing .The most of classification algorithms are based on spectral information .Here, in order to achieve an high classification accuracy, we can use the spatial information of data. Integration of hidden morkov random field that optimize spatial information by minimizing energy functions, with support vector machine that is an powerful method for classification of hyperspectral data, can improve classification accuracy in final classified map  properly. The purpose of this study is to improve the classification accuracy with a limited of training samples by combination of support vector machine algorithm and hidden morkov random field. In this study, tow hyperspectral dataset from Hyperion and AVIRIS sensors has been used. After the applying radiometric corrections like correcting embedded lines and remove bad bands , atmospheric correction Hyperion dataset done by FLAASH method and AVIRIS dataset by IAR algorithm. MNF transformation was used in order to dimensionally reduction and the endmembers were extracted from PPI band and then in order to spectral classification, used from SVM method. Finally, to improve classification accuracy in the final classified map, hidden Markov random field (HMRF) was used. So that after the extracting of Components from PCA and MNF Transformations, computing of some statistic parameters of classes in SVM classified map in order to use in inputs model and so configuration of iterations, SVM-HMRF model was applied.

The results show that the proposed model (SVM-HMRF) has improved overall classification accuracy in both of data sets. For example, the improved classification accuracy on some of land uses, were around 25 percent. Also regions of final classified map is much more homogeneous and salt and pepper nose drastically reduced.

Among the various natural hazards, floods may be considered as the most devastating factor that inflicts great damage on human societies. Therefore, the importance of estimating flood damage and its scope in planning to reduce damages and determine points with high risk is very important. The aim of this study is to determine the extent of flood hazard using OLI satellite data. For this reason, a window of OLI satellite images of Landsat 8 was acquired before and after the Dezful flood of April 25, 2016. First, preprocessing operations include radiometric and atmospheric corrections of images were done, and the principal component analysis was then used to reduce the correlation of the data. Data processing was performed using a Support Vector Machine algorithm with linear and polynomial kernels. In order to train the Support Vector Machine algorithm, training samples for each class (agricultural land, flood extent, water resources, settlement areas, and recreational areas along the river boundary) were harvested at the user level. In order to evaluate the similarity of the classes and the degree of correlation between the samples, the quantitative assessment method of the Jeffries Matusita was performed. The results showed that the flood area was 11593.26 ha, the highest damage was due to agricultural land with a destruction of 8467.45 ha and recreational and tourist areas along the riverbank with a destruction of 2659.14 ha.

The urban areas of the world have complex physical-geometric characteristics of various elements, which are rapidly expanding. Having accurate knowledge of user characteristics, identifying and detection complications from planning management needs, optimizing urban and environmental settings. Combining hyperspace images and high resolution spatial images can provide effective assistance in detection, extracting and producing maps of the elements of a city environment. The purpose of this study is to evaluate the fusion of hyperspectral and high resolution imagery. In this regard, hyperspectral data related to the Hyperion sensor were first corrected in terms of non-usable bands and inferior bands. After the FLAASH module, the atmospheric correction of this data was used. In the following, the image of the Hyperion with a panicramatic band of the ALI sensor is geometric correction and The re-sampling operation was converted to a 10-meter pixel. Finally, using the algorithms of Grim Schmidt and Pc Spectral and IHS, the image was combined with the image of ALI. Then, using the Panchromatic Band, the Orb View image was re-geometric correction and re-sampling, and converted to a 1.5-pixel pixel. Finally, the combined operations with the Orb View Panchromatic Band again were carried out, the combined results showed that the Gram method Schmidt had the best performance spectral and spatial.

The aim of this study is to use of the FFT_IHS method to increase the accuracy of data extraction from OLI of Landsat 8 data. For this purpose, a window of OLI images of Ardabil County was selected and, after applying the necessary preprocesses include atmospheric correction, the multispectral and panchromatic bands were fused with the FFT_IHS method. In order to evaluate the capability of FFT_IHS method to increase the accuracy of information extraction, the training data were taken from the before and after applying this method. Correlation of training data’s was evaluated using the Jeffries Matusita index and the training data’s were classified into 8-classes using Support Vector Machine algorithm. The results showed that image classification before the fusion of bands has a overall accuracy of 88.3% and a kappa coefficient of 0.87 and after fusion with FFT_IHS, the overall accuracy is increased to 96.3% and the Kappa coefficient is to 0.96.

The aim of this study is to evaluate Pixel Purity Index(PPI) and Sequential Maximum Angle Convex Cone(SMACC) algorithms In order to extract the spectrum of Ghezel Dash copper by using the hyperspectral Image of Hyperion Sensor level 1R. For this purpose, a window of the Hyperion image, Ghezel Dash district of Khoy was selected. Then, in order to extract the pure spectra of the material to the pre-processing of the image, including radiometric correction, Contain the removal of bad bands, The strip lines containing noises, spectral effects removal, and atmospheric correction were used to convert radiation values to reflection. Next, the MNF transform was applied to reduce image dimension and noises. In the next step, the purest pixels were obtained using the PPI and SMACC algorithms. And the results was classified eith SAM classifier. In the final step, by using the ground control points we measured the accuracy and precision of the algorithms in mining minerals spectra. The overall accuracy and Kappa coefficients for the PPI algorithm are respectively 74.12% and 0.61 and for the SMACC algorithm it was 69.23% and 0.57, respectively. Also, the number of minerals pure pixels in the PPI algorithm was greater than that the SMACC.

The aim of this study was identification of ecosystem units in Arasbaran forests and determination of effective environmental factors and biodiversity indices on their distribution. For this purpose, 130 circular plots with an area of ​​314 m2 were examined in a random - systematic design with 300 × 300 m dimensions. Tree and shrub species in the original sample plots were measured. Species, canopy cover and vegetation cover were considered as basic measurement criteria. Herbaceous species were studied in micro sample plots, each 3.14 m2. Six ecological groups were classified using two-way analysis of indicator species (TWINSPAN) and comparative analysis (RA) methods. Analysis of groups was performed by applying analysis of variance and discriminant analysis based on topographic and biodiversity index. The discriminative analysis showed that altitude and gradient-direction as two topography variable, and the Margalef richness index, evenness indices of Smith - Wilson, Sheldon and Simpson, and heterogeneity (diversity) indices of Simpson, Hill (N1) and Shannon-Wiener were the five important indices which were significant (α = 0.01) which confirmed the accuracy of the classification at 83.8%. Therefore, it was concluded that topographical characteristics had more influence on the formation of ecosystem units than plant biodiversity indices. We also concluded that the ecological classification system can present a suitable prediction of environmental characteristics.

Obtaining more accurate and updated information about the forest area is one of the basic factors in sustainable management of this area. Acquiring this information is more beneficial in terms of time and cost through classification of remote sensing data. In this paper, Landsat8 (OLI) data from Maroons Behbahan riparian forest that is located in Khoozestan province of Iran were used for mapping and better management of riparian forest. Preprocessing operation including radiometric and atmospheric correction was applied to the data. Supervised classification algorithms including maximum likelihood (MLC) and support vector machine (SVM) with seven and three classes were used for classification. In order to evaluate the capability of support vector machine, three categories of training data with 241, 141 and 41 numbers with four kernels of SVM (linear, radial basic function, sigmoid and polynomial) were used. The results indicate that mapping of Maroons riparian forest using Landsat images is possible and the best result was acquired using SVM –polynomial method by three classes with overall accuracy and kappa coefficient of (99/24) % and (0/97) respectively. Also, the findings showed that with reduction of number of classes from seven to three, the accuracy of classification is increased. By reducing the number of samples to moderate, significant difference in accuracy of classification was not observed, but by more reduction of samples, the accuracy of results is reduced.

In past years the lack of accurate understanding of exist resource and use of uncontrol and illegal led to many valuable natural resources are being destroyed completely abolished or placed in such a way that exploitation They consistently unavailable,so that stable use of them is not possible. Undoubtedly, natural forest reserves in Khuzestan province, which is one of the valuable natural resources of the country are also not been spared. One of the major tree species in these forests were destroyed critically endangered species is Shisham ) Dalbegia sisso(.The purpose of this investigation, study and evaluation of regeneration and seeds germination in site of Dalbegia sisso.
In order to investigate the natural regeneration of Dalbergia sisso reservery, the natural regeneration in 30 micro plots (3*3 m) was investigated randomly. So the qualitative and quantitative properties of seedling were measured and investigated respectly. Also in order to investigate the condition of seed germination of Shisham trees, the seeds were collected from the mature trees and from the ground floor, as wet seeds and dry seeds respectively, and sent to Khazar laboratory of seed. The physical properties of seeds such as germination capacity, viability percent and suitable treatments for seeds germination were determined in the laboratory. For comparison of different treatments, the ANOVA and Tukey test were used.
The results showed that the highest rate of natural regeneration on the floor height more than 100 cm and a height of less than 25 cm is the lowest in class. The study of natural regeneration in Shisam natural reserves showed the regeneration in reservery was only the root sucker. Shishama qualitative study showed that the highest percentage of natural regeneration of trees in floor height of less than 25 cm and the lowest percentage in more than 100 cm height classes respectively. Seed test results indicated that if the seeds are sown in cold water immediately and without treatment, after three weeks with an important loss of viability 7 percent. Moreover, if the dry seeds are sown in cold water immediately and without treatment, after three weeks of treatment lowest viability and eight days in the cold water would be the highest viability.
The quantitive and qualitative investigation of natural regeneration showed that the maximum of qualitative percent were seen in height class of below 25 and the minimum of qualitative percent were seen in height class of above 100 cm. The results of seeds germination tests and viability showed that seeds Shishman cold water without treatment with the lowest percentage of seed viability (5%) and the treatment of cold water for 8 days had the highest viability (39%).

For achievement tosustainable development and conservation of natural ecosystems and their biodiversity,study on topographic factors and their effects is necessary.Regarding to topographical condition of Arasbaran forests of Iran,slope is an important ecological factor controlling many other dependant variables.So the inventory grid was designed by a random-systematic method with dimension of 300×300 meter and 160 sample circle plot with 314 square meters area were taken in Arasbaranforests.Shrub and arborous species were measured in main sample plot.Species,the percentage of coverage crown and the number of basis were considered as the measurement standards of plant coverage.Grassy species were measured in micro sample plots each with3.14 square meter.In micro sample plots,species and the percentage of the grass coverage were both determined.Site slope weredividedtothree classes including(0-25,25-50,and over50percent)in order to determined the biodiversity of plant species in different slope classes,and then indices of Margalef,Menhinick,Modified Nee, SmithandWilson,Simpson and Shanon-Wiener was calculated in each class.To determine the difference between slope classes we used one-way variance analysis and multi range comparing Duncan test.The results show evenness index of the range of plant do not have significant impact, however, its effect on the richness and heterogeneity index is a mean.Also the results showed that the index of species richness has a significant role in increasing biodiversityof region studied with increasing slope.Coefficient of variation toidentify the best indicator of the statistics and Pearson correlation coefficient was used.The result showed that to measure of richness,Margalef index,to measure of the evenness,Modified index and to measure of the heterogeneity,the Shanon-Wiener index,are the best indicators.