حمیدرضا ریاحی بختیاری

Zagros forests, as the largest forest ecosystem in Iran, are mainly composed of oak and pistachio species. Although Pistacia mutica has the ability to adapt to adverse environmental conditions, for optimal establishment and growth, like other tree species, it needs environmental conditions appropriate with its ecological needs. Diameter and height growth in these trees are slow due to climatic conditions, so the study of the quantitative and qualitative condition of the forest by measuring and monitoring quantitative characteristics will not be accompanied by accurate results. In contrast, studying the biochemical and biophysical properties of the leaves and canopy of these trees can provide a more appropriate way for studying and monitoring them. Plant chlorophyll and moisture are important parameters in determining the physiological status, health condition, and stress status of trees. It is possible to estimate these parameters from remote sensing and proximity data using radiation transfer models that work according to the physics laws and how electromagnetic radiation interacts with trees. The PROSPECT4 model is one of the newest models proposed to estimate the amount of chlorophyll, water content, and leaf dry matter per unit area based on spectral reflectance measurements. In recent years, fires, pests and diseases, climate change, and drought have gradually affected the growth and quality of pistachio species. Since the first signs of stress in trees appear in their leaves, in this study, the need to investigate the quantitative and qualitative status of this species based on the biochemical parameters of its leaves through non-destructive methods of proximity was considered.

20 Pistacia trees were randomly selected in the Kood Siyah forest of Felard section of Chaharmahal va Bakhtiari province. The amount of chlorophyll parameters, equivalent water thickness and leaf dry matter were calculated in the laboratory. Spectral reflectance of leaf samples was measured by SVC HR-1024 spectrometer. Spectral data and values of leaf biophysical and biochemical parameters were entered in the ARTMO toolbox. Then, PROSPECT4 radiation transfer model was used to simulate spectral reflection and estimate water and leaf chlorophyll of Pistacia mutica. Combining the simulations with least squares regression, the performance of PROSPECT4 model in estimating chlorophyll content and leaf water of this species was evaluated.

In order to evaluate the model in estimating chlorophyll content and leaf water content, R2 and RMSE indices were used between the measured and estimated values. The results showed that the PROSPECT4 model in combination with the PLS model has good accuracy in estimating leaf water content (R2 = 0.73, RMSE = 0.0028) and leaf chlorophyll (R2 = 0.72, RMSE = 2.61). The results of paired T-test of spectral indices showed that ARI,, ARI2,, DWSI,,NDWI and p550 indices were not significantly different between the measured and simulated reflectance.

Based on the results of this study, the combination of radiation transfer model with regression methods such as PLS has great power in predicting tree parameters. Estimation of forest quality parameters in a vast area of Zagros forests using satellite data along with other radiation transfer models using several tree species and a range of parameters, as well as techniques such as ancillary information, multiple solutions, and other regression methods of simulating spectral reflectance can be performed.

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.

Remote sensing has found a special technique and effective in the geological studies and mineral identification for the determination of minerals in the primary detection. The purpose of this study is to recognize the high purity calcite mineral regions in carbonate units using Aster and sentinel 2 images. Sampling of rocks and laboratory analysis using XRF and XRD for verification. The results showed that 97.22% the purity of calcite mineral (CaCO3) is recognized in carbonate unit. The Sentinel 2 auxiliary data improved the spatial resolution of Aster visible and infrared shortwave bands to 10m. As well as CR, MF and SAM algorithms were calculated beside the calcite spectral index (CI) and RGB:831 image for calcite mineralization. By comparing the results of the algorithms, MF image had resembled to calcite spectral index image, so it was used in SAM method and calcite classification map was calculated. The results of comparing showed that calcite minerals categorized in the SAM method with the calcite index, 83.66% of calcite areas was correctly assigned to this mineral. Therefore, using spectral indexes and classification methods, minerals can be identified with the lowest cost and acceptable accuracy.

In order to plan and manage the land and its changes, it is necessary to identify and evaluate the factors affecting it. Land use / cover changes are one of the main factors in global environmental change that is defined as a change in the type of land use; it is one of the major factors changing hydrologic flow, land erosion and destruction of biodiversity. The main purpose of this study was to assess the trends of land use changes in Beheshtabad Watershed of Chaharmahal and Bakhtiari Province with an area about 3847 square kilometers by using remote sensing and GIS during a 25-year period. In this research, first, analyzing and pre-processing the satellite images of Landsat 5 TM sensors from 1991 and 2008 were done, and Landsat 8 of OLI sensor of 2016 was applied. Then, by using the hybrid classification method, 5 land use classes including pasture lands, urban-building lands, agricultural lands, garden lands and bare lands, land use maps for the three time periods were prepared. The overall accuracy of the obtained land use maps for 1991, 2008 and 2016, was 92.17%, 94.29% and 93.41%, respectively, indicating the acceptable accuracy of the maps. Then, the process of land use change and the contribution of each land use classes and the percentage of changes in each land use class were determined in two study periods. The results of this study showed some changes occurred in the studied watershed. The total area of pasture lands during two periods indicated the decreasing trend, but urban-building and garden lands during two periods represented the increasing one. Agricultural lands during the first period indicated the decreasing trend and during the second period showed the increasing trend, while bare lands during the first period showed the increasing trend and during the second period, reflected the decreasing trend. In general, it should be noted that in the Beheshtabad watershed, we could see an increase in the replacement of pastures by urban-building class, rainfed agriculture, gardens, and bare lands, the incidence of destruction in the region.

Increasing urban population growth, industrialization and climate change have led to environmental pollution and therefore attracted attention of city managers and planners to green space, due to its key role in reducing pollution and climate effects. In this study, using remote sensing, geographic information system and field works, the quantity and quality of urban green space in a district one of Shahrekord municipality has been investigated. QuickBird satellite images with high spatial resolution were used to calculate per capita green space and the distribution of green spaces in the concerned municipal district of Shahrekord city. To discriminate different land uses in the study area, image classification was performed using NDVI index and maximum likelihood algorithm. To assess the accuracy of the classification results, the ground truth map was prepared using field works and recording of ground points by GPS. Based on the error matrix, the overall accuracy of 91% and kappa coefficient of 0.85 were obtained. The total per capita green space (public and non-public green spaces) and per capita park for each person was 13.87 and 12.18 square meters respectively. Moreover, the results showed that the distribution of green spaces does not follow a specific pattern, but distributed equally and fairly throughout the region. Finally, with the recognition of empty spaces without any residential applications, suitable places for green spaces establishment and compensating for the lack of per capita park were proposed.

The forest type map is one of the most important thematic maps for forest ecosystem management. Forest mapping using field methods or aerial photos is labor-intensive and time consuming. In contrast, satellite data with its own characteristics like large and repetitive coverage, update and useful information in various wavelengths provides a good opportunity in this regard. This research was carried out with the aim of providing forest type map of central Zagros forests (Chahartagh forest reservoir), of Iran, using the Landsat 8 Operational Land Imager (OLI) data, in August 2016.Two ground-truth maps based on tree density and tree crown area were prepared by field surveying. Moreover, ancillary data such as tree species, location and crown area was taken. In order to increase the spatial resolution of multispectral bands, various image fusion techniques were applied. The best result obtained by the maximum likelihood algorithm with kappa coefficient and overall accuracy values of 57 and 63%, respectively. Due to high species diversity in this area the results showed that the OLI images have a moderate capability to produce forest type maps in Zagros forest.

Producing digital elevation models (DTMs) for forested areas is a challenging task. Nowadays, because of the capability of LiDAR pulses to penetrate through the vegetation canopy, airborne laser scanner technology has attracted enormous interest for DEM extraction especially in forested terrain. Producing Digital Terrain Models from airborne LiDAR data consist of two main steps of filtering and interpolation. Several automatic filtering algorithms for laser scanning data have been proposed. The capability of these algorithms to separate ground and non-ground points is not the same. The aim of this study is to combine two algorithms to achieve higher accuracy. In order to separate ground from non-ground points at first, noise was removed from LiDAR data, then the third moment (skewness) was calculated and finally slope-based algorithm was applied. The skew amount of LiDAR data was equal to 0.48; so the points with greater height were recognized as object points and removed from point clouds. Some of the non-ground points were recognized by mistake as the ground points which were removed by applying slope-based algorithm. Slope-based algorithm with five threshold slopes 18, 20, 22, 24 and 26 degrees was applied on point clouds and after calculating type I, type II and total errors the best threshold slope of 22 degrees was found and implemented. Finally, a digital terrain model was produced by B-spline interpolation method. The results showed 21 cm vertical accuracy for produced digital elevation model which comparing to previous works was acceptable. User's and producer's accuracy for ground points was 93.38 and 94.5 and for non-ground points was 94.5 and 95.2, respectively. This accuracy indicates that the ground and non-ground points were separated very carefully.

Temperature is one of the most significant climatic parameters and is among the main factors affecting urban planning. It is a significant determinant of the dedicated facility in the cities and can even determine the urban structure, shape and texture. Land Surface Temperature is a key indicator for energy balance at the Earth and can be used as an input for models of climate change, Urban Heat Islands, etc. The Land Surface Temperature in Metropolitan is higher than the surrounding area and this phenomenon called the Urban Heat Island. Karaj metropolitan is the third largest city and the second host city of Iran. Given this position, it is necessary to require to check the temperature in this metropolitan more than ever. In this study, the Land Surface Temperature of the Karaj Metropolitan was extracted by using Landsat8 images (4 images) in years of 2013 and 2014. Methods used to extract the Land Surface Temperature include: Mono Window, SEBAL (Surface Energy Balance Algorithm for Land), Stephan-Boltzmann, Single-Channel (developed by Munoz and Sobrino, 2003), Single-Channel (developed by Munoz et al, 2014), Split Window and Landsat Project Science Office. All used methods were compared by using Mean Absolute Error (MAE) indices and finally, the best method was chosen by its proximity to ground data. Results showed that the best method is SEBAL (Band 11) with the MAE equal to 7.98. In general, band 11 of Landsat 8 compared to band 10 has produced more reliable results for extracting Land Surface Temperature. Further analysis of the different dates indicated that the images of the second half of the year produced more accurate and reliable results compared to the first half of the year.

With the advent of the high spatial resolution satellites in recent years, feature extraction has attracted the attention of many researchers. Road has a great importance in infrastructure, transportation and management. The extraction of urban road network has been the subject of numerous studies. Because of its low contrast, complex geometric structure and far away from crowded city centers, mountain road extraction from satellite images has been less studied. In this study to extract mountain roads QuickBird satellite imagery was used. In order to increase the spatial resolution of multispectral images, panchromatic and multispectral bands were fused using novel Esri method. Rod extraction was performed using advanced object-oriented classification methods and the results were evaluated. The overall accuracy for Object-oriented rule-based and Example-based classification methods was 93% and 92% respectively. Considering the results, road's central axis was extracted from the region generated by the Object-oriented rule-based method. Final accuracy of central axis was 87% and RMSE 1.1 pixel comparing to the reference data. Due to the Topographic complexity of the area and spatial resolution of the images, the results showed that advanced Object-oriented classification method has a good capability to extract homogeneous terrestrial objects especially the roads.

Land surface temperature is a key indicator of energy balance. Besides, it serves as input data for models of climate change, agriculture, meteorology, urban heat islands, choosing the best time to agricultural activities, study of volcanic and geothermal activity, and fire detection. In this study land surface temperature has been extracted by available methods using 4 images of TM and ETM+ sensors of Landsat in span years of 1985 to 2003. The methods of land lurface temperature extraction included landsat project science office, mono window, SEBAL, Stefan-Boltzmann and single channel. Because of the multiplicity of methods and the number of images used in this study using a statistical method is required. It is required to determine the most efficient extraction method of land surface temperature, which is close to the existing field data. The statistical indicator used in this study was a mean absolute error (MAE). The results indicated that Stefan-Boltzmann method was the best method for both TM and ETM+ sensors. The MAE values for TM and ETM+ were 4.3 and 6.8 respectively, which showed a minimum value among other results.