جستجوی مقالات مرتبط با کلیدواژه « سنجش از دور » در نشریات گروه « جنگلداری »

Today, reforested stands are one of the most important sources of forest carbon storage and one of the factors that reduce the process of destruction of natural areas. Above-ground biomass (AGB) plays an essential role in sustainable forest management and reducing global warming and is an important source of information. Allometric equations are an important tool for quantifying above-ground biomass in forests. In recent years, remote sensing techniques using non-parametric methods such as the Random Forest algorithm have been widely used to estimate forest tree biomass. In this research, the ability of Sentinel 2 data using the random forest algorithm to estimate the above-ground biomass of Arabdagh reforested stands in Golestan province was evaluated. In this study, 180 circular sample plots with an area of 400 square meters were measured using the cluster sampling method and the diameter at breast height (DBH) and tree height (H) were measured. Also, the exact coordinates of the centers of the sample plots were recorded using DGPS. Then, using the prepared allometric equations, the above-ground biomass of trees was calculated. In this study, Sentinel 2 pre-processed radiometric and geometrical data were used, and based on that, different vegetation indices were prepared. In the implementation of the random forest algorithm, the relationship between the characteristics of biomass as a dependent variable and the spectral values of vegetation indices as independent variables were investigated. Modeling was done using 75% of sample plots (135 sample plots) with random forest algorithm and validation of estimates was done using 25% of sample plots (45 sample plots). The results showed that NDVI and GNDVI indices had the highest correlation in the estimation of above-ground biomass and the random forest algorithm with 310 trees and 5 predictors and the percentage root mean square error of 35.83% and the coefficient of determination 0.51 was able to estimate the above-ground biomass of Arabdagh reforested stands. Also, the results showed that using the data of Sentinel 2, the random forest algorithm has estimated the above-ground biomass of trees more than the actual values. There is no significant difference at the 95% probability level between the estimated and real above-ground biomass values (p-value > 0.05). Also, among the independent variables used. The results of this research showed that Sentinel 2 data has been able to estimate the above-ground biomass of Arabdagh reforested stands with acceptable accuracy. According to the results of this article, it can be said that the information of the main bands and spectral indices played an important role in the estimation of above-ground biomass.

The surface layer of the road suffers various damages with the passage of time due to traffic and atmospheric factors, and their quality decreases. Therefore, quick and accurate extraction of layer-surface anomaly is very important for effective monitoring of road health status. In order to improve the efficiency of surface layer inspection, today drones are used as a useful tool to obtain reliable information in the field of road surface layer.

This research was conducted with the aim of revealing the top layer damage of forest roads using UAV images and image processing techniques on a 3.6 km long road in Dr. Bahramnia educational and research forest, Golestan province. The images obtained from the UAV were prepared using photogrammetric processing calculations and orthomosaic image and digital model of the ground height, then the resulting images were used to identify and check the top layer pits using three supervised learning algorithms: nearest neighbor, K-nearest neighbor And the supporting machine was examined and evaluated.

The results showed that the orthomosaic image obtained from photogrammetric calculations has high accuracy. Also, checking the accuracy of the algorithms used to classify and identify potholes showed that these algorithms have a good ability to identify the damage of the road surface layer. Nearest Neighbor, K-Nearest Neighbor, and Support Vector Machine estimated the top layer failures with accuracy of 92.04, 94.31, and 96.59 percent, respectively.

The support vector machine algorithm as a supervised learning algorithm with 96.59 percent accuracy had the highest classification accuracy compared to the other two algorithms and as a suitable method for classifying and identifying failures in this The research was introduced. The resulting UAV images and supervised learning algorithms can be used to identify the abnormality of forest roads including potholes.

The utilization of lignocellulosic crop residues, which remain after the final product in the agricultural sector, can be considered as a valuable resource for generating renewable energy. However, due to their dispersed distribution and the high cost associated with their collection, this task poses significant challenges. Currently, the application of spatial modeling to assess the sustainable potential of lignocellulosic residue biomass for renewable energy production in the agricultural sector is limited. Moreover, no such studies have been reported in the country to date. Therefore, the objective of this study is to assess the renewable energy production potential and identify suitable locations for bioenergy facilities that process lignocellulosic residues using cluster analysis within a geographic information system (GIS) environment.

To accomplish the objectives, descriptive-quantitative data regarding the production level and production area in the agricultural sector for the crop year 2020-21, as published by the Ministry of Agriculture, were collected and analyzed. Furthermore, spatial information pertaining to agricultural farms in Golestān province was gathered through satellite imagery, which was subsequently processed using cluster analysis and hotspot analysis within a geographic information system (GIS) framework. The biomass quantity in each field was estimated for four conventional crops: wheat, rapeseed, rice, and soybean, on a per-unit area basis. Subsequently, separate projection was conducted for each of these crops within the case study of Golestān province.

In the crop year of 2020-21, the total arable land area amounted to 12.9 million hectares, with a nationwide crop harvest of 91.8 million tons. Cereals accounted for the highest proportion (71%), while grain products had the lowest share. Golestān province, ranked 7th, possessed an agricultural area of 661 thousand hectares, contributing 4.27% of the country's total crop production, which amounted to 3.9 million hectares. Among the cereal production group, wheat had the highest production level at 63%, while grain corn had the lowest. Notably, Gonbad-e-Kavoos and Bandargaz counties exhibited the highest production levels and the largest areas for the cereal production group. Through the spatial analysis, the estimated amount of harvestable residue biomass was 840,000 Mg, derived from a total of 84,104 identified farms, with the largest proportion being wheat straw. The cluster analysis of the identified farms revealed that out of a total of 35,508 clusters, 15% belonged to the cold cluster category, while 18% fell into the hot cluster category. Furthermore, hotspot analysis identified four suitable sites for bioenergy sites within the study area. The available biomass can be transported to these potential sites at an average cost of $20.83 per kilometer per megagram.

The findings of this study highlight there is substantial biomass quantity within the agricultural sector of Golestān province, a significant portion of which is currently burned annually, resulting in the generation of large amounts of greenhouse gases and widespread environmental pollution. However, this biomass resource holds significant potential for the production of renewable energy if effectively harnessed with support from both the public and private sectors.

Land cover change has become one of the most challenges due to the interaction of negative effects of climate change with human activities. In this study, land cover changes in the Hendijan Basin with a focus on the impact on dust sources located in Khuzestan province were investigated. Areas of forests, rangelands, woodlands, water, agricultural lands, residential and barren lands were extracted during 30 years at 1987 and 2017 using Landsat image processing. Dust sources located in Khuzestan have been monitored and land cover changes in have been revealed. The results show that trend of land cover changes inside and outside Khuzestan province is different from each other. In fact, the highest rate of change compared to beginning of study is occurred in mountainous part of the Hindijan Basin is due to increasing lakes of dams (9.7 times) and in the plain part due to the expansion of residential lands and also increasing cultivated area (8 and 4.9 times). Also, 936.31 square kilometers, has been added to barren lands, at the same time, the area of rangelands and forests has decreased 367.60 and 2633.40 square kilometers, respectively. Detection of changes in Khuzestan dust sources shows that the east and southeast area of Ahvaz, which was covered with vegetation and water (seasonal floods and flood plains) in winter of 1366, has become barren land in 1396. Monitoring land cover changes and reporting results helps managers make more informed decisions.

Advances in the field of UAVs and sensors, has provided access to high-resolution images and 3D data of them can be used to monitor of forests and evaluate the characteristics of trees. In this study, low-cost UAV sensors and algorithms SFM images have been used to extract the crown height model. then Local Maxima algorithm was used to detect trees.

Current research was conducted in the Sisangan Forest Park that located in 30 km east of Nowshahr. Based on the Orthomosaic of drone images, six sample plots with 30 to 30 m dimensional were designed.

A total of 209 trees were recorded as ground reference, which allowed the algorithm to accurately detect 139 of them with the F-Score of 0.63.  Applied algorithm detected 88 points by mistake, and lost 72 trees. Our r findings showed that the less variety of species and the crown of the trees are symmetrical and in a crown diameter range, the performance of the detection algorithm is better. Also, by using LiDAR sensors, better results may be achieved. generally, the results of this investigation indicated that the in open- canopy forests with the tangier and Also, the trees are symmetrical trees, the acceptable outcome might be access.

Overall, the local Maxima algorithm did not detect single-trees. These results therefore need to be interpreted with caution in broadleaves forests.

One way to study the problems of forests, to study its changes over the years and access to accurate information, is to use remote sensing technology and satellite data. The use of GIS as one of the most important tools for analyzing the extent, location of degradation and auxiliary factor to identify the cause and factors in the solution of the phenomenon of deforestation. Research shows that between 1990 and 2000, an average of 6.8 hectares of forest area in Ilam province decreased. On the other hand, the phenomenon of oak tree droughts and deforestation has intensified due to severe droughts and land use changes, especially constructions that have been done in forest areas. Therefore, this study was conducted to investigate the distribution of oak tree drought with physiographic factors and also land use change in Zagros forest areas in Ilam province.

In conducting this research, first the digital elevation model of the province was prepared based on topographic maps, then based on this model, information layers of slope class, geographical directions and elevation classes were extracted. In order to prepare the oak tree drought site location layer, in addition to processing satellite images, the location of oak tree drought trees was collected at 100 points in the province. To ensure the accuracy of the obtained maps, the accuracy assessment method was used, which is based on comparing the map extracted from satellite data with ground reality information such as field perceptions and visits, existing maps and the expert knowledge of the interpreter. In the following, the location map of the distribution of dry masses was overlapped and integrated with slope classes, geographical directions, altitude classes and land use. Then the frequency of dead trees in the units of the mentioned layers was calculated and presented as a percentage.

According to the physiographic units of the region, the highest amount of dry masses is related to the altitude of 1200 to 1600 meters, slope class of 0 to 15% and the geographical direction of the southwest. The lowest of them are in the altitude floor less than 400 and more than 2400 meters, the slope class is more than 30% and the geographical directions are northeast and southeast. The highest frequency of oak tree drying in Ilam province is in the classification of forest areas related to thin class 42%, medium 39% and dense forest areas 5%, respectively.

In general, it can be stated that physiographic factors (slope, geographical directions and altitude) as well as land use changes in different forms and different intensities and weaknesses can contribute to the destruction of temples. Zagros and especially in the area of ​​Ilam province to be effective. Based on the obtained results, dried oak trees have different distribution in physiographic units. In the study of this distribution, the use of satellite images, especially in the study of forest areas, both in terms of the phenomenon of oak tree drying and in terms of deforestation due to land use changes or other natural factors, has many applications.

The present study was conducted to classify the sensitivity degree of mangrove habitats of the Hormozgan province based on canopy percentage (as an indicator of the structural characteristics of forest). For this purpose, by field survey and recording of mangrove crown diameter in the sample plots, the percentage of canopy cover in each sample plot was calculated. Then, by analyzing satellite images and mapping NDVI of habitats, a regression relation was fitted between the percentage of canopy cover in the sample plots and the corresponding NDVI values ​​at each vegetation level and by applying the regression relation on the NDVI map, canopy cover percentage spatial changes were mapped at each habitat. Finally, the map of the susceptibility of the Khamir, Tiab, and Jask habitats to three classes of low, medium and high was prepared using the mangrove canopy cover map and GIS techniques. The results showed that the average percentages of the canopy cover in the Khamir, Tiab, and Jask were 62, 43, and 71%, respectively, that indicate that Jask and Tiab have the lowest and highest sensitivity to environmental stresses and disturbances, respectively. Local geomorphologic features, sediment yield, coastal installations, economic and social conditions of the area, environmental pollutants, and sea level rise are among the influencing factors that cause differences in the degree of susceptibility between habitats. Producing accurate information on the sensitivity of mangrove habitats of Iran, this study has provided preliminary prerequisites for implementing climate change adaptation programs and prioritize conservation and restoration activities of mangroves.

In recent years, oak trees in the Zagros forests have suffered a lot of fatalities for unclear reasons. Determining the rate and severity of forest crown density changes is important for the investigation and management of these forests. This research was carried out with the aim of determining the amount of crown cover changes in the forest area of Dashtebarm in Fars province under the influence of oak drying phenomena using satellite data in three different periods. To this end, QuickBird satellite images dating back to 2005 (before the oak trees dying), images with high spatial resolution, derived from Google Earth 2011 (during oak tree decay) and 2014 (after oak trees dying) used. First, the images were matched using precise ground control and biometric sampling closest neighbors based on the 2011 image. The results of the geometric matching of the images showed that the image QuickBird (2005) with root mean square error of 0.25 and QuickBird image taken from Google Earth (2014) with root mean square error of 0.56, were corrected. Then, 1000 meter square circle pieces with a radius of 17.85 meters, were designed and installed on GIS software, and the crown cover was taken in the samples. The results of this study showed that the average percentage of crown cover of trees in pre-drying period of oak trees was 19.17%, at drying time, 13.33%, and 11.16% for the period after drying of trees. According to the results, it was found that the crown crowns of oak trees decreased from pre-drying to post-drying periods and there was a need for reduction operations in areas with reduced crown cover.

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.

The aim of this study was to predict and investigate land use change trends in Manasht - ghalarang protected area in Ilam province using Landsat satellite imagery (L5 TM-1984, L5TM-1999, L8 OLA-2014). After geometric and atmospheric correction on satellite images, four land uses including agriculture, garden, pasture and forest were identified in the study area. For classification of satellite images used the comparison of maximum likelihood (MLC) and k nearest neighbor (KNN) methods, and for identifying user changes from post-classification comparison methods, the difference of vegetation index (NDVI) and principal component analysis (PCA) were used. Prediction of land use changes in 2024 was performed using the automated fusion cell and Markov chain model. The classification of images used with the MLC method is higher than KNN method. According to the classification results, during the period 1984–2014, decreases in rangelands and forests and increase in gardens and agricultural were observed. In the comparative method after classification most changes happened in period of 1984-1999 were related to forest conversion to agriculture, while in period of 1999-2014 were to forest to pasture. Forecasts of changes for 2024 showed that agricultural land-uses increased by 1.10%, garden by 0.52% and rangelands and forests decreased by 0.76% and 0.86 %, respectively. Based on the results, the use of Landsat images in combination with the Markov model in modeling land use change, had high accuracy and can be effective in decision-making and management of the region.

Reduced rainfall and drought occurrence by restricting the migration of mangroves to the uplands have increased the vulnerability of these ecosystems to sea level rise. Therefore, monitoring the changes in the landward and seaward extent of mangroves can provide valuable information on the response and degree of vulnerability of these ecosystems to sea level rise. The aim of this study was to investigate the changes in the landward and seaward extent of mangroves along the coasts of the Hormozgan province. To this end, the mangroves in the Khamir, Tiab and, Jask habitats were mapped using satellite imagery in 1986, 1998 and 2016 and changes in the landward and seaward extent of mangroves were investigated during the periods before and after 1998 (the period of occurrence of long-term droughts on the southern coasts of Iran). The results showed that in the post-1998 period, which corresponds to long periods of drought on the southern coast of Iran, the landward extent of the mangroves was reduced (272.7 ha reduction in Khamir) or the trend of development of landward extent has decreased dramatically, such that in Tiab and Jask the increase in the landward extent has decreased from 463 and 165 ha to 35 and 13 ha, respectively. In addition to the significant effect of reduced rainfall, human factors such as livestock grazing and fueling (especially for the mangroves located in Khamir), development of aquaculture and pollutants have also played an important role in the regression of these habitats.

In this research, the status of forests in Ilam city with the aim of preparing the spatial distribution map of oak forests degradation using remote sensing and geographic information system was investigated. In order to achieve the extent and location of degradation in the studied area, the forest area map for 2001 using Landsat 7 ETM+ satellite images and forest area map for 2013 using OLI Satellite Landsat 8 images, Field visits and a method of determining educational samples were prepared. To investigate the factors affecting on degradation, first, the spatial variables including distance from residential areas, distance from roads, slope, aspect and altitude were extracted from topographic maps of 1: 50000. Then the degradation location data of the region forests with the physiographic and human spatial variables was entered the model. For modeling and estimating the spatial distribution of degradation in the study area forests, logistic regression and step-by-step method and forward step type were used. According to the results obtained from the statistical model, the development of human-made areas, population growth, reducing the distance of roads from the forest areas, the middle altitudes and reducing the slope percentage that triggered agroforestry activities and increase of agricultural land area had the highest impact on the degradation of the Ilam city forests, respectively. In fact, of 73349 hectares of forests in Ilam city, 5311 hectares (7.2%) have been reduced, of which 2125 hectares (2.9%) are related to the phenomenon of oak drying and 3186 hectares (4.3%). ) It is related to the construction of settlements, road construction, etc. Based on this, a spatial distribution map of the destruction of forests in Ilam city was prepared.

In this research, the capability of Rapid Eye satellite imagery for mapping the crown distribution of oak trees in Zagros forests was investigated in the Dashtebarm forest area of ​​Kazeroun, Fars province. In this study, data quality was investigated geometrically and radiometrically and geometric correction of the images was done using a linear method and using precision ground control points. In order to investigate the use of artificial bands obtained from appropriate processes in the classification process, images of appropriate plant spectrum indices were created by mapping the bands and images of the main components using the principal components analysis (PCA index). The vegetation map of crowns of trees was measured by measuring the crowns of trees in square sample samples with an area of ​​400 square meters in a randomized way. 70% of samples were selected as educational sample and 30% of the rest were randomly selected. Two-point and polygonal classification with two maximum likelihood algorithms and support vector machines was performed on the original image, the processed bands, and the main image composition of the processed bands. The results of the accuracy assessment of the maps in this study showed that the highest overall accuracy and Kappa coefficient were 98.52% and 0.97, respectively, in the point of processing with processed bands and maximum likelihood algorithms, as well as with the image composition The original is with processed bands and support vector machines algorithm (SVM). Also, in the polygonal classification, the highest overall accuracy and kappa coefficient of the maps classified using the processed bands were 87.50% and 0.75 with the maximum likelihood algorithm and 90.78% and coefficient Kappa 0.81 has been supported by the car engine algorithm. In general, the results of this study showed that Rapid Eye images are suitable for preparing the crown distribution map of forest trees in Zagros forests.

Remote sensing-based methods were developed for resolving the limits of direct measurement methods for evapotranspiration. The purpose of this study was to investigate the capability of Landsat 8 satellite imagery and the SEBAL model for estimating actual instantaneous (ETINST) and daily (ET24) evapotranspiration in the forest ecosystem. For this reason, Landsat 8 satellite imagery was utilized at four dates for the forests of the Ramsar city and after image processing and performing necessary calculations the map of NDVI, surface temperature, emissivity and albedo were prepared and all energy components at the surface of evapotranspiration including net radiation, soil heat flux, sensible heat flux and latent heat flux were estimated and amount of instantly ET, ETINST and ET24 were conducted as a map. Accuracy of estimated ETINST and ET24 were evaluated based on the three hour data from synoptic Ramsar weather station data using FAO Penman-Monteith method. Estimated average of ETINST and ET24 were 0.45 and 3.98 and calculated average of these parameters were 0.55 and 4.62 respectively. Results showed that the differences are relatively small and range between 8.38 and 15.17 percent in instantaneous scale and between 11.41 and 18.34 in daily scale. Mean Relative Difference Percentage for ETINST and ET24 were 11.45 and 14.27, respectively reflecting high capability of the overall ability of the approach used in this study to estimate evapotranspiration. In conclusion based on results showed that used method in this study has the capability of producing evapotranspiration map in north forests of Iran in spring and summer

In recent years, oak decline phenomenon has caused severe damages in Zagros forests. To deal with and managed this crisis, prior to any action, having accurate information about the status and distribution area of this phenomena is necessary. Using satellite data is one of methods to achieve information on the extent and severity of die back. For this purpose, map of oak decline severity was prepared in four levels for some parts of Ilam forests using Worldview-2 satellite data. Maximum likelihood, naive bayes, K-nearest neighbors and artificial neural network classification algorithm were used. The results showed that among different classification methods, the results of artificial neural network classification algorithm had most overall accuracy with 72.83%. Moreover, our results confirmed that the Worldview-2 satellite data can illustrate the severity of oak decline.

Using remote sensing data is an applied method to estimate above ground biomass. In this study, satellite radar data of ALOS-2, with the full polarization and the optical data of Sentinel-2, has been used to estimate the aboveground biomass in the Nav-e Asalem forests, Gilan province. The backscattering coefficients at different polarization, the texture measures and target decomposition features of SAR images, obtained original and synthetic bands from optical images in three different combinations of radar images, optical images and the composition of radar and optical images, as inputs to the Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models were used. In order to measure aboveground biomass, 149 sample plots were laid out. Evaluation of ANN and MLR models using R2 and RMSE statistics showed that in all cases the ANN was better performance to estimate the aboveground biomass than MLR. The best results showed that the ANN from combined optical and radar data with R2 and RMSE, 0.86 and 31.62 Mg/ha (15.34%), respectively, can be the best applied method to estimate the aboveground biomass. The results of radar images and optical separately, with the R2 and RMSE for the modeling of aboveground biomass have been shown, respectively, 0.57 and 49.17 Mg/ha (23.85%) by radar images and 0.7 and 39.53 Mg/ha (19.17%) by the optical images, superior modeling to estimate aboveground biomass represents by optical imaging. The overall and more accurate results to estimate of aboveground biomass have been shown when we used combined radar and optical images with the ANN model.

According the available statistics, the average annual rate of approximately 700 ha of forested areas are subject to severe degradation caused by wildfires in Kermanshah province located in western Iran. However, the heterogeneous spatial distribution of fires is mainly driven by factors such as the occurrence of harsh mountainous areas, which is coupled with the accessibility problems that notably constrain the ability of administrative units to achieve reliable quantitative information e.g. on the size of the burned areas and the level of damages. The satellite imagery provide useful information on various spatiotemporal scales which enable a more realistic assessment of consequences of forest fire. Therefore, this study aims at testing the performance of LANDSAT TM data for burned area mapping across a selected test site in Zagros Mountains. To this aim, the required imagery was obtained for 1389 (2010) over Qoori Qaleh area of Kermanshah Province. A set of digital image processing methods were applied, including band ratioing, principal component analysis and analysis of separability amongst the burned area classes. The resulted dataset was further classified by a supervised Maximum Likelihood approach. The results showed the TM band 4 as well as a composite of bands 3, 4, 7 and the first principal components of bands 1-3 and 4-7 yielded an overall accuracy of 90 and 92.75% and kappa coefficients of 0.8 and 0.86, respectively. Thus the applied dataset is concluded to be appropriate for burned area mapping in such areas.

To assess the physiological states، the stress impact and ultimately the overall health condition of living plants، measuring moisture content is a well-known choice. Alteration in plant water is more likely occurs not only after prolonged drought and fire prone forests، but also in more sparse and weaken stands. Hyperspectral remote sensing techniques try to provide means to quantify the leaf moisture content with low cost، high-speed، online and up-to-date information. In this study the relationship between spectral reflectance of Quercus brantii leaves and fuel moisture content، equivalent water thickness and specific leaf weight was estimated. The sampling was done on 40 randomly selected oak trees in southern and western aspects in central Zagros range. To acquire spectral reflectance the leaves were scanned through wavelengths 350 to 2500 nm، using a ASD Fieldspec® field spectrometer. For each tree، leaves were picked up from four canopy directions. Then the leaf wet and dry weights as well as their masses were determined. Water Index (WI) spectral was most significantly correlated to fuel moisture content (P<0/001، r=0/22). In addition، WI and Normalized Difference Vegetation Index (NDVI) showed high correlation coefficient with both fuel moisture content and specific leaf weight. None of the spectral indices could establish significant relationship with equivalent water thickness. On the other hand، there was also no significant difference between fuel moisture content، water equivalent thicknesses، and specific leaf weight with spectral indices in four of the tree crown direction. Nevertheless، there was significant difference between leaf equivalent water thickness and indices NDVI، NDSI [1108، 1242] and NDSI [1108، 1457] in both western and southern aspects.

Inappropriate land use triggers and accelerates desertification process in arid and semi arid regions. The aim of this study was to evaluate the desertification impacts in Qom province through land cover/ land use changes in 33 years using Land sat 1976 TM, 1992 and 2009. Population data, livestock numbers and SPI indices were also compared during this period. First, geometric, radiometric and topographic preprocessing techniques were applied to the images and land use/cover maps were extracted using supervised classification (Maximum likelihood) and fuzzy method. The maps accuracies were assessed through overall accuracy and Kappa statistics. According to the results, the maps produced by Fuzzy methods had more accuracy than the Maximum likelihood method. The results also indicated that the size of rangelands decreased from 784281.to 662910 hectares and 34112 hectares of lands were transformed into salty areas during this period. The results showed that the size of urban and industrial areas and agricultural lands in the province were increased to 2864 and 131308 hectares, respectively. The Namak Lake was also completely dried in 2009. The results revealed that dam constructions, pumping underground water, continuous droughts and increasing livestock numbers were among the reasons of accelerating the desertification trend in Qom province especially in the eastern part; hence both management and environmental variables were responsible for these changes. Holistic and adaptive management approaches in natural resources management, and especially in water resource management can restore the province into the past conditions.

Govater Creek is one of the creeks in GovaterBayat the end of east southern of Sistan and BaluchestanProvince. Mangrove’s communities in this bay only consist of grey mangrove (Avicennia marina). In order to study the changes of mangrove vegetation area and density in Govater Creek from 1957 to 2006 (for 49 years) aerial photos with scales of 1:55000 and 1:20000 in 1957 and 1966, Corona satellite image in 1965, Landsat satellite image TM/ETM related to 1998 and 2001, IRS satellite image in 2006 and QuickBird satellite image in 2006 were used. Results showed that the changes rate of mangrove’s forest for this period has not been uniform but changes have been positive and area was changed from 101.19 to 149.18 ha. This change has average growing rate of 0.98 ha per year. The most of changes is related to years between 1966 until 1998. Generally we can conclude that before 1966, decreases have been in both density class (i.e. dense and sparse) and from 1966 to 1998, increases is observed specially in low density class and then until 2006 trend of changes has been slow and negative.