babak naimi

The gap analysis based on species distribution modeling (SDMs) is a essential tool for evaluating the performance of Protected Areas and identifying conservation gaps. Improving the accuracy and reliability of SDMs can significantly aid in implementing strategies under the Global Biodiversity Framework. This study aimed to enhance gap analysis methods through deep learning-based modeling and to overcome some limitations of traditional gap analysis approaches by using a fuzzy approach to calculate habitat suitability, ultimately assessing the efficacy of gap analysis in identifying conservation gaps. The study employed the CNN-SDM deep learning-based method to identify complex relationships between species' spatial distributions and environmental conditions. WorldClim data and records of 45 amphibian species from the GBIF in Southwest Asia were used to calibrate the model. After generating habitat suitability maps, species were weighted according to their threat levels in the IUCN Red List, creating a weighted species richness map. To address the limitations of binary data, a fuzzy approach was used to represent habitat quality on a continuous scale from 0 to 1. Future predictions were made using the SSP126 and SSP585 climatic scenarios for the years 2060 and 2100. The comparison of weighted species richness maps and stacked richness revealed that 43% of threatened amphibian habitats were overlooked by the stack binary method. Results confirmed that under the worst climate scenario, while 85% of suitable habitats remain within protected areas, the average pixel value of habitat suitability within these areas significantly decreases, with fuzzy habitat suitability declining for 97% of the species. The CNN-SDM model demonstrated high accuracy, with an AUC of 0.94 and a TSS of 0.89. This study highlights the need for enhancing gap analysis methods and addressing the limitations of traditional approaches

In recent years, one of the most significant environmental crises has been the phenomenon of wind erosion and dust emission. Wind erosion is considered one of the effective factors in desertification and land degradation in dry regions. The process of wind erosion, due to the transport of soil nutrients along with fine particles, is recognized as one of the limiting factors for soil fertility in many parts of the world. Evaluating this type of erosion and estimating the soil loss requires the installation of various measuring stations. Establishing and equipping these stations and providing the necessary equipment require large costs and a long time. In addition to direct measurement methods in wind erosion, the use of modeling results, especially in combination with remote sensing techniques, to study and predict environmental effects, trends, and risk assessment has greatly contributed to research in the last two decades. Therefore, this study aims to monitor and investigate the warning zones of wind erosion in northwest Golestan Province using the wind erosion hazard index (WEHI) model.                                 

To assess wind erosion in the western part of Golestan Province, the WEHI model was implemented. This model predicts the sensitivity of the landscape to wind erosion by considering a set of surface and climatic thresholds and using a geographic information system (GIS). In this model, wind erosion severity is determined in three classes: low, moderate, and severe, by multiplying wind speed by the percentage of bare soil and dividing it by the percentage of soil moisture percentage. To monitor wind erosion, the normalized difference moisture index (NDMI) was used to evaluate soil moisture, and the modified bare soil index (MBI) was used to assess bare soil. For validation, these indices were compared to field data and plots in each working unit. Additionally, three statistical parameters, Pearson correlation coefficient, coefficient of determination (R2), and root mean square error (RMSE), were employed to calculate the correlation between these indices and ground data. Furthermore, a Markov chain model was used to examine changes in wind erosion classes. Finally, after monitoring wind erosion and considering a threshold value for this model, the area of warning zones during the statistical period was investigated.

The results obtained from the WEHI model indicate severe wind erosion intensity in the working units of Atark floodplain deposits, saline lands, longitudinal dunes, Barchan dunes, bare lands, and margins area of wetlands. According to the WEHI model, the region was divided into three classes: low (54% frequency), moderate (21% frequency), and severe (25% frequency). The model classified the northern regions into severe and moderate classes, while the southern areas of the region fell into the low wind erosion class. High correlation coefficients between the WEHI model indices based on remote sensing and field data demonstrate the model's ability to monitor wind erosion over time and at different scales. Wind erosion monitoring results showed that the high wind erosion class increased from 59,940.88 ha to 71,698.3 ha, indicating an increase of 11,757.43 ha. Spatial analysis of wind erosion classes indicated that most changes occurred in central areas, with most areas around the Sangartappeh playa and central regions changed to severe wind erosion class, while western, eastern, and central areas changed to the low wind erosion class. Finally, monitoring the warning zones revealed an increase of 41,000 hectares in the areas under warning, in the western, northwestern, and central regions.

In this study, the performance of the WEHI model in assessing wind erosion risk in the western part of Golestan Province was confirmed. Although factors such as roughness, soil structure, and organic matter content are not directly considered in the model, they are indirectly incorporated in scoring the percentage of bare soil. To control wind erosion in these areas, planting salt-tolerant species and implementing soil fertility enhancement strategies, such as soil mulching in heavy-textured areas, are recommended. Finally, mechanical operations and the establishment of windbreak networks are suggested for controlling wind erosion in abandoned land units. This research can serve as a useful approach for planning and managing vulnerable areas to wind erosion in northwest Golestan Province.

Desertification involves processes that are both the product of natural factors and the mismanagement of human beings. Adafic parameters and processes affecting soil condition, climate, groundwater, vegetation and management are the most important factors affecting the phenomenon of desertification in many arid and semi-arid regions. These parameters are investigated using different models in different regions.Therefore, this study was conducted to evaluate the desertification intensity based on groundwater and soil criteria in the west of Golestan province.

To determine the work unit map, topographic, geological, aerial photo interpretation, panchromatic band and multispectral Landsat satellite images and field visits were used. In this study, the Madalus method was used to prepare a map and evaluate the desertification situation. The geostatistical methods used in this research include kriging method, local estimator method (GPI), radial function method (RBF) and distance distance method (IDW). in Madalus model, 6 criteria and 20 indicators were used to evaluate the intensity of desertification. The scoring of each of the indicators was determined at the unit level in the region. The map of each criterion is also obtained through the geometric mean relationship between the indicators. To evaluate the accuracy of the model results, the desertification class in each of the work units according to the field evidence, observational and visual evaluation was performed and then the statistical comparison of the model desertification class with an expert opinion was performed. SPSS software and Mann-Whitney non-parametric test were used to validate the model results.

In this study, according to the histogram of the data, the normality of the parameters was investigated. The mean square squared error (RMSE) was used to determine the most appropriate interpolation method. The weighted average score of desertification intensity of area 135 was obtained, which indicates the middle class. In terms of zoning of desertification intensity, the region was classified into three classes: low and insignificant with a frequency of 27 and medium with a frequency of 60 and severe with a frequency of 13. Among the criteria of desertification, the criterion of management and policy with an average weight of 148 points is the dominant and effective criterion of desertification, followed by the criterion of vegetation (145), the criterion of soil (141), the criterion of erosion (138), the criterion of climate. (122) and groundwater criteria (121) were in the next rank of effective desertification criteria in the region. Also, the most important indicators of desertification are the indicators of drought resistance, conservation operations and soil salinity, respectively. These indicators have increased the trend of desertification in the work units of abandoned lands, saline and wetland lands and saline lands located in the northeastern parts of the region. Based on the spatial distribution of classes with low and insignificant desertification intensity in the southern and eastern part of the region, the middle class in the western, central and northern parts and finally the severe desertification class are located in the northeastern parts of the region.

According to the obtained results, which indicate high evapotranspiration of the region, expansion of land salinity, unprincipled road construction and incomplete drainage.it seems that the management of desertification in the west of Golestan province, as one of the agricultural hubs, should be in the managerial priority of the officials and experts of the executive departments.Accordingly, it is proposed in order to control the process of desertification and achieve sustainable development in the region.treatment of industrial and domestic effluents for reuse for various purposes, Use of modern irrigation systems for agricultural lands, Placing low-yield crops in terms of water consumption in the region's crop rotation and As well as the necessary training to justify farmers to use pesticides and chemical fertilizers in the area.

Human-induced climate change has had worldwide impacts on coastal human and natural systems. Sea-level rise (SLR) due to climate change is one of the biggest challenges human civilization is facing in coastal regions today. Over the past 25 years of satellite altimetry, global sea level rose at an average rate of 3.3 mm/yr. (13 in. /century), but this rate is accelerating. Global mean sea-level rise (SLR) will continue during the 21st century, very likely at an increased rate. Climate related SLR has major adverse effects on coastal systems. Assessing the vulnerability of individual coastal areas to future inundation is essential in order to develop adaptive management strategies to cope with the future impacts of sea-level rise. In this vulnerability assessment, exposure of the 292 km coast of southern Iran (Jask County) along the sea of Oman has been studied. To identify areas that would be inundated by future sea-level rise we applied NOAA’s modified bathtub model, and used high estimates of projected global sea-level rise for all RCP scenarios for the year 2100 from the IPCC Fifth Assessment. The results of inundation mapping indicate that under RCP 2.5 scenario (with 0.55 m of SLR) about 299 sq. km. would be inundated. For RCP 4.5 and 6 (with 0.63 m of SLR), about 334 sq. km., and under extreme sea- level rise (RCP 8.5 and 0.82 m SLR) about 449 sq. km. will be inundated. In addition the results indicate although only 1% of the entire coastal area falls into the urban/built category, this includes the most populous city in the county, 2 strategic ports, as well as an airport and industrial zones. Assessing the socio-economic and ecological vulnerability of coastal areas subject to future sea-level rise can assist government officials, managers or decision makers in developing appropriate action plans to adapt to and mitigate sea-level rise impacts. This is particularly timely along the southern coast of Iran where development is in an early stage.

In this research, the desertification process is evaluated using 3 climate indicators and 4 groundwater indices in the form of a modal model in a period of 20 years (2008-2017) in the west of Golestan province with the aim of providing a desertification warning system.Then, by determining the threshold limits for the influential indicators in the desertification process of the region, the desertification early warning system was presented based on four indicators. The results of the index change the trend showed that drought indices, total soluble materials, electrical conductivity, and groundwater depth had the greatest impact on desertification in the region, respectively. Desertification warning maps have shown an increase in drought indicators, total solubility, and electrical conductivity from west to east. Also, the groundwater depth index in the central areas has the highest warning range. According to the results obtained from monitoring desertification indicators, the electrical conductivity index during the years 1999, 2001, 2004 to 2006 and 2009, the Total dissolved solids index in the years 1998, 1999, 2001, 2004, 2006 and 2007, Groundwater depth index of 2001, 2002, 2006, 2008 and 2014, Finally, the drought index is in the warning range during the years 1999 to 2003, 2006 to 2008, 2011, 2014, 2015 and 2016. Irrigated lands, drylands and wetlands in the East region affected by desertification warning have been that on this basis is suggested for better management of the land, the threshold defined in this study as the basis for the work to be placed.

The studies show that the mean temperature of the Hyrcanian region has been increased by 0.74 siliceous degrees in the twenty recent years. In this research, the current and future distribution of Chestnut-leaved oak (Quercus castaneifolia C.A.M.) was studied using an ensemble method including five different species distribution models. We used five general circulation models under two reprehensive concentration pathway (RCPs) to project the impact of climate change on the distribution of Chestnut-leaved oak by 2070. Also, the stable, gained and lost suitable habitat of Chestnut-leaved oak were determined using range size change analyses under climate change. The results showed that the area of suitable habitats with high certainty would be increased by 16.80%, while it would be decreased by 29.80% under RCP 8.5 by 2070. The results of the range size analyses of oriental beech showed 54.12% of suitable habitat would be stable under RCP 4.5 while it would be decreased by 23.85% under RCP 8.5 by 2070. Also, the gained suitable habitats would be 62.68% and 42.36% under RCP 4.5 and RCP 8.5, respectively. It is suggested that the climate change impacts should be considered in the management decisions and conservation plans related to the Chestnut-leaved oak in the Hyrcanian forests. Further, the suitability of habitats under climate change should be accounted for plantation and reforestation by Chestnut-leaved oak.

In recent years, global biodiversity has experienced a steeper decline over in human history. Climate change and anthropogenic interventions including habitat destruction, introduced species, and pollution are important sources of current threats to biodiversity and that loss of animal or plant species have a dramatic effect on ecosystem services. The current study aimed to prepare species distribution model of western rabbit (Lepus europeus), a species with high distribution, and oriental rabbit (Lepus tolai), a low mobility species in north, northwest, and northeast regions of Iran was conducted. First, layers of climate variables extracted from the BIOCLIM database. In next step, correlation of climate variables for each species was analyzed and then highly correlated variables were excluded from the modeling process. Finally, nine species distribution of the sdm package (GLM, GAM, BRT, SVM, RF, MARS, CART, FDA and MaxEnt) were used in the R softwares. The results demonstrated that suitable areas for Lepus europeus generally are western and southwest regions and for Lepus tolai generallyare semi-dense forests in north regions of Iran. In addition, max temperature of warmest month (BIO5) and precipitation of warmest quarter (BIO18) were the most influential variables for Lepus europeus and Lepus tolai, respectively. The results of this study can be used to better management of mentioned regions with emphasis on conserve biodiversity of Lepus europeus and Lepus tolai species.

In the recent decades, a lot of Researches which are focused on Predicting the geographical distribution potential of species with goal Identifying hot habitat areas. Iran has a specific situation and varied habitats. As well as, there is a limitation in management and conservation in Iran, so conserving of Hot habitat areas is the best way to conserve a lot of species in the large landscapes. CARNIVORA has eight families in Iran, which the most of species are in IUCN Red List. The main goal of the project was making hotspot map of FELIDAE, which has a key role in biodiversity conservation of the country. In this study, we used 19 climate variables from worldclim database and presence data felid species; and used RF, SVM, MaxEnt and BRT models in Software R for species distribution modeling. After the overlay of species distribution maps using the collecting layer technique in ArcGIS10.5, to create a model with a high degree of reliability, a comprehensive map for Iranian felids hotspots was prepared using layer multiplication method. As a result, felids hotspots occurred in central deserts of Iran, parts in the north and northeast of Iran and parts of the Zagros Mountain Range in west. According to matching of prediction map of felids hotspot with map of Iranian protected areas, the pattern of habitat potential has a relatively small overlap with protected areas and have overlap with 39 protected areas. The results showed that, the RF model was the best model with excellent reliability.

Due to global climate change, climate change in Iran is inevitable and the results of the climate studies clearly show that the Hyrcanian climate has been warmer over the past 50 years. In this research, the current and future distribution of oriental beech (Fagus orientalis Lipsky) was studied using an ensemble method including five different species distribution models. We used five general circulation models under four existing reprehensive concentration pathway (RCPs) to project the impact of climate change on the distribution of oriental beech by 2070. Also, the stable, gained and lost suitable habitat of oriental beech were determined using range size change analyses under climate change. The results showed that the area of suitable habitats with high certainty would be decreased by 72.79% and 89.88% under RCP 2.6 and RCP 8.5, respectively, by 2070. The results of the range size analyses of oriental beech showed only 24.84% of suitable habitat would remain in the most optimistic condition (RCP 2.6) and stable the suitable habitat would decrease by 7.13% under RCP 8.5 by 2070. Also the lowest and highest rate of suitable habitats lost will be by 72.79% and 89.85% under RCP2.6 and RCP 8.5, respectively, by 2070. It is suggested that the climate change impacts should be considered in the management decisions and conservation plans related to the oriental beech in the Hyrcanian forests

Carnivores have always been exposed to threat-inducing processes because of their placement at the top of the food pyramid. Nowadays, approximately 65% of carnivores are listed as Critically Endangered (CR) or Vulnerable (V) on the IUCN Red List of threatened species. On a global scale, in the order Carnivora the Hyaenidae family is the smallest with only four species. Hyaena hyaena is the only member of the Hyaenidae family found in Iran, and is listed as Near Threatened (NT) in the IUCN Red List. The current study was aimed at modelling the species distribution of the striped hyaena (Hyaena hyaena), which is wide in Iran. Using nineteen layers of so-called Bioclimatic variables, the correlations between those variables were analyzed and then highly correlated variables were excluded from the modelling process. Finally, eight species distribution models from the sdm package (GLM, GAM, BRT, SVM, RF, MARS, CART, and FDA) in R software were used. Given that the output of species distribution models is, unavoidably, often uncertain, one possible solution is to estimate the difference between projections and, to reduce the uncertainty, the use of an ensemble prediction system instead of using a single modelling method. Therefore, after determining the potential habitats of the Hyaena hyaena using those eight mentioned models and by using the ensemble prediction system, the best regions for the distribution of this species in Iran were estimated. The results of this study showed that annual mean temperature, seasonal precipitation and precipitation during the warmest season have the greatest influence on the distribution of Hyaena hyaena. In general, the FDA, GAM, BRT, CART and GLM models are fairly reliable, the MARS model is very reliable, and SVM and RF models are completely reliable. The results showed that the GLM, GAM, BRT, MARS, CART, RF models demonstrated that the suitable areas for Hyaena hyaena are generally the central regions of Iran, while the SVM and FDA models predicted the southern margin of the Caspian sea as containing the best regions for the distribution of this species. The difference in the predictions of each model makes confirms the uncertainty between the models which confirms the necessity of using the ensemble method. The results of the ensemble model showed that the most suitable regions for the Hyaena hyaena species distribution are the semi-arid and central steppe regions of Iran.

Environmental pollutants are considered among the factors disturbing natural ecosystems. Among them, heavy metals due to their toxic effects and having a high bio-accumulation are known as one of the most dangerous pollutants. This leads to the concentration of these metals in the food chain, in the top of the pyramid. Many of these metals are natural components of aquatic ecosystems and some of them play a critical role in the survival of living organisms. However, if the concentration of heavy metals exceeds a certain limits, aquatic life will be threatened and ecosystem degradation will be occurred. Heavy metals have not the ability to be refined in aquatic ecosystems, so with gradual blurring of these ecosystems, they can easily accumulate in sediments. In fact, it can be said that the marine sediments are considered often as a final repository for the accumulation of metals. So, we can say that the sediments are considered as an important indicator of pollution and it is used to estimate the amount of pollution in the environment, especially in aquatic ecosystems. The spatial distribution of toxic metals in marine sediment in the explanation on the contamination history of aquatic ecosystems and location of pollution sources is very important and effective. Spatial and visual assessment of the pollutants is important for a better understanding of the threats of pollution sources. This can be achieved by Geographical Information System (GIS) techniques. A GIS-based and geostatistical approach provides the possibility of the spatial datasets processing. Using of the geostatistical principles, in addition to describing the spatial pattern of the observed data, provides the possibility of creating contamination maps with minimum variance. It is believed that the spatial assessment and visualization of the pollutants is essential to better understanding of threats of the pollution sources. For this reason it is recommended to use GIS techniques in the studies about the distribution of pollutants in the environment. Persian Gulf supply major part of the global oil and gasenergy and is known as a potentially oil contaminated ecosystem. Since there is only a narrow exchangeable path between the Persian Gulf and Oman Sea, it takes a very long time to transfer water in the entire Persian Gulf into open seas. One of the most important and sensitive ecosystems in the Persian Gulf is mangrove forests. Mangrove ecosystems have many environmental and social-economical functions. Therefore, metal contaminants entering to Persian Gulf through the exploration of oil and tanker shipping, due to the high toxicity and bioavailability, are one of the main concerns for these ecosystems. The purpose of this study is an investigation on metals of lead, mercury and cadmium concentrations in surface sediments of mangrove forests. This is in order for getting a correct spatial distribution pattern of these pollutants in this very sensitive ecosystem. The spatial distribution modeling involves using GIS and principles of geostatistics. Study area of this research is the mangrove forests in the Hormozgan province (in 55˚33'42" to 55˚47'23" E and 26˚46'21" to 26˚58'49" N). Sampling was conducted from 42 stations in three parts of Qeshm Island, Khamir Port, and middle part from the surface sediments 0-5c min, March 2010. Geographic locations and characteristics of each station were recorded and finally samples were transported to the laboratory in boxes of ice-containing. To determine the concentrations of lead and cadmium after preparation and digestion of the samples, they were analyzed by graphite furnace atomic absorption spectrophotometer Model AA-67OG. To determine the concentrations of mercury, the samples were also placed into the freeze dryer at -63°C for 48 hours. They were crushed to be prepared for analysis. In order to measure the total mercury, the 0.03 to 0.05 g of each sample was placed directly in the Advanced Mercury Analyzer Model AMA254. This is designed specifically for determination of mercury concentration in liquid and solid samples. To determine the level of contamination in surface sediments of mangrove forests, the mean values of the calculated concentrations for metals were compared with the NOAA and SQGs standards. In fact, these standards represent concentrations of pollutants that in the lower values of them, biological effects are rarely observed. On the other hand, if the concentration of pollutants is greater than this amounts, many incompatible biological effects will be occurred. In order to model the spatial variations of toxic metals of mercury, lead and cadmium in surface sediments of mangrove forests, seven of different geostatistical methods were used. These methods were: Radial Basis Functions, Local Polynomial Interpolation, Global Polynomial Interpolation, Inverse Distance Weighting, Simple Kriging, Universal Kriging, and Ordinary Kriging. The cross-validation method was used to compare the methods used in this study and selection of the most appropriate geostatistical method. For performance evaluation of geostatistical methods, the statistical parameters such as Mean Bias Error (MBE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were examined. The results showed that the highest concentrations for the metals lead and cadmium were in Qeshm Island and the highest concentration of mercury was found in the Kamir port. However, Duncan's test results at 1% significance level were indicative of the lack of significant differences between the estimated concentrations for each metal in the three investigated parts. The mean concentrations of the metals lead and cadmium in the mangrove forests were obtained 1.86 and 0.21 µg/g, respectively, and that of mercury obtained 8/04 ng/g. In order to understand and evaluate the contamination of surface sediments of mangrove forests in these metals, specified concentrations for them were compared with NOAA and SQGs guidelines and the results showed a significant difference for all metals in the study area (P<0.05) and the concentrations were lower than these standards. In order to make a modeling the spatial variations in the studied variables, before any calculations, test of the normality was conducted with Kolmogorov-Smirnov test on data sets. The results of the tests showed that mercury and lead follows a normal distribution trend. Cadmium values were transformed to their logarithms to obey normal distribution. Finally, after the implementation of geostatistical methods on a dataset, the simple kriging because of having the lowest values for RMSE and MAE and nearing the Mean Bias Error (MBE) parameter to zero was selected as the best approach for modeling the spatial variations of the variables. Superiority of kriging methods can be due to the nature of these methods in minimizing variance of the estimation and use of the variogram techniques in the modeling the spatial distribution of the pollutants. The best experimental variogram was plotted for each of the metals after fitting and checking of various models. The spatial structures of these metals were studied and the results showed that the spatial distribution of mercury, lead and cadmium are following the gaussian, exponential and circular models, respectively. Conclusion Although concentration of the metals in mangrove forests in terms of toxicity for organisms is not threatening, but this is one of the most serious dangers that is threatening the mangrove ecosystems in Persian Gulf. The sediments of these forests are low-oxygen and water-saturated that takes a time to clean up oil pollution in these areas. This makes the oil floats on water that may cause death of the tree roots in mangrove forests. Finally, the structures and relationships in the mangrove forests may be broken down. But as laboratory analysis of sediment samples in this study showed, fortunately a number of natural factors reduce the accumulation of toxic pollutants in mangrove forests. First, the flow is counterclockwise in the Persian Gulf. This can convey clean waters into Persian Gulf from northern parts of the Qeshm Island. After crossing the northwest and western parts of the Persian Gulf, where there are the highest concentration of oil wells and oil exploration projects, it gets out from southern parts of the island. Therefore, it is expected that the sediments of northern parts of Qeshm have less pollution in comparison with those of southern parts. This is also confirmed by the results of previous studies. It is also noteworthy that the issue of the mangrove forest floor is full of microorganisms that can degrade petroleum compounds into simpler substances. This can be revealed by the toxic metals found in crude oil before accumulation in surface sediments, by marine counterclockwise currents driven toward the inner parts of the Persian Gulf,i.e., where they deposited. On the other, Qeshm Island is located in such a way that serves as a protective barrier to protect physically a large portion of the mangrove forests against contaminants. In this study, due to the lack of extensive sampling of the total mangrove forest areas, we used of geostatistical methods to model the distribution pattern of toxic metals. In general, we can say that employing human development in data processing techniques and using remote sensing and GIS, mapping of pollutions in environment (water, soil and air) is easier and faster and that geostatistical methods are very useful due to spatial extent and the problems associated with sampling.

Coastal areas are the confluence of interaction between earth, large bodies of water and atmosphere. It is constantly affected by natural motion and human interference in the balance of nature. Generally speaking, everywhere in the world coastal areas are very resourceful and provide better condition for human habitat, and therefore, attract human populations and become centers for civic developments. As a result of excessive use, and in some cases abuse of the resources by human beings, adverse conditions develop and cause a decline in coastal areas ecosystem such as; mangroves, coral reefs,cliffs, beaches, tidal flats and estuaries. Such condition makes it imperative to draw urgent attention for protection and rehabilitation of coastal areas. In current research the Environmental Sensitivity Index (ESI) of NOAA and the Particularly Sensitive Sea Area (PSSA) method of IMO were applied to the region under consideration. These methods are credible and worldly accepted for such investigations, in particular where pollutant source include petrochemical materials. The result of study utilizing Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices of the Strengths-Weaknesses-Opportunities-Threats (SWOT) way of analysis indicated that both methodsproduce acceptable results with scores above 3.0. However, weighted scores of NOAA method, using both IFE and EFE matrices, came out somewhat higher than those for IMO method, and considered better choice for application in the coastal areas of Siatan and Baluchestan province.