ECOPERSIA
Volume:10 Issue: 3, Summer 2022

In this study, more expedition work has been done to clarify distribution map of the Bungarus persicus. In addition, providing more specimens to confirm occurrence of the species.

The Persian Krait, Bungarus persicus, was described based on two specimens from Baluchistan, southeastern Iran. On 5 September 2020, collected from the Tidar region, Bashagard, Hormozgan Province, southern Iran. Also, another road killed individual, was found at the same location. Further evidences provided that indicate the local population of Bungarus persicus in southeastern Iran belongs to the Bungarus sindanus complex.

However, further molecular studies on Iranian Bungarus are necessary to clarify the species validity of Bungarus persicus and evaluate its similarity with the other congeners.

This research investigates the impact of land cover on dust distribution in the southern part of Khuzestan province in the period of 2000 to 2018.

We used the Landsat 7 and 8 satellite data in 2000, 2009, and 2018 to extract land cover. The land cover map was prepared using the decision tree classification. Aerosol data was extracted using the aerosol optical depth index from the Modis Terra and Aqua sensors. Finally, the relationship between land cover changes and dust index was analyzed.

The results of land cover maps showed a 5% decrease in rangeland cover; a 4.3% increase in salt marshes area; and, a 0.2% decrease in water bodies. The results also showed that the maximum aerosol index in 74% for Hindijan, Ahvaz, and Bandar Mahshahr. The maximum value of this index has increased in recent years. The highest percentage of land-use changes between 2000 and 2018 are bare lands to saline lands, rangelands to bare lands, and bare lands to croplands, respectively. We believe that salt lands by an increase in area by 68195 ha are the main cause of the increase in dust storms in the study area.

Our results confirm the need to reconsider land use management and restore the basic functionality of the region's ecosystems to prevent the occurrence of grave consequences of aerosol accumulation in the atmosphere.

Understanding the patterns of land use and land cover (LULC) change is important for efficient environmental restoration. This study focused on changes in LULC patterns of the Koupal watershed in Khouzestan Province over 22 years.

Multi temporal satellite imagery of the Landsat series (1998 and 2020) were preprocessed and used to extract LULC maps by bayes discriminant and Maximum likelihood rule. Reliability of classified maps were checked using confusion matrix.Transition matrix and change rate were computed by Change detection analysis.  

The results of the change detection analysis shows that vegetation cover witness of dramatic decrease and changed from 27.6% to 0.06%, followed by water body reduction from 8.59% to 0.79% and bare land decrease from 57.9% to 51% of whole area. The results indicates a rapid expansion of cropland from 5.44% to 41.25% of total area. Sand dune increased from 1.08% of total area in 1998 to 2.75% in 2020 and build up area shows a growth from0.27% of total area. Change matrix revealed that 93% of cropland remained unchanged, followed by bare land (71%), built up (53%), water body (7%), sand dune (6%) and vegetation (0.05%). This indicates that vegetation experienced the most significant loss and highest conversion during this period, with almost 73% of its total area converted to cropland and bare land (22%) and the rest to other land uses.

These results establish LULC trends in past 22 years and provide crucial data useful for planning and sustainable land use management.

Monitoring variations in macroalgal assemblages is a crucial issue for the preservation and management program of coastal waters. This study was conducted to determine the seasonal and spatial distribution patterns, and composition of macroalgal communities along the eastern coasts of Qeshm Island, Iran.

Seasonal sampling was conducted at three different sites of different tidal levels on the eastern coasts of Qeshm Island. Random samples of macroalgae were collected at three stations, seasonally. The species were identified and the dry weight of each species was used to calculate the macroalgae abundance. The Species richness and the Diversity indices were calculated to evaluate the distribution pattern and composition of the macroalgal community.

As a result, 51 species (4 Chlorophyta, 21 Phaeophyta, and 26 Rhodophyta) were identified. The seasonal and spatial dominant species were found to be Padina sp. and Hypnea sp., and a distribution pattern was seen to have increasing macroalgal biomass from the upper to lower intertidal level. The sampling sites shared more than 50% similarity of their macroalgal species, indicating a relatively homogeneous distribution. The highest (18.1±4.3 gr drywt m-2) and lowest (8.27±2.1 gr drywt m-2) mean of total seaweed biomass were recorded in winter and summer, respectively.

The assemblage composition of macroalgae significantly differs between hot and cold seasons, and there was no substantial compositional variation of seaweeds communities along the tidal gradient. The macroalgal distribution was largely homogeneous with no significant difference among the research areas at sampling seasons.

An attempt was made to understand the influence of climate change on thefuture potential distribution of Centaurea balsamita
In the world. Centaurea balsamita is an annual plant from thesunflower family (Asteraceae) that invades fallow and slope lands worldwide. Climate change has caused extreme weather events and has had widespread impacts on the global ecosystem, including
thedistribution of plant species.The CLIMEX software is used globally for analyzing potential distributions of species.

The experiments were conducted in Mashhad, Khorasan Razavi Province.In the present study,CLIMEX software was used to study thepotential distribution of this plant in the world at present and future climate conditions.CLIMEX software requires five climate variables, including average, maximum, and minimum monthly temperature, precipitation and relative humidity at 9 Am and 3 Pm.These data were obtained from various sources such as "CRU TS v. 4.03" and used for the model predictions. Following the data collection, the values were adjusted and incorporated into the CLIMEX Modelling software.Using the literature data, we collected information onThe biology and ecology of Centaurea balsamita relevant for modeling the distribution of this species in Iran and worldwide underCurrent and future climatic conditions.

Our results revealed that in current conditions, Europe, Asia, and North America are suitable locations for this invasive weed dispersal, and most parts of Europe have optimal conditions (20≤ EI) for dispersal of C. balsamita. It is likely that the suitable C. balsamita habitat area will be wider in some parts of the world such as Asia, America, and Europe under future climatic

The main aim of this study was to assess the efficacy of two important signal processing approaches i.e., wavelet transform and ensemble empirical mode decomposition (EEMD) on the performance of convolutional neural network (CNN).

The study was performed in two watersheds i.e., Kasilian and Bar-Erieh watersheds. In the first step, the CNN based runoff modeling was done in its single form i.e., using the original data as input. In the next step the input data was decomposed into several different sub-components i.e., approximation and details using Wavelet transform and Intrinsic Mode Functions (IMFs) using EEMD. Then the decomposed data were imported to the CNN model as input and combined Wavelet-CNN and EEMD-CNN models were provided.

The results showed that CNN in its single form could not estimate the one day ahead runoff with an acceptable accuracy. CNN in its original form had a moderate performance (with NRMSE of 83 and 66%). However, application of Wavelet transform and EEMD in combination with CNN produced acceptable results. It was shown that Wavelet transform had a higher impact (with NRMSE of 48 and 26%) on the performance of CNN in comparison to EEMD (with NRMSE of 52 and 61%).

This study showed that signal processing approaches can enhance the ability of deep learning methods such as CNN in predicting runoff values for one day ahead. However, the impact of signal processing methods on the performance of deep learning methods are not equal.

Like many other countries, Iran has been exposed to the COVID-19 pandemic and its different economic and environmental implications. So, the research studies the economic and environmental consequences of COVID-19 in the Makoran coast of Sistan and Baluchistan province that supplies nearly 60% of the demand for fish in Iran.

The research is an applied study in terms of goal and a descriptive study in terms of data collection method, which was conducted by the cross-sectional survey method.

A comparison of the period of January-June 2020 (COVID-19 conditions) versus the similar period in the year 2019 (normal conditions) in the economic sector showed that the fishermen’s income, catch rate, and employment have decreased and their unemployment has increased. In the environmental sector, the pandemic and the related restrictions have increased seawater pollution and waste production.

 These changes, which have seemingly increased by the culmination of the COVID-19 pandemic, have had irreparable consequences for the fisheries sector.Suggestions: Governmental support from the management of demand and supply markets and its plans to support fishermen are recommended as approaches to tackling the crisis. Given the emergence of new strains of COVID-19, such as alpha, beta, gamma, delta, and omicron, it is necessary to conduct long-term studies to determine the full range of the pandemic.