نشریه تحقیقات منابع طبیعی تجدید شونده
سال چهاردهم شماره 2 (پیاپی 40، پاییز و زمستان 1402)

Detailed information on forest combination is required for many environmental, monitoring, and forest protection purposes. The link between ecology and remote sensing provides valuable information for the study of forest trees to facilitate the study of ecosystem performance and to measure the spatial distribution of vegetation. In recent years, the use of modern remote sensing methods and techniques based on UAVs have been used for regular updating of forest inventory. In this research, different data sources including multi-spectral and RGB images with very high spatial resolution, were used for tree species recognition in plain forests of Noor City located in Mazandaran province. Also, taking images was performed in the growing season to prepare a time series of UAV-RGB images for investigating the effect of tree crown phonological changes on classification accuracy.  Following orthomosaic generation, RGB (NGB, NRB) and multi-spectral (NDVI, CIgreen) indices were calculated and the random forest classification method was used for forest species classification. Based on single-time images, late April images provided the highest overall accuracy (75%). However, the results of the time series obtained from RGB images showed an increase in accuracy of up to 86%. Species identification based on multispectral images obtained from the Sequoia sensor also provided 85% accuracy. The results showed that the single-time image at the appropriate time using a UAV-RGB, compared to taking a time series and using a UAV equipped with multispectral sensors, has acceptable and less expensive results for tree recognition in the study area.

Species richness is one of the biodiversity parts calculated by counting the number of species present in a given area. In this study, the species richness of the birds in Bushehr Province was examined to identify biodiversity hotspots and assess their conservation status. To achieve this, bird presence data were collected, and the conservation status of each species was determined. Subsequently, using a grid system with one-by-one-kilometer cells, table of distribution of birds species richness was prepared, and biodiversity hotspots were calculated using the Getis-Ord Gi statistic in a geographic information system (GIS). The results indicated that out of 320 bird species with continuous observation records in Bushehr Province, none were categorized as critically endangered according to the 2021 IUCN Red List. However, three bird species included saker falcon(Falco cherrug), Steppe Eagle(Aquila nipalensis), and Egyptian Vulture(Neophron percnopterus) were classified as Endangered, and Marbled duck(Marmaronetta angustirostris), Common pochard(Aythya ferina), Long-tailed duck(Clangula hyemalis), Macqueen's Bustard(Chlamydotis macqueenii), Socotra cormorant(Phalacrocorax nigrogularis), Pallid Harrier(Circus macrourus), and Eastern Imperial Eagle(Aquila heliaca) were considered as vulnerable. Furthermore, the study of species richness revealed that 21.58% of the study area was covered by the presence of these 320 species, with species diversity ranging from one to 26 birds per cell. The calculation of hotspots at the province level also identified the presence of 30 hotspots, covering a total area of 151311.90 hectares (6.33% of the province's area). These hotspots were mostly located in close proximity to major rivers, rangelands, and agricultural lands, particularly date palm orchards, emphasizing the need for greater attention to these habitats for bird conservation.

In this research, the effect of soaking process and ethanol-acetone washing as well as fermentation pre-treatment by bread yeast in Cypress (Cupressus arizonica) has been investigated. The samples were subjected to pre-treatment with bread yeast and without pre-treatment, washing with pure water and removal of extractives in organic solvent and subjected to dynamic test by forced vibration test in free-free beam. Both methods of soaking process and ethanol-acetone washing resulted in a significant decrease in density and vibration damping, while the values of acoustic conversion efficiency showed an increase. The amount of improvement of acoustic properties due to ethanol-acetone washing was more than water washing. Fermentation pretreatment increased the changes in both water washing and ethanol-acetone washing methods. The dynamic modulus of elasticity did not show any significant change water washing, ethanol-acetone washing and fermentation pretreatment. In general, the ethanol-acetone washed samples under fermentation pretreatment left the highest acoustic indices.

Road ecology studies are very important and valuable for monitoring the impact of forest roads on biodiversity. This study was conducted in order to investigate the effect of forest roads on the biodiversity of herbaceous species, tree regeneration and lichen in the Kalardasht forests. Sampling was done in the sample plots at distances of 0, 15, 45, 75, and 105 meters from the roadside in two controlled and harvested areas. The indices used were Margalf's species richness, Shannon-Wiener's biodiversity and Pielou's evenness index. The effects of roads on vegetation diversity in relation to soil characteristics were also investigated. The results showed that road construction in the harvested stand caused the destruction of tree regeneration, herbaceous and lichen, but the physical and chemical properties of the soil were not affected. By increasing the distance from the road, the diversity and richness of regeneration in both control and harvested areas has increased and the evenness index has decreased. On the other hand, the diversity and richness of herbaceous species has shown a significant decrease. This study reports the distance of 15 to 45 meters of the buffering effect of the roadside on the indices of biodiversity and the richness of tree regeneration, herbaceous cover and lichens. Most of the physical and chemical properties of the soil have also increased with increasing distance from the road. Also, there was a significant correlation between most of the physical and chemical properties of the soil with the regeneration of trees and herbaceous species.

Because the government in countries with abundant natural resources depends on one or more limited resources, if there are trade barriers, national income decreases and economic growth slows down. The research question is whether the effect of non-resource exports on economic growth is non-linear and whether the real effective exchange rate, which has a direct role on the flow of exports, can lead to the Dutch disease phenomenon or not? The aim of this study is to investigate the nonlinear effect of natural resource abundance on economic growth by focusing on non-resource export and the real effective exchange rate variables for the 16 selected OPEC member countries during the period between 2000 and 2021. In order to analyze the data, GMM method has been used. Findings showed that the abundance of natural resources has led to a decrease in economic growth, which indicates the occurrence of the resource curse phenomenon in the studied countries. Also, the second power of the natural resources abundance variable has a positive and significant effect, based on which it can be concluded that the natural resources abundance variable has a non-linear effect on economic growth in the studied countries.

In this research, the water quality of Sefidroud River during the years 2013-2018 using Landsat 8 satellite images as well as 10 qualitative chemical and physical parameters including Ca2+, Na+,Mg2+, (Cl-,SO4-2, HCO3-, TDS, EC, TH and pH were studied in three hydrometric stations. Drinking water quality index (WQI) was calculated and its relationship with satellite bands and band ratios (28 parameters) was analyzed using univariate and multivariate regression models. The results of the univariate regression model showed that the WQI index with band 5 and the ratio of band B4/B3 had a linear and power correlation at a significance level of 1% with coefficient of determination (R2) of 0.55 and 0.51, respectively. The implementation of the stepwise linear multivariate regression model of WQI with all the studied bands and ratios showed that the three band 5 variables and band ratios B4/B3 and B6/B5 were correlated with WQI, with an R2 of about 0.80 at a 5% significance level. After preparing the spatial changes map of WQI using multivariate linear regression model, the results indicated that the water quality in the head branches of Sefidroud, that is, the Qezaluzen and Shahroud rivers, was lower compared to the lower areas and Manjil Dam Lake, although the water quality of the Qezeluzen and Shahroud rivers was lower. Shahrood was weak, but after entering Manjil Dam, it became a good water class. However, the WQI of the water coming out of the dam had gradually increased by passing through the agricultural lands, residential and industrial areas along the river until it reached the Caspian Sea, and it had weakened water class. In general, the results of the research showed that the use of Landsat 8 satellite images and multivariable regression model has a high power for water quality monitoring.

The occurrence of landslides in mountainous areas may cause serious damage to road infrastructure, and may also lead to human deaths. Therefore, the purpose of this study is to landslide susceptibility mapping using advanced machine learning algorithms in Sarovabad city. In this study, landslide susceptibility was determined using two advanced data mining algorithms including random forest (RF) and decision tree (DT). First, the point file of 166 landslides occurred in Sarovabad city was considered as the landslide inventory map. The landslide points are divided into training data (70%) and validation data (30%). A total of 16 parameters including slope, aspect, elevation, river proximity, road proximity, river density, fault proximity, fault density, road density, precipitation, land use, NDVI, lithology, earthquake, stream power index (SPI) and topographic wetness index (TWI) were used in order to landslide susceptibility mapping. Finally, the performance of the models was evaluated using the ROC curve. The results of the ROC showed that the decision tree and random forest models have AUC values of 0.942 and 0.951, respectively. Therefore, the random forest model has the highest AUC value compared to the decision tree and was the best model for predicting the risk of landslides in the future in the study area. Landslide potential maps are efficient tools; so that they can be used for environmental management, land use planning and infrastructure development.