Due to growing demand and reduction of water resources and increasing pollution of water,driven by dramatic population and economic growth, arid and semi-arid land's imminent water problems are nowadays aggravatin...Due to growing demand and reduction of water resources and increasing pollution of water,driven by dramatic population and economic growth, arid and semi-arid land's imminent water problems are nowadays aggravating. This study aims to determine the most appropriate management strategies for balancing the Abhar plain aquifer using the SWOT coupled with AHP technique. The results indicate that weaknesses prevail over strengths as well as threats over opportunities. The placement in the quarter of weaknesses-threats with a defensive strategy indicates the critical condition of the Abhar plain aquifer. The most appropriate solutions to achieve the goal of balancing the groundwater were prioritized by AHP method. According to results, improper management of water consumption with a weight of 72.5% is the most destructive factor in reducing groundwater resources. Among the types of consumption, the effect of an agricultural factor carries a weight of 74.2%. The exploitation of illegal wells, overdraft of exploitation license provisions of wells, reduction of precipitation and traditional irrigation methods were selected as the destructive factors causing the deteriration of groundwater resources. Also, with filling the illegal wells,changing the type of cultivation and greenhouse crops cultivation, installing a smart water meter,observance the provisions of the water exploitation license, implementing integrated pressurized irrigation systems, benefiting from suitable climatic conditions and geographical location for cultivating and developing the low-water use species and industries and on the other hand, with implementing artificial recharge to control the surface water resources and reduce abstraction from groundwater aquifers, the adverse trend of Abhar Plain groundwater resources can be controlled.展开更多
This study aimed to investigate the interaction between regions with different climatic conditions(arid vs. semi-arid) and management(protected vs. unprotected) on the turnover and nestedness of vegetation in relation...This study aimed to investigate the interaction between regions with different climatic conditions(arid vs. semi-arid) and management(protected vs. unprotected) on the turnover and nestedness of vegetation in relation to physical, chemical and biological properties of soils in the Ilam Province of Iran. In each of the two regions, we sampled 8 sites(4 managed and 4 unmanaged sites) within each of which we established 4 circular plots(1000 m^2) that were used to investigate woody species, while two micro-plots(1 m×1 m) were established in each 1000-m^2 plot to analyze herbaceous species. In each sample unit, we also extracted three soil samples(0–20 cm depth) for measuring soil properties. The results indicated that the interaction between region and conservational management significantly affected the percent of canopy cover of Persian oak(Quercus brantii Linddl), soil respiration, substrate-induced respiration, as well as beta and gamma diversities and turnover of plant species. The percent of oak canopy cover was positively correlated with soil silt, electrical conductivity, available potassium, and alpha diversity, whereas it was negatively correlated with plant turnover. In addition, plant turnover was positively related to available phosphorus, while nestedness of species was positively related to organic carbon and total nitrogen. According to these results, we concluded that physical, chemical, and biological characteristics of limited ecological niche generally influenced plant diversity. Also, this study demonstrated the major contribution of the beta diversity on gamma diversity, especially in semi-arid region, because of the higher heterogeneity of vegetation in this area.展开更多
The relationships between different aspects of diversity(taxonomic,structural and functional)and the aboveground biomass(AGB)as a major component of global carbon balance have been studied extensively but rarely under...The relationships between different aspects of diversity(taxonomic,structural and functional)and the aboveground biomass(AGB)as a major component of global carbon balance have been studied extensively but rarely under the simultaneous influence of forest dieback and management.In this study,we investigate the relationships between taxonomic,functional and structural diversity of woody species(trees and shrubs)and AGB along a gradient of dieback intensity(low,moderate,high and no dieback as control)under two contrasted management conditions(protection by central government vs.traditional management by natives)in a semi-arid oak(Quereus brantii Lindl.)forest ecosystem.AGB was estimated and taxonomic diversity,community weighted average(CWM)and functional divergence indices were produced.We found that the aerial biomass was significantly higher in the intensively used area(14.57(±1.60)t/hm^(2))than in the protected area(8.70(±1.05)t/hm^(2))due to persistence of some large trees but with decreasing values along the dieback intensity gradient in both areas.CWM of height(H),leaf nitrogen content(LNC)and leaf dry matter content(LDMC)were also higher in the traditional managed area than in the protected area.In contrast,in the protected area,the woody species diversity was higher and the inter-specific competition was more intense,explaining a reduced H,biomass and LDMC.Contrary to the results of CWM,none of the functional diversity traits(FDvar)was affected by dieback intensity and only FDvar values of LNC,leaf phosphorus content(LPC)and LDMC were influenced by management.We also found significantly positive linear relationships of AGB with CWM and FDvar indices in the protected area,and with taxonomic and structural diversity indices in the traditional managed area.These results emphasize that along a dieback intensity gradient,the leaf functional traits are efficient predictors in estimating the AGB in protected forests,while taxonomic and structural indices provide better results in forests under a high human pressure.Finally,species identity of the dominant species(i.e.,Brant’s oak)proves to be the main driver of AGB,supporting the selection effect hypothesis.展开更多
Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two archite...Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.展开更多
Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately use...Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.展开更多
Time and motion studies were conducted to evaluate the impact of selective cutting type on Chainsaw productivity in Caspian forests,the selective cutting performed as single and group selection cutting in the region.T...Time and motion studies were conducted to evaluate the impact of selective cutting type on Chainsaw productivity in Caspian forests,the selective cutting performed as single and group selection cutting in the region.The selective cutting was performed on a 42-ha tract with an average slope of 30%.The results indicate that felling time for per tree is most affected by diameter at breast height,the distance among harvested trees in single-tree selection method and diameter at breast height in group selection method.The production rate in single and group selection cutting were 21.2 m 3 h-1 and 28.4 m 3 h-1 for one person,respectively.Considering the gross and net production rate in single and group selection cutting,the unit cost was 1.11 USD m-3 and 0.88 USD m-3,respectively.The results indicate that group selection cutting can be more profitable than single-tree selection method.展开更多
Background:Suitable habitat and landscape structure play a pivotal role in the success of forest restoration projects.This study aimed to model the habitat suitability of wild almond(Amygdalus scoparia Spach)using thr...Background:Suitable habitat and landscape structure play a pivotal role in the success of forest restoration projects.This study aimed to model the habitat suitability of wild almond(Amygdalus scoparia Spach)using three individual species distribution models(SDMs),i.e.,backpropagation artificial neural network(BP-ANN),maximum entropy(MaxEnt),generalized linear model(GLM),as well as the ensemble technique along with measuring the landscape metrics and analyzing the relationship between the distribution of the suitable habitat of the species in different landform classes in Fars Province,southern Iran.Results:There was no clear difference in the prediction performance of the models.The BP-ANN had the highest accuracy(AUC=0.935 and k=0.757)in modeling habitat suitability of A.scoparia,followed by the ensemble technique,GLM,and MaxEnt models with the AUC values of 0.890,0.887,and 0.777,respectively.The highest discrimination capacity was associated to the BP-ANN model,and the highest reliability was related to the ensemble technique.Moreover,evaluation of variable importance showed that the occurrence of A.scoparia was strongly dependent on climatic variables,particularly isothermality(Bio 3),temperature seasonality(Bio 4),and precipitation of driest quarter(Bio 17).Analysis of the distribution of species habitat in different landform classes revealed that the canyon,mountain top,upland drainage,and hills in valley classes had the highest suitability for the species establishment.Conclusions:Considering the importance of landform in the establishment of plant habitats,the combination of the outputs of the SDMs,landform,and the use of landscape metrics could provide both a clear view of habitat conditions and the possibility of analyzing habitat patches and their relationships that can be very useful in managing the remaining forests in semi-arid regions.The canyon,mountain top,and upland drainage classes were found to be the most important landforms to provide the highest suitable environmental conditions for the establishment of A.scoparia.Therefore,such landforms should be given priority in restoration projects of forest in the study area.展开更多
文摘Due to growing demand and reduction of water resources and increasing pollution of water,driven by dramatic population and economic growth, arid and semi-arid land's imminent water problems are nowadays aggravating. This study aims to determine the most appropriate management strategies for balancing the Abhar plain aquifer using the SWOT coupled with AHP technique. The results indicate that weaknesses prevail over strengths as well as threats over opportunities. The placement in the quarter of weaknesses-threats with a defensive strategy indicates the critical condition of the Abhar plain aquifer. The most appropriate solutions to achieve the goal of balancing the groundwater were prioritized by AHP method. According to results, improper management of water consumption with a weight of 72.5% is the most destructive factor in reducing groundwater resources. Among the types of consumption, the effect of an agricultural factor carries a weight of 74.2%. The exploitation of illegal wells, overdraft of exploitation license provisions of wells, reduction of precipitation and traditional irrigation methods were selected as the destructive factors causing the deteriration of groundwater resources. Also, with filling the illegal wells,changing the type of cultivation and greenhouse crops cultivation, installing a smart water meter,observance the provisions of the water exploitation license, implementing integrated pressurized irrigation systems, benefiting from suitable climatic conditions and geographical location for cultivating and developing the low-water use species and industries and on the other hand, with implementing artificial recharge to control the surface water resources and reduce abstraction from groundwater aquifers, the adverse trend of Abhar Plain groundwater resources can be controlled.
基金Ilam University is kindly acknowledged for its financial support for this research work
文摘This study aimed to investigate the interaction between regions with different climatic conditions(arid vs. semi-arid) and management(protected vs. unprotected) on the turnover and nestedness of vegetation in relation to physical, chemical and biological properties of soils in the Ilam Province of Iran. In each of the two regions, we sampled 8 sites(4 managed and 4 unmanaged sites) within each of which we established 4 circular plots(1000 m^2) that were used to investigate woody species, while two micro-plots(1 m×1 m) were established in each 1000-m^2 plot to analyze herbaceous species. In each sample unit, we also extracted three soil samples(0–20 cm depth) for measuring soil properties. The results indicated that the interaction between region and conservational management significantly affected the percent of canopy cover of Persian oak(Quercus brantii Linddl), soil respiration, substrate-induced respiration, as well as beta and gamma diversities and turnover of plant species. The percent of oak canopy cover was positively correlated with soil silt, electrical conductivity, available potassium, and alpha diversity, whereas it was negatively correlated with plant turnover. In addition, plant turnover was positively related to available phosphorus, while nestedness of species was positively related to organic carbon and total nitrogen. According to these results, we concluded that physical, chemical, and biological characteristics of limited ecological niche generally influenced plant diversity. Also, this study demonstrated the major contribution of the beta diversity on gamma diversity, especially in semi-arid region, because of the higher heterogeneity of vegetation in this area.
基金the Islamic Azad Univercity of Chalusand Ilam University,Iran(research team managed by Dr.Mehdi HEYDARI)for financial support of the research。
文摘The relationships between different aspects of diversity(taxonomic,structural and functional)and the aboveground biomass(AGB)as a major component of global carbon balance have been studied extensively but rarely under the simultaneous influence of forest dieback and management.In this study,we investigate the relationships between taxonomic,functional and structural diversity of woody species(trees and shrubs)and AGB along a gradient of dieback intensity(low,moderate,high and no dieback as control)under two contrasted management conditions(protection by central government vs.traditional management by natives)in a semi-arid oak(Quereus brantii Lindl.)forest ecosystem.AGB was estimated and taxonomic diversity,community weighted average(CWM)and functional divergence indices were produced.We found that the aerial biomass was significantly higher in the intensively used area(14.57(±1.60)t/hm^(2))than in the protected area(8.70(±1.05)t/hm^(2))due to persistence of some large trees but with decreasing values along the dieback intensity gradient in both areas.CWM of height(H),leaf nitrogen content(LNC)and leaf dry matter content(LDMC)were also higher in the traditional managed area than in the protected area.In contrast,in the protected area,the woody species diversity was higher and the inter-specific competition was more intense,explaining a reduced H,biomass and LDMC.Contrary to the results of CWM,none of the functional diversity traits(FDvar)was affected by dieback intensity and only FDvar values of LNC,leaf phosphorus content(LPC)and LDMC were influenced by management.We also found significantly positive linear relationships of AGB with CWM and FDvar indices in the protected area,and with taxonomic and structural diversity indices in the traditional managed area.These results emphasize that along a dieback intensity gradient,the leaf functional traits are efficient predictors in estimating the AGB in protected forests,while taxonomic and structural indices provide better results in forests under a high human pressure.Finally,species identity of the dominant species(i.e.,Brant’s oak)proves to be the main driver of AGB,supporting the selection effect hypothesis.
基金conducted by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)funded by the Ministry of Science and ICT。
文摘Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.
基金financial supported by the Iran National Science Foundation(INSF)through research project No.96004000the GIS research group(Ton Duc Thang University)for supports via the research project“GIS-based applications for solving realworld problems”。
文摘Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA)based on Deep Belief Network(DBN)with Back Propagation(BP)algorithm optimized by the Genetic Algorithm(GA).For this task,a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE)technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy,root mean square error(RMSE),and area under the receiver operatic characteristic curve(AUC)were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC=0.989)and prediction accuracy(AUC=0.985),and based on the validation dataset it outperforms benchmark models including LR(0.885),LMT(0.934),BLR(0.936),ADT(0.976),NBT(0.974),REPTree(0.811),ANFIS-BAT(0.944),ANFIS-CA(0.921),ANFIS-IWO(0.939),ANFIS-ICA(0.947),and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.
文摘Time and motion studies were conducted to evaluate the impact of selective cutting type on Chainsaw productivity in Caspian forests,the selective cutting performed as single and group selection cutting in the region.The selective cutting was performed on a 42-ha tract with an average slope of 30%.The results indicate that felling time for per tree is most affected by diameter at breast height,the distance among harvested trees in single-tree selection method and diameter at breast height in group selection method.The production rate in single and group selection cutting were 21.2 m 3 h-1 and 28.4 m 3 h-1 for one person,respectively.Considering the gross and net production rate in single and group selection cutting,the unit cost was 1.11 USD m-3 and 0.88 USD m-3,respectively.The results indicate that group selection cutting can be more profitable than single-tree selection method.
基金supported by the University of Zabol,Iran(Project code:PR-UOZ 97-8).
文摘Background:Suitable habitat and landscape structure play a pivotal role in the success of forest restoration projects.This study aimed to model the habitat suitability of wild almond(Amygdalus scoparia Spach)using three individual species distribution models(SDMs),i.e.,backpropagation artificial neural network(BP-ANN),maximum entropy(MaxEnt),generalized linear model(GLM),as well as the ensemble technique along with measuring the landscape metrics and analyzing the relationship between the distribution of the suitable habitat of the species in different landform classes in Fars Province,southern Iran.Results:There was no clear difference in the prediction performance of the models.The BP-ANN had the highest accuracy(AUC=0.935 and k=0.757)in modeling habitat suitability of A.scoparia,followed by the ensemble technique,GLM,and MaxEnt models with the AUC values of 0.890,0.887,and 0.777,respectively.The highest discrimination capacity was associated to the BP-ANN model,and the highest reliability was related to the ensemble technique.Moreover,evaluation of variable importance showed that the occurrence of A.scoparia was strongly dependent on climatic variables,particularly isothermality(Bio 3),temperature seasonality(Bio 4),and precipitation of driest quarter(Bio 17).Analysis of the distribution of species habitat in different landform classes revealed that the canyon,mountain top,upland drainage,and hills in valley classes had the highest suitability for the species establishment.Conclusions:Considering the importance of landform in the establishment of plant habitats,the combination of the outputs of the SDMs,landform,and the use of landscape metrics could provide both a clear view of habitat conditions and the possibility of analyzing habitat patches and their relationships that can be very useful in managing the remaining forests in semi-arid regions.The canyon,mountain top,and upland drainage classes were found to be the most important landforms to provide the highest suitable environmental conditions for the establishment of A.scoparia.Therefore,such landforms should be given priority in restoration projects of forest in the study area.
