ali asghar naghipour - Academia.edu (original) (raw)

Papers by ali asghar naghipour

Research paper thumbnail of Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Remote Sensing

Accurate plant-type (PT) detection forms an important basis for sustainable land management maint... more Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO’s software fra...

Research paper thumbnail of Assessing the potential distribution of Juniperus excelsa M. Bieb. under current and future climate scenarios in the Chaharmahal va Bakhtiari province, Iran

Species distributions models (SDMs) are increasingly used to predict species' potential range... more Species distributions models (SDMs) are increasingly used to predict species' potential range shift or extinction risk in response to future climate change. Here, we used the ensemble predictions of the models in order to estimate the impact of climate change on the geographical distribution of Juniperus excelsa M. Bieb. in Chaharmahal va Bakhtiari province in the Central Zagros region, Iran. We projected climate change impacts for 2050 based on four scenarios of the increase in the greenhouse gases in the general circulation model MRI-CGCM3. We then used the bioclimatic and topographic variables to create a model ensemble from six different SDM algorithms including Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), Artificial Neural Network (ANN), Generalized Boosting Method (GBM), Multivariate Adaptive Regression Splines (MARS), and Random Forest (RF). The findings indicated that 26.5% of the study area (4393.98 km2) is suitable for the J. excelsa. Annual pr...

Research paper thumbnail of Modeling Current and Future Potential Distributions of Caspian Pond Turtle (Mauremys caspica) under Climate Change Scenarios

Iranian Journal of Applied Ecology, Mar 1, 2022

Although turtles are the most threatened taxonomic group within the reptile class, we have a very... more Although turtles are the most threatened taxonomic group within the reptile class, we have a very limited understanding of how turtles respond to climate change. Here, we evaluated the effects of climate changes on the geographical distribution of Caspian pond turtle (Mauremys caspica). We used an ensemble approach by combining six species distribution models including artificial neural network, generalized boosted model, generalized linear model, flexible discriminant analysis, random forest and multivariate adaptive regression splines. To predict the future distribution, modelling projection of MRI-CGCM3 was used for the year 2070 under four scenarios of representative concentration pathways (RCP). Based on the findings, the suitable habitat of Caspian pond turtle was estimated to be about 835941 km 2 (about 8.73%) of the study area. Our model projections showed that about 26 to 33% of the current suitable habitats will be unsuitable by 2070 due to climate change. The annual precipitation (24.56%), precipitation of wettest quarter (24.28%), precipitation seasonality (16.93%) and temperature seasonality (14.64%) had the highest contribution to model performance of Caspian pond turtles. Overall, our findings emphasize the need for a comprehensive understanding of the complex effects of climate change on the species, specially turtles.

Research paper thumbnail of Influence of Exclosure on Carbon Sequestration of Soil and Plant Biomass in Semi Arid Rangelands of North Khorasan Province

Research paper thumbnail of Effect of integrated fire period and intensity grazing on plant species diversity in the semi-steppe rangeland of Chaharmahal and Bakhtiari Province

Iranian Journal of Range and Desert Research, 2020

Research paper thumbnail of Predicting of fire occurrence using Bayesian belief network in Chaharmahal and Bakhtiari province

Research paper thumbnail of Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Remote Sensing, Sep 6, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Land Cover Change Detection and Prediction in Sefiddasht-Borujen Basin Using Ca-Markov

Desert Management, Feb 19, 2021

The aim of this study is to evaluate the land cover changes in the basin of Sefiddasht-Borujen us... more The aim of this study is to evaluate the land cover changes in the basin of Sefiddasht-Borujen using remote sensing Using remote sensing data, land cover maps of satellite images of 1998, 2009, and 2018 were prepared and classified. Then, using the image differencing method, land cover changes for the time periods of 1998 to 2018 were detected. Finally, predicted land cover changes were investigated in each land cover using a CA-Markov model. To predict the probable changes for the year of 2028, the 2018 land cover was modeled using 1998-2009 images by applying of the CA-Markov method of change detection. Next, the resulted of modeled 2018 land cover map were compared with the ground truth map of this year. The results of both maps showed relatively similarity and there was a slight difference between these predicted and classified images of 2018. Therefore, this method was used to predict 2028 land cover image too. The results of change detection for the years 1998 to 2018 indicates the reduction of 8339 hectares of agricultural lands in the study area, as well as 11824 ha from rangelands. Conversely, the bare land increased 14601 ha. According to predicted map for 2028, the largest incremental change in the bare land will be 16476 ha. Estimates show that 8664 hectares of these lands will be from agricultural lands, but approximately 8580 ha will be transformed into the bare land and about 224 ha to residential-industrial lands. Rangelands also will be reduced by13055 ha including 11663 ha to bare land and 1069 ha will be transformed into residential-industrial areas. 16476 ha will be added to bare land and 1420 ha to residential-industrial areas. The results of the present study can be used for future planning for the study area.

Research paper thumbnail of Application of ensemble modelling method in predicting the effects of climate change on the distribution of Fritillaria imperialis L

Journal of Plant Research #R##N#(Iranian Journal of Biology), Sep 23, 2019

Research paper thumbnail of Assessment of Desertification Status in Sefiddasht-Boroujen Watershed (Chaharmahal and Bakhtiari Province) Using MEDALUS Model

Journal of RS and GIS for Natural Resources, Jun 6, 2021

Research paper thumbnail of Evaluation of drought resistance and yield in PGPR-primed seeds of Festuca arundinacea Schreb under different levels of osmotic potential and field capacity

Journal of Pure and Applied Microbiology, 2015

Research paper thumbnail of 311 | Naghipour et al

The effects of fire on density, diversity and richness of soil seed bank in semi-arid rangelands ... more The effects of fire on density, diversity and richness of soil seed bank in semi-arid rangelands of central Zagros region, Iran

Research paper thumbnail of Modeling climate change impacts on the distribution of an endangered brown bear population in its critical habitat in Iran

Science of The Total Environment

Research paper thumbnail of Changes in Soil Organic Carbon, Nitrogen and Phosphorus in Modified and Native Rangeland Communities (Case study: Sisab Rangelands, Bojnord)

Journal of Rangeland Science, 2011

A bstract. Converting the native rangelands to simplified agro nomic communities causes some chan... more A bstract. Converting the native rangelands to simplified agro nomic communities causes some changes in soil carbon, nitrogen and phosphorus. Establishing of perennial plant communities on formerly cultivated rangelands is expected to stabilize soil properties and increase the amount of C, N, P stored in rangeland soils, but there is little information on what plant communities are the most effective for improvement of soil C, N, P reserves. The purpose of this study was to compare soil C, N, and P pools in ungrazed native rangelands with plant community of Festuca-Centurea with those ungrazed pastures established by sowing non-native perennial grasses ( Agropyron elongatum and Agropyron desertorum), shrubs (Kochia prostrata), and wheat cultivation in continuous dry land farming system. Study site was located in Sisab Research Station in North Khorasan Province, Iran. The results showed that the total C, N and P contents in the soils under modified plant communities were less than t...

Research paper thumbnail of The Influence of Climate Change on distribution of an Endangered Medicinal Plant (Fritillaria Imperialis L.) in Central Zagros

Journal of Rangeland Science, 2019

Climate change has a great impact on the species distribution range and many endangered plant spe... more Climate change has a great impact on the species distribution range and many endangered plant species. Fritillaria imperialis as a species that is native to Central Zagros, Iran is a medicinal plant with great ecological and commercial profits. Its population has decreased considerably and the species would be endangered in later decades. Understanding the habitat needs of this species, evaluating habitat conditions, and forecasting its potential habitat are important for protecting F. imperialis. The presence of F. imperialis points recorded from our field surveys in Chaharmahal-va-Bakhtiari province as a part of Central Zagros, Iran in spring 2017. In order to model its distribution based on correlation analysis, two topographic variables and eight bioclimatic ones as the input of Maximum Entropy model (MaxEnt) were used. The results showed that temperature seasonality (55.1%) and precipitation of driest quarter (22.9%) were important factor drivers of F. imperialis suitable habit...

Research paper thumbnail of The effect of fire on carbon sequestration of soil and plant biomass in Semi-Steppe rangelands of Central Zagros region, Iran

Research paper thumbnail of Distribution Modeling of Foraging Habitats for Egyptian Vulture (Neophron percnopterus) in Kermanshah Province, Iran

Iranian Journal of Applied Ecology, 2020

Research paper thumbnail of Assessing the potential distribution of Juniperus excelsa M. Bieb. under current and future climate scenarios in the Chaharmahal va Bakhtiari province, Iran

Scientific Reports in Life Sciences, Sep 1, 2021

Research paper thumbnail of Predicting the Potential Distribution of Crataegus azarolus L. under Climate Change in Central Zagros, Iran

Global climate change has had a significant impact on biodiversity and altered the geographical d... more Global climate change has had a significant impact on biodiversity and altered the geographical distribution of many plant species. In this study, ensemble modeling based on seven species distribution models was used to predict the effect of climate change on the spatial distribution of Crataegus azarolus L. in Chaharmahal-va-Bakhtiari province, located in the Central Zagros region, Iran. We used 113 presence points of the species and physiographic, land cover, and bioclimatic variables. Predicting the geographical distribution of the C. azarolus in the future (years 2050 and 2070) was made based on four scenarios of the increase in the greenhouse gases (RCPs: Representative Concentration Pathways) in the general circulation model of MRI-CGCM3. Based on the results, about 20% (3292.192 km) of the study area can be considered as the suitable habitat of C. azarolus. Precipitation Seasonality, Isothermality, and Mean Temperature of the Wettest Quarter had the highest contribution to th...

Research paper thumbnail of Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

Remote Sensing

Vegetation Types (VTs) are important managerial units, and their identification serves as essenti... more Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Ra...

Research paper thumbnail of Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Remote Sensing

Accurate plant-type (PT) detection forms an important basis for sustainable land management maint... more Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO’s software fra...

Research paper thumbnail of Assessing the potential distribution of Juniperus excelsa M. Bieb. under current and future climate scenarios in the Chaharmahal va Bakhtiari province, Iran

Species distributions models (SDMs) are increasingly used to predict species' potential range... more Species distributions models (SDMs) are increasingly used to predict species' potential range shift or extinction risk in response to future climate change. Here, we used the ensemble predictions of the models in order to estimate the impact of climate change on the geographical distribution of Juniperus excelsa M. Bieb. in Chaharmahal va Bakhtiari province in the Central Zagros region, Iran. We projected climate change impacts for 2050 based on four scenarios of the increase in the greenhouse gases in the general circulation model MRI-CGCM3. We then used the bioclimatic and topographic variables to create a model ensemble from six different SDM algorithms including Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), Artificial Neural Network (ANN), Generalized Boosting Method (GBM), Multivariate Adaptive Regression Splines (MARS), and Random Forest (RF). The findings indicated that 26.5% of the study area (4393.98 km2) is suitable for the J. excelsa. Annual pr...

Research paper thumbnail of Modeling Current and Future Potential Distributions of Caspian Pond Turtle (Mauremys caspica) under Climate Change Scenarios

Iranian Journal of Applied Ecology, Mar 1, 2022

Although turtles are the most threatened taxonomic group within the reptile class, we have a very... more Although turtles are the most threatened taxonomic group within the reptile class, we have a very limited understanding of how turtles respond to climate change. Here, we evaluated the effects of climate changes on the geographical distribution of Caspian pond turtle (Mauremys caspica). We used an ensemble approach by combining six species distribution models including artificial neural network, generalized boosted model, generalized linear model, flexible discriminant analysis, random forest and multivariate adaptive regression splines. To predict the future distribution, modelling projection of MRI-CGCM3 was used for the year 2070 under four scenarios of representative concentration pathways (RCP). Based on the findings, the suitable habitat of Caspian pond turtle was estimated to be about 835941 km 2 (about 8.73%) of the study area. Our model projections showed that about 26 to 33% of the current suitable habitats will be unsuitable by 2070 due to climate change. The annual precipitation (24.56%), precipitation of wettest quarter (24.28%), precipitation seasonality (16.93%) and temperature seasonality (14.64%) had the highest contribution to model performance of Caspian pond turtles. Overall, our findings emphasize the need for a comprehensive understanding of the complex effects of climate change on the species, specially turtles.

Research paper thumbnail of Influence of Exclosure on Carbon Sequestration of Soil and Plant Biomass in Semi Arid Rangelands of North Khorasan Province

Research paper thumbnail of Effect of integrated fire period and intensity grazing on plant species diversity in the semi-steppe rangeland of Chaharmahal and Bakhtiari Province

Iranian Journal of Range and Desert Research, 2020

Research paper thumbnail of Predicting of fire occurrence using Bayesian belief network in Chaharmahal and Bakhtiari province

Research paper thumbnail of Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Remote Sensing, Sep 6, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Research paper thumbnail of Land Cover Change Detection and Prediction in Sefiddasht-Borujen Basin Using Ca-Markov

Desert Management, Feb 19, 2021

The aim of this study is to evaluate the land cover changes in the basin of Sefiddasht-Borujen us... more The aim of this study is to evaluate the land cover changes in the basin of Sefiddasht-Borujen using remote sensing Using remote sensing data, land cover maps of satellite images of 1998, 2009, and 2018 were prepared and classified. Then, using the image differencing method, land cover changes for the time periods of 1998 to 2018 were detected. Finally, predicted land cover changes were investigated in each land cover using a CA-Markov model. To predict the probable changes for the year of 2028, the 2018 land cover was modeled using 1998-2009 images by applying of the CA-Markov method of change detection. Next, the resulted of modeled 2018 land cover map were compared with the ground truth map of this year. The results of both maps showed relatively similarity and there was a slight difference between these predicted and classified images of 2018. Therefore, this method was used to predict 2028 land cover image too. The results of change detection for the years 1998 to 2018 indicates the reduction of 8339 hectares of agricultural lands in the study area, as well as 11824 ha from rangelands. Conversely, the bare land increased 14601 ha. According to predicted map for 2028, the largest incremental change in the bare land will be 16476 ha. Estimates show that 8664 hectares of these lands will be from agricultural lands, but approximately 8580 ha will be transformed into the bare land and about 224 ha to residential-industrial lands. Rangelands also will be reduced by13055 ha including 11663 ha to bare land and 1069 ha will be transformed into residential-industrial areas. 16476 ha will be added to bare land and 1420 ha to residential-industrial areas. The results of the present study can be used for future planning for the study area.

Research paper thumbnail of Application of ensemble modelling method in predicting the effects of climate change on the distribution of Fritillaria imperialis L

Journal of Plant Research #R##N#(Iranian Journal of Biology), Sep 23, 2019

Research paper thumbnail of Assessment of Desertification Status in Sefiddasht-Boroujen Watershed (Chaharmahal and Bakhtiari Province) Using MEDALUS Model

Journal of RS and GIS for Natural Resources, Jun 6, 2021

Research paper thumbnail of Evaluation of drought resistance and yield in PGPR-primed seeds of Festuca arundinacea Schreb under different levels of osmotic potential and field capacity

Journal of Pure and Applied Microbiology, 2015

Research paper thumbnail of 311 | Naghipour et al

The effects of fire on density, diversity and richness of soil seed bank in semi-arid rangelands ... more The effects of fire on density, diversity and richness of soil seed bank in semi-arid rangelands of central Zagros region, Iran

Research paper thumbnail of Modeling climate change impacts on the distribution of an endangered brown bear population in its critical habitat in Iran

Science of The Total Environment

Research paper thumbnail of Changes in Soil Organic Carbon, Nitrogen and Phosphorus in Modified and Native Rangeland Communities (Case study: Sisab Rangelands, Bojnord)

Journal of Rangeland Science, 2011

A bstract. Converting the native rangelands to simplified agro nomic communities causes some chan... more A bstract. Converting the native rangelands to simplified agro nomic communities causes some changes in soil carbon, nitrogen and phosphorus. Establishing of perennial plant communities on formerly cultivated rangelands is expected to stabilize soil properties and increase the amount of C, N, P stored in rangeland soils, but there is little information on what plant communities are the most effective for improvement of soil C, N, P reserves. The purpose of this study was to compare soil C, N, and P pools in ungrazed native rangelands with plant community of Festuca-Centurea with those ungrazed pastures established by sowing non-native perennial grasses ( Agropyron elongatum and Agropyron desertorum), shrubs (Kochia prostrata), and wheat cultivation in continuous dry land farming system. Study site was located in Sisab Research Station in North Khorasan Province, Iran. The results showed that the total C, N and P contents in the soils under modified plant communities were less than t...

Research paper thumbnail of The Influence of Climate Change on distribution of an Endangered Medicinal Plant (Fritillaria Imperialis L.) in Central Zagros

Journal of Rangeland Science, 2019

Climate change has a great impact on the species distribution range and many endangered plant spe... more Climate change has a great impact on the species distribution range and many endangered plant species. Fritillaria imperialis as a species that is native to Central Zagros, Iran is a medicinal plant with great ecological and commercial profits. Its population has decreased considerably and the species would be endangered in later decades. Understanding the habitat needs of this species, evaluating habitat conditions, and forecasting its potential habitat are important for protecting F. imperialis. The presence of F. imperialis points recorded from our field surveys in Chaharmahal-va-Bakhtiari province as a part of Central Zagros, Iran in spring 2017. In order to model its distribution based on correlation analysis, two topographic variables and eight bioclimatic ones as the input of Maximum Entropy model (MaxEnt) were used. The results showed that temperature seasonality (55.1%) and precipitation of driest quarter (22.9%) were important factor drivers of F. imperialis suitable habit...

Research paper thumbnail of The effect of fire on carbon sequestration of soil and plant biomass in Semi-Steppe rangelands of Central Zagros region, Iran

Research paper thumbnail of Distribution Modeling of Foraging Habitats for Egyptian Vulture (Neophron percnopterus) in Kermanshah Province, Iran

Iranian Journal of Applied Ecology, 2020

Research paper thumbnail of Assessing the potential distribution of Juniperus excelsa M. Bieb. under current and future climate scenarios in the Chaharmahal va Bakhtiari province, Iran

Scientific Reports in Life Sciences, Sep 1, 2021

Research paper thumbnail of Predicting the Potential Distribution of Crataegus azarolus L. under Climate Change in Central Zagros, Iran

Global climate change has had a significant impact on biodiversity and altered the geographical d... more Global climate change has had a significant impact on biodiversity and altered the geographical distribution of many plant species. In this study, ensemble modeling based on seven species distribution models was used to predict the effect of climate change on the spatial distribution of Crataegus azarolus L. in Chaharmahal-va-Bakhtiari province, located in the Central Zagros region, Iran. We used 113 presence points of the species and physiographic, land cover, and bioclimatic variables. Predicting the geographical distribution of the C. azarolus in the future (years 2050 and 2070) was made based on four scenarios of the increase in the greenhouse gases (RCPs: Representative Concentration Pathways) in the general circulation model of MRI-CGCM3. Based on the results, about 20% (3292.192 km) of the study area can be considered as the suitable habitat of C. azarolus. Precipitation Seasonality, Isothermality, and Mean Temperature of the Wettest Quarter had the highest contribution to th...

Research paper thumbnail of Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

Remote Sensing

Vegetation Types (VTs) are important managerial units, and their identification serves as essenti... more Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Ra...