Iranian Journal of Remote Sensing & GIS

علمی - پژوهشی

Investigating and Modeling the Effect of the Composition and Arrangement of the Landscapes of Yazd City on the Land Surface Temperature Using Machine Learning and Landsat-8 and Sentinel-2 Data

Mohammad Mansourmoghaddam; Iman Rousta; Mohammad Sadegh Zamani; Mohammad Hossein Mokhtari; Mohammad Karimi Firozjaei; Seyed Kazem Alavipanah

Volume 15, Issue 3 , September 2023, Pages 1-26

https://doi.org/10.48308/gisj.2023.102195

Abstract

The effect of urban thermal islands due to intersections with major environmental challenges of the 21st century is one of the most important studies on environmental phenomena, and in this regard, the study of the land surface temperature gives a clear perspective of the thermal islands in cities, which, ... Read More

علمی - پژوهشی

Investigation of Topographic Changes in Open Pit Mines by Radar Interferometry (Case Study: Sangan- Khaf iron Ore Mines)

Mahvash Naddaf; seyedReza Hosseunzadeh; Jose Martin; mahnaz jahadi; Naser Hafezimoghaddas

Volume 15, Issue 3 , September 2023, Pages 27-40

https://doi.org/10.48308/gisj.2023.102345

Abstract

In the early 1990s, radar interferometry was introduced and used as a useful tool in the study of all phenomena that cause land surface deformations. If the land surface deforms between two radar images, a surface displacement map can be created with millimeter resolution and accuracy. This paper reports ... Read More

علمی - پژوهشی

Improving the Classification of Hyperspectral Images Using the Combined Model of CapsNet and the Extreme Gradient Boosting

Pouya Ahmadi; Tayebe Managhebi; Hamid Ebadi; Behnam Asghari

Volume 15, Issue 3 , September 2023, Pages 41-60

https://doi.org/10.48308/gisj.2023.102347

Abstract

With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In ... Read More With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure. With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure.In order to improve the classification accuracy, the feature extraction approach through the designed network and the classification by the Extreme Gradient Boosting was compared with the classification method by the global deep network. The proposed capsule approach consists of 3 basic layers: 1) Prime caps, which are capsules of size 8 and 32 with 9 × 9 filters and movement step 2, 2) Digitcaps with 10 16-dimensional capsules, and 3) fully connected layer. The results of examining two approaches for deep networking as well as combining capsule networks with XGBoost reinforcement tree algorithm were compared. Approaches such as SVM, RF-200, LSTM, GRU and GRU-Pretanh were considered to compare the proposed approach based on the configurations mentioned in their research.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined. The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined.The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.

علمی - پژوهشی

Presenting a Strategic Model of Intelligent Monitoring of Construction Violations in Urban Management (Case Study: Mashhad Metropolis Area and Privacy)

Mahdi Fahmideh Modami; Masoud Ayaz; Ahmad Alajeh Gardi; mahdi javanshiri

Volume 15, Issue 3 , September 2023, Pages 61-82

https://doi.org/10.48308/gisj.2023.102392

Abstract

The dramatic increase in construction in recent decades has been accompanied by an increase in the number of construction violations in urbanized areas and has overshadowed the urban management and planning system, so preventing unauthorized urban construction is one of the main problems of city managers. ... Read More

علمی - پژوهشی

Mapping Phenological Stages of Two Rangeland Species Using MODIS Growing Degree Days in Chaharmahal and Bakhtiari Province

Reza Jafari; Morteza Ansari; Mostafa Tarkesh

Volume 15, Issue 3 , September 2023, Pages 83-102

https://doi.org/10.48308/gisj.2023.102437

Abstract

Temperature is the most important parameter for studying spatiotemporal phenological changes in plants. Thus, the current study was aimed to investigate the potential of MODIS land surface temperature (LST) data for mapping growing degree days (GDD) and different phenological stages of Bromus tomentllus ... Read More

علمی - پژوهشی

Estimation of Fire Area in Iranian Vegetation Using MODIS and Alos-2 Data

morteza Sharif; aboozar kiani

Volume 15, Issue 3 , September 2023, Pages 103-124

https://doi.org/10.48308/gisj.2023.102682

Abstract

Forest fires worldwide cause severe damage to vegetation, soil and natural habitats, resulting in direct and indirect negative environmental impacts such as deforestation, climate change and drought. Therefore, identifying and determining the hazards of vegetation that suffer from fire is crucial for ... Read More Forest fires worldwide cause severe damage to vegetation, soil and natural habitats, resulting in direct and indirect negative environmental impacts such as deforestation, climate change and drought. Therefore, identifying and determining the hazards of vegetation that suffer from fire is crucial for their management and development. The proliferation of remote sensing images such as the active fire products of the Terra and Aqua satellites over the past two decades has been one of the most essential methods in detecting these fires. However, the active fire product of the MODIS sensor in previous studies has shown that they alone do not provide good results in fire-affected areas. Therefore, it is necessary to evaluate vegetation with basic maps. The aim of this study was to investigate two types of plant products and to discover the active fire of MODIS sensor and FNF-JAXA forest and non-forest cover maps for better separation of burnt areas of vegetation in Iran between July 1 and 160 2020. The results show the highest area of fire on Julius 144 with more than 49 thousand hectares and Julius 128 with more than 45 thousand hectares. However, the largest area of the fire, forest cover is estimated at 120 to 160 in 2020 with more than 14 thousand hectares. Khuzestan province had the highest area of fires in the period under study that most of these areas in agricultural lands and the three provinces of Fars, Kohgiluyeh and Boyer-Ahmad and Bushehr had the highest area of fires in forest cover. The highest frequency of fires was observed in agricultural lands, the main reason for which could be human intervention. The evaluation of the results showed that the use of the FNF-JAXA product (accuracy of 87.4% and a Kappa coefficient of 0.85) compared to MODIS products (accuracy of 80.3% and a Kappa coefficient of 0.78) in the separation of forest areas has better capabilities. However, the ability of MODIS products to distinguish between pasture and agricultural vegetation is an important advantage, which the FNF-JAXA product does not have. In general, the findings of the research show that the MODIS product and FNF-JAXA maps can be used as reference maps to distinguish different types of vegetation that are subject to fire, in damage assessment and management.

علمی - پژوهشی

Ecological Drought Monitoring of Middle Zagros Based on Landsat 7 Satellite Data and Climate Data (Case Study: Lorestan Province)

Hamid Reza Matinfar; Aliakbar Shamsipor; Hadis Sadeghi

Volume 15, Issue 3 , September 2023, Pages 125-142

https://doi.org/10.48308/gisj.2023.102804

Abstract

Vegetation plays an important role in protecting water and soil resources, stabilizing carbon and improving air quality. In Middle Zagros, forest and pasture vegetation is very important in terms of protecting soil and water resources and sustaining economic activities. In this research, using the Google ... Read More Vegetation plays an important role in protecting water and soil resources, stabilizing carbon and improving air quality. In Middle Zagros, forest and pasture vegetation is very important in terms of protecting soil and water resources and sustaining economic activities. In this research, using the Google Earth Engine platform and Landsat 7 satellite images, the drought of Middle Zagros (Lorestan province) was monitored with vegetation indices NDVI, SAVI and VCI, as well as meteorological drought index SPI for the statistical period of 2020-2000. To calculate the SPI index, the precipitation data of 9 synoptic meteorological stations with appropriate spatial distribution and the length of the statistical period (2020-2000) were used, and the processing was done in DPI software. In order to calculate the plant indices, first, all the geometrically corrected satellite images of the ETM+ sensor of the Landsat satellite were called for Lorestan province for each year. At this stage, an average of 52 images were called for each year. Then the images with less than 5% cloud cover were selected and processed. The results of the VCI index showed that mainly the studied area was affected by mild drought during the statistical period of 2020-2000. The year 2008 had the highest amount of drought related to the middle class with 5880.6 hectares among the studied years. The results of the SPI index showed that there was a moderate drought in 2010, a severe drought in 2008 and 2017, a mild drought in 2006, and a severe drought in 2019. The results of NDVI and SAVI indices also show the increase of thin vegetation classes and areas without vegetation by 1.331679 and 115164 hectares, respectively, and the decrease of normal and dense vegetation by 446160.7 and 682.4 hectares respectively per year. 2008 was compared to 2006 and 2007. Based on the results of all three investigated indicators, the favorable conditions of vegetation cover and ecological threat were obtained in 2016, 2019 and 2020. The highest level of this coordination between SPI meteorological drought and vegetation indices was observed in 2008 and 2010 and to some extent in 2019. In general, the results show that the increase or decrease of vegetation can be caused by the occurrence or absence of drought, while other factors such as land use changes should also be considered.

Positioning lands prone for cultivating ficus fars province using fuzzy logic and the GIS approach

Ali shamsoddini; hasan mehrzad; babraz karimi

Volume 10, Issue 3 , January 2019, , Pages 1-16

Abstract

Agriculture is one of the most important economic parts in each country, which each product requires specific climatic and environmental conditions. So climatologists pay special attention to landuse planning and managing ecological resources with appropriate methods. The purpose of this study is to ... Read More

Cropland Mapping through Integration of Segmentation and Classification Techniques in Google Earth Engine

Alireza Taheri Dehkordi; Mohammad Javad Valadanzouj; Alireza Safdarinezhad

Volume 14, Issue 1 , June 2022, , Pages 1-20

https://doi.org/10.52547/gisj.14.1.1

Abstract

Map of croplands is one of the information layers required in the efficient management of these lands. Having such maps makes it possible to monitor agricultural fields during the growing season continuously. In this study, a solution to produce map of Shahrekord’s agricultural lands in two agricultural ... Read More

Applying the SAM Supervised Classification Algorithm to Prepare a Map of Rock Units Using Satellite Images

P Zeaiean Firozabadi; P Safarbeyranvand; A Hosseinhgholizade; R Hasanitabar; M Safarbeyranvand

Volume 10, Issue 1 , June 2018, , Pages 1-16

Abstract

The issue of the mapping of rock units in an ever-expanding area has now reached a point where the detection and classification of rock units is carried out through the aid of hyperspectral image. In this research, Hyperion images are used in the light of the work of previous researchers and the application ... Read More

Modeling of semi-steppe rangelands degradation in Isfahan Province using MODIS images

Fatemeh Hadian; Reza Jafari; Hossein Bashari; Mostafa Tarkesh

Volume 11, Issue 3 , November 2020, , Pages 1-20

https://doi.org/10.52547/gisj.11.3.1

Abstract

Plants are one of the most important components of the ecosystem which are affected by natural and human factors. Therefore, the study of net primary production (NPP) is one of the main subjects in ecology. The main purpose of this research was to model spatial and temporal distributions of NPP ... Read More

Investigating and Modeling the Effect of the Composition and Arrangement of the Landscapes of Yazd City on the Land Surface Temperature Using Machine Learning and Landsat-8 and Sentinel-2 Data

Mohammad Mansourmoghaddam; Iman Rousta; Mohammad Sadegh Zamani; Mohammad Hossein Mokhtari; Mohammad Karimi Firozjaei; Seyed Kazem Alavipanah

Volume 15, Issue 3 , September 2023, , Pages 1-26

https://doi.org/10.48308/gisj.2023.102195

Abstract

علمی - پژوهشی

Effects of Climate Change on Vegetation Phenology of Urmia Lake Basin Using AVHRR Time Series Data

farzaneh hadadi; Davod Ashourloo; Alireza Shakiba; Aliakbar Matkan

Articles in Press, Accepted Manuscript, Available Online from 17 April 2021

https://doi.org/10.48308/gisj.2021.100998

Abstract

Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various ... Read More Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various researches, remote sensing indices are used as one of the new methods in identifying climate change. One of the important indices of remote sensing is the phonological characteristics of vegetation, which in recent studies has shown great potential in identification and estimation of vegetation. In the present study, using the 5-day normalized vegetation index (NDVI) time series of NOAA-AVHRR images and plant phenology parameters, vegetation changes in rangelands and dryland areas of Lake Urmia Basin during 1984-2013 were investigated. Climatic temperature and precipitation data was obtained from the meteorological stations of Lake Urmia basin and was compared with the results of satellite images. The results of time series analysis over thirty years of statistical period in Lake Urmia basin showed that the beginning of the growing season in Oshnavieh, Saghez and Sarab started earlier in 2013 than in 1984. But in the Maragheh area it began later. The end of the growing season in Oshnaviyeh, Saghez and Takab has ended earlier. Also, the peak growth parameter in the above mentioned vegetation reached its maximum value earlier. The length of the growing season has been decreased in the cities of Oshnavieh, Maragheh and Saghez, respectively. The results of statistical analysis obtained from satellite images and climatic data showed that changes in phonological parameters are location dependent and also decreased and increased in cold nights and hot days at the beginning of the growing season, respectively. But at the end of the growing season, the warm days have increased. These changes increased the slope of the plant growth phenology curve at the time of plant aging and ultimately reduced the length of the growing season.

علمی - پژوهشی

Comparison of the efficiency of local climatic zone algorithm in separating built-up area compared ‎to built-up indices

Najme Satari; Malihe Erfani; FATEMEH Jahanishakib

Articles in Press, Accepted Manuscript, Available Online from 20 July 2022

https://doi.org/10.48308/gisj.2022.102576

Abstract

Trend analysis of growth of cities and predicting their changes in the future are essential for spatial ‎planning. For this purpose, it is necessary to map build-up areas. In many areas, especially in arid ‎climate, it is not possible to separate the build-up areas from the surrounding land cover ... Read More

علمی - پژوهشی

Improving the accuracy of ground surface ozone concentration estimation using satellite products and machine learning

Rasoul Atashi Deligani; Mina Moradizadeh; Behnam Tashayo

Articles in Press, Accepted Manuscript, Available Online from 21 September 2022

https://doi.org/10.48308/gisj.2022.102758

Abstract

Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in ... Read More Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in Tehran. For this purpose, pollutant concentration and meteorological data were used along with the satellite product of land surface temperature (LST) in the period from 2015 to 2021. After calculating the correlation between ozone concentration and independent parameters, ozone concentration modeling was done in five different modes in terms of input parameters and learning method and applying data refinement. In the first and second mode, modeling was done using pollutant concentration and meteorological data through multivariate linear regression method. The only difference between these two modes is the filtering of the input data using the WTEST method in the second mode. In the third mode, the LST product was added to the input data, and in the fourth and fifth mode, ozone modeling was done using multilayer neural network and recurrent neural network, respectively. The comparison of the five modes showed that the modeling of the first to fifth stages with adjusted coefficient of determination of 0.5, 0.64, 0.69, 0.74 and 0.8 were able to recover the ozone concentration, respectively. It was also found that among different pollutants, nitrogen monoxide, nitrogen dioxide and nitrox have the greatest impact on ozone concentration, just as temperature, humidity and wind speed are the most influential among meteorological data. Although the use of WTEST statistics led to the identification and elimination of inconsistencies and errors in the observations of pollution measurement stations, the neural network learning method showed better performance in modeling than multivariate regression due to its less sensitivity to noise. As a notable result, adding the LST product to the input data brought a 5% increase in accuracy in estimating ozone concentration.

علمی - پژوهشی

A new forecasting approach using the combination of machine learning to predict flood susceptibility (case study: Karun catchment)

Bhareh gharedaghy; amir ghasemzadeh

Articles in Press, Accepted Manuscript, Available Online from 15 October 2022

https://doi.org/10.48308/gisj.2022.102813

Abstract

Due to its high environmental diversity, Iran has a high rank in crises caused by natural disasters. Flood as one of the natural disasters, following the rapid growth of cities and climate changes in many regions, has caused severe social and economic, health and environmental damage. For this reason, ... Read More

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علمی - پژوهشی

The advances, challenges and perspectives in the correction field of free Night light satellite image

Fatemeh Ahmadi; yasser Ebrahimian Ghajari; Abbas Kiani

Articles in Press, Accepted Manuscript, Available Online from 15 November 2022

https://doi.org/10.48308/gisj.2022.102886

Abstract

Abstract: Remote sensing provides a powerful data source for mapping urban areas and monitoring urban dynamics on various scales. Among remote sensing data, images taken at night provide an effective way to monitor human activities on a global scale. Because the features and capabilities of these images ... Read More Abstract: Remote sensing provides a powerful data source for mapping urban areas and monitoring urban dynamics on various scales. Among remote sensing data, images taken at night provide an effective way to monitor human activities on a global scale. Because the features and capabilities of these images can enable separating urban areas and other human activities, the main feature of which is the use of light at night by accurately measuring the location, from the background without light. Via providing uninterrupted and continuous monitoring from the night world perspective, these images provide valuable source and results of human activities from the past to the present; the time series analysis of this data is highly valuable for discovering, estimating and monitoring social and economic dynamics in countries, especially sub-regions where there are no official statistics. With the development of night data satellite sensors in recent years and new research conducted in the field of night-time data, this study aims to review the advances in night-time sensors, introduce the existing data and products, review and express the advantages and disadvantages of each one and review methods and solutions presented in previous research for solving the existing problems and limitations in order to improve these images. Therefore, according to the reviews of 225 articles on night light from various credible journals, the results demonstrated that 65% of the available articles were from night light data in the urban field (59% related to urban dynamics extracting and studying and 6% for population surveys) and 35% for non-urban field (17% for energy consumption, 13% for economic issues and 5% for other issues such as pollution) in a more detailed look. Articles that have tried to provide methods for correcting night images can be categorized as follows: in the DMSP-OLS data, 44% of the articles were urban surveys, 14% were for economic purposes, 38% for the general purpose of correction and 4% for other purposes. Visible and infrared imaging radiometer suite (VIIRS) data were 57% for economic purposes, 36% for urban surveys and 7٪ for other purposes. The results and findings of this study can provide a general overview for researchers to familiarize and understand the trends of various studies in the use of night light data. It can also help researchers choose the right data and algorithm according to their purpose and study field.

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علمی - پژوهشی

Zoning of soil shear strength parameters (Case study: Kermanshah)

Hassan Sharafi; Reza Faraji

Articles in Press, Accepted Manuscript, Available Online from 15 November 2022

https://doi.org/10.48308/gisj.2022.102887

Abstract

In order to know the site, obtaining soil strength parameters is necessary and at the same time costly and time-consuming. In this research, using 135 geotechnical boreholes drilled in Kermanshah, zonation of soil shear strength parameters (friction angle and cohesion) using ArcGIS software and ordinary ... Read More

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علمی - پژوهشی

Application of Sentinel-2 satellite data in investigating land cover changes in the bed of Anzali wetland

Maryam Haghighi Khomami; Mohammad Panahandeh; Mohammad javad tajaddod; Fariborz Jamalzad Fallah; Mahsa Abdoli

Articles in Press, Accepted Manuscript, Available Online from 16 November 2022

https://doi.org/10.48308/gisj.2022.102888

Abstract

Wetlands as an integral part of the global ecosystem in flood prevention or mitigation, feeding aquifers and providing unique habitat for plants and animals and other services and benefits are key elements of a regional conservation strategy. Anzali International Wetland in Guilan Province is one of ... Read More

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علمی - پژوهشی

An analysis of the trend changes in water resources and factors affecting it in the the central plateau of Iran using satellite products

Mohsen Ebrahimi; Zohre Ebrahimi-Khusfi

Articles in Press, Accepted Manuscript, Available Online from 16 November 2022

https://doi.org/10.48308/gisj.2022.102889

Abstract

The Central Plateau of Iran, due to climate changes and the reduction of available water resources on one hand, and the increase in population and the consequent increase in demand on the other hand, is facing a severe water crisis. The science of remote sensing and the availability of numerous satellite ... Read More The Central Plateau of Iran, due to climate changes and the reduction of available water resources on one hand, and the increase in population and the consequent increase in demand on the other hand, is facing a severe water crisis. The science of remote sensing and the availability of numerous satellite products have made it possible to monitor the process of changes in various environmental parameters, especially surface and underground water sources, with appropriate accuracy. For this purpose, using the Google Earth Engine system, 16 different satellite products including different environmental parameters such as precipitation, temperature, evaporation and transpiration, soil moisture, runoff, total water storage (GRACE), vegetation cover index and water surface area were received and prepared for the time period 2000-2022. Then, using the non-parametric Mann-Kendall test and the Sen’s slope estimator, the change trend of these parameters was investigated. According to the results, the changes in earth's gravity, which indicates the level of underground water, as well as the area of water surfaces, which indicates surface water resources, and soil moisture, showed a significant decreasing trend. On the other hand, maximum temperature, minimum temperature, potential evaporation and transpiration and NDVI index have a significant increasing trend. Despite the decrease in water surface area, the vegetation cover index has increased, which indicates the increase in the area under cultivation of agricultural products and excessive harvesting of underground water resources, which is also confirmed by the decreasing trend of the GRACE satellite product. The correlation coefficients between parameters with significant trends also showed that there is a significant correlation between GRACE and NDVI parameters, minimum temperature, maximum temperature, soil moisture and area of surface water bodies and and in future researches, satellite products related to these parameters can be used to evaluate the condition of surface and underground water resources in other regions and basins of Iran.

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علمی - پژوهشی

Monitoring the changes of vegetation phenological cycles in Ahvaz city using remote sensing images

morteza Sharif; S Attarchi

Articles in Press, Accepted Manuscript, Available Online from 16 November 2022

https://doi.org/10.48308/gisj.2022.102890

Abstract

The phenological cycle of plants plays an important role in the global carbon cycle. Considering the importance of the role of plants in the urban ecosystem and its role in the health of socity, it is necessary to study and monitor the phonological cycle of plants in different seasons of the year in ... Read More The phenological cycle of plants plays an important role in the global carbon cycle. Considering the importance of the role of plants in the urban ecosystem and its role in the health of socity, it is necessary to study and monitor the phonological cycle of plants in different seasons of the year in urban areas at different spatio-temporal scales In this study, the two widely used NDVI and EVI indices calculated from OLI sensor of landsat satellite and MOD13Q1 product of MODIS sensor images were used to investigate the plant phenology cycle in Ahvaz metropolitan area from the period 2015 to December 2019. In this research, satellite images were retrieved and prepared through the Google Earth Engine platform. Then, according to the type of vegetation, the phenological cycle of the plants was obtained based on the vegetation indices and compared with the phenological cycle obtained from the ground surveys. Due to the possibility of noise and pixels with spectral mixing, Savitzky-Golay filter was used to smooth the phenological cycle of plants. The results show the increasing trend in the values of both NDVI and EVI indices by 0.03 and 0.04 in the OLI sensor and 0.01 in the MOD13Q1 product (annually), respectively. These changes were positive in January, March, October, November and December on both sensors. Differences were observed in both sensors during plant phenology phases. The largest difference between two sensors was observed in 2018 and 2019. This shows, in case the weather provides better condition, the plant chlorophyll content will increase. This will lead to the difference between the results of both sensors. The growing season transition periods obtained from the OLI sensor showed more detail than the MODIS medium resolution dataset. The MODIS sensor shows the growing season period start earlier than the OLI sensor. In general, according to the MODIS product, the duration of the growing season (between the beginning of the growing season (mid-winter) and the end of growing season (EOS) (early summer) is four-month. The lowest difference between the periods of the growing season of plants with ground observations in OLI and MODIS sensors, was 7 and 10 daye for Start of growing season (SOS), respectively. The biggest defference was observed at the peak of the growing season with 20 and 35 days, and for the end of growing season (EOS), 20 days later and 20 days earlier, respectively, according to ground observations. However, the length of growing season (LOS) in the OLI sensor is about five months. That the results of OLI sensor are closer to ground observations.This difference is due to the increase in heterogeneous conditions in the target phenomena and/or the spatial resolution of the MODIS sensor images. It is concluded that, the results of the OLI sensor improve our understanding of human interactions with natural environment in urban areas. Therefore, addressing them in future studies can mitigate many environmental challenges and provide more realistic information for planning.

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علمی - پژوهشی

Optimizing the results of ML-Based GMDH algorithm in order to increase the accuracy of dust detection and horizontal visibility depth through TLBO algorithm

مهدی Amiri; Farzad Amiri; Mohammad Hossein Pourasad; Seyfollah Soleimani

Articles in Press, Accepted Manuscript, Available Online from 23 November 2022

https://doi.org/10.48308/gisj.2022.102900

Abstract

Clean air quality, as one of the most essential needs of living organisms, has been compromised by natural and artificial activities. Dust storms have been constantly increasing in recent years, which have resulted in countless social, economic and environmental health damages for residents of southern ... Read More

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علمی - پژوهشی

Spatial analysis and modeling of Covid-19 outbreak using spatial regression techniques

Somayeh Rafati alashti; Abozar Ramezani; Alireza Sadeghinia

Articles in Press, Accepted Manuscript, Available Online from 26 November 2022

https://doi.org/10.48308/gisj.2022.102903

Abstract

The Covid-19 pandemic is considered as a geographical phenomenon, so spatial analysis and its geographical impact on decision-making are very important. Geographic information system and spatial analysis can play an important role in analysis of the spread of Covid-19 at the global level. This study ... Read More

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علمی - پژوهشی

Classification of Hyperspectral Images Using a Combination of Features Extracted from the Weighted Local Kernel Matrix of Spectral and Fractal Features

Behnam Asghari Beirami; Mehdi Mokhtarzade

Articles in Press, Accepted Manuscript, Available Online from 04 January 2023

https://doi.org/10.48308/gisj.2023.103055

Abstract

The use of spatial features to improve the classification accuracy of hyperspectral images has become popular in recent years. Various methods for spectral-spatial classification of hyperspectral images have been introduced to date, and relevant research is being conducted to develop methods with a more ... Read More

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علمی - پژوهشی

Crop mapping using a combination of Sentinel-1 and 2 images in Ardabil province

Ali shamsoddini; Bahar Asadi

Articles in Press, Accepted Manuscript, Available Online from 25 January 2023

https://doi.org/10.48308/gisj.2023.103095

Abstract

Identifying and mapping crops provides important information for managing agricultural lands and estimating the area under cultivation of crops. This study investigates the importance of red edge bands for segregation of crops including wheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet ... Read More

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علمی - پژوهشی

Evaluation of spatio-temporal downscaling algorithms of MODIS data to Sentinel-2 data in different land cover classes

Nahid Haghshenas; Ali Shamsoddini

Articles in Press, Accepted Manuscript, Available Online from 25 January 2023

https://doi.org/10.48308/gisj.2023.103096

Abstract

Normally, images with a high resolution (temporal or spatial) are available, while there is a limitation in accessing images which are simultaneously high spatial and temporal resolution. While, in some applications, access to images with high spatial and temporal resolution is necessary. Therefore, ... Read More

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Original Article

Identifying Spatial and Temporal Patterns of Urban Activities Using Mobile Phone Data

Hanieh Zhendeh Khatibi; Afshin Shariat Mohaymany; Matin Shahri

Articles in Press, Accepted Manuscript, Available Online from 25 January 2023

https://doi.org/10.48308/gisj.2023.103097

Abstract

Recently, the use of big data from mobile devices has received considerable attention in transportation studies. The need to do activities is the main inducement for urban trip generation. In addition, urban activities and their patterns vary over space and time. Mobile phone data, as a kind of continuous ... Read More

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علمی - پژوهشی

effect of salt dust storms on the health of plants in the eastern basin of The Urmia Lake.

fariba gilreyhan; Khalil Valizadeh Kamran; davood mokhtari; ali akbar rasouli

Articles in Press, Accepted Manuscript, Available Online from 25 January 2023

https://doi.org/10.48308/gisj.2023.103099

Abstract

Urmia Lake is one of the largest saltwater lakes in the world, which unfortunately is drying up and has caused many dangers and concerns, especially in relation to salt dust in its dried areas. Therefore, in this research, we tried to investigate the relationship between vegetation and dust in the cities ... Read More Urmia Lake is one of the largest saltwater lakes in the world, which unfortunately is drying up and has caused many dangers and concerns, especially in relation to salt dust in its dried areas. Therefore, in this research, we tried to investigate the relationship between vegetation and dust in the cities around Lake Urmia. Salinity in plants causes physiological disorders, salt stress causes growth, photosynthesis, protein, respiration, energy production, premature senescence, water reduction in plants. Considering these effects, it was tried to evaluate the overall health of plants by using related indicators including NDVI, CIre, GCI, CRI2, NDWI, NDII, MSI, PSRI. These indicators evaluate the amount of plant water, plant water stress, photosynthesis capacity, plant growth and water deficit, the amount of chlorophyll, nitrogen and pigments, which are related to plant energy and health. According to these indicators, the health of plants is generally in a favorable condition, and mostly the highest numerical values of the indicators were assigned to gardens. Using Landsat and Sentinel 2 images and the NDVI index, the vegetation changes of the region were determined in the period from 2010 to 2020, and then using the MERRA-2 database, the amount of dust concentration was also extracted for the mentioned years. The results showed that the average NDVI in the studied area follows a constant trend with an overall average of 0.2957 and sometimes it increases or decreases due to the influence of external factors such as dust. Based on this, the highest (0.3495) average NDVI is related to 2018 and the lowest (0.2579) is related to 2013. Also, two methods of linear and logarithmic regression were used to investigate the relationship between vegetation cover and dust, and the results showed that based on the linear (0.7703) and logarithmic (0.7915) regression, the highest coefficient of explanation between the two mentioned indicators was in May. have been.

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علمی - پژوهشی

Potatoes fields mapping based on the phenology feature and Support Vector Machine utilizing Google Earth Engine platform

Salman Goodarzdashti; Mohamad Seifi; Mahshid Kohandel; Davoud Ashourloo; Hossein Aghighi

Articles in Press, Accepted Manuscript, Available Online from 06 February 2023

https://doi.org/10.48308/gisj.2023.103148

Abstract

Potatoes are the fourth most cultivated crop worldwide. Regarding the strategic role of this crop in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation /prediction at any scale. Although remote sensing (RS) approaches based on ... Read More Potatoes are the fourth most cultivated crop worldwide. Regarding the strategic role of this crop in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation /prediction at any scale. Although remote sensing (RS) approaches based on optical and/or microwave sensors have been widely employed to monitor cultivated land (including crop area, type, condition, and yield forecasting), the identification of potato planting areas using RS data has not been much addressed. Hence this study addresses the literature gap by suggesting an effective potato mapping approach that uses the time series of the Sentinel-2 (S2) images, Google Earth Engine (GEE) platform and machine learning methods. Since most crops have specific spectral and temporal characteristics during the growing season, this research has presented a method to discriminate potato fields from other crops using time series images without explicit thresholding. We employed 1648 ground truth data to optimize, train, and evaluate the model at the study site, which includes potatoes and other fields. A handheld GPS receiver was used to collect these data. The performance of this approach is evaluated by conducting a set of experiments in Hamedan and Bahar cities, as the regions grow more potatoes than any other places in Iran. Accurate identification of potato fields was completed by extracting the required features, namely the potato phenology feature and NDVI medians, from the time series of the S2 satellite bands. After that, these features were utilized as the input parameters to Support Vector Machine (SVM) technique. In order to train the most optimal SVM model using RBF kernel, Gamma and C values were optimized with the help of the 5-fold cross-validation method. These values were then employed during the algorithm's implementation on GEE platform. The estimated overall accuracy and Kappa coefficient are 90.9% and 0.82 for Hamedan and 93.3% and 0.87 for Bahar, respectively. The results of this research indicate the efficiency of SVM technique in potato acreage mapping. Moreover, the selected features such as potato phenology feature can be considered as discriminating features for improved identifying of crop farms.

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علمی - پژوهشی

Comparison of four PLSR, RF, GRNN and SVR algorithms to estimate sugarcane sheath moisture during growing season using Sentinel-2 satellite imagery

maryam soltanikazemi; Saeid Minaei; Hossein Shafizadeh Moghadam; AliReza Mahdavian

Articles in Press, Accepted Manuscript, Available Online from 08 February 2023

https://doi.org/10.48308/gisj.2023.103163

Abstract

Sheath moisture is an important parameter during the growth period of sugarcane, which is of special importance from the perspective of water stress and field irrigation management. Remote sensing data has a high capacity to update crop growth monitoring systems. In this regard, satellite images that ... Read More

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علمی - پژوهشی

Modeling the spatial distribution of Dengue fever disease in Iran using Maxent entropy model and Genetic Algorithm

Mohammad Karimi; Sajjad Haghi; Ahmad ali Hanafi Bojd

Articles in Press, Accepted Manuscript, Available Online from 07 March 2023

https://doi.org/10.48308/gisj.2023.103268

Abstract

Dengue fever is Chickenpox is a contagious and viral disease that is transmitted by two species of Aedes Egyptian mosquitoes and Aedes albopicus mosquitoes and is spreading rapidly in the world. The main purpose of this study is to model the spatial distribution of carriers of this disease in Iran. Due ... Read More Dengue fever is Chickenpox is a contagious and viral disease that is transmitted by two species of Aedes Egyptian mosquitoes and Aedes albopicus mosquitoes and is spreading rapidly in the world. The main purpose of this study is to model the spatial distribution of carriers of this disease in Iran. Due to the lack of sufficient carrier data in the country, carrier data available worldwide and also in Asia, in two different scales were used. Among the most important aspects of this research, we can name the use of heterogeneous layer as an auxiliary factor to analyze the presence points and reduce spatial autocorrelation and the use and comparison of two species distribution models based on presence data for Selecting the optimal modeling method. In this regard, first, using the maximum entropy method (MaxEnt) and a type of genetic algorithm called GARP (GARP), the level of habitat suitability in the world with a spatial resolution of 5 km for both species was modeled. To evaluate the mentioned models, the variables of population density, climate, vegetation density, altitude and soil organic carbon were considered. Due to the high accuracy of the MaxEnt method, using this method, the habitat suitability of the Asian continent with a resolution of 900 m was modeled for both species. The values under the curve (AUC) for the two types of carriers were calculated to be 0.9. The results showed that the northern and southern provinces of the country have higher habitat suitability for both species, with the difference that Aedes aegypti species in the southern to eastern parts of the Oman Sea coast has a higher probability of distribution. In implementing the MaxEnt method for Aedes albopicus, the provinces in western Iran were also identified as desirable, which was not properly modeled on a smaller scale. In February 2016, unfortunately, a small number of mosquitoes and AIDS mosquito eggs were discovered in Bandar Lengeh, which was exactly what this research had predicted. The results of this study can be used in line with planning for population management of these vector insects to control the disease at the same time as monitoring populations during epidemic seasons.

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علمی - پژوهشی

Evaluation of the effect of drought on vegetation in Iran using satellite images and meteorological data

mostafa karampour; samad khosravi yeganeh; Behrouz Nasiri

Articles in Press, Accepted Manuscript, Available Online from 18 April 2023

https://doi.org/10.48308/gisj.2023.103394

Abstract

In the coming years, drought is one of the potential threats to low rainfall countries, especially Iran. Iran has always faced big and small droughts. The geographical location and natural conditions of the country are such that severe and weak droughts have occurred in it. Evaluating the effects of ... Read More

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علمی - پژوهشی

An Improved Approach for Spectral-spatial Classification of Hyperspectral Images using Weighted Genetic and Neural Network Algorithms

Davood Akbari; Ali Ashrafi; Mostafa Yaghoobzadeh

Articles in Press, Accepted Manuscript, Available Online from 18 April 2023

https://doi.org/10.48308/gisj.2023.103395

Abstract

The Hyperspectral remote sensing technology has many applications in classifying land covers and studying their changes. With recent developments and the creation of images with high spatial resolution, the simultaneous use of spectral and spatial information in the classification of hyperspectral images ... Read More

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علمی - پژوهشی

Building Detection with Special Roofing in Hyperspectral Images using Marker-based Hierarchical Algorithm

Davood Akbari

Articles in Press, Accepted Manuscript, Available Online from 25 April 2023

https://doi.org/10.48308/gisj.2023.103401

Abstract

The complexity and large volume of data from hyperspectral sensors have led to the consideration of more specialized and advanced methods of data analysis in order to extract information. One of the analyzes performed on hyperspectral images is target detection. With recent developments and the creation ... Read More

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علمی - پژوهشی

Investigating the trend of night temperature changes in Iran and the effect of carbon dioxide

Sajedeh Morady; Majid Rezaeibanafsheh

Articles in Press, Accepted Manuscript, Available Online from 31 May 2023

https://doi.org/10.48308/gisj.2023.103545

Abstract

In this article, night temperature changes in relationship with CO2 emissions in Iran were studied using AIRS satellite images and time series graphs, regression, correlation and Mann-Kendall tests and maps. This research was conducted for the statistical period of 2003-2016 and the months of January, ... Read More

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علمی - پژوهشی

Using network analysis process model and fuzzy techniques in order to determine the optimal range of construction of 63.20 kV substation in GIS platform

Abolfazl Habibitabar; sahar Alian; Davoud Soleimanian

Articles in Press, Accepted Manuscript, Available Online from 20 June 2023

https://doi.org/10.48308/gisj.2023.103606

Abstract

The energy injection point from the transmission network to the distribution network is the 63.20 kV substations. Determining the location of the substation is technically and economically important for regional electricity companies and electricity distribution companies. The purpose of this research ... Read More

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علمی - پژوهشی

Evaluation of classical statistical methods for estimating and reconstructing the daily temperature in Iran

Mohammad Khaledi; Ghasem Zarei

Articles in Press, Accepted Manuscript, Available Online from 24 July 2023

https://doi.org/10.48308/gisj.2023.103725

Abstract

In order to be able to make decisions and manage in the field of sustainable development of natural resources, knowledge of climatic parameters is one of the most important and effective topics of this matter. Meanwhile, the temperature parameter, as one of the most influential climate parameters, plays ... Read More

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علمی - پژوهشی

Modeling the concentration distribution of NO2 and O3 pollutants with an appropriate spatial resolution by combining ground and satellite data.

Amir Hadian; Mina Moradizadeh

Articles in Press, Accepted Manuscript, Available Online from 24 July 2023

https://doi.org/10.48308/gisj.2023.103726

Abstract

Air pollution is one of the most important crises that most countries are facing today due to the progress of industry and technology. The country of Iran and especially the city of Tehran is not exempt from this phenomenon. Air pollution measurement stations at city level, in spite of the high accuracy ... Read More

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علمی - پژوهشی

Identifying and monitoring urban growth and expansion changes using spectral un-mixing of remote sensing imagery and geographic information systems techniques at the micro level, (A case study of Amol city)

Sedigheh Lotfi; Toheed Alizadeh

Articles in Press, Accepted Manuscript, Available Online from 24 July 2023

https://doi.org/10.48308/gisj.2023.103727

Abstract

Land is a vital natural resource for human survival and the basis of all ecosystem services. However, land degradation in the form of land use and land cover changes has been a severe problem in the world. Rural-to-urban migration in developing countries, as well as the conversion of many nearby villages ... Read More

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علمی - پژوهشی

Sensitivity analysis of the spectral response of the plant leaf to the biophysical-biochemical variables using the PROSPECT radiative transfer model

M Hajeb

Articles in Press, Accepted Manuscript, Available Online from 24 July 2023

https://doi.org/10.48308/gisj.2023.103728

Abstract

To determine the effect of the leaf biochemical contents on its spectral reflectance behavior via remote sensing (RS) can help to understand the process of the ecosystem and its parameters such as plant water stress. The present study aimed to do a quantitative analysis of the effect of leaf parameters, ... Read More

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علمی - پژوهشی

Evaluation of environmental criteria affecting citizens' health using geographic information system (case example: Agheh Zaman neighborhood of Sanandaj city)

Mir Saeed Moosavi; Aida Ghafoori; Mahsa Faramarzi Asl

Articles in Press, Accepted Manuscript, Available Online from 17 October 2023

https://doi.org/10.48308/gisj.2023.231601.1157

Abstract

The problems in urban life, such as the diminution of the role of the environment as a place for the presence of citizens and their physical activity, the increase in vehicles and as a result inactivity and the increase in non-communicable diseases, have caused the concern of the world community about ... Read More The problems in urban life, such as the diminution of the role of the environment as a place for the presence of citizens and their physical activity, the increase in vehicles and as a result inactivity and the increase in non-communicable diseases, have caused the concern of the world community about public health. The aim of the research is to evaluate subjectively and objectively the environmental criteria affecting the general health of the residents in three physical, psychological and social dimensions in Agha Zaman neighborhood of Sanandaj city. The collection of subjective and objective research data was done sequentially in terms of time, in this regard, four phases of the research were defined. The first phase is the subjective qualitative assessment of the residents regarding their mental and social health, the second phase is the collection of quantitative subjective data of the residents regarding their physical health, the third phase is the quantitative objective assessment of the environment, and the fourth phase is the examination of the correlation between the research variables. Subjective data was obtained using a questionnaire and objective data was obtained using a geographic information system. To analyze the relationship between neighborhood environmental criteria and physical activities and to determine the relationship between variables, regression analysis was used in SPSS software. Objective data such as the type of block arrangement and the example of the spatial pattern of the neighborhood, the land use layer and the road network, etc., were calculated by the geographic information system and entered into the index of space syntax to determine the pedestrianization of the neighborhood. Finally, the relationships and effects of environmental criteria on public health have been identified using regression analysis in Lisrel software. The results showed that the two measures of comfort and tranquility of the environment with a score of 23 and social interactions and neighborhood culture with a score of 21 respectively have the greatest impact on mental and social health. Also, environmental criteria such as mix of uses with a score of 5.671, visual and aesthetic qualities with a score of 7.961, and special infrastructure for pedestrians and bicycles with a score of 8.475 have the greatest impact on physical work, leisure and sports activities, respectively. As a result, they have physical health. According to the obtained data, Namaki Street with a score of 21 has the highest level of connection and interconnection, depth and control with the whole neighborhood and has the highest level of pedestrian circulation in the neighborhood. The general results show that due to the direct impact and relationship of environmental criteria of urban design on physical activities and on the other hand, the existence of a positive and meaningful relationship between physical activities and general health, it was found that the general health of residents is related to the environmental criteria of urban design. Yes, but this relationship does not happen directly but with the intervention of physical activities.

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علمی - پژوهشی

Spatial Monitoring of Drought Using Indicators of Remote Sensing, case study: Sistan and Baluchestan Province

کمال امیدوار; massumeh nabavi zadeh

Articles in Press, Accepted Manuscript, Available Online from 17 October 2023

https://doi.org/10.48308/gisj.2023.231123.1156

Abstract

Due to the statistical limitations of the data of the Grace satellite, a common time period of 16 years was chosen from 2002 to 2017. Two indices VHI and TSDI were obtained a high correlation in the south, center and some areas of the north. Finally, results of the research were validated by GLDAS ... Read More

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Original Article

Land use change detection based on satellite images in the Haraz watershed plain

Fatemeh Shokrian; Karim Solaimani

Articles in Press, Accepted Manuscript, Available Online from 27 November 2023

https://doi.org/10.48308/gisj.2023.232979.1176

Abstract

Investigating land use changes requires the integration of layers in a certain period. This research aims to investigate land use changes in Haraz Plain from 1980 to 2021. Therefore, Landsat data was used to measure the changes. By applying atmospheric, geometric and radiometric corrections, image enhancement ... Read More

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علمی - پژوهشی

Seismic hazard assessment using arithmetic-weighted overlay method based on earthquake potential index (EPI), the southwestern Iran

sasan motaghed; amin nakhlian; lotfolla emadali; nasrolla eftekhari; heshmatalla mahmoudian

Articles in Press, Accepted Manuscript, Available Online from 29 November 2023

https://doi.org/10.48308/gisj.2023.229646.1133

Abstract

In this study, an integrated geographic information system (GIS) has been used to assess the seismic hazard in the southwestern region of Iran. In data preparing, the important factors in earthquake hazard were identified and relevant thematic data layers including digital elevation model (DEM), the ... Read More

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علمی - پژوهشی

Estimating the runoff coefficient by combining Arc CN-Runoff, SCS-CN and ICAR empirical relationship (Case study: Selseleh Study area - Lorestan province)

yazdan yarahmadi; hojatolah younesi; Ahmad Godarzi; saeed rostami

Articles in Press, Accepted Manuscript, Available Online from 29 November 2023

https://doi.org/10.48308/gisj.2023.228883.1120

Abstract

Determining the value of the runoff coefficient is one of the biggest problems and the main source of uncertainty in many water resources projects. The aim of the current research is to estimate the runoff coefficient by combining Arc CN-Runoff, SCS-CN and ICAR experimental relationship in the Selseleh ... Read More Determining the value of the runoff coefficient is one of the biggest problems and the main source of uncertainty in many water resources projects. The aim of the current research is to estimate the runoff coefficient by combining Arc CN-Runoff, SCS-CN and ICAR experimental relationship in the Selseleh watershed. Selseleh Study area is located in the north of Lorestan province and is one of the sub-watersheds of Kashkan. In order to carry out this research, data including digital elevation model information, land use classes, soil texture and meteorological and hydrological statistics (rainfall and runoff) related to the research area were used. The map of the land use layer and soil hydrological groups was entered into the Arc CN-Runoff tool environment, and Intersect was applied on two layers the Land soil layer was prepared, and the runoff coefficient map was prepared based on this layer. Finally, the runoff coefficient was estimated in three conditions dry, medium and wet moisture conditions and a comparison was made. The results of the research showed that the runoff coefficient (CR) in the Selseleh Study area is 0.26, 0.53, and 0.77, respectively, in dry, medium, and wet conditions. Therefore, the dry humid state has decreased by 68% compared to the average, and the more humid state has increased by 37% compared to the average. Investigating the correlation between the runoff coefficient and watershed characteristics showed that the runoff coefficient is influenced by the six physiographic features of the Study area: area, slope, length of the waterway and Gravelius coefficient, the maximum height and density of the waterway, which has a significant effect on the value of the runoff coefficient in this Study area. The value of the runoff coefficient in the ICAR method for the entire Study area was 0.48. In the Selseleh Study area, the runoff coefficient has a decreasing trend from April to September. The type of land and soil use in the basin under study is one of the influential factors that affect the runoff coefficient and, consequently, the peak discharge of the Study area. Also, in early spring, the flood potential is high in the said Study area. Among the measures to increase water infiltration are the establishment of a rainwater collection system and the operation of the Farrow meter along with the increase of plant cover with seeding and planting and intercropping of pasture plants.

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علمی - پژوهشی

Land use and land cover classification by combining GLCM, SNIC, and machine learning algorithms in Google Earth Engine environment (case study: part of the lands of North Mahabad, West Azerbaijan)

mahdi naderi

Articles in Press, Accepted Manuscript, Available Online from 29 November 2023

https://doi.org/10.48308/gisj.2023.233271.1181

Abstract

In recent decades, land use and land cover changes information has been successfully derived from remote sensing data at various levels, from local to global scale. Accurate and frequent monitoring of these changes is required for urban planning and sustainable management of land resources. In this study, ... Read More In recent decades, land use and land cover changes information has been successfully derived from remote sensing data at various levels, from local to global scale. Accurate and frequent monitoring of these changes is required for urban planning and sustainable management of land resources. In this study, an object-oriented approach using a combination of GLCM, SNIC, and machine learning algorithms is presented to classify the LULC of a part of the lands of North Mahabad, West Azerbaijan, in 2019 using satellite images in Google Earth Engine. For this purpose, after preparing the initial dataset, which contains the bands of Sentinel-1 and Sentinel-2 images, the ALOS digital surface model, and NDVI, BSI, SAVI, and total scattering power indices, two pixel-based and object-oriented approaches, as well as the random forest algorithm, were used to classify land use and land cover, and their results were compared to explain the best approach in terms of the accuracy of the various classes. In the object-oriented approach, textural measures were extracted by applying the GLCM matrix to the initial dataset. Due to the increase in the number of bands, the PCA method was used to reduce the dimensions of the image. Finally, by combining the segmentation layer obtained from the SNIC algorithm and the PC1 layer, the random forest algorithm was considered to produce land use and land cover maps of the study area. According to the research findings, the object-oriented approach performed better than the pixel-based approach in classifying various land use classes in the study area, with an overall accuracy and kappa coefficient of 86.40% and 0.8307, respectively, compared to 82.73% and 0.8028. The results of the accuracy evaluation criteria showed that the producer accuracy of most of the classes except for corn, fall irrigated vegetables, and wheat, and barley irrigated in the object-oriented approach was higher than the pixel-based method, and their classification accuracy was more than 90%. Additionally, water, build-up, corn, and sugar beet classes have the highest user accuracy in the object-oriented LULC map. The findings showed that the appropriate determination of the super-pixel size of the SNIC clustering algorithm and the use of GLCM texture criteria effectively improved the performance of the proposed approach in land use and land cover classification.

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Volume 1 (2009)

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