Maximum Likelihood Classification Method Research Articles

Analysis of Land Cover Change in Karanganyar District in 2013 and 2022 Based on Story Maps

Abstract Land use change is influenced by human activities that affect the land cover of an area. Most of the Karanganyar Regency area is still agricultural land and as the population increases from year to year, it has an impact on land conversion. The purpose of this study is to analyze land cover changes in Karanganyar Regency in 2013 and 2022, analyze the factors that influence land change, and present information on land cover changes using Story Maps. The research method used qualitative descriptive analysis with the Maximum Likelihood Classification method and the results tested for accuracy were obtained from the Kappa method. The results showed that land cover change from 2013 to 2022 was dominated by residential land and paddy fields as much as 11946.10 ha or 14.87% and 14777.35 ha or 18.40%. Factors of land cover change are population growth, relatively cheap land value, easy accessibility, and easy licensing mechanism for infrastructure development. The land cover of Karanganyar Regency from 2013 to 2022 is represented in Story Maps, displaying maps and analysis with an attractive and interactive display.

Spatiotemporal Land Use/Land Cover Changes and Impact on Urban Thermal Environments: Analyzing Cool Island Intensity Variations

This study pioneers the comprehensive evaluation of the spatiotemporal evolution of land use/land cover (LULC) in Hangzhou city, introducing the novel water body shape index (WBSI) to analyze its seasonal impacts on the urban thermal environment and urban cool island (UCI) effects, uncovering distinct patterns of thermal regulation. It particularly investigates how distance gradients and the water body shape index (WBSI) influence land surface temperature (LST) in the urban core. The region’s climate, featuring hot summers and cold winters, highlights significant seasonal LST variations. Addressing a gap in existing UCI research, the analysis extends beyond the typical large-scale planning focus to include small-scale, high-resolution aspects. Employing remote sensing and geographic information system (GIS) analysis techniques, this study analyzes the seasonal dynamics in Hangzhou’s central urban area. High-resolution LST data, obtained through single-channel inversion and resolution enhancement algorithms, are crucial to this analysis. This study employs the maximum likelihood classification method to analyze land use and land cover changes from 1990 to 2020. This analysis reveals potential drivers of urban thermal environment changes, such as the expansion of residential and commercial areas and the reduction in green spaces. Different regions in LST data are delineated to assess the cool island effect, and the complexity of water body boundaries is quantified using the water body shape index. Spatial and temporal patterns of LST changes are investigated using multivariate regression and time-series analysis models. We identified significant changes in LULC over the past 30 years in Hangzhou, closely correlating with a continuous rise in LST. This observation underscores a clear finding: the strategic importance of blue–green infrastructure in mitigating urban heat, a novel insight that extends the current understanding of urban thermal dynamics. A clear and novel finding of this study is that the intensity of the cool island effect from large water bodies not only diminishes with distance but is intricately influenced by the complexity of their shapes, as quantified by the WBSI, whereas the complexity of their boundaries enhances this effect. Additionally, the regulatory role of the cool island effect is observed to vary seasonally, being most pronounced in summer and less so in autumn and winter, thereby demonstrating a positive impact. In conclusion, our findings innovatively highlight how the specific shapes of water bodies, quantified through the water body shape index (WBSI), emerge as critical, yet previously underappreciated, drivers in modulating the urban thermal environment. This underscores a new avenue for urban planning, advocating for the strategic design of water bodies within urban landscapes. It also finds that spatial factors and seasonal variations significantly affect the intensity of the cool island effect. These findings offer valuable evidence for urban planning and climate change adaptation, emphasizing balancing natural elements with the built environment in urban design.

Identifying soil groups and selecting a high-accuracy classification method based on multi-textural features with optimal window sizes using remote sensing images

Determining the spatial distribution of soil groups accurately is crucial for managing soil resources. However, limitations persist in the mapping of soil groups using multi-textural features derived from remote sensing images. Identification of the optimal window size for multi-textural feature extraction and the most effective classification method for soil group recognition using remote sensing multi-textural features remains unresolved. In this study, we investigated soil groups in a representative area of the Jiaodong Peninsula. We extracted the mean and entropy texture parameters for various window sizes (3 × 3 to 25 × 25 in odd increments) from Landsat 8 images to determine the optimal sizes for multi-textural feature extraction. The efficacy of identifying soil groups via textural features was analyzed using maximum likelihood classification (MLC), support vector machine (SVM), artificial neural network (ANN), and random forest (RF) methods to ascertain the most suitable classification approach. The results indicate that the optimal window sizes were 19 × 19 for the mean parameter and 23 × 23 for the entropy parameter. The SVM method outperformed the MLC, ANN, and RF methods in terms of the classification accuracy. Notably, the SVM classification method reached a peak accuracy of 71.61% when combining multi-textural features with the optimal windows. This demonstrates the feasibility of different soil groups using multi-textural information from remote sensing images. These findings have notable implications in guiding digital soil mapping using multi-textural features.

Directions of land degradation in the greater Istanbul metropolitan area: A view from four decades

AbstractIn an era of dynamic urbanization, especially in densely populated regions such as the Greater Istanbul Metropolitan Area, land degradation (LD) is a widespread and multifaceted phenomenon. Hence, there is a need for monitoring LD to track the direction of urban sprawl and respond rationally. In this context, the purpose of this study was to investigate the magnitude and directions of LD in Istanbul from 1984 to 2022. The originality of the study lies in the development of the CAI_A index, which is a quantitative measure of the degree of anthropogenic impact. This study utilized Landsat 5 and Landsat 8 satellite imagery from 1984, 2002, and 2022, employing a maximum likelihood classification method to determine land cover and land use changes. The results show that the area of LD by anthropogenic activity has increased by an average of nearly 40 percentage points in each district of Istanbul over the past four decades, significantly impacting both natural and semi‐natural areas. Furthermore, a noteworthy observation was made regarding forests in the northern part of the study area, which demonstrated stability and resilience to LD. The results of the study are important for the urban environment because they facilitate a better understanding of the impact of human activities on mitigating LD in the context of climate change, among other things. Moreover, the results identify specific areas in need of targeted monitoring and remedial actions regarding LD.Research Highlights Istanbul's urbanization has been consistent since 1984. Urban expansion primarily contributes to agricultural land degradation. Land degradation clusters near settlements and on the southern coast. State forests are crucial for preserving natural areas and resisting degradation. Forest services are threatened by fragmented urban expansion.

Application of NDVI Sentinel-2Ac Imagery for Agricultural Land Development in Food Buffer Area of New Capital City Indonesia

Paser Regency is one of the food buffer areas for the New Capital City (IKN) of Indonesia in East Kalimantan Province. The relocation of the capital to IKN increases the number of populations and food demand as well. To meet food needs in IKN in the future, the development of agricultural land is needed. This study aimed to identify the type and direction of land use in Paser Regency to ensure food self-sufficiency. Sentinel-2 satellite Imagery with the Maximum Likelihood Classification (MLC) method was used in this study. The sentinel data from 2018 to 2022 was analyzed to identify the existing land use types over the past five years. The land use direction was determined based on the existing land use type, soil type, and land slope class. The results with an accuracy value for the overall test of 91.68% and kappa accuracy of 0.86 showed a change in land use from 2018 to 2022. Eight types of main land used were identified such as non-swamp rice fields, swamp rice fields, oil palm plantations, moors, shrubs, forests, settlements, and water bodies. The application of NDVI Sentinel-2Ac Imagery can identify a potential area of 13,249.7 hectares for rice field development and can significantly increase rice production to ensure food self-sufficiency in IKN.

Land Cover Change Analysis on The Coast of Surabaya Indonesia (Case Study: Eastern Part of Surabaya, 1994 – 2022)

Surabaya is one of the coastal cities which experiences regular land cover changes. The construction of new settlements and changes in land use functions have a significant impact on the coastal environment of Surabaya. This research is a development of previous research which aims to determine changes in land cover on the coast of Surabaya (especially on the Eastern Part of Surabaya) using remote sensing data. Landsat satellite imagery data on 1994, 2003, 2018 and 2022 were used with maximum likelihood classification method. The results showed that the Surabaya coast experienced changes in various land cover classes (urban areas, vegetation, mangroves, ponds, bare land and water bodies). During 1994-2022, Surabaya’s coastal area increased by 10.72 km2 and about 13 km2 pond area is reduced due to changes in coastal land use functions. Eastern part of Surabaya is coastal area which growing rapidly due to the accretion phenomenon (especially in the Keputih and Medokan Ayu sub-districts). The average accretion and erosion rate was measured using DSAS analysis through 1994 to 2022. The average accretion and erosion were showed the value respectively 21.69 m/year, and −2.43 m/year. The coast of East Surabaya contributes 50% of mangrove forests existence in Surabaya.

EVALUATION AND PREDICTION OF LAND USE, AND LAND COVER CHANGES USING REMOTE SENSING AND CA-ANN MODEL IN HUONG HOA DISTRICT, QUANG TRI PROVINCE

Evaluation of land use and land cover change (LULC) is necessary for densely vegetated areas like Huong Hoa district, Quang Tri province. It is a basis for sustainable development strategies. Therefore, the study aims to evaluate the LULC change in the 10-year period of 2013–2023 by using Landsat 8 satellite image data with the Maximum Likelihood Classification method and to predict future LULC changes. The LULC maps for 2013, 2018, and 2023 are accurate, with Kappa coefficients of 0.82–0.85. In the period of 2013–2023, the dense vegetation area tended to decrease by 1.4%. The decrease was mainly due to the transition to sparse vegetation cover. Bare land increased by 0.5%, and the built-up area decreased by 0.6%. Meanwhile, the water bodies changed slightly. The prediction of LULC change with the CA-ANN model in the QGIS MOLUSCE plugin is based on the history of LULC change and two spatial variables: DEM and distance to the road. The accuracy of the CA-ANN model is satisfactory, with an overall accuracy of 86% and a Kappa coefficient of 0.76. In the simulated LULC of 2033, dense vegetation is predicted to keep a higher decrease (2%) in the area compared with the LULC of 2023. Sparse vegetation steadily increased by 1.3% over the subsequent 10 years. Similarly, the built-up area, water boddies, and bare land extended slightly by 0.5, 0.1, and 0.1%, respectively. The CA-ANN model in the QGIS MOLUSCE plugin is suitable for the simulated LULC changes for the studied area.

Satellite-Based Monitoring of Growing Agricultural Water Consumption in Hyper-Arid Regions

Land-use change has a key role in hydrologic processes and biodiversity. Although many satellite-based studies have been conducted to reveal the interaction between land-use changes in hydrological processes worldwide, the land-use change impact on agricultural water consumption in hyper-arid regions is poorly understood. Here, we investigate increased agricultural water consumption in the Qom province, a hyper-arid region in Iran, using derived imageries from Landsat 5 Tm and Landsat 8 OLI during the last three decades. We used maximum likelihood classification (MLC) and decision tree classification (DTC) to analyze the satellite images. The MLC method showed that croplands have increased from 30,547 ha in 1989 to 39,255 ha in 2019 (i.e., a 29% increase). In this period, the total orchard area increased from 3904 ha to 6307 ha, revealing a growth of 61%. In the DTC method, the increases in the cropland and orchard areas were, respectively, 34% and 60%. Although both MLC and DTC satisfied the accuracy criteria, the former was more consistent than the latter concerning ground data and documented statistics. Satellite-based and MLC results showed an increase in agricultural water consumption from 152 million cubic meters (MCM) in 1989 to 209 MCM in 2019, showing a 38% increase (i.e., 1.27% annually). Our findings send an alarming message for policymakers to reduce the water demand through sustainable agricultural practices in the Qom province, where the agricultural sector uses approximately 90% of annual water demand.

Land Cover Classification Using Sentinel 2A Image in Kolaka Subdistrict, Kolaka Regency, Southeast Sulawesi

Land cover in Kolaka District continues to change. Mapping and identifying land cover types using the Maximum Likelihood method is more accurate than other methods. This research aims to analyze the capabilities of Sentinel 2A Imagery and the Maximum Likelihood classification method for mapping and identifying land cover types in Kolaka District. This research was carried out from July to September 2023 and was carried out in 4 stages, namely the first stage of image pre-processing by carrying out the layer stacking process. The second stage is image analysis and classification. The third stage is carrying out a Ground Check, and the fourth stage is validation and accuracy testing. The value of the accuracy test results with Overall Accuracy (OA) is 88.75% which is in the good category. The results of the land cover classification obtained 8 land cover classes, namely secondary dry land forest covering an area of 3974.20 Ha or 31.84%, plantation land cover covering an area of 3,886.87 Ha or 31.14%, dense bushes covering an area of 1,641.42 Ha or 13.15%, mixed dry land agricultural land cover covering an area of 1,415.62 Ha or 11.34%, residential land cover of 744.26 or 5.96%, paddy field cover of 613.53 Ha or 4.92%, open land cover of 148.66 Ha or 1.19% and water body land cover of 56.22 Ha or 0.45% of the total area of Kolaka District.

A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps

Cryo electron microscopy (cryo-EM) is used by biological research to visualize biomolecular complexes in 3D, but the heterogeneity of cryo-EM reconstructions is not easily estimated. Current processing paradigms nevertheless exert great effort to reduce flexibility and heterogeneity to improve the quality of the reconstruction. Clustering algorithms are typically employed to identify populations of data with reduced variability, but lack assessment of remaining heterogeneity. Here we develope a fast and simple algorithm based on spatial filtering to estimate the heterogeneity of a reconstruction. In the absence of flexibility, this estimate approximates macromolecular component occupancy. We show that our implementation can derive reasonable input parameters, that composition heterogeneity can be estimated based on contrast loss, and that the reconstruction can be modified accordingly to emulate altered constituent occupancy. This stands to benefit conventionally employed maximum-likelihood classification methods, whereas we here limit considerations to cryo-EM map interpretation, quantification, and particle-image signal subtraction.

Towards the Early Detection of Gymnodinium catenatum Algal Blooms in the Northern Gulf of California

The annual occurrence of harmful algal blooms (HABs) of the dinoflagellate Gymnodinium catenatum in the northern Gulf of California (NGC) during winter and spring has negative ecological, economic, and social impacts on the local coastal population. G. catenatum produces paralytic shellfish toxins, and a robust monitoring program of the species is necessary to sustain mitigation actions against their detrimental effects. Here, we applied the maximum-likelihood classification (MLC) method to classify satellite images from MODIS and Sentinel-3 to evaluate their effectiveness to detect G. catenatum. Different classes associated with the presence of the species were developed from data of two HABs that occurred in 2015 and 2017. Two classes derived from Sentinel-3 data from the 2017 HAB allowed the detection of this species. These Sentinel-3 classes adequately represented the temporal and geographical distribution of G. catenatum in the region and the no-bloom condition during the summer. The concordance between the detection of the Sentinel-3 classes on the west coast of the NGC and the recorded presence of G. catenatum (75% of concordance) in the area indicates that the MLC method could be applied for early detection of the species in the NGC, using Sentinel-3 full resolution images.

The Implementation of Classification and Spectral Transformation Methods, Based on Extraction Landsat Imagery for Growth Center Mamminasata Region

Urban growth begins from a center and affects its surrounding area, and this is due to the emergence of additional centers that will serve as a growth pole. Assessment of the dynamics of urban growth, some data, and extraction analysis methods are needed. This research examines various information by extracting of growth center area of Landsat 8 OLI / TIRS 2017 and utilizing spectral resolution on Landsat images in the Mamminasata region. The classification method tested is the OBIA classification and the Maximum Likelihood classification methods. Simultaneously, the Landsat image’s spectral resolution is maximized by spectral channel transformation sensitive to the building object. It is usually called the urban index. This study indicates that the OBIA classification method and NDBI spectral transformation proved to produce maps. It can also show center location growth with the best accuracy, especially in the Mamminasata region. Total accuracy from the accuracy-test matrix is 71.48% for building intensity and 78.57% for growth centers.

Spatio-Temporal Variability Analysis of Vegetation Dynamics in China from 2000 to 2022 Based on Leaf Area Index: A Multi-Temporal Image Classification Perspective

Leaf Area Index (LAI) is one of the most important biophysical parameters of vegetation, and its dynamic changes can be used as a reflective indicator and differentiation basis of vegetation function. In this study, a VCA–MLC (Vertex Component Analysis–Maximum Likelihood Classification) algorithm is proposed from the perspective of multi-temporal satellite LAI image classification to monitor and quantify the spatial and temporal variability of vegetation dynamics in China since 2000. The algorithm extracts the vegetation endmembers from 46 multi-temporal images of MODIS LAI in 2011 without the aid of other a priori knowledge and uses the maximum likelihood classification method to select the categories that satisfy the requirements of the number of missing periods, absolute distance, and relative distance for the rest pixels to be classified, ultimately dividing the vegetation area of China into 10 vegetation zones called China Vegetation Functional Zones (CVFZ). CVFZ outperforms MCD12Q1 and CLCD land cover datasets in the overall differentiation of vegetation functions and can be used synergistically with other land cover datasets. In this study, CVFZ is used to cut the constant vegetation-type pixels of MCD12Q1 during 2001–2022. The results of the LAI mean time series decomposition of each subregion using the STL (Seasonal-Trend Decomposition based on Loess) method show that the rate of vegetation greening ranges from 9.02 × 10−4 m2m−2yr−1 in shrubland subregions to 2.34 × 10−2 m2m−2yr−1 in savanna subregions. In relative terms, the average greening speed of forests is moderate, and savannas tend to have the fastest average greening speed. The greening speed of grasslands and croplands in different zones varies widely. In contrast, the average greening speed of shrublands is the slowest. In addition, CVFZ detected grasslands with one or two phenological cycles, broadleaf croplands with one or two phenological cycles, and shrublands with no apparent or one phenological cycle.

Soil erosion variations along land use and land cover dynamics in Matenchose watershed, Rift Valley Basin, Southern Ethiopia

AbstractThe global community recognizes land use and land cover change (LULC) as a primary cause of ecological modification that has a considerable impact on natural resources, particularly soil and water resources. The aim of this research was to investigate land use change's influences on soil erosion in the Matenchose watershed of Ethiopia in 1991, 2003, and 2020. The maximum likelihood classification (MLC) method was used in the study for supervised image analysis. Soil erosion was estimated using the geographic information system (GIS), remote sensing, and the Revised Universal Soil Loss Equation (RUSLE) model. According to the LULC data from 1991, the watershed was mostly covered by grassland (35%), while in 2003 and 2020, it was typically enclosed by cultivated land (36%) and (52%), respectively. The watershed's mean annual soil erosion rate grew significantly from 13 t/ha in 1991 to 18 t/ha in 2003 to 21 t/ha in 2020. Based on the current soil loss rate result, the Matenchose watershed was divided into five priority groups for soil management practices. In contrast, the watershed is made up of 2052 ha (21%) of areas with high to very high erosion risk, 3304 ha (33%) of areas with moderate erosion risk, and 2866 ha (29%) of areas with severe erosion risk. Based on the average annual rate of soil erosion, several vital subwatersheds were identified for potential future land management‐related actions. Over the 29 years, the area of grassland and forest decreased while agricultural and settlement areas expanded, and they contributed to the enhanced hazards of soil erosion. Particularly vulnerable to erosion are the watershed's hilly and steeper areas. The identified subwatersheds that are most at risk of erosion should be given priority for upcoming LULC initiatives, proper participatory watershed planning and management, and measures to conserve soil and water to preserve the Matenchose watershed's soil resources.

Mapping and simulating the spatial-temporal changes in the Lagos wetland ecosystem: A step-by-step approach to creating a carbon-neutral, sustainable urban city

BackgroundLagos is the largest megacity in Africa, with numerous lagoons and wetlands. Due to anthropogenic activities, Lagos’ wetlands have undergone significant alterations in the last few decades. The UN's Sustainable Development Goals (SDGs) and the campaign programs for carbon neutrality in the city offer a plan of action that recognizes the necessity of managing water-related habitats, such as wetlands. Wetlands are essential to accomplishing the SDGs because they provide a solution to many global issues like climate change, food insecurity, and water scarcity. In some ways, each of the SDGs is related to wetlands, but SDGs 6 and 13 are particularly significant to this study. The application of remote sensing and Geographic Information Systems (GIS) is a crucial tactic for monitoring the achievement of these SDGs. ObjectivesThe study aims to map and simulate the spatial-temporal changes in the Lagos wetland from 2000 to 2050 for the achievement of a carbon-neutral city. MethodsIn this study, Landsat ETM+ (2000, 2010) and OLI 2020 were used to monitor the spatial-temporal changes in Lagos wetlands. The maximum likelihood classification method was adopted for the supervised classification. The simulation analysis was done with the cellular automata-Markov model. ResultsThe findings show that the water body increased throughout the study, rising from 658.76 km2 in 2000 to 681.60 km2 in 2010, and to 712.0 km2 in 2020. Marshy areas have decreased progressively from 39.41 km2 in 2000 to 14.48 km2 in 2010 and 0.078 km2 in 2020. Swamps and mangrove wetland classes had changed from 448.24 km2 and 406.29 km2 in 2000 to 317.74 km2 and 538.87 km2 respectively in 2010, and to 435.29 km2 and 405.33 km2 respectively in 2020. The simulated results show that water bodies, swamps, marshy areas, and mangroves will respectively change to 711.99 km2 and 747.11 km2, 405.26 km2 and 400.16 km2, 0.15 km2 and 0.13 km2, as well as 455.29 km2 and 455.30 km2 in the simulated years 2030–2050. ConclusionThe study shows that Lagos has witnessed spatial-temporal changes in its wetland classes within the last two decades. The results prove that remote sensing and GIS are effective tools for studying water-related habitats such as wetlands.

Detection of flood inundation in serayu watershed using landsat-8 and sentinel-2 satellite imagery

Floods are natural disasters that occur routinely almost every year in various regions in Indonesia, especially on the island of Java. According to the National Disaster Management Authority (BNPB), in 2001-2021 there were 5542 floods in Java. Determining the area of flood inundation is very important to know the potential and risk of the impact of flooding in an area. The purpose of this study is to develop a method of detecting flood inundation areas based on Landsat-8 and Sentinel-2 satellite imagery data using the Normalized Difference Water Index (NDWI) parameter. The Maximum Likelihood Classification (MLC) method was chosen to classify inundated areas and non-flooded areas. The Landsat-8 and Sentinel-2 satellite images were selected during the wet months that usually rain, January, February, March, November and December in 2020. The results show that the Landsat-8 satellite can detect water inundation better than the Sentinel-2 satellite. The results are influenced by the difference in recording time between the two satellites, the presence of clouds that cover the research location, and Landsat-8 imagery data that does not cover the entire location area.

Monitoring Land Changes Using Remote Sensing Methods and Spatial Information System in the Area of Kahrizak Waste Disposal Center

With more than 50 years of proximity to Tehran metropolis, the Kehrizak waste center has had destructive effects on the environment with the development of Tehran and the significant increase in waste production, including the change of land use units that are always affected by natural events, human actions, social issues and Economic. In this research, using Landsat 8 satellite images and remote sensing technique, the land use changes of Kahrizek waste center in thematic maps that are more accessible to users. Five vegetation indices NDVI, EVI, SAVI, LAI and VCI and six supervised classifications are investigated. Several methods were evaluated, parameters of overall accuracy and kappa coefficient. For this research, 4 classes of soil, water, building (urban area) and agricultural land were selected, and the results showed that the maximum likelihood classification method was the best method with an overall accuracy of 90.99% and a kappa coefficient of 0.85 and high similarity. Then, using the maximum likelihood classification method as the most accurate method, a user map was prepared from all images from 2011 to 2022. After calculating the area of ​​the floors, the results showed that the area of ​​the building floor increased by 71% and the area of ​​the agricultural land floor decreased by 80%.

Investigation and Prediction of the Land Use/Land Cover (LU/LC) and Land Surface Temperature (LST) Changes for Mashhad City in Iran during 1990–2030

Studies on how cities are affected by urban heat islands (UHI) are critical nowadays for a better understanding of the connected effects and for providing helpful insights for sustainable city development planning. In this study, Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper+ (ETM+), and Landsat-8 Operational Land Imager (OLI) images were used to assess the dynamics of the spatiotemporal pattern of land use/land cover (LU/LC) and land surface temperature (LST) in the metropolitan city of Mashhad, Iran in the period between 1990 and 2019. The Markov chain model (MCM) was used to predict LU/LC and LST for 2030. In the analyzed LU/LC maps, three LU/LC classes were distinguished, including built-up land (BUL), vegetated land (VL), and bare land (BL) using the maximum likelihood (ML) classification method. The collected data showed different variations in the geographical pattern of Mashhad LST during the research period that impacted the LST in this metropolis. The study evaluated the variations in LU/LC classes and evaluated their impact on the LST. The value of the LST was positively correlated with the occurrence of the built-up land (BUL), and with the bare land areas, while it was negatively correlated with the occurrence of the VL areas. The analysis of changes observed over three decades with 10-year intervals and the prediction of the LU/LC and LST for 2030 constitute an important contribution to the delineation of the dynamics of long LU/LC and LST records. These innovative results may have an important impact on policymaking fostering environmental sustainability, such as the control and management of urban expansion of Mashhad in connection with UHI.

Investigating the Impact of Construction in Urban Areas on Air Pollution in the Troposphere Using Sentinel Satellite Images: A Case Study in District 22 West of Tehran, Iran

In recent years removing environmental pollution has become one of the main concerns of human life. Air quality in cities depends on weather conditions and the amount of pollutants produced. Today, air pollution is one of the most complex problems of human societies, which has left many negative effects on the health of living beings, especially humans. In this research, the average monthly and annual concentrations of air contaminators in the air pollution measurement stations of the General Directorate of Environmental Protection of Tehran province in the years 2013 to 2019 were examined. By using google earth engine site [1]. Processes that GEE (Google Earth Engine) contains a consolidated resource of open-access RS (Remote Sensing) datasets, along with a variety of algorithms to extract information for Earth’s surface monitoring [2], and using maximum likelihood classification method it recognized that examining the spatial changes of pollutants in the areas where there was more construction, the amount of contaminators was also higher. In terms of time, however, no special changes have been observed for all gases. For AI (Aerosol Index) pollutant, no very specific changes were observed within the region. We have seen a growing trend for carbon monoxide pollutant. For the nitrogen oxide pollutant, we have not seen any growth during this period of time. For sulfur dioxide, we have seen a growing trend. Also, in terms of seasonal changes, air pollutants are more concentrated in winter.

Impact of land use change on urban heat island in Etsoko West, Southern Nigeria

The phenomenon of urbanization and population growth is a major global concern, as both emerging and old cities continue to expand, particularly in developing countries. Nigeria's urban population has grown dramatically over the last five decades, resulting in shrinking green spaces and urban forest, as well as increased urban island heat. As a result, this study looked into the existing and predicted land use and land cover (LULC), as well as land surface temperature change in Etsako West LGA of Edo State, Nigeria. The supervised maximum likelihood classification method was used to classify the town into four classes using Landsat data from 1987, 2001, and 2020. Similarly, LULC for the year 2040 was predicted using the Markov and CA-markov models. The Landsat Project manual was used to extract the land surface temperature. It was discovered that the built-up area increased over time, from 28.1 km2 in 2001 to 103.07 km2 in 2020, with a projected 166.93 km2 in 2040. Furthermore, forestland decreased throughout time, but the decline was more pronounced in 2020, with decline of twice that of 2001. Cultivated/grassland land has increased over time and is expected to continue to do so in 2040, but surface water hasn't changed significantly. The average land surface temperature climbed from 25.67 °C in 1987 to 32.39 °C in 2020. The environmental impact of urban sprawl and rising urban heat cannot be overstated, as it includes both direct and indirect human and environmental consequences. As a result, eco-friendly strategies were recommended in ensuring sustainable urban growth and reducing urban island heat.