Akib Javed - Profile on Academia.edu (original) (raw)
Papers by Akib Javed
Remote sensing, Mar 1, 2024
This study proposes a fusion approach to enhancing urban remote sensing applications by integrati... more This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.
Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data
Environmental Science and Pollution Research
Remote sensing, Feb 13, 2024
The integration of optical and SAR datasets through ensemble machine learning models shows promis... more The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine learning classifiers (random forest and extreme gradient boost (XGBoost)) classifiers using an integration of optical and SAR features and simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textures and enhanced modified indices to extract features for the year 2023. The classification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three significant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seoul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an optical feature. Results showed an overall accuracy of 81% for UIS classification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperformed the RF algorithm and Dynamic World (DW) data product and comparatively showed higher classification accuracy. Still, all three results show poor separability with bare soil class compared to ground truth data. XGBoost outperformed random forest and Dynamic World in classification accuracy, highlighting its potential use in urban remote sensing applications.
Natural disaster science and mitigation engineering: DPRI reports, Oct 12, 2021
Flash floods are unexpected, localized flood events that occur when an exceptional amount of rain... more Flash floods are unexpected, localized flood events that occur when an exceptional amount of rain falls happens over a short period of time. In South Asia, it is mostly disastrous, for example, in 2017 flash floods killed approximately 1200 people from India, Nepal, and Bangladesh. However, it is also common in Dhaka megacity, Bangladesh due to its geographic location, monsoon climatic condition
Review of Spectral Indices for Urban Remote Sensing
Photogrammetric Engineering and Remote Sensing, Jul 1, 2021
Urban spectral indices have made promising improvements in the last two decades in urban land use... more Urban spectral indices have made promising improvements in the last two decades in urban land use land cover studies through mapping, estimation, change detection, time-series analyzing, urban dynamics, monitoring, modeling, and so on. Remote sensing spectral indices are unsupervised, unbiased, rapid, scalable, and quantitative in information extraction. Hence, we aimed to summarize the most relevant urban spectral indices by focusing on multispectral, thermal, and nighttime lights indices. We use the search terms "urban index", "built-up index", "normalized difference built-up area (NDBI )", "impervious surface index", and "spectral urban index" to collect relevant literature from the "Web of Science Core Collection" database. We found that all urban spectral indices developed since 2003, except NDBI. This review will help understand the applications of urban spectral indices, the selection of indices based on available spectral bands, and their merits and demerits.
The Simulation and Prediction of Land Surface Temperature Based on SCP and CA-ANN Models Using Remote Sensing Data: A Case Study of Lahore
Photogrammetric Engineering and Remote Sensing, Dec 1, 2022
Over the last two decades, urban growth has become a major issue in Lahore, accelerating land sur... more Over the last two decades, urban growth has become a major issue in Lahore, accelerating land surface temperature (LST) rise. The present study focused on estimating the current situation and simulating the future LST patterns in Lahore using remote sensing data and machine learning models. The semi-automated classification model was applied for the estimation of LST from 2000 to 2020. Then, the cellular automata-artificial neural networks (CA-ANN) module was implemented to predict future LST patterns for 2030 and 2040, respectively. Our research findings revealed that an average of 2.8 °C of land surface temperature has increased, with a mean LST value from 37.25 °C to 40.10 °C in Lahore during the last two decades from 2000 to 2020. Moreover, keeping CA-ANN simulations for land surface temperature, an increase of 2.2 °C is projected through 2040, and mean LST values will be increased from 40.1 °C to 42.31 °C by 2040. The CA-ANN model was validated for future LST simulation with an overall Kappa value of 0.82 and 86.2% of correctness for the years 2030 and 2040 using modules for land-use change evaluation. The study also indicates that land surface temperature is an important factor in environmental changes. Therefore, it is suggested that future urban planning should focus on urban rooftop plantations and vegetation conservation to minimize land surface temperature increases in Lahore.
Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also i... more Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.
The Cellular Automata Approach in Dynamic Modelling of Land Use Change Detection and Future Simulations Based on Remote Sensing Data in Lahore Pakistan
Photogrammetric Engineering and Remote Sensing, 2023
Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic a... more Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation.
Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data
Environmental Science and Pollution Research, Dec 6, 2022
Expansion of Urban Impervious Surfaces in Lahore (1993–2022) Based on Gee and Remote Sensing Data
Photogrammetric Engineering & Remote Sensing
Impervious surfaces are an essential component of our environment and are mainly triggered by hum... more Impervious surfaces are an essential component of our environment and are mainly triggered by human developments. Rapid urbanization and population expansion have increased Lahore's urban impervious surface area. This research is based on estimating the urban imper- vious surface area ( uisa ) growth from 1993 to 2022. Therefore, we aimed to generate an accurate urban impervious surfaces area map based on Landsat time series data on Google Earth Engine ( gee ). We have used a novel global impervious surface area index ( gisai ) for impervious surface area ( uisa ) extraction. The gisai accomplished significant results, with an average overall accuracy of 90.93% and an average kappa coefficient of 0.78. We also compared the results of gisai with Global Human Settlement Layer-Built and harmonized nighttime light ( ntl ) isa data products. The accuracy assessment and cross-validation of uisa results were performed using ground truth data on ArcGIS and gee. Our research findings rev...
Environmental impact assessment of tobacco farming in northern Bangladesh
Heliyon
Development of normalized soil area index for urban studies using remote sensing data
Geofizika
This paper presents two novel spectral soil area indices to identify bare soil area and distingui... more This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined us...
Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data
Environmental Science and Pollution Research
The Simulation and Prediction of Land Surface Temperature Based on SCP and CA-ANN Models Using Remote Sensing Data: A Case Study of Lahore
Photogrammetric Engineering & Remote Sensing
Over the last two decades, urban growth has become a major issue in Lahore, accelerating land sur... more Over the last two decades, urban growth has become a major issue in Lahore, accelerating land surface temperature (LST) rise. The present study focused on estimating the current situation and simulating the future LST patterns in Lahore using remote sensing data and machine learning models. The semi-automated classification model was applied for the estimation of LST from 2000 to 2020. Then, the cellular automata-artificial neural networks (CA-ANN) module was implemented to predict future LST patterns for 2030 and 2040, respectively. Our research findings revealed that an average of 2.8 °C of land surface temperature has increased, with a mean LST value from 37.25 °C to 40.10 °C in Lahore during the last two decades from 2000 to 2020. Moreover, keeping CA-ANN simulations for land surface temperature, an increase of 2.2 °C is projected through 2040, and mean LST values will be increased from 40.1 °C to 42.31 °C by 2040. The CA-ANN model was validated for future LST simulation with an...
The Cellular Automata Approach in Dynamic Modelling of Land Use Change Detection and Future Simulations Based on Remote Sensing Data in Lahore Pakistan
Photogrammetric Engineering & Remote Sensing
Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic a... more Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) ...
Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also i... more Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using ...
Review of Spectral Indices for Urban Remote Sensing
Photogrammetric Engineering & Remote Sensing, 2021
Urban spectral indices have made promising improvements in the last two decades in urban land use... more Urban spectral indices have made promising improvements in the last two decades in urban land use land cover studies through mapping, estimation, change detection, time-series analyzing, urban dynamics, monitoring, modeling, and so on. Remote sensing spectral indices are unsupervised, unbiased, rapid, scalable, and quantitative in information extraction. Hence, we aimed to summarize the most relevant urban spectral indices by focusing on multispectral, thermal, and nighttime lights indices. We use the search terms "urban index", "built-up index", "normalized difference built-up area (NDBI )", "impervious surface index", and "spectral urban index" to collect relevant literature from the "Web of Science Core Collection" database. We found that all urban spectral indices developed since 2003, except NDBI. This review will help understand the applications of urban spectral indices, the selection of indices based on available sp...
Comparative occupational health risk between tobacco and paddy farming people in Bangladesh
SSM - Mental Health, 2022
The study tries to find out the scenario of black fever among the Santal people in Bangladesh. Sa... more The study tries to find out the scenario of black fever among the Santal people in Bangladesh. Santal patient health seeking behaviors related with their community people decision, free treatment consideration, preferable healthcare option. Those the entire thing is related with culture. The study is explorative and to some extent descriptive in nature that enforces to adopt mixed with qualitative and quantitative data as well as secondary and primary data. Research shows that 81% patient depend too much on treatment of indigenous physician (Kabiraj). Also barriers of accessing health care are the prevailing factor for health seeking behavior. 92% respondents said awareness and knowledge regarding black fever has too much impact. 43% people are influenced by church and Non-Governmental Organization (N.G.O) during decision making regarding treatment. 54% patients state that, skin turns into more black after taking medicine. Economic condition of lower class people has too much impact...
Effects of Agricultural Practices on Biodiversity in Bangladesh
American Journal of Environmental Protection, 2018
Biodiversity in Bangladesh is deteriorating gradually due to many anthropogenic activities. Agric... more Biodiversity in Bangladesh is deteriorating gradually due to many anthropogenic activities. Agricultural practices along with modern farming techniques causing depletion of biodiversity. The study was conducted to determine the impacts of agricultural practices on biodiversity in the study area. Cultivable land increase only 1% but total cultivable land came under irrigation. Cropping in wetland area got popular and increase 95% in term of land area. As water level goes down, digging pond in wetland area became a trend recently. Simultaneously, the use of submersible water pumps gaining popularity. Agricultural practices shift dramatically from inorganic fertilizer to organic fertilizer and chemical fertilizer. The numbers of pesticides use reached three folds. On the other hand, floral and faunal species is losing its diversity in the study area. Total, 29% fruit plants, 38% timber plant and 42% medicinal plant species extinct. In case of fauna, 33% wild animal, 26% birds and 46% f...
Remote sensing, Mar 1, 2024
This study proposes a fusion approach to enhancing urban remote sensing applications by integrati... more This study proposes a fusion approach to enhancing urban remote sensing applications by integrating SAR (Sentinel-1) and optical (Landsat-8) satellite datasets. The fusion technique combines feature-based fusion and simple layer stacking (SLS) to improve the accuracy of urban impervious surface (UIS) extraction. SAR textures and modified indices are used for feature extraction, and classification is performed using the XGBoost machine learning algorithm in Python and Google Earth Engine. The study focuses on four global cities (New York, Paris, Tokyo, and London) with heterogeneous climatic zones and urban dynamics. The proposed method showed significant results. The accuracy assessment using random validation points shows an overall accuracy of 86% for UIS classification with the SLS method, outperforming single-data classification. The proposed approach achieves higher accuracy (86%) compared to three global products (ESA, ESRI, and Dynamic World). New York exhibits the highest overall accuracy at 88%. This fusion approach with the XGBoost classifier holds potential for new applications and insights into UIS mapping, with implications for environmental factors such as land surface temperature, the urban heat island effect, and urban pluvial flooding.
Mapping impervious surface area increase and urban pluvial flooding using Sentinel Application Platform (SNAP) and remote sensing data
Environmental Science and Pollution Research
Remote sensing, Feb 13, 2024
The integration of optical and SAR datasets through ensemble machine learning models shows promis... more The integration of optical and SAR datasets through ensemble machine learning models shows promising results in urban remote sensing applications. The integration of multi-sensor datasets enhances the accuracy of information extraction. This research presents a comparison of two ensemble machine learning classifiers (random forest and extreme gradient boost (XGBoost)) classifiers using an integration of optical and SAR features and simple layer stacking (SLS) techniques. Therefore, Sentinel-1 (SAR) and Landsat 8 (optical) datasets were used with SAR textures and enhanced modified indices to extract features for the year 2023. The classification process utilized two machine learning algorithms, random forest and XGBoost, for urban impervious surface extraction. The study focused on three significant East Asian cities with diverse urban dynamics: Jakarta, Manila, and Seoul. This research proposed a novel index called the Normalized Blue Water Index (NBWI), which distinguishes water from other features and was utilized as an optical feature. Results showed an overall accuracy of 81% for UIS classification using XGBoost and 77% with RF while classifying land use land cover into four major classes (water, vegetation, bare soil, and urban impervious). However, the proposed framework with the XGBoost classifier outperformed the RF algorithm and Dynamic World (DW) data product and comparatively showed higher classification accuracy. Still, all three results show poor separability with bare soil class compared to ground truth data. XGBoost outperformed random forest and Dynamic World in classification accuracy, highlighting its potential use in urban remote sensing applications.
Natural disaster science and mitigation engineering: DPRI reports, Oct 12, 2021
Flash floods are unexpected, localized flood events that occur when an exceptional amount of rain... more Flash floods are unexpected, localized flood events that occur when an exceptional amount of rain falls happens over a short period of time. In South Asia, it is mostly disastrous, for example, in 2017 flash floods killed approximately 1200 people from India, Nepal, and Bangladesh. However, it is also common in Dhaka megacity, Bangladesh due to its geographic location, monsoon climatic condition
Review of Spectral Indices for Urban Remote Sensing
Photogrammetric Engineering and Remote Sensing, Jul 1, 2021
Urban spectral indices have made promising improvements in the last two decades in urban land use... more Urban spectral indices have made promising improvements in the last two decades in urban land use land cover studies through mapping, estimation, change detection, time-series analyzing, urban dynamics, monitoring, modeling, and so on. Remote sensing spectral indices are unsupervised, unbiased, rapid, scalable, and quantitative in information extraction. Hence, we aimed to summarize the most relevant urban spectral indices by focusing on multispectral, thermal, and nighttime lights indices. We use the search terms "urban index", "built-up index", "normalized difference built-up area (NDBI )", "impervious surface index", and "spectral urban index" to collect relevant literature from the "Web of Science Core Collection" database. We found that all urban spectral indices developed since 2003, except NDBI. This review will help understand the applications of urban spectral indices, the selection of indices based on available spectral bands, and their merits and demerits.
The Simulation and Prediction of Land Surface Temperature Based on SCP and CA-ANN Models Using Remote Sensing Data: A Case Study of Lahore
Photogrammetric Engineering and Remote Sensing, Dec 1, 2022
Over the last two decades, urban growth has become a major issue in Lahore, accelerating land sur... more Over the last two decades, urban growth has become a major issue in Lahore, accelerating land surface temperature (LST) rise. The present study focused on estimating the current situation and simulating the future LST patterns in Lahore using remote sensing data and machine learning models. The semi-automated classification model was applied for the estimation of LST from 2000 to 2020. Then, the cellular automata-artificial neural networks (CA-ANN) module was implemented to predict future LST patterns for 2030 and 2040, respectively. Our research findings revealed that an average of 2.8 °C of land surface temperature has increased, with a mean LST value from 37.25 °C to 40.10 °C in Lahore during the last two decades from 2000 to 2020. Moreover, keeping CA-ANN simulations for land surface temperature, an increase of 2.2 °C is projected through 2040, and mean LST values will be increased from 40.1 °C to 42.31 °C by 2040. The CA-ANN model was validated for future LST simulation with an overall Kappa value of 0.82 and 86.2% of correctness for the years 2030 and 2040 using modules for land-use change evaluation. The study also indicates that land surface temperature is an important factor in environmental changes. Therefore, it is suggested that future urban planning should focus on urban rooftop plantations and vegetation conservation to minimize land surface temperature increases in Lahore.
Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also i... more Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.
The Cellular Automata Approach in Dynamic Modelling of Land Use Change Detection and Future Simulations Based on Remote Sensing Data in Lahore Pakistan
Photogrammetric Engineering and Remote Sensing, 2023
Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic a... more Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation.
Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data
Environmental Science and Pollution Research, Dec 6, 2022
Expansion of Urban Impervious Surfaces in Lahore (1993–2022) Based on Gee and Remote Sensing Data
Photogrammetric Engineering & Remote Sensing
Impervious surfaces are an essential component of our environment and are mainly triggered by hum... more Impervious surfaces are an essential component of our environment and are mainly triggered by human developments. Rapid urbanization and population expansion have increased Lahore's urban impervious surface area. This research is based on estimating the urban imper- vious surface area ( uisa ) growth from 1993 to 2022. Therefore, we aimed to generate an accurate urban impervious surfaces area map based on Landsat time series data on Google Earth Engine ( gee ). We have used a novel global impervious surface area index ( gisai ) for impervious surface area ( uisa ) extraction. The gisai accomplished significant results, with an average overall accuracy of 90.93% and an average kappa coefficient of 0.78. We also compared the results of gisai with Global Human Settlement Layer-Built and harmonized nighttime light ( ntl ) isa data products. The accuracy assessment and cross-validation of uisa results were performed using ground truth data on ArcGIS and gee. Our research findings rev...
Environmental impact assessment of tobacco farming in northern Bangladesh
Heliyon
Development of normalized soil area index for urban studies using remote sensing data
Geofizika
This paper presents two novel spectral soil area indices to identify bare soil area and distingui... more This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined us...
Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data
Environmental Science and Pollution Research
The Simulation and Prediction of Land Surface Temperature Based on SCP and CA-ANN Models Using Remote Sensing Data: A Case Study of Lahore
Photogrammetric Engineering & Remote Sensing
Over the last two decades, urban growth has become a major issue in Lahore, accelerating land sur... more Over the last two decades, urban growth has become a major issue in Lahore, accelerating land surface temperature (LST) rise. The present study focused on estimating the current situation and simulating the future LST patterns in Lahore using remote sensing data and machine learning models. The semi-automated classification model was applied for the estimation of LST from 2000 to 2020. Then, the cellular automata-artificial neural networks (CA-ANN) module was implemented to predict future LST patterns for 2030 and 2040, respectively. Our research findings revealed that an average of 2.8 °C of land surface temperature has increased, with a mean LST value from 37.25 °C to 40.10 °C in Lahore during the last two decades from 2000 to 2020. Moreover, keeping CA-ANN simulations for land surface temperature, an increase of 2.2 °C is projected through 2040, and mean LST values will be increased from 40.1 °C to 42.31 °C by 2040. The CA-ANN model was validated for future LST simulation with an...
The Cellular Automata Approach in Dynamic Modelling of Land Use Change Detection and Future Simulations Based on Remote Sensing Data in Lahore Pakistan
Photogrammetric Engineering & Remote Sensing
Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic a... more Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) ...
Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also i... more Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using ...
Review of Spectral Indices for Urban Remote Sensing
Photogrammetric Engineering & Remote Sensing, 2021
Urban spectral indices have made promising improvements in the last two decades in urban land use... more Urban spectral indices have made promising improvements in the last two decades in urban land use land cover studies through mapping, estimation, change detection, time-series analyzing, urban dynamics, monitoring, modeling, and so on. Remote sensing spectral indices are unsupervised, unbiased, rapid, scalable, and quantitative in information extraction. Hence, we aimed to summarize the most relevant urban spectral indices by focusing on multispectral, thermal, and nighttime lights indices. We use the search terms "urban index", "built-up index", "normalized difference built-up area (NDBI )", "impervious surface index", and "spectral urban index" to collect relevant literature from the "Web of Science Core Collection" database. We found that all urban spectral indices developed since 2003, except NDBI. This review will help understand the applications of urban spectral indices, the selection of indices based on available sp...
Comparative occupational health risk between tobacco and paddy farming people in Bangladesh
SSM - Mental Health, 2022
The study tries to find out the scenario of black fever among the Santal people in Bangladesh. Sa... more The study tries to find out the scenario of black fever among the Santal people in Bangladesh. Santal patient health seeking behaviors related with their community people decision, free treatment consideration, preferable healthcare option. Those the entire thing is related with culture. The study is explorative and to some extent descriptive in nature that enforces to adopt mixed with qualitative and quantitative data as well as secondary and primary data. Research shows that 81% patient depend too much on treatment of indigenous physician (Kabiraj). Also barriers of accessing health care are the prevailing factor for health seeking behavior. 92% respondents said awareness and knowledge regarding black fever has too much impact. 43% people are influenced by church and Non-Governmental Organization (N.G.O) during decision making regarding treatment. 54% patients state that, skin turns into more black after taking medicine. Economic condition of lower class people has too much impact...
Effects of Agricultural Practices on Biodiversity in Bangladesh
American Journal of Environmental Protection, 2018
Biodiversity in Bangladesh is deteriorating gradually due to many anthropogenic activities. Agric... more Biodiversity in Bangladesh is deteriorating gradually due to many anthropogenic activities. Agricultural practices along with modern farming techniques causing depletion of biodiversity. The study was conducted to determine the impacts of agricultural practices on biodiversity in the study area. Cultivable land increase only 1% but total cultivable land came under irrigation. Cropping in wetland area got popular and increase 95% in term of land area. As water level goes down, digging pond in wetland area became a trend recently. Simultaneously, the use of submersible water pumps gaining popularity. Agricultural practices shift dramatically from inorganic fertilizer to organic fertilizer and chemical fertilizer. The numbers of pesticides use reached three folds. On the other hand, floral and faunal species is losing its diversity in the study area. Total, 29% fruit plants, 38% timber plant and 42% medicinal plant species extinct. In case of fauna, 33% wild animal, 26% birds and 46% f...