Shahfahad is a Senior Research Fellow (SRF) at the Department of Geography, Jamia Millia Islamia. He is doing pursuing a PhD in Urban growth and Climate change. His areas of research are urban studies, climate change, land-use change, ecosystem services, urban landscape and use of remote sensing in urban and land resource studies. The main themes of his research are Urban Environment: issues and challenges, Climate Change and the use of Geoinformatics in Urban studies. His PhD title is 'Impact of Urbanization on Climatic Conditions in India: A Comparative Analysis of Delhi and Mumbai Metro Cities Using Geospatial Technique'.
The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC)... more The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC) pattern and seasonal variation in urban thermal comfort (UTC) and land surface temperature (LST). This study examines the seasonal variation in LST and UTC and quantify the importance of LULC pattern in influencing LST in eight Indian semi-arid cities using Landsat 8 datasets. Random forest regression (RFR) model has been applied to quantify the influence of LULC in determining LST. The study shows that bare soil and open spaces in outskirts of selected cities have comparatively high LST than the core of cities. The mean LST varies seasonally from 12.84 • C in Udaipur to 26.18 • C in Jaipur in winter and summer. Analysis of UTC shows that autumn and winter seasons have better UTC than spring and summer seasons. RFR analysis shows a robust correlation (R2 > 0.85) between LULC and LST across all cities. Notably, built-up areas, open land, and vegetation cover exert the greatest influence on LST. Study suggests that targeted LULC planning may effectively address UTC problem in semi-arid cities and enhance sustainability. Study may help in understanding the seasonality thermal environment and assist in mitigating UHI and maintain UTC in these cities.
The decrease in vegetation cover due to urban expansion poses serious challenge to urban sustaina... more The decrease in vegetation cover due to urban expansion poses serious challenge to urban sustainability. Protected areas (PAs) are the most effective tools to prevent the loss of urban vegetation cover and to control urban expansion. Hence, this study aims to assess the importance of PAs in protecting urban vegetation and the urban expansion in the mega city of Delhi. For this, Landsat datasets were used for land use and land cover (LULC) mapping and then land cover change rate (LCCR) and land cover intensity (LCI) were calculated. For assessing urban expansion dynamics, mean landscape expansion index (MLEI) and the area-weighted LEI (AWLEI) were calculated. To evaluate the significance of PAs in protecting vegetation cover, kernel density estimation (KDE) was applied to assess the spatial variation and concentration of vegetation cover under different PAs. The result shows that urban expansion in Delhi was initially characterized by edge expansion during 1991-2001, followed by outlying expansion of built-up area during 2001-2021, while infilling of open and vegetated areas by built-up area was consistent during 1991-2021. Vegetation cover on the other hand, has followed a fluctuating trend in the city, but has overall it has declined from 13.36% to 9.30% during 1991-2021. The vegetation cover has declined significantly in eastern, northern, and western parts of Delhi but has increased significantly in central and southern parts, especially during 2001-21. This is because the central and southern parts of Delhi are well planned and have several PAs while the western, northern, and eastern parts of Delhi are unplanned regions and have only a few PAs. The KDE chart shows that the PAs have played an important role in protecting the vegetation cover in Delhi with R 2 value > 0.70. Hence, this study suggests to give special emphasis on preservation and expansion of PAs in urban planning for the long-term conservation of urban vegetation cover and sustainable urban development.
Amongst various form of urbanization induced climate change, changing thermal environment is the ... more Amongst various form of urbanization induced climate change, changing thermal environment is the most widely studied and understood phenomenon. The impervious surfaces in urban areas absorb and re-emit the heat from solar radiation more than those of natural landscape which causes an elevated temperature in global cities. Due to increasing impervious surfaces and emissions from anthropogenic sources, the diurnal temperature range (DTR) is declining in cities while the frequency of extreme temperature events (TX X) is increasing. Hence, in this study, the trend of DTR and TX X has been examined in Delhi and Mumbai mega cities of India. For this study, India Meteorological Department (IMD) provided daily temperature data for 13 meteorological stations in Mumbai and 21 meteorological stations in Delhi. The DTR and TX X have been analysed using the RClimDex-Extraqc package while the trend of DTR and TX X has been analysed using the innovative trend analysis (ITA). The result showed that during 1991-2018, DTR has declined by about 1.5 °C in Delhi and about 0.2-0.4 °C in Mumbai, while TX X has increased by about 0.1-1.4 °C in Delhi and about 4 °C in Mumbai. The trend analysis of DTR and TX X using ITA showed that the DTR has a declining trend in both the cities while TX X has an increasing trend. The declining DTR and increasing TX X may increase the vulnerability to heat waves for the city dwellers and deteriorate the urban thermal comfort in both the cities.
The cities of arid and semi-arid regions have distinctive landscape patterns and large-scale vari... more The cities of arid and semi-arid regions have distinctive landscape patterns and large-scale variations in soil moisture and vegetation cover which causes significant variations in land surface temperature (LST) and surface urban heat island intensity (SUHII) pattern. Therefore, the study aims to analyse the seasonal and spatial variation in LST and SUHII in the eight semi-arid cities of India in response to soil moisture and vegetation conditions. LST was retrieved from the thermal bands of Landsat data and then SUHII was calculated. The global Moran's I was used to analysis the spatial pattern of SUHII. The result shows that the mean SUHII was higher during spring and summer seasons to a tune of 0.2 to 1.0 °C in comparison to the winter and autumn season. SUHII zones exhibit seasonal variation in coverage, with high and very high zones increasing during spring and summer, while low and very low zones increase during autumn and winter. Furthermore, the highest LST was noticed in outskirt areas of the selected cities. The regression coefficient shows that soil moisture is closely associated with SUHII, while there is a weak association between vegetation condition and SUHII. This indicates that soil moisture has a higher impact on SUHII than vegetation condition in semi-arid environment. Global Moran's I showed that the SUHII had a clustered distribution pattern across all cities. The outcome of this study may provide useful insight for the urban planners in SUHII mitigation in the selected cities as well as in other semi-arid cities of the world with similar geographical conditions.
In the cities of developing countries like India, rapid and uncontrolled urbanization has been ta... more In the cities of developing countries like India, rapid and uncontrolled urbanization has been taking place due to continuous population growth in last few decades. As a result, land use/ land cover (LU/LC) is changing very fast in the cities of developing countries. Therefore, this study aims to examine the changes in LU/LC pattern in Delhi during 1991-2018 and simulate the future LU/LC pattern of Delhi for 2030. The LU/LC pattern mapping was done from Landsat datasets using k-means clustering technique. The cellular automata (CA) technique was integrated with artificial neural network (ANN) for simulating the future LU/LC patterns. The projected LU/LC pattern shows that Delhi's built-up area will increase to nearly 60% of the total area of city, while cropland and open land will decrease to 19.86 and 0.15%, respectively. The highest increase in built-up area was observed in the northern, western, and southwestern sub-districts of Delhi. Outcomes of the study may be used for future land use planning in Delhi and other cities. In addition, they can also provide valuable insights for the development of transportation network and other facilities and amenities in the areas of future urban expansion.
Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor... more Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat Operational Land Imager (OLI) and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy than OLI. The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran's I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor.
Environmental Science and Pollution Research, 2022
In the era of global urbanization, the cities across the world are experiencing significant chang... more In the era of global urbanization, the cities across the world are experiencing significant change in the climate pattern. However, analysing the trend and pattern of rainfall over the urban areas has a number of challenges such as availability of long-term data as well as the uneven distribution of rain-gauge stations. In this research, the rainfall regionalization approach has been applied along with the advanced statistical techniques for analysing the trend and pattern of rainfall in the Delhi metropolitan city. Fuzzy C-means and K-means clustering techniques have been applied for the identification of homogeneous rainfall regions while innovative trend analysis (ITA) along with the family of Mann–Kendall (MK) tests has been applied for the trend analysis of rainfall. The result shows that in all rain-gauge stations of Delhi, an increasing trend in rainfall has been recorded during 1991–2018. But the rate of increase was low as the trend slope of ITA and Sen’s slope in MK tests are low, which varies between 0.03 and 0.05 and 0.01 and 0.16, respectively. Furthermore, none of the rain-gauge stations have experienced a monotonic trend in rainfall as the null hypothesis has not been rejected (p value > 0.05) for any stations. Furthermore, the study shows that ITA has a better performance than the family of MK tests. The findings of this study may be utilized for the urban flood mitigation and solving other issues related to water resources in Delhi and other cities.
Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor... more Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat OLI and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy (92.4%) than OLI (83.4%). The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran’s I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor.
Rapid and uncontrolled population growth along with economic and industrial development, especial... more Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error...
According to the World Urbanization Prospects of United Nations, the global urban population has ... more According to the World Urbanization Prospects of United Nations, the global urban population has increased rapidly over past few decades, reaching about 55% in 2018, which is projected to reach 68% by 2050. Due to gradual increase in the urban population and impervious surfaces, the urban heat island (UHI) effect has increased manifold in the cities of developing countries, causing a decline in thermal comfort. Therefore, this study was designed to model the spatio-temporal pattern of UHI and its relationships with the land use indices of Delhi and Mumbai metro cities from 1991 to 2018. Landsat datasets were used to generate the land surface temperature (LST) using mono window algorithm and land use indices, such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBal), normalized difference moisture index (NDMI), and modified normalized difference water index (MNDWI). Additionally, the urban hotspots (UHS) were identified and then the thermal comfort was modelled using the UTFVI. The results showed that maximum (30.25-38.99 °C in Delhi and 42.10-45.75 °C in Mumbai) and minimum (17.70-23.86 °C in Delhi and 19.06-25.05 °C in Mumbai) LST witnessed steady growth in Delhi and Mumbai from 1991 to 2018. The LST gap decreases and the UHI zones are being established in both cities. Furthermore, the UHS and worst-category UTFVI areas increased in both cities. This research can be useful in designing urban green-space planning strategies for mitigating the UHI effects and thermal comfort in cities of developing countries. Keywords Land use indices • Urban heat island • Urban Hotspots • Urban thermal field variation index • Thermal comfort
Journal of the India Society of Remote Sensing, 2021
The rapid urbanization and land-use/land-cover (LU/LC) changes have resulted in the unplanned and... more The rapid urbanization and land-use/land-cover (LU/LC) changes have resulted in the unplanned and unsustainable growth of the Indian cities. This has resulted in a number of environmental issues such as escalating the urban heat island (UHI) intensity over the cities. Therefore, this study was designed to model and quantify the UHI dynamics of Mumbai city in response to the LU/LC change during 1991-2018 using temporal Landsat datasets. The result shows a significant decline in vegetation cover from 215.8 to 129.27 km 2 , while the built-up areas have almost doubled, i.e., from 173.09 to 346.02 km 2 in the Mumbai city during 1991-2018. As a consequence of this, a significant increase in the LST has been noticed in both urban heat island (UHI) and non-UHI zones. Although the areas under UHI zones have not increased significantly, the land surface temperature (LST) gap (difference between minimum and maximum LST) has declined in the Mumbai city from 30.04°C in 1991 to 20.7°C in 2018. Further, the minimum and mean LST over each LU/LC classes have also shown a significant increase. On the other hand, the regression analysis shows that the association between UHI and normalized difference built-up index (NDBI) has increased in the city, while the association of vegetation density (NDVI) and normalized difference bareness index (NDBaI) has declined in the city. The study can provide useful insights into the process of urban planning and policy makings for urban spatial planning and UHI mitigation strategies. Keywords Land-use/land-cover change Á Land surface temperature Á Urban heat island Á Vegetation and built-up density Á Mumbai city
Journal of the Indian Society of Remote Sensing, 2021
The coastal area supports millions of population in terms of livelihood, settlement and social ac... more The coastal area supports millions of population in terms of livelihood, settlement and social activities across the world and India. The increasing rate of socioeconomic activities made the coasts susceptible to various hazards. Therefore, this study is aimed to examine the coastal vulnerability of Vishakhapatnam Coastal district using remote sensing and geographic information system. To fulfill this objective, six physical indicators, i.e., geomorphology, land use/land cover, coastal slope, shoreline change rate, etc., were prepared using the multi-temporal datasets of 1991, 2001, 2011 and 2018 and mean tidal height has been considered to calculate the coastal vulnerability index (CVI). The indicators selected for the analysis of coastal vulnerability have been integrated using the rank and weighted methods. The shoreline change has been detected using the digital shoreline analysis system (DSAS). Analytical hierarchy process (AHP) has been used for calculating weights of various indices. The CVI values obtained using different indicators are 2.6 (min) and 14.39 (max). Based on the CVI values, the coast is classified as five classes of vulnerability, i.e., very low (0-4.9) covering 42.5 km, low (4.9-7.3) which covers 29.49 km, moderate (7.3-9.6) covering 23.46 km, high (9.6-12.0) which covers 34.61 km, and very high (12.0-14.39) covering 7.5 km. This integrated study is found useful for exploring the accretion and erosion processes and also for vulnerability mapping in the coastal tract of Vishakhapatnam district.
Groundwater scarcity is one of the most concerning issues in arid
and semi-arid regions. In this ... more Groundwater scarcity is one of the most concerning issues in arid and semi-arid regions. In this study, we develop and validate a novel artificial intelligence that is a coupling of five ensemble benchmark algorithms e.g., artificial neural network (ANN), reduced-error pruning trees (REPTree), radial basis function (RBF), M5P and random forest (RF) with particle swarm optimization (PSO) for delineating GWP zones. Further, nine parameters used for the GWP modelling and to test and train the proposed PSObased models. Additionally, this study proposes a receiver operating characteristic (ROC) based sensitivity analysis for GWP modelling. Multicollinearity test, information gain ratio, and correlation attribute evaluation methods used to choose important parameters for the proposed GWP model. The result shows that drainage density, elevation, and land use/land cover have a higher influence on the GWP using correlation attribute evaluation methods. Results showed that the hybrid PSO-RF model performed better than other proposed hybrid models.
Coastline or Shoreline calculation is one of the important factors in the finding of coastal accr... more Coastline or Shoreline calculation is one of the important factors in the finding of coastal accretion and erosion and the study of coastal morphodynamic. Coastal erosion is a tentative hazard for communities especially in coastal areas as it is extremely susceptible to increasing coastal disasters. The study has been conducted along the coast of Vishakhapatnam district, Andhra Pradesh, India with the help of multi-temporal satellite images of 1991 2001, 2011 and 2018. The continuing coastal erosion and accretion rates have been calculated using the Digital Shoreline Analysis System (DSAS). Linear regression rate (LRR), End Point Rate (EPR) and Weighted Linear Regression (WLR) are used for calculating shoreline change rate. Based on calculations the district shoreline has been classified into five categories as high and low erosion, no change and high and low accretion. Out of 135 km, high erosion occupied 5.8 km of coast followed by moderate or low erosion 46.2 km. Almost 34.7 km coastal length showed little or no change. Moderate accretion is found along 30.5 km whereas high accretion trend found around 17.8 km. The outcome of shows that erosion is prevailing in Vishakhapatnam taluk, Ankapalli taluk, Yellamanchili taluk whereas most of the Bhemunipatnam coast is accreting. Natural and manmade activities and phenomena influence the coastal areas in terms of erosion and accretion. The study could be used for further planning and development and also for disaster management authority in the decision-making process in the study area. ARTICLE HISTORY
This study was designed to compare the pattern of land surface temperature (LST) over four metro ... more This study was designed to compare the pattern of land surface temperature (LST) over four metro cities of India (Mumbai, Chennai, Delhi, and Kolkata) selected on a longitudinal basis in relation to the built-up and vegetation indices. Two different methods were employed for the retrieval of LST, i.e., mono-window algorithm (MWA) and split-window algorithm (SWA) on the Landsat 8 (OLI/TIRS) datasets, to analyze the spatial pattern of LST over selected cities in relation to normalized differential built-up index (NDBI) and normalized differential vegetation index (NDVI). The result shows that the LST was high over the densely built areas while low over the densely vegetated areas. The highest LST, NDBI, and NDVI were found in Mumbai, while Kolkata records the lowest LST and NDVI. Furthermore, the spatial analysis of LST shows that the LST was high in central parts of all cities except in the case of Delhi where some peripheral areas also record high LST. The comparison from in situ LST (field observations) reveals that the SWA has higher accuracy in the retrieval of LST in maritime areas like Mumbai and Chennai because it reduces the atmospheric effects, while the MWA has higher accuracy for inland areas like Delhi. The spatial relationships of LST with NDVI and NDBI show that vegetation cover has more impact on LST in Delhi while low in Chennai and Mumbai, and the built-up surfaces have a higher impact on LST in Chennai and Mumbai than Kolkata and Delhi.
Groundwater in Delhi Metropolitan Region (DMR) is suffering from multiple catastrophes, viz., asy... more Groundwater in Delhi Metropolitan Region (DMR) is suffering from multiple catastrophes, viz., asymptotic increases in groundwater withdrawal, reduced recharge due to erratic rainfall, and variable soil type. In this study, we examined long-term trends in groundwater levels across the DMR from 1996 to 2018. Station level data collected by the Central Groundwater Board for 258 stations at the seasonal scale were visualized and interpreted using geospatial analysis. The spatial patterns of the trends in groundwater levels revealed increasing depths of groundwater levels, except the Yamuna River floodplains. The main cause for the decline is related to the rapid growth in population accompanied with high-density impervious urban land uses, leading to lower levels of recharge vs unlimited withdrawal of groundwater for daily needs. In addition, the local geology in the form of clayey soils in northwest DMR also contributed to the lower levels of recharge. The results of the analysis enabled us to establish the trend and delineate the zones of differential recharge. Furthermore, the level of contaminants were analyzed at the district level for fluorides and nitrates. The presence of fluoride contamination was mostly concentrated in the northwestern district, while the nitrate exceedance was more widespread. These findings will help in achieving the 6th Sustainable Development Goal (SDG) of United Nations by 2030 as well as goals identified in Delhi's master plan of 2041.
The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC)... more The cities in semi-arid and arid regions generally exhibit distinctive land use land cover (LULC) pattern and seasonal variation in urban thermal comfort (UTC) and land surface temperature (LST). This study examines the seasonal variation in LST and UTC and quantify the importance of LULC pattern in influencing LST in eight Indian semi-arid cities using Landsat 8 datasets. Random forest regression (RFR) model has been applied to quantify the influence of LULC in determining LST. The study shows that bare soil and open spaces in outskirts of selected cities have comparatively high LST than the core of cities. The mean LST varies seasonally from 12.84 • C in Udaipur to 26.18 • C in Jaipur in winter and summer. Analysis of UTC shows that autumn and winter seasons have better UTC than spring and summer seasons. RFR analysis shows a robust correlation (R2 > 0.85) between LULC and LST across all cities. Notably, built-up areas, open land, and vegetation cover exert the greatest influence on LST. Study suggests that targeted LULC planning may effectively address UTC problem in semi-arid cities and enhance sustainability. Study may help in understanding the seasonality thermal environment and assist in mitigating UHI and maintain UTC in these cities.
The decrease in vegetation cover due to urban expansion poses serious challenge to urban sustaina... more The decrease in vegetation cover due to urban expansion poses serious challenge to urban sustainability. Protected areas (PAs) are the most effective tools to prevent the loss of urban vegetation cover and to control urban expansion. Hence, this study aims to assess the importance of PAs in protecting urban vegetation and the urban expansion in the mega city of Delhi. For this, Landsat datasets were used for land use and land cover (LULC) mapping and then land cover change rate (LCCR) and land cover intensity (LCI) were calculated. For assessing urban expansion dynamics, mean landscape expansion index (MLEI) and the area-weighted LEI (AWLEI) were calculated. To evaluate the significance of PAs in protecting vegetation cover, kernel density estimation (KDE) was applied to assess the spatial variation and concentration of vegetation cover under different PAs. The result shows that urban expansion in Delhi was initially characterized by edge expansion during 1991-2001, followed by outlying expansion of built-up area during 2001-2021, while infilling of open and vegetated areas by built-up area was consistent during 1991-2021. Vegetation cover on the other hand, has followed a fluctuating trend in the city, but has overall it has declined from 13.36% to 9.30% during 1991-2021. The vegetation cover has declined significantly in eastern, northern, and western parts of Delhi but has increased significantly in central and southern parts, especially during 2001-21. This is because the central and southern parts of Delhi are well planned and have several PAs while the western, northern, and eastern parts of Delhi are unplanned regions and have only a few PAs. The KDE chart shows that the PAs have played an important role in protecting the vegetation cover in Delhi with R 2 value > 0.70. Hence, this study suggests to give special emphasis on preservation and expansion of PAs in urban planning for the long-term conservation of urban vegetation cover and sustainable urban development.
Amongst various form of urbanization induced climate change, changing thermal environment is the ... more Amongst various form of urbanization induced climate change, changing thermal environment is the most widely studied and understood phenomenon. The impervious surfaces in urban areas absorb and re-emit the heat from solar radiation more than those of natural landscape which causes an elevated temperature in global cities. Due to increasing impervious surfaces and emissions from anthropogenic sources, the diurnal temperature range (DTR) is declining in cities while the frequency of extreme temperature events (TX X) is increasing. Hence, in this study, the trend of DTR and TX X has been examined in Delhi and Mumbai mega cities of India. For this study, India Meteorological Department (IMD) provided daily temperature data for 13 meteorological stations in Mumbai and 21 meteorological stations in Delhi. The DTR and TX X have been analysed using the RClimDex-Extraqc package while the trend of DTR and TX X has been analysed using the innovative trend analysis (ITA). The result showed that during 1991-2018, DTR has declined by about 1.5 °C in Delhi and about 0.2-0.4 °C in Mumbai, while TX X has increased by about 0.1-1.4 °C in Delhi and about 4 °C in Mumbai. The trend analysis of DTR and TX X using ITA showed that the DTR has a declining trend in both the cities while TX X has an increasing trend. The declining DTR and increasing TX X may increase the vulnerability to heat waves for the city dwellers and deteriorate the urban thermal comfort in both the cities.
The cities of arid and semi-arid regions have distinctive landscape patterns and large-scale vari... more The cities of arid and semi-arid regions have distinctive landscape patterns and large-scale variations in soil moisture and vegetation cover which causes significant variations in land surface temperature (LST) and surface urban heat island intensity (SUHII) pattern. Therefore, the study aims to analyse the seasonal and spatial variation in LST and SUHII in the eight semi-arid cities of India in response to soil moisture and vegetation conditions. LST was retrieved from the thermal bands of Landsat data and then SUHII was calculated. The global Moran's I was used to analysis the spatial pattern of SUHII. The result shows that the mean SUHII was higher during spring and summer seasons to a tune of 0.2 to 1.0 °C in comparison to the winter and autumn season. SUHII zones exhibit seasonal variation in coverage, with high and very high zones increasing during spring and summer, while low and very low zones increase during autumn and winter. Furthermore, the highest LST was noticed in outskirt areas of the selected cities. The regression coefficient shows that soil moisture is closely associated with SUHII, while there is a weak association between vegetation condition and SUHII. This indicates that soil moisture has a higher impact on SUHII than vegetation condition in semi-arid environment. Global Moran's I showed that the SUHII had a clustered distribution pattern across all cities. The outcome of this study may provide useful insight for the urban planners in SUHII mitigation in the selected cities as well as in other semi-arid cities of the world with similar geographical conditions.
In the cities of developing countries like India, rapid and uncontrolled urbanization has been ta... more In the cities of developing countries like India, rapid and uncontrolled urbanization has been taking place due to continuous population growth in last few decades. As a result, land use/ land cover (LU/LC) is changing very fast in the cities of developing countries. Therefore, this study aims to examine the changes in LU/LC pattern in Delhi during 1991-2018 and simulate the future LU/LC pattern of Delhi for 2030. The LU/LC pattern mapping was done from Landsat datasets using k-means clustering technique. The cellular automata (CA) technique was integrated with artificial neural network (ANN) for simulating the future LU/LC patterns. The projected LU/LC pattern shows that Delhi's built-up area will increase to nearly 60% of the total area of city, while cropland and open land will decrease to 19.86 and 0.15%, respectively. The highest increase in built-up area was observed in the northern, western, and southwestern sub-districts of Delhi. Outcomes of the study may be used for future land use planning in Delhi and other cities. In addition, they can also provide valuable insights for the development of transportation network and other facilities and amenities in the areas of future urban expansion.
Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor... more Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat Operational Land Imager (OLI) and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy than OLI. The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran's I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor.
Environmental Science and Pollution Research, 2022
In the era of global urbanization, the cities across the world are experiencing significant chang... more In the era of global urbanization, the cities across the world are experiencing significant change in the climate pattern. However, analysing the trend and pattern of rainfall over the urban areas has a number of challenges such as availability of long-term data as well as the uneven distribution of rain-gauge stations. In this research, the rainfall regionalization approach has been applied along with the advanced statistical techniques for analysing the trend and pattern of rainfall in the Delhi metropolitan city. Fuzzy C-means and K-means clustering techniques have been applied for the identification of homogeneous rainfall regions while innovative trend analysis (ITA) along with the family of Mann–Kendall (MK) tests has been applied for the trend analysis of rainfall. The result shows that in all rain-gauge stations of Delhi, an increasing trend in rainfall has been recorded during 1991–2018. But the rate of increase was low as the trend slope of ITA and Sen’s slope in MK tests are low, which varies between 0.03 and 0.05 and 0.01 and 0.16, respectively. Furthermore, none of the rain-gauge stations have experienced a monotonic trend in rainfall as the null hypothesis has not been rejected (p value > 0.05) for any stations. Furthermore, the study shows that ITA has a better performance than the family of MK tests. The findings of this study may be utilized for the urban flood mitigation and solving other issues related to water resources in Delhi and other cities.
Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor... more Since its advent in 1972, the Landsat satellites have witnessed consistent improvements in sensor characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat OLI and OLI-2 satellites in land use land cover (LULC) mapping has been made. For this, image fusion techniques have been applied to enhance the spatial resolution of both OLI and OLI-2 multispectral images, and then a support vector machine (SVM) classifier has been used for LULC mapping. The results show that LULC classification from OLI-2 has better accuracy (92.4%) than OLI (83.4%). The validation of classified LULC maps shows that the OLI-2 data is more accurate in distinguishing dense and sparse vegetation as well as darker and lighter objects. The relationship between LULC maps and surface biophysical parameters using Local Moran’s I also shows better performance of the OLI-2 sensor in LULC mapping than the OLI sensor.
Rapid and uncontrolled population growth along with economic and industrial development, especial... more Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error...
According to the World Urbanization Prospects of United Nations, the global urban population has ... more According to the World Urbanization Prospects of United Nations, the global urban population has increased rapidly over past few decades, reaching about 55% in 2018, which is projected to reach 68% by 2050. Due to gradual increase in the urban population and impervious surfaces, the urban heat island (UHI) effect has increased manifold in the cities of developing countries, causing a decline in thermal comfort. Therefore, this study was designed to model the spatio-temporal pattern of UHI and its relationships with the land use indices of Delhi and Mumbai metro cities from 1991 to 2018. Landsat datasets were used to generate the land surface temperature (LST) using mono window algorithm and land use indices, such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBal), normalized difference moisture index (NDMI), and modified normalized difference water index (MNDWI). Additionally, the urban hotspots (UHS) were identified and then the thermal comfort was modelled using the UTFVI. The results showed that maximum (30.25-38.99 °C in Delhi and 42.10-45.75 °C in Mumbai) and minimum (17.70-23.86 °C in Delhi and 19.06-25.05 °C in Mumbai) LST witnessed steady growth in Delhi and Mumbai from 1991 to 2018. The LST gap decreases and the UHI zones are being established in both cities. Furthermore, the UHS and worst-category UTFVI areas increased in both cities. This research can be useful in designing urban green-space planning strategies for mitigating the UHI effects and thermal comfort in cities of developing countries. Keywords Land use indices • Urban heat island • Urban Hotspots • Urban thermal field variation index • Thermal comfort
Journal of the India Society of Remote Sensing, 2021
The rapid urbanization and land-use/land-cover (LU/LC) changes have resulted in the unplanned and... more The rapid urbanization and land-use/land-cover (LU/LC) changes have resulted in the unplanned and unsustainable growth of the Indian cities. This has resulted in a number of environmental issues such as escalating the urban heat island (UHI) intensity over the cities. Therefore, this study was designed to model and quantify the UHI dynamics of Mumbai city in response to the LU/LC change during 1991-2018 using temporal Landsat datasets. The result shows a significant decline in vegetation cover from 215.8 to 129.27 km 2 , while the built-up areas have almost doubled, i.e., from 173.09 to 346.02 km 2 in the Mumbai city during 1991-2018. As a consequence of this, a significant increase in the LST has been noticed in both urban heat island (UHI) and non-UHI zones. Although the areas under UHI zones have not increased significantly, the land surface temperature (LST) gap (difference between minimum and maximum LST) has declined in the Mumbai city from 30.04°C in 1991 to 20.7°C in 2018. Further, the minimum and mean LST over each LU/LC classes have also shown a significant increase. On the other hand, the regression analysis shows that the association between UHI and normalized difference built-up index (NDBI) has increased in the city, while the association of vegetation density (NDVI) and normalized difference bareness index (NDBaI) has declined in the city. The study can provide useful insights into the process of urban planning and policy makings for urban spatial planning and UHI mitigation strategies. Keywords Land-use/land-cover change Á Land surface temperature Á Urban heat island Á Vegetation and built-up density Á Mumbai city
Journal of the Indian Society of Remote Sensing, 2021
The coastal area supports millions of population in terms of livelihood, settlement and social ac... more The coastal area supports millions of population in terms of livelihood, settlement and social activities across the world and India. The increasing rate of socioeconomic activities made the coasts susceptible to various hazards. Therefore, this study is aimed to examine the coastal vulnerability of Vishakhapatnam Coastal district using remote sensing and geographic information system. To fulfill this objective, six physical indicators, i.e., geomorphology, land use/land cover, coastal slope, shoreline change rate, etc., were prepared using the multi-temporal datasets of 1991, 2001, 2011 and 2018 and mean tidal height has been considered to calculate the coastal vulnerability index (CVI). The indicators selected for the analysis of coastal vulnerability have been integrated using the rank and weighted methods. The shoreline change has been detected using the digital shoreline analysis system (DSAS). Analytical hierarchy process (AHP) has been used for calculating weights of various indices. The CVI values obtained using different indicators are 2.6 (min) and 14.39 (max). Based on the CVI values, the coast is classified as five classes of vulnerability, i.e., very low (0-4.9) covering 42.5 km, low (4.9-7.3) which covers 29.49 km, moderate (7.3-9.6) covering 23.46 km, high (9.6-12.0) which covers 34.61 km, and very high (12.0-14.39) covering 7.5 km. This integrated study is found useful for exploring the accretion and erosion processes and also for vulnerability mapping in the coastal tract of Vishakhapatnam district.
Groundwater scarcity is one of the most concerning issues in arid
and semi-arid regions. In this ... more Groundwater scarcity is one of the most concerning issues in arid and semi-arid regions. In this study, we develop and validate a novel artificial intelligence that is a coupling of five ensemble benchmark algorithms e.g., artificial neural network (ANN), reduced-error pruning trees (REPTree), radial basis function (RBF), M5P and random forest (RF) with particle swarm optimization (PSO) for delineating GWP zones. Further, nine parameters used for the GWP modelling and to test and train the proposed PSObased models. Additionally, this study proposes a receiver operating characteristic (ROC) based sensitivity analysis for GWP modelling. Multicollinearity test, information gain ratio, and correlation attribute evaluation methods used to choose important parameters for the proposed GWP model. The result shows that drainage density, elevation, and land use/land cover have a higher influence on the GWP using correlation attribute evaluation methods. Results showed that the hybrid PSO-RF model performed better than other proposed hybrid models.
Coastline or Shoreline calculation is one of the important factors in the finding of coastal accr... more Coastline or Shoreline calculation is one of the important factors in the finding of coastal accretion and erosion and the study of coastal morphodynamic. Coastal erosion is a tentative hazard for communities especially in coastal areas as it is extremely susceptible to increasing coastal disasters. The study has been conducted along the coast of Vishakhapatnam district, Andhra Pradesh, India with the help of multi-temporal satellite images of 1991 2001, 2011 and 2018. The continuing coastal erosion and accretion rates have been calculated using the Digital Shoreline Analysis System (DSAS). Linear regression rate (LRR), End Point Rate (EPR) and Weighted Linear Regression (WLR) are used for calculating shoreline change rate. Based on calculations the district shoreline has been classified into five categories as high and low erosion, no change and high and low accretion. Out of 135 km, high erosion occupied 5.8 km of coast followed by moderate or low erosion 46.2 km. Almost 34.7 km coastal length showed little or no change. Moderate accretion is found along 30.5 km whereas high accretion trend found around 17.8 km. The outcome of shows that erosion is prevailing in Vishakhapatnam taluk, Ankapalli taluk, Yellamanchili taluk whereas most of the Bhemunipatnam coast is accreting. Natural and manmade activities and phenomena influence the coastal areas in terms of erosion and accretion. The study could be used for further planning and development and also for disaster management authority in the decision-making process in the study area. ARTICLE HISTORY
This study was designed to compare the pattern of land surface temperature (LST) over four metro ... more This study was designed to compare the pattern of land surface temperature (LST) over four metro cities of India (Mumbai, Chennai, Delhi, and Kolkata) selected on a longitudinal basis in relation to the built-up and vegetation indices. Two different methods were employed for the retrieval of LST, i.e., mono-window algorithm (MWA) and split-window algorithm (SWA) on the Landsat 8 (OLI/TIRS) datasets, to analyze the spatial pattern of LST over selected cities in relation to normalized differential built-up index (NDBI) and normalized differential vegetation index (NDVI). The result shows that the LST was high over the densely built areas while low over the densely vegetated areas. The highest LST, NDBI, and NDVI were found in Mumbai, while Kolkata records the lowest LST and NDVI. Furthermore, the spatial analysis of LST shows that the LST was high in central parts of all cities except in the case of Delhi where some peripheral areas also record high LST. The comparison from in situ LST (field observations) reveals that the SWA has higher accuracy in the retrieval of LST in maritime areas like Mumbai and Chennai because it reduces the atmospheric effects, while the MWA has higher accuracy for inland areas like Delhi. The spatial relationships of LST with NDVI and NDBI show that vegetation cover has more impact on LST in Delhi while low in Chennai and Mumbai, and the built-up surfaces have a higher impact on LST in Chennai and Mumbai than Kolkata and Delhi.
Groundwater in Delhi Metropolitan Region (DMR) is suffering from multiple catastrophes, viz., asy... more Groundwater in Delhi Metropolitan Region (DMR) is suffering from multiple catastrophes, viz., asymptotic increases in groundwater withdrawal, reduced recharge due to erratic rainfall, and variable soil type. In this study, we examined long-term trends in groundwater levels across the DMR from 1996 to 2018. Station level data collected by the Central Groundwater Board for 258 stations at the seasonal scale were visualized and interpreted using geospatial analysis. The spatial patterns of the trends in groundwater levels revealed increasing depths of groundwater levels, except the Yamuna River floodplains. The main cause for the decline is related to the rapid growth in population accompanied with high-density impervious urban land uses, leading to lower levels of recharge vs unlimited withdrawal of groundwater for daily needs. In addition, the local geology in the form of clayey soils in northwest DMR also contributed to the lower levels of recharge. The results of the analysis enabled us to establish the trend and delineate the zones of differential recharge. Furthermore, the level of contaminants were analyzed at the district level for fluorides and nitrates. The presence of fluoride contamination was mostly concentrated in the northwestern district, while the nitrate exceedance was more widespread. These findings will help in achieving the 6th Sustainable Development Goal (SDG) of United Nations by 2030 as well as goals identified in Delhi's master plan of 2041.
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Papers by SHAH FAHAD
and semi-arid regions. In this study, we develop and validate a
novel artificial intelligence that is a coupling of five ensemble
benchmark algorithms e.g., artificial neural network (ANN),
reduced-error pruning trees (REPTree), radial basis function (RBF),
M5P and random forest (RF) with particle swarm optimization
(PSO) for delineating GWP zones. Further, nine parameters used
for the GWP modelling and to test and train the proposed PSObased
models. Additionally, this study proposes a receiver operating
characteristic (ROC) based sensitivity analysis for GWP modelling.
Multicollinearity test, information gain ratio, and correlation
attribute evaluation methods used to choose important parameters
for the proposed GWP model. The result shows that drainage
density, elevation, and land use/land cover have a higher influence
on the GWP using correlation attribute evaluation methods.
Results showed that the hybrid PSO-RF model performed better
than other proposed hybrid models.
and semi-arid regions. In this study, we develop and validate a
novel artificial intelligence that is a coupling of five ensemble
benchmark algorithms e.g., artificial neural network (ANN),
reduced-error pruning trees (REPTree), radial basis function (RBF),
M5P and random forest (RF) with particle swarm optimization
(PSO) for delineating GWP zones. Further, nine parameters used
for the GWP modelling and to test and train the proposed PSObased
models. Additionally, this study proposes a receiver operating
characteristic (ROC) based sensitivity analysis for GWP modelling.
Multicollinearity test, information gain ratio, and correlation
attribute evaluation methods used to choose important parameters
for the proposed GWP model. The result shows that drainage
density, elevation, and land use/land cover have a higher influence
on the GWP using correlation attribute evaluation methods.
Results showed that the hybrid PSO-RF model performed better
than other proposed hybrid models.