Skip to main content
SHAH FAHAD

    SHAH FAHAD

    Jamia Millia Islamia, Geography, Graduate Student
    • Shahfahad is a Senior Research Fellow (SRF) at the Department of Geography, Jamia Millia Islamia. He is doing pursuin... moreedit
    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.... 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 most widely studied and understood phenomenon. The impervious surfaces in urban areas absorb and re-emit the heat from solar radiation more... 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 variations in soil moisture and vegetation cover which causes significant variations in land surface temperature (LST) and surface urban heat... 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 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... 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 characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat Operational Land Imager... 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.
    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... 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 characteristics, which have significantly improved accuracy. In this study, a comparison of the accuracy of Landsat OLI and OLI-2 satellites... 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, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC)... 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 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... 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
    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... 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
    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.... 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 study, we develop and validate a novel artificial intelligence that is a coupling of five ensemble benchmark algorithms e.g., artificial... 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 accretion and erosion and the study of coastal morphodynamic. Coastal erosion is a tentative hazard for communities especially in coastal areas... 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 cities of India (Mumbai, Chennai, Delhi, and Kolkata) selected on a longitudinal basis in relation to the built-up and vegetation indices.... 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., asymptotic increases in groundwater withdrawal, reduced recharge due to erratic rainfall, and variable soil type. In this study, we examined... 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 flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the... more
    The flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the devastating disasters from nature, but for developing nations like Bangladesh, flooding becomes worse. Due to the dynamic and complex nature of the flooding, the prediction of flooding sites was usually very difficult for flood management. But the artificial intelligence and advanced remote sensing techniques together could predict and identify the possible sites, which are vulnerable to flooding. The present work aimed to predict and identify the flooding sites or flood susceptible zones in the Teesta River basin by employing state-of-the-art novel ensemble machine learning algorithms. We developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results. Twelve factors, which are considered as the conditioning factors, and 413 current and former flooding points were identified for flooding susceptibility modelling. The Information Gain ratio statistical technique was utilized to determine the influence of the factors for flooding. We applied receiver operating characteristic curve (ROC) for validation of the flood susceptible models. The Freidman test, Wilcoxon signed-rank test, Kruskal-Wallis test and Kol-mogorov-Smirnov test were applied together for the first time in flood susceptibility modelling to compare the models with each other. Results showed that more than 800 km 2 area was predicted as the very high flood susceptibility zones by all algorithms. The ROC curve showed that all models achieved more than 0.85 area under the curve indicating highly accurate flood models. For flood susceptibility modelling, the bagging with M5P performed superior, followed by bagging with RF, bagging with REPtree and bagging with RT. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate measures to avoid potential damages. Electronic supplementary material The online version of this article (https://doi.
    this study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time... more
    this study analyzes and forecasts the long-term Spatio-temporal changes in rainfall using the data from 1901 to 2015 across India at meteorological divisional level. The Pettitt test was employed to detect the abrupt change point in time frame, while the Mann-Kendall (MK) test and Sen's innovative trend analysis were performed to analyze the rainfall trend. The Artificial Neural Network-Multilayer Perceptron (ANN-MLP) was employed to forecast the upcoming 15 years rainfall across India. We mapped the rainfall trend pattern for whole country by using the geo-statistical technique like Kriging in ArcGIS environment. Results show that the most of the meteorological divisions exhibited significant negative trend of rainfall in annual and seasonal scales, except seven divisions during. Out of 17 divisions, 11 divisions recorded noteworthy rainfall declining trend for the monsoon season at 0.05% significance level, while the insignificant negative trend of rainfall was detected for the winter and pre-monsoon seasons. Furthermore, the significant negative trend (−8.5) was recorded for overall annual rainfall. Based on the findings of change detection, the most probable year of change detection was occurred primarily after 1960 for most of the meteorological stations. The increasing rainfall trend had observed during the period 1901-1950, while a significant decline rainfall was detected after 1951. The rainfall forecast for upcoming 15 years for all the meteorological divisions' also exhibit a significant decline in the rainfall. The results derived from ECMWF ERA5 reanalysis data exhibit that increasing/ decreasing precipitation convective rate, elevated low cloud cover and inadequate vertically integrated moisture divergence might have influenced on change of rainfall in India. Findings of the study have some implications in water resources management considering the limited availability of water resources and increase in the future water demand. Rainfall is a key part of hydrological cycle and alteration of its pattern directly affect the water resources 1. The changing pattern of rainfall in consequence of climate change is now concerning issues to water resource managers and hydrologists 2. Srivastava et al. 3 and Islam et al. 4 reported that the changes of rainfall quantities and frequencies directly changing the stream flow pattern and its demand, spatiotemporal allocation of runoff , ground water reserves and soil moisture. Consequently, these changes showed the widespread consequences on the water resource, environment, terrestrial ecosystem, ocean, biodiversity , agricultural and food security. The drought and flood like hazardous events can be occurred frequently because of the extreme changes of rainfall trend 5. Gupta et al. 6 documented that the amount of soil moisture for crop production is totally determined by the amount of rainfall. The monsoon rainfall plays a vital role for agriculture in India. 68% of cultivated land to the total cultivated land of India is occupying by the rain fed agriculture which supports 60% of livestock population and 40% of human population 7. Hence, the research on the climate change or most specifically on the changes of rainfall occurrences and its allocation are the most significant way for sustainable water resource management. Therefore, the sustainable development of agriculture in India requires the noteworthy research on the identification and
    During past four decades, in post economic reforms period, Delhi and its surrounding regions has attracted a large number of populations which led to the rapid transformation of its LULC pattern. Therefore, this study is aimed to analyze... more
    During past four decades, in post economic reforms period, Delhi and its surrounding regions has attracted a large number of populations which led to the rapid transformation of its LULC pattern. Therefore, this study is aimed to analyze the LULC changes during 1990-2018 as well as the growth and pattern of built-up surfaces in relation to the population growth and migration in the suburbs of Delhi metropolitan city which is also known as the National Capital Region (NCR). The Landsat 5 (TM) and Landsat 8 (OLI/TIRS) data has been used for the LU/LC classification of Delhi NCR. The K means clustering technique was applied on the Landsat data for the LULC classification and then the change detection technique was used to quantify the LULC change. The result shows that the considerable changes in LULC have occurred with continuous increase in built-up area and open/fallow land and decrease in agriculture land and vegetation over the study time period. Built-up area increased by about 326 percent and open/fallow land by 44 percent while the agricultural land and vegetation cover have decreased by 12 percent and 34 percent of the total area of study respectively during the study period. Built-up area has mostly increased at the expense of agricultural land and vegetation cover while vegetation cover has been transformed into Built-up area, Ridge and Agriculture. The statistical analysis shows that the association between built-up expansion and the population and migrants varies from weak to high but the coefficient of determination was always positive.
    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)... 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 (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.
    The ecosystems provide a range of material as well as non-material services that contribute to human well-being as well as supply necessary resources for the organisms. The land use/ land cover (LU/LC) changes have been taken place due to... more
    The ecosystems provide a range of material as well as non-material services that contribute to human well-being as well as supply necessary resources for the organisms. The land use/ land cover (LU/LC) changes have been taken place due to several natural and anthropogenic reasons, which significantly influence the ecosystem services. Therefore, the present study aimed to explore the minor variations of ecosystem services provided by the particular land use types of the study area. Therefore, we have divided the study area into nine grids. The land use land cover classifications have been performed using support vector machine techniques (SVM) for 1999-2019. Based on the multi-temporal land use land cover maps, we have used the global coefficient value of 1997 and 2003 for valuation of ecosystem services for different land use types. Then we have employed elasticity techniques to analyse the response of land use land cover changes over the ecosystem service valuation. The findings showed that the overall built-up area has increased by 29.14% since 1999, while the overall water-body has decreased by 15.81%. Therefore, the ecosystem services provided by water-body have been decreased correspondingly and the 29.14% areas that converted to built-up area from others land use types do not able to provide any ecosystem services and the ecosystem service values become nil, which is not suitable for good health ecosystem. Therefore, the study can be the foundation to the planners and scientists to prepare sustainable plans for the management of local ecosystem based on minorly study on the impact of LULC changes on the ecosystem services.
    The population growth in urban areas leads to the expansion of built-up area which leads to a number of serious problems like environmental pollution , destruction of urban ecology, climatic modification etc. In this study, we have tried... more
    The population growth in urban areas leads to the expansion of built-up area which leads to a number of serious problems like environmental pollution , destruction of urban ecology, climatic modification etc. In this study, we have tried to assess the linkages and association between population growth and built-up expansion in Surat city. Landsat satellite data (TM, ETM? and OLI/TIRS) has been used for 1991, 2001, 2011 and 2019 to extract the built-up area while the demographic data of the city was obtained from the Census of India and SMC. The built-up area has been extracted using index based built-up index (IBI) method. The association between urban expansion rate (RUE) and population growth rate (PGR), distribution of population and built-up area and the population and built-up density was analyzed using linear regression technique. The result shows that both the population and built-up area of Surat has increased rapidly but the rate of increase of built-up area is higher than the population. The statistical analysis
    Special economic zones (SEZs) concept has evolved across the globe and several developing countries have adopted this policy for set-up of industrial zones that focus on exports and helps in generating economic opportunity and able to... more
    Special economic zones (SEZs) concept has evolved across the globe and several developing countries have adopted this policy for set-up of industrial zones that focus on exports and helps in generating economic opportunity and able to attract Foreign direct investment. So SEZs can be defined as a geographically delimited area which is physically secured, has single window clearance system, less complicated administrative unit and duty free environment. So objective behind this paper is to evaluate the performance of SEZs in India in terms of generation of employment, attraction of Foreign Direct Investment (FDI) and contribution in Indian export. To fulfill the objective data related to employment, investment and export from SEZs are collected from different sources like Ministry of Commerce and Industries, economic survey of India and official website of SEZ. These data are organized in Ms Excel and simple tabular analysis is done. Simple and compound growth rate is calculated to show the change in the performance of SEZs. It is found in the study that SEZs helps in generation of employment, attraction of FDI and contribution in Indian export. At present scenario 1688.34 thousand people is working in SEZs at national level, FDI in SEZs is 433142 crores in 2017 and export output from SEZs in 2016 was 467337 crores which is 19.88% of total export from India.
    Special economic zone (SEZ) is a geographically delimited area which is physically secured, has single management and administrative unit and duty free environment (Zeng, 2015). In India SEZs established to solve the problem of... more
    Special economic zone (SEZ) is a geographically delimited area which is physically secured, has single
    management and administrative unit and duty free environment (Zeng, 2015). In India SEZs established to
    solve the problem of infrastructural deficiency, complex business procedure, bureaucratic hassles and
    barriers raised by monetary, trade, fiscal, taxation, tariff and labour policies (Doharmann, 2008). SEZ in
    India was conceived by the Commerce and Industries Minister Murosoli Maran during a visit to Special
    Economic Zone in China in 1999. The scheme was announced at the time of annual review of Export Import
    Policies from 1.4.2000. The basic idea is to establish the zone as area where economic activities could take
    place free from all rules and regulations and to give them operational flexibility. So this paper aims at
    analyzing establishment of SEZ in India and to assess their spatial and sectoral distribution. To fulfill this
    objective data related to notification, establishment of SEZs, spatial and sectoral distribution of SEZs and
    contribution of SEZs in Indian GDP are collected from different sources such as Development
    Commissioner of SEZ and ministry of commerce and industries on temporal basis. These data are tabulated
    in MS Excel, where analysis part is carried out. The whole research is based on descriptive research and
    comparative study and analytical logic developed through the understandings from various research papers,
    reports, books, journals, newspapers and online data bases. Simple growth rate is calculated to show the
    temporal change in approval of SEZs in India. It is found in this study that SEZs causes uneven
    geographical developments in India. Recent developments of SEZs suggest that advanced state and city
    regions have attracted much of the investment in SEZs like Maharashtra, Andhra Pradesh, Tamil Nadu,
    Karnataka, Gujarat and Haryana have attracted a large number of SEZs in comparison to other states while
    under-developed areas have been ignored by the SEZ developers. In the sector-wise composition of SEZs,
    majority of IT/ITES SEZs are either formally approved or notified. However, as far as SEZs in principle are
    concerned, the numbers of Multi-product SEZs are greater as compared to other categories of SEZs. Typewise
    distribution of SEZs provides that most of the SEZs belong to the category of IT/ITES, but a large area
    is allocated to multi-product SEZs. Size-wise distribution of SEZs shows that maximum numbers of SEZs
    are either tiny or small. But most of the area is allocated to large SEZs. It has also been observed that most
    of the tiny SEZs carry out IT/ITES activities and almost all the large size SEZs are Multi-product SEZs. It is
    evident from the analysis that SEZs contributes in the export and GDP of India.
    built-up area in the periphery (Chadchan and Shankar, Int J Sustainable Built Environ 1:36–49, 2012; Pandey and Seto, J Environ Manag 148:53–66, 2015). With these physical changes, i.e. decrease in green cover and increase in built-up,... more
    built-up area in the periphery (Chadchan and Shankar, Int J Sustainable Built Environ 1:36–49, 2012; Pandey and Seto, J Environ
    Manag 148:53–66, 2015). With these physical changes, i.e. decrease in green cover and increase in built-up, the land surface
    temperature (LST) is bound to increase. The green area is a basic need of any city because it is a must for a healthy life and also
    maintains the aesthetic and ecological beauty in the urban areas (Low et al. 2007). The present study aims to analyse the
    association between built-up, green cover and land surface temperature for which district-level analysis of the normalised
    differential built-up index (NDBI), normalised differential vegetation index (NDVI) and land surface temperature (LST) has
    been done over the urban area of Delhi. In this study, Landsat 7 (ETM+ SLC) for 2003, Landsat 5 (TM) for 2010, and Landsat 8
    (OLI/TIRS) for 2017 have been used together with Survey of India (SOI) toposheet of Delhi at 1:25,000. Indices like NDBI,
    NDVI and LST are calculated for 2003, 2010 and 2017 using the spectral radiance model (SRM), the mono-window algorithm
    (MWA) and the split window algorithm (SWA). Thereafter, district-wise NDBI, NDVI and LST are extracted by using clip tools
    in ArcGIS 10.5 software. To analyse the relationship between built-up and green cover with LST, correlation is done in SPSS
    software and a scatter diagram is made to assess the correlation amongst the variables. The further surface temperature profile is
    created to know which part of the Delhi has the highest and lowest temperatures on a particular surface. The study shows that
    NDVI and LST are negatively correlated with each other as vegetation has a cooling effect on the land surface temperature
    whereas NDBI and LST are positively correlated with each other. The studies show a change in the distribution of vegetation
    cover and gradually increase in the built-up land which results in the increase in land surface temperature to about 3.31 °C in the
    last 14 years. The result shows thatMWA give the most accurate result in this study since RMSE ofMWA is the lowest (0.71 °C)
    amongst the three algorithms used in the study. Temporal analysis of land surface temperature by all the three algorithms shows
    the increase in land surface temperature of Delhi between 2003 and 2017.
    Public open spaces and green cover are a connection between people and nature and are necessary to retain the quality of the urban landscape. Rapid urban growth and population increase put tremendous pressure on public open spaces, which... more
    Public open spaces and green cover are a connection between people and nature and are necessary to retain the quality
    of the urban landscape. Rapid urban growth and population increase put tremendous pressure on public open spaces,
    which reduces the quality of the urban landscape. The fast urban growth leads to utilize remaining green and open areas
    of the city for different purposes. As a result, the size of the urban green and open space is decreasing at an alarming
    rate. Therefore, the study analyses the availability of per capita public open spaces to assess the landscape quality. The
    green space available per capita in Delhi is about 20 m2, and public open space is 30 m2. Weights have been assigned
    to five variables selected to assess the landscape quality, and then, it was multiplied by the respective z score (scalefree)
    of each variable. Finally, we have calculated composite index score to develop a landscape quality index. The per
    capita share of public open spaces in East Delhi is 7.01 m2 to its total area. Wards with high population density have a
    comparatively low proportion of public open spaces. It is seen that most of the wards did not match the criteria of WHO
    and UN for per capita availability of public open spaces. The landscape quality index shows that in more than two-thirds
    of the wards, the landscape quality is poor. The wards of the central and northern parts of the study area are densely
    populated and have a low concentration of public open spaces; therefore, they have the least index score on landscape
    quality. At the same time, the wards of southeastern and eastern parts, where the population density is the lowest, the
    score of the landscape quality index is high.
    Delhi is the second largest metropolitan city of India after Mumbai which is still growing. In any city smooth traffic movement is a prerequisite for its development but it is very difficult to achieve because of increase in population,... more
    Delhi is the second largest metropolitan city of India after Mumbai which is still growing. In
    any city smooth traffic movement is a prerequisite for its development but it is very difficult
    to achieve because of increase in population, commercial and industrial activities. Besides
    this high vehicle ownership and poor supporting public transport facilities cause serious
    problems of transport. Primary data related to user satisfaction is collected through a
    structured interview done in the area of Hauz Khas metro station in Delhi in June 2018. It is
    seen in the study that there is rapid growth of population in Delhi i.e. 5.64% average annual
    growth rate between 1981 and 2011 and the number of vehicles and road length also
    increased during this period but increase of length of road is not as much as the increase in
    the number of vehicles. Vehicles increased with about 49% annual average growth rates
    whereas length of road increased at about 4% annual average growth rate. It is found in the
    study that people are satisfied with the service as it provides a safe, secured and fast mode
    of transport.