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The most expedient means of analysing global climate change is to analyse the precipitation along with major components of the global atmospheric circulation. Like other Coastal areas, Coastal Andhra is also vulnerable to extreme weather events. So, in this study, the trend and pattern of the precipitation of the Coastal Andhra have been analyzed using daily and monthly rainfall data of 36 years (1983–2018). The standardized precipitation index, rainfall anomaly index and Mann–Kendall tests have been used to analyse the trend and pattern of precipitation. The result showed that the average annual rainfall was 161 cm in 1983 which first declined to 147 cm in 1991 but increased to 181 cm in 2001 and again declined rapidly to 91 cm in 2018. Apart from this normalized difference vegetation index (NDVI) has been used for the years 1983, 1991, 2001, 2011 and 2018 for the validation of dryness and wetness. The statistical analysis shows that rainfall in the study area shows a declining trend at the rate of − 1.27 cm per year as per the result obtained by the Sen, slope. Further, the association between mean annual rainfall and NDVI is found to be very strong with a higher positive value of the coefficient of determination.
Mirza Razi Imam Baig; Shahfahad; Mohd Waseem Naikoo; Aijaz Hussain Ansari; Shakeel Ahmad; Atiqur Rahman. Spatio-temporal analysis of precipitation pattern and trend using standardized precipitation index and Mann–Kendall test in coastal Andhra Pradesh. Modeling Earth Systems and Environment 2021, 1 -20.
AMA StyleMirza Razi Imam Baig, Shahfahad, Mohd Waseem Naikoo, Aijaz Hussain Ansari, Shakeel Ahmad, Atiqur Rahman. Spatio-temporal analysis of precipitation pattern and trend using standardized precipitation index and Mann–Kendall test in coastal Andhra Pradesh. Modeling Earth Systems and Environment. 2021; ():1-20.
Chicago/Turabian StyleMirza Razi Imam Baig; Shahfahad; Mohd Waseem Naikoo; Aijaz Hussain Ansari; Shakeel Ahmad; Atiqur Rahman. 2021. "Spatio-temporal analysis of precipitation pattern and trend using standardized precipitation index and Mann–Kendall test in coastal Andhra Pradesh." Modeling Earth Systems and Environment , no. : 1-20.
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 km2, while the built-up areas have almost doubled, i.e., from 173.09 to 346.02 km2 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.
Shahfahad; Mohd Rihan; Mohd Waseem Naikoo; Mohd Akhter Ali; Tariq Mahmood Usmani; Atiqur Rahman. Urban Heat Island Dynamics in Response to Land-Use/Land-Cover Change in the Coastal City of Mumbai. Journal of the Indian Society of Remote Sensing 2021, 49, 2227 -2247.
AMA StyleShahfahad, Mohd Rihan, Mohd Waseem Naikoo, Mohd Akhter Ali, Tariq Mahmood Usmani, Atiqur Rahman. Urban Heat Island Dynamics in Response to Land-Use/Land-Cover Change in the Coastal City of Mumbai. Journal of the Indian Society of Remote Sensing. 2021; 49 (9):2227-2247.
Chicago/Turabian StyleShahfahad; Mohd Rihan; Mohd Waseem Naikoo; Mohd Akhter Ali; Tariq Mahmood Usmani; Atiqur Rahman. 2021. "Urban Heat Island Dynamics in Response to Land-Use/Land-Cover Change in the Coastal City of Mumbai." Journal of the Indian Society of Remote Sensing 49, no. 9: 2227-2247.
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.
Shahfahad; Swapan Talukdar; Mohd. Rihan; Hoang Thi Hang; Sunil Bhaskaran; Atiqur Rahman. Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environment, Development and Sustainability 2021, 1 -29.
AMA StyleShahfahad, Swapan Talukdar, Mohd. Rihan, Hoang Thi Hang, Sunil Bhaskaran, Atiqur Rahman. Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environment, Development and Sustainability. 2021; ():1-29.
Chicago/Turabian StyleShahfahad; Swapan Talukdar; Mohd. Rihan; Hoang Thi Hang; Sunil Bhaskaran; Atiqur Rahman. 2021. "Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities." Environment, Development and Sustainability , no. : 1-29.
This study is aimed to analyze the dynamics of land use/ land cover (LU/LC) change in a newly created special economic zone, Gautam Buddha Nagar district during 2003–2015. The Landsat satellite data has been used to map the LU/LC pattern of 2003 and 2015 of the study area, using indices overlay method. Consequently, the indices overlay have been created using three land-use indices, i.e. modified normalized difference water index (MNDWI), soil adjusted vegetation index (SAVI), and enhanced built-up and bareness index (EBBI), and then the maximum likelihood classifier (MLC) has been used for the LU/LC classification. The result illustrates that the built-up area (419.35%) and open land (388.36%) have increased during 2003–2015 while the cropland (− 34.38%), scrubland (− 73.25%), and water bodies (− 58.37%) have declined. Further, northern parts of the district have experienced maximum change in the LU/LC while the southern parts have experienced comparatively low change. The study also reveals that the increase in the built-up area occurred mostly at the cost of cropland and scrubland. The statistical analysis shows that the EBBI and SAVI have high relationships with LU/LC while the MNDWI has a comparatively low relationship. The study concludes that cropland and scrubland are the main LU/LC types that get transformed due into the built-up area the study area and the SAVI, MNDWI, and EBBI are the good indicators in the study of LU/LC classification and change analysis.
Babita Kumari; Shahfahad; Mohammad Tayyab; Ishita Afreen Ahmed; Mirza Razi Imam Baig; Mohd. Akhter Ali; Asif; Tariq Mahmood Usmani; Atiqur Rahman. Land use/land cover (LU/LC) change dynamics using indices overlay method in Gautam Buddha Nagar District-India. GeoJournal 2021, 1 -19.
AMA StyleBabita Kumari, Shahfahad, Mohammad Tayyab, Ishita Afreen Ahmed, Mirza Razi Imam Baig, Mohd. Akhter Ali, Asif, Tariq Mahmood Usmani, Atiqur Rahman. Land use/land cover (LU/LC) change dynamics using indices overlay method in Gautam Buddha Nagar District-India. GeoJournal. 2021; ():1-19.
Chicago/Turabian StyleBabita Kumari; Shahfahad; Mohammad Tayyab; Ishita Afreen Ahmed; Mirza Razi Imam Baig; Mohd. Akhter Ali; Asif; Tariq Mahmood Usmani; Atiqur Rahman. 2021. "Land use/land cover (LU/LC) change dynamics using indices overlay method in Gautam Buddha Nagar District-India." GeoJournal , no. : 1-19.
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.
Mirza Razi Imam Baig; Shahfahad; Ishita Afreen Ahmad; Mohammad Tayyab; Sarfaraz Asgher; Atiqur Rahman. Coastal Vulnerability Mapping by Integrating Geospatial Techniques and Analytical Hierarchy Process (AHP) along the Vishakhapatnam Coastal Tract, Andhra Pradesh, India. Journal of the Indian Society of Remote Sensing 2020, 49, 215 -231.
AMA StyleMirza Razi Imam Baig, Shahfahad, Ishita Afreen Ahmad, Mohammad Tayyab, Sarfaraz Asgher, Atiqur Rahman. Coastal Vulnerability Mapping by Integrating Geospatial Techniques and Analytical Hierarchy Process (AHP) along the Vishakhapatnam Coastal Tract, Andhra Pradesh, India. Journal of the Indian Society of Remote Sensing. 2020; 49 (2):215-231.
Chicago/Turabian StyleMirza Razi Imam Baig; Shahfahad; Ishita Afreen Ahmad; Mohammad Tayyab; Sarfaraz Asgher; Atiqur Rahman. 2020. "Coastal Vulnerability Mapping by Integrating Geospatial Techniques and Analytical Hierarchy Process (AHP) along the Vishakhapatnam Coastal Tract, Andhra Pradesh, India." Journal of the Indian Society of Remote Sensing 49, no. 2: 215-231.
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.
Shahfahad; Babita Kumari; Mohammad Tayyab; Ishita Afreen Ahmed; Mirza Razi Imam Baig; Mohammad Firoz Khan; Atiqur Rahman. Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arabian Journal of Geosciences 2020, 13, 1 -19.
AMA StyleShahfahad, Babita Kumari, Mohammad Tayyab, Ishita Afreen Ahmed, Mirza Razi Imam Baig, Mohammad Firoz Khan, Atiqur Rahman. Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arabian Journal of Geosciences. 2020; 13 (19):1-19.
Chicago/Turabian StyleShahfahad; Babita Kumari; Mohammad Tayyab; Ishita Afreen Ahmed; Mirza Razi Imam Baig; Mohammad Firoz Khan; Atiqur Rahman. 2020. "Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India." Arabian Journal of Geosciences 13, no. 19: 1-19.
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.
Swapan Talukdar; Pankaj Singha; Susanta Mahato; Shahfahad; Swades Pal; Yuei-An Liou; Atiqur Rahman. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing 2020, 12, 1135 .
AMA StyleSwapan Talukdar, Pankaj Singha, Susanta Mahato, Shahfahad, Swades Pal, Yuei-An Liou, Atiqur Rahman. Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing. 2020; 12 (7):1135.
Chicago/Turabian StyleSwapan Talukdar; Pankaj Singha; Susanta Mahato; Shahfahad; Swades Pal; Yuei-An Liou; Atiqur Rahman. 2020. "Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review." Remote Sensing 12, no. 7: 1135.