scholarly journals ANALYSIS OF SPATIO-TEMPORAL URBAN DYNAMICS IN 11 SMART CITIES OF UTTAR PRADESH, INDIA

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 557-564
Author(s):  
R. Verma ◽  
P. K. Garg
Keyword(s):  
Land Use ◽  
Nearest Neighbor ◽  
Smart Cities ◽  
Urban Expansion ◽  
Uttar Pradesh ◽  
Temporal Analysis ◽  
Sudden Increase ◽  
Aggregation Index ◽  
Detection Techniques ◽  
Spatio Temporal

Abstract. Urban planning in smart cities needs to be done in a “Smart” way. One way is to analyze the urbanisation pattern by spatio-temporal change detection techniques. Classified data such as, for years 1985, 1995 and 2005 Decadal Land use data for India and for year 2015, Copernicus Global Land service Dynamic Land Cover layers (CGLS-LC100 products) are used to perform multi-temporal analysis of the 11 smart cities of Uttar Pradesh state of India namely "Agra", "Aligarh", "Bareilly", "Jhansi", "Kanpur", "Lucknow", "Moradabad", "Prayagraj", "Rampur", "Saharanpur" and "Varanasi". Dynamics of Urban expansion are studied utilizing concepts of Landscape Metrics calculated by FRAGSTATS and also Shannon’s Entropy Values (Hn) over the 11 smart cities. Largest Patch Index (LPI), Landscape Shape Index (LSI), Aggregation Index (AI) and Mean Euclidean Nearest Neighbor Distance (ENN_MN) are metrics used to characterize urbanisation. Results indicate rise in value of LSI over the years from 1985 and with sudden increase in year 2015 for Built-up patches, corroborating more complexity in shapes of Built-up patches in all 11 cities. Kanpur, showing large values of LPI indicates the sudden increase of Built-up land use class over the years. The decreasing value of ENN_MN over the years indicates less centrality for built-up pixels in urbanisation. AI is unchanged for Built-up patches for 1985–1995 but decrease in year 2015 indicates less compactness which is due to dispersion of built-up pixels. High values of Hn over the years indicating dispersion of urbanisation in all 11 smart cities except Agra, also validates results.

scholarly journals SPATIO-TEMPORAL LAND USE CHANGE ANALYSIS IN DIRECTIONAL ZONES OF LUCKNOW CITY, INDIA

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 181-186
Author(s):  
R. Verma ◽  
P. K. Garg
Keyword(s):  
Land Use ◽  
Nearest Neighbor ◽  
Urban Expansion ◽  
Temporal Analysis ◽  
Shape Index ◽  
Change Analysis ◽  
Aggregation Index ◽  
North West ◽  
North East ◽  
Spatio Temporal

Abstract. Dynamic changes in urbanisation of a city is best analyzed through spatio-temporal analysis of classified data. Decadal Land use data for India for years 1985, 1995 and 2005 and Copernicus Global Land service Dynamic Land Cover layers (CGLS-LC100 products) for year 2015 have been used to conduct analysis for multi-temporal analysis of urban expansion and its dynamics using Landscape Metrics by FRAGSTATS and Shannon’s Entropy Values (Hn) over the 4 directional zones of Lucknow city namely North-East (NE), South-East (SE), South-West (SW) and North-West (NW). The metrics used to find characteristics of urbanisation are Landscape Shape Index (LSI), Largest Patch Index (LPI), Mean Euclidean Nearest Neighbor Distance (ENN_MN) and Aggregation Index (AI). Results showed the increase in LSI for Built-up patches over the years from 1985 to 2015, explaining the increase in complexity of shapes of Built-up patches in all zones. The increase in LPI indicates the increase of Built-up land use class over the years but also the convergence of urbanisation in the study area as indicated by lower entropy values. NW zone of Lucknow city area being poor in Vegetation is having highest ENN_MN which is decreasing over the years indicating more centrality. AI is same for Built-up patches from 1985 to 2015 which is due to either edge-filling or outlying urban growth in study area in all 3 change durations 1985-1995, 1995–2005 and 2005–2015. Among all 4 zones of Lucknow city, decrease in vegetation is major factor to urbanisation in city over the years.

scholarly journals Spatio-Temporal Analysis Of Urban Expansion And Future Growth Patterns Of Lahore, Pakistan

2021 ◽  
Vol 14 (3) ◽  
pp. 41-53
Author(s):  
Muhammad Nasar-u-Minallah ◽  
Sahar Zia ◽  
Atta-ur Rahman ◽  
Omer Riaz
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Population ◽  
Urban Expansion ◽  
Temporal Analysis ◽  
Growth Patterns ◽  
Land Use Land Cover ◽  
The Past ◽  
Spatio Temporal ◽  
Lahore City

Lahore, a metropolis and 2nd largest city of Pakistan, has been experiencing rapid urban expansion over the past five decades. The socio-economic development and growth of the urban population have caused the rapid increase of urban expansion. The increase in the built-up area of Lahore has seen remarkable growth during the past five decades. This study is aimed at detecting the Spatio-temporal changes in land use land cover and evaluating the urban expansion of Lahore since 1973. The conversion of land to other uses is primarily because of growth in urban population, whereas the increase in economic activities is the central reason for the land-use changes. In this study, temporal Landsat imageries were integrated with demographic data in the GIS environment to quantify the spatial and temporal dynamics of land use land cover (LULC) changes and urban expansion of Lahore city. The supervised image classification of maximum likelihood algorithm was applied on Landsat MSS (1973 and 1980), TM (1990), ETM+ (2000), TM (2010), and OLI/TIRs (2020) images, whereas a postclassification comparison technique was employed to detect changes over time. The spatial and temporal analysis revealed that during the past five decades, the built-up area of Lahore city has expanded by ~ 532 km2. It was found from the analysis that in Lahore city the urban expansion was primarily at the cost of loss of fertile agricultural land, vegetation, and other cultivable land use. The analysis further revealed that the structure and growth pattern of Lahore has mainly followed road network and linear expansion. The results indicate that this accretive urban expansion is attributed to socio-economic, demography, conversion of farmland, rural-urban migration, proximity to transportation routes, and commercial factors. This study envisions for decision-makers and urban planners to devise effective spatial urban planning strategies and check the growth trend of Lahore city.

scholarly journals SPATIO-TEMPORAL ANALYSIS OF LAND USE DYNAMICS AND ITS POTENTIAL IMPLICATIONS ON LAND SURFACE TEMPERATURE IN LAHORE DISTRICT, PUNJAB, PAKISTAN

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 359-367
Author(s):  
M. Sohail ◽  
S. S. F. Ali ◽  
E. Fatima ◽  
D. A. Nawaz
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Temporal Analysis ◽  
Kappa Statistics ◽  
Spatio Temporal

Abstract. The rapid population growth and the urge in people to move to big cities for their settlement upshot in urban expansion. While stepping into the corridor of the 21st century, the utility of remote sensing and GIS techniques in various fields has made things understandable and thus enhances the ways of investigation for better decision making and management. The paper presents the Landsat Satellite series based Land Surface Temperature retrieval concerning land use/ land cover changes over Lahore District, Punjab, Pakistan. The Spatio-temporal analysis was performed from 1980–2020. We availed high-resolution Landsat and Sentinel-2 Satellite imagery to perform Normalized Difference Vegetation Index and Supervised classification. Cloud-free satellite data was acquired from June, July, or August. Data pre-processing including atmospheric and terrain corrections were performed using ERDAS Imagine. The Red, NIR, and Thermal bands were utilized for LST estimation. ArcGIS 10.22 was used for making maps, analysis, and interpretations. The Spatio-temporal analysis of LULC and LST for the area indicates a great urbanization trend over the past forty years. People are migrating from small towns and villages to the metropolitan city of Pakistan for their livelihoods, and settlements. The built-up/urban land has expanded over the period with excessive construction that has affected the Land surface temperature. The area where human activity has increased shows higher LST’s as compared to green lands. The excessive construction has taken off the agricultural land, while the River Ravi still flows with a changing course and less water table. The COVID-19 pandemic hit in 2020 put everything on lockdown had an impact on environmental restoration due to fewer emissions and human activities. The overall classification accuracy of the images yielded substantial-high Kappa statistics of 80 %, 88%, 82%, 82.41%, and 87.76% for 1980, 1990, 2000, 2010, and the 2020 images, respectively. The unplanned urbanization is leading the Lahore District to serious environmental issues and climate change impacts. The need of the hour is to properly plan and manage the area for the coming generations to have a healthier and sustainable place to breathe in.

scholarly journals Spatio-temporal variation of water supply in Guizhou Province, China

Water Policy ◽  
2016 ◽  
Vol 19 (1) ◽  
pp. 181-195 ◽  
Author(s):  
Huiqing Han ◽  
Yuxiang Dong
Keyword(s):  
Land Use ◽  
Water Supply ◽  
Urban Expansion ◽  
Guizhou Province ◽  
Water Yield ◽  
Freshwater Ecosystem ◽  
Yield Model ◽  
Invest Model ◽  
Spatio Temporal ◽  
The Impact

Water supply is an important freshwater ecosystem service provided by ecosystems. Water shortages resulting from spatio-temporal heterogeneity of climate condition or human activities present serious problems in the Guizhou Province of southwest China. This study aimed to analyze the spatio-temporal changes of water supply service using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, explore how climate and land-use changes impact water supply provision, and discuss the impact of parameters associated with climate and land-use in the InVEST model on water supply in the region. We used data and the model to forecast trends for the year 2030 and found that water supply has been declining in the region at the watershed scale since 1990. Climate and land-use change played important roles in affecting the water supply. Water supply was overwhelmingly driven by the reference evapotranspiration and annual average precipitation, while the plant evapotranspiration coefficients for each land-use type had a relatively small effect. The method for sensitivity analysis developed in this study allowed exploration of the relative importance of parameters in the InVEST water yield model. The Grain-for-Green project, afforestation, and urban expansion control should be accelerated in this region to protect the water supply.

scholarly journals Modeling of Urban Growth Using Cellular Automata and GIS Case of Benslimane in Morocco

2020 ◽  
Author(s):  
BENCHELHA MOHAMED ◽  
Benzha Fatiha ◽  
Rhinane Hassan ◽  
BENCHELHA SAID ◽  
BENCHELHA TAOUFIK ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Growth ◽  
Urban Expansion ◽  
Confusion Matrix ◽  
Spatial Transformation ◽  
Urban Growth Modeling ◽  
Main Component Analysis ◽  
Spatio Temporal ◽  
Main Component

In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.

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