Fig 2 - uploaded by Shrihari Surathkal
Content may be subject to copyright.
Source publication
Urban settlements in developing countries are, at present, growing five
times as fast as those in developed countries. This paper presents the
urban expansion and land use/land cover changes in the fast urbanizing
coastal area of the Dakshina Kannada district in Karnataka state, South
India, during the years 1983-2008 as a case study. Six Indian Re...
Similar publications
Land use/land cover (LULC) change can strongly affect carbon storage in terrestrial ecosystems. The rapid development of China’s economy has formed different functional regions. These functional regions profoundly affect land use patterns. Thus, assessing the carbon storage induced by LULC changes is significant for green development. Selecting the...
Indian cities are expanding at an unprecedented rate. The speed of development poses a challenge for urban planners, as the expansion of cities frequently outpaces the planning process. This leads to further challenges for urban planners, namely i) the database for the planning is often outdated and ii) processes and patterns of unplanned urban gro...
Citations
... The study of land use land cover (LULC) changes is very important in defining how the land is presently used and delivers a starting point for current and future planning (Abdullah et al., 2019;Hussain & Karuppannan, 2023;Yamamoto & Ishikawa, 2020). The LULC has become a fundamental component in present approaches for managing environmental changes and observing natural resources (Akram et al., 2022a(Akram et al., , 2022bBhagyanagar et al., 2012;Orimoloye et al., 2018). The LULC changes occur when there are alterations in how land is used or in the composition of the land cover (Akram et al., 2018;Masood et al., 2022). ...
Change in land use land cover (LULC) is driven by human activities and drives changes that limit the accessibility of services and products for livestock and humans. This study aimed to develop the spatio-temporal changes in LULC, vegetation cover, and moisture index using multi-temporal satellite data in Southern Punjab, Pakistan. Analysis of satellite data was accomplished using ArcGIS 10.4 software. Based on ground-truthing, the supervised classifica-tion technique (maximum likelihood algorithm) was used to achieve the LULC classification. The LULC change analysis revealed that vegetation area converted to the build-up area by 7.17 % and bare soil converted to urban areas by 1.68 % from 2000 to 2021. Average NDVI values were calculated at 0.23, 0.17, 0.19, and 0.14 for 2000, 2007, 2014, and 2021, respectively. In the study area, average NDMI values were observed at 0.28, 0.25, 0.2, and 0.15 for 2000, 2007, 2014, and 2021, respectively. Based on our study, the general trend in Southern Punjab is a decrease in vegetation cover, moisture index, and forest area due to an increase in build-up areas. Assessments of LULC and NDMI changes, as well as estimates of the effect on the environment, are necessary for many policy decisions and planning.
... The land use change researches provide us very much useful information towards improved information of the past practices, present land use change patterns and the future land use trends as crucial for policy makers and land use management [24][25][26]. Many studies have been conducted in various areas of Pakistan to determine the land use and it's causing factors and impacts. ...
Specific objective of this study was to find out the distribution of various land use changes in District Layyah from 2000 to 2020 using geographic information system (GIS) and remote sensing (RS) techniques, and the forces or factors that lead to land use change. District Layyah has experienced remarkable land use and land cover (LULC) changes for the past three decades. Three Landsat satellite images i.e. thematic mapper (TM), Landsat enhanced thematic mapper plus (ETM+) and operational land imager (OLI)/ TIRS for the years 2000, 2010 and 2020 were acquired from USGS website in order to detect the land use changes. By using ERDAS Imagine software, the maximum likelihood classification was employed in order to classify the images. The spatial and spectral distribution of five land use types was made including i.e. Water, Built-up, Vegetation, Desert, Bare and Sparse land. Ground Truth points were noted and these points were used for the validation and classification of the images. This accuracy showed an overall accuracy rate of 85% with a Kappa coefficient of 0.9 which demonstrated the basic classification method because the images used in the research were highly good. Results showed that the rise was revealed in Vegetation, Built-up and Water land uses from the year 2000 to 2020. On the other side, the decrease in Bare and Sparse land and Desert land use was calculated. The main driving factors behind these LULC changes were found the growth in population, agro-technological advancement and various physical factors (e.g. availability of water and so on), resulting an increase in built-up area. Present research will be beneficial in understanding the most important land use changes to estimate the future change trends in various land use classes for policy making and land use management.
... Remotely sensed satellite imagery has demonstrated its ability to distinguish earth's surface features (Saadat et al., 2011) and play a significant role in LULC mapping (Vivekananda et al., 2021) by providing a reliable and consistent source of information (Owen et al., 1998;Jensen, 2007;Alam et al., 2020). Remote sensing, in combination with Geographic Information System (GIS) and Global Positioning System (GPS), has proven to be an extremely useful and powerful tool for studying the spatial-temporal dynamics of LULC changes (Bhagyanagar et al., 2012;Fichera et al., 2012;Arveti et al., 2016;Belal and Moghanm, 2011). However, it may still be difficult to explicitly enumerate the causes of such dynamic changes (Yesuph and Dagnew, 2019). ...
... Computer-based algorithms are very efficient in clustering image pixels based on the spectral signatures to derive LULC maps. Some of the supervised classification algorithms are: Maximum Likelihood Classifier (MLC) (Owen et al., 1998;Benzer, 2010;Fichera et al., 2012;Shi et al., 2015;Rwanga and Ndambuki, 2017;Chamling and Bera, 2020;Yonaba et al., 2021), Parallelepiped, Minimum Distance, Mahalanobis Distance, Support Vector Machine (Foody et al., 2006;Bhagyanagar et al., 2012;Lin et al., 2015;Dibs et al., 2020). Unsupervised ISODATA and K-Mean algorithms were also adopted for LULC change detection (Saadat et al., 2011;Gadrani et al., 2018;Bonato et al., 2019;Talukdar et al., 2020). ...
... These pixels were independent of the sites choosen during the training phase. The number of pixels ranges from fifty to thousands (Foody et al., 2006;Bhagyanagar et al., 2012;Kamusoko et al., 2013;Pal and Ziaul, 2017;Roy and Inamdar, 2019;Chamling and Bera, 2020;Thakur et al., 2020;Hossain and Moniruzzaman, 2021) and even greater than thousands (Lin et al., 2015;Costa et al., 2018;Yan et al., 2019;Van Leeuwen et al., 2020). In total, 348, 350, and 330 reference pixels (approx. ...
In recent decades, rural areas in the surroundings of metropolitan cities have undergone substantial development in India. This has resulted in changes in land use and land cover (LULC) patterns having a significant impact on hydrological processes and climate change. Therefore, comprehending such modifications is critical for the decision-making process, and the development of a sustainable environment. The current study focused on comprehending the spatio-temporal dynamics of LULC changes and their repercussions on variations in Land Surface Temperature (LST) profiles over a two-decade period in the Dodballapur Taluk, Bengaluru Rural District of Karnataka State in India. The study also investigates the relationship between LULC changes, Land Surface Temperature (LST), and spectral indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). Landsat 7 ETM + satellite images acquired in the years, 2001, 2010, and 2019 were used in the study. The Supervised Maximum Likelihood Classifier (MLC) technique was adopted to classify images and assess spatio-temporal LULC changes and analyse transformations of several LULC classes viz. barren land (BL), built-up land (BUL), crop land (CL), pasture (P), uncultivated land (UCL), shrub land (SL), and water bodies (WB). The accuracy assessment ensured the veracity of the image classification. The results showed that a) built-up, crop, and shrub land have been increased by 75.54%, 38.18%, and 52.57% respectively, b) barren, pasture, and uncultivated land have been increased by 22.94%, 4.89%, and 50.62% respectively; and c) the area covered with surface water bodies has been significantly reduced by 635.58%. The analysis of LST changes revealed a 2.09 °C increase in mean LST with a variance of 2.75 °C, showing considerable fluctuations in cold and hot weather conditions. The regression study of scatterplots for LST-NDVI revealed triangular space (R² = −0.234), LST-NDBI revealed right-angled triangle space (R² = 0.567), and LST-NDWI also exhibited right-angled triangular space (R² = −0.533). Intriguingly, the relationship between NDVI and NDBI exhibited a crescent shaped scatterplot with R² value of −0.448. The findings of the study can assist policymakers and decision-makers in developing and implementing policies for sustainable rural development and water resource management.
... Urbanization refers to an index of transformation from conventional rural economies to modernized industrial one [3][4][5]. It usually occurs in an unplanned and irregular manner, resulting in profound transformation in patterns of land use and land cover [6,7]. Rapid urbanization with inadequate planning can have a negative impact on the environment with growing population [1,8,9]. ...
Urban flooding has become a significant concern across many towns and cities in the Asia Pacific. Vulnerability and its components must be understood in order to minimize flood risks. Rapid urban growth occurs in developing countries, resulting from unplanned settlements growing along the rivers, and coastlines are at greater risk. On average, a total of 40% of the world's population lives in narrow coastal belts that take up 7% of the total world land area. Coastal areas are urbanizing at an unprecedented rate that is posing a common threat to humans and ecosystems. Low-lying coastal areas are especially susceptible to climate change related coastal hazards such as; sea level rise, storm surge, coastal flooding, land subsidence etc. This study has been carried out across four talukas of South Goa district, India's smallest state, located along the Arabian sea. The low-lying coastal belt of South Goa district is dotted with world famous sandy beaches of Palolem, Agonda, Colva etc. which attract millions of tourists every year. The present study has assessed the spatio-temporal growth of built-up land in low-lying coastal areas (Marmugao, Salcette, Quepem and Canacona) of South goa district. Google Earth Engine platform was used to estimate Normalized Difference Build Index (NDBI) based on Landsat ETM+/OLI imageries for 2009, 2015 and 2020 to determine and map spatio-temporal changes in the total built-up area. The result revealed that there had been a rapid built-up area increment in South goa coastal belt by 24.94 Sq. Km between 2009 (88.46 Sq. Km) and 2015 (113.40 Sq. Km) and by 15.14 Sq. Km between 2015 (113.40 Sq. Km) and 2020 (128.54 Sq. Km). The main driving force behind this phenomenon is the extensive land use changes for haphazard tourism development (in Salcette and Canacona) and immigration (in Marmugao). However, the conversion of traditional paddy fields and modification of natural drainage system to increase built-up areas can significantly increase the physical and social vulnerability in low lying areas of Salcette and Canacona against the coastal hazards. This study may help urban planners/ authorities to let the region develop in sustainable manner.
... According to research, socioeconomics, neighbourhoods, topography, and geolocation all have an impact on urban expansion [40][41][42][43]. In this study, we selected nine factors in three categories as independent variables (Table 2) based on the referenced literature [44,45]. ...
The expansion and evolution of urban areas are the most perceptible manifestations of the transformation of the urban spatial form. This study uses remote sensing images of Nanjing from 2001, 2006, 2011, 2016, and 2021, along with socio-economic data to analyse the spatio-temporal characteristics of the city’s urban expansion. Furthermore, we utilize a binary logistic regression to quantitatively analyse the driving forces in each stage. We find that from 2001 to 2021, Nanjing’s urban area expanded approximately 3.97 times. Notably, the city started moving from a stage of medium-speed development to rapid development in 2006, and then slowed down and returned to medium-speed development in 2011. The urban land mainly expanded in the north, northeast, southeast, and southwest directions in a lopsided cross-shape roughly along the northwest-southeast direction; meanwhile, the city’s centre of gravity continuously moved towards the southeast. Among the driving factors, neighbourhood (distance from planned commercial centres, railways, and highways), topography, and geolocation (distance from the Yangtze River, and elevation) had a greater, albeit inhibitory effect on urban expansion. However, the effects of different socio-economic factors (GDP per capita, resident population, secondary and tertiary industry, etc.) varied across different time periods.
... The future management of a city necessitates the present-day data (Bhatta 2010). Lack of data at regional scale often poses challenges in developing countries like India (Bhagyanagar et al. 2012). Mangaluru is no different from that. ...
Urbanisation induced land use and a land cover change is a proliferating phenomenon worldwide. The impacts of chaotic urban growth are all well documented. To overcome the ill effects of urbanisation, continuous monitoring and sustainable planning are the keys. As the future urban growth in India is predicted to take place in small cities of less than a million populations, it is the crucial time to plan a strategy for sustainable urban growth. Mangaluru, being a small, emerging city has the basic fertility required for urban growth. But the deficiencies of spatial data availability and lack of insight for impending urbanisation scenario would hamper the sustainable management of urban growth in the region. In this context, the current study proposes to map land use and land cover and monitor the spatiotemporal dynamics of urban growth in Mangaluru urban agglomeration, India, during 1972–2018. The present study employs techniques of remote sensing and geographic information system (GIS) and uses Landsat series of imageries. The land use and land cover changes and dynamics of urban growth have been studied using change detection, Shannon entropy, and spatial trend analysis. The study reveals that the rapid increase in the built-up area is largely induced by intense in fill growth in the core range of the city which is further corroborated by the Shannon entropy value, which indicates the compact nature of city growth. It has been found that built-up area has increased from 3.68% in 1972 to 18.79% in 2018. Agricultural land on the other hand decreased from 36.51–11.22% during 1972–2018 period. The spatial trend of change analysis represents the expansion of built-up land along with the transport network, and predominantly following a north–south course. Landsat image classification has also shown that Land use and land cover have largely changed during 1972–2018.
... Land-use changes play an important role in maintaining a global ecosystem balance [19], the human variables for example government policies and the environment largely impacts the land-use changes [20,21]. A better understanding of the land-use changes can be acquired from the past practices, present patterns of the land use, and the future land use trajectory [22,23,24]. ...
Land modification and its allied resources have progressively become a severe problem presently pulling the worldwide attention and now it rests at the central point of the conservation of the environment and sustainability. The present research aimed to examine the land-use changes and their impact on agricultural production using remote sensing and GIS techniques over the study area that comprised of Tehsil Shorkot, District Jhang, Punjab, Pakistan. Images were pre-processed by using the Arc GIS and ERDAS Imagine 15 software for stacking of the layers, sub-setting, and mosaicking of the satellite bands. After the pre-processing of the images, supervised image classification scheme was applied by employing a maximum likelihood algorithm to recognize the land-use changes which have been observed in the area under study. The area under water was occupied 9.6 km2 in 2010 that increased to 21.04km2in 2015and decreased to 19.4 km2in 2020. Built-up land was16.6 km2in 2010 that increased to 19.4 km2 in 2015 and 26.8 km2in 2020. The total area under vegetation was computed as 513.2 km2 in 2010 that increased to 601.6km2 in 2015and further increased to 717.7 km2in 2020. Forest land use showed decreasing trend as the covered area in 2010 was occupied 90.8 km2that decreased to 86.7km2in 2015 and further decreased to 61.84km2in 2020. In 2010, barren land use was occupied 528.54 km2that considerably decreased to429.64 km2in 2015 further decreased to 333.1 km2in 2020. Barren land drastically decreased into watered, built-up, and vegetation land uses. The findings of this study will be helpful for the future conservation of various land-use types, urban and regional planning, and an increase in agricultural production of various crops in the study area.
... Land-use changes play an important role in maintaining a global ecosystem balance [19], the human variables for example government policies and the environment largely impacts the land-use changes [20,21]. A better understanding of the land-use changes can be acquired from the past practices, present patterns of the land use, and the future land use trajectory [22,23,24]. ...
Land modification and its associated resources have grown considerably to be aserious issue that is currently attracting attention on a global scale, and they now form the core ofenvironmental protection and sustainability. The current study used remote sensing and GIS techniquesto evaluate land-use changes and their impacts on agricultural productivity over the study area, whichincluded Tehsil Shorkot, District Jhang, Punjab, Pakistan. Arc GIS and ERDAS Imagine 15 softwarewere used for image pre-processing in order to stack the layers, sub-set them, and mosaic the satellitebands. After pre-processing the photos, a maximum likelihood technique was used in a supervisedimage classification scheme to identify the land-use changes that had been noticed in the research area.The goal of the current study was. In 2010, there were 9.6 km2 under water. By 2015, there were 21.04km2, and by 2020, there were 19.4 km2. In 2010, there were 16.6 km2 of built-up land; this numberrose to 19.4 km2 in 2015 and 26.8 km2 in 2020. The total area covered by vegetation was estimated tobe 513.2 km2 in 2010, 601.6 km2 in 2015, and 717.7 km2 in 2020. The area covered by forest land usedeclined with time, from 90.8 km2 in 2010 to 86.7 km2 in 2015 to 61.84 km2 in 2020, indicating adownward trend. The area used for bare land in 2010 was 528.54 km2, which significantly reduced to429.64 km2 in 2015 and then to 333.1 km2 in 2020. The area of arid terrain that was once used foragriculture has dramatically shrunk. The results of this research will be beneficial for future land-useplanning, urban and regional development, and a growth in agricultural production of different crops inthe study area.
... Olaniyi et al. [16] studied the driving factors of coastal land use change in Malaysia and found that urbanization is an important factor of the changing coastal land use. Bhagyanagar et al. [17] studied urban expansion and land use change in the coastal area of Dakshina Kannada in India from 1983 to 2008, and identified that activities brought by ports and the drivers of urbanization can increase the forces of urbanization and land changes. Schweizer et al. [18] studied land use changes and forest distribution in the coastal plain of Mississippi in the United States, and concluded that the changes in land use in coastal areas are due to the continuous expansion of neighboring cities rather than regional economic trends. ...
... Correct spatial analysis of LST is becoming more important in providing information about LULC properties [23], and the use of Landsat data has become one of the main resources to observe the LST on regional as well as local scales [24][25][26]. To analyze and quantify the urban heat island effect, researchers have used satellite images like Landsat 8 Operational Land Imager (OLI) [27,28]. Climatic variations and extreme weather events negatively influenced the crop growth and their development, which may reduce the crop output significantly [29]. ...
The rapid increase in urbanization has an important effect on cropping pattern and land use/land cover (LULC) through replacing areas of vegetation with commercial and residential coverage, thereby increasing the land surface temperature (LST). The LST information is significant to understand the environmental changes, urban climatology, anthropogenic activities, and ecological interactions, etc. Using remote sensing (RS) data, the present research provides a comprehensive study of LULC and LST changes in water scarce and climate prone Southern Punjab (Multan region), Pakistan, for 30 years (from 1990 to 2020). For this research, Landsat images were processed through supervised classification with maps of the Multan region. The LULC changes showed that sugarcane and rice (decreased by 2.9 and 1.6%, respectively) had less volatility of variation in comparison with both wheat and cotton (decreased by 5.3 and 6.6%, respectively). The analysis of normalized difference vegetation index (NDVI) showed that the vegetation decreased in the region both in minimum value (−0.05 [1990] to −0.15 [2020]) and maximum value (0.6 [1990] to 0.54 [2020]). The results showed that the built-up area was increased 3.5% during 1990–2020, and these were some of the major changes which increased the LST (from 27.6 to 28.5°C) in the study area. The significant regression in our study clearly shows that NDVI and LST are negatively correlated with each other. The results suggested that increasing temperature in growing period had a greatest effect on all types of vegetation. Crop-based classification aids water policy managers and analysts to make a better policy with enhanced information based on the extent of the natural resources. So, the study of dynamics in major crops and surface temperature through satellite RS can play an important role in the rural development and planning for food security in the study area.
