Tracing land use and land cover change in peri-urban Delhi, India, over 1973–2017 period

Paul, Saikat Kumar; Saxena, K. G.; Nagendra, Harini; Lele, Nikhil

doi:10.1007/s10661-020-08841-x

Cited by 37 publications

(13 citation statements)

References 53 publications

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“…Twelve independent factors were derived based on previous literature and discussion with experts to be included in the model [4,14,15,37,40,46,47]. Then, two factors (economic activities and urban master plans) were excluded from the study due to a lack of data for the study area.…”

Section: Methodsmentioning

confidence: 99%

“…Many studies have discussed the land use/cover change in Delhi and its impacts [34][35][36]. However, so far, little attention has been paid to analyzing the pattern of urban expansion in Delhi and the driving forces that influence this expansion [37]. Therefore, this research seeks to analyze the driving factors that influence urban expansion in Delhi and predict the pattern of urban expansion.…”

Section: Introductionmentioning

confidence: 99%

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Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

et al. 2021

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During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

et al. 2021

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“…The expansion in urban areas causes decline in vegetated areas, cropland, and fallow land thereby contributing to climate change. Paul et al (2021) also showed expansion in urban areas at annual rates of 38.6% with decline in agricultural area at rate of 2.1% for peripheral Delhi during the 1973-2017. So, climate change is one of the emerging threats to human health in Indian cities.…”

Section: Climate Changementioning

confidence: 92%

Air Pollution, Climate Change, and Human Health in Indian Cities: A Brief Review

Kaur

Pandey

2021

Front. Sustain. Cities

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Climate change and air pollution have been a matter of serious concern all over the world in the last few decades. The present review has been carried out in this concern over the Indian cities with significant impacts of both the climate change and air pollution on human health. The expanding urban areas with extreme climate events (high rainfall, extreme temperature, floods, and droughts) are posing human health risks. The intensified heat waves as a result of climate change have led to the elevation in temperature levels causing thermal discomfort and several health issues to urban residents. The study also covers the increasing air pollution levels above the prescribed standards for most of the Indian megacities. The aerosols and PM concentrations have been explored and hazardous health impacts of particles that are inhaled by humans and enter the respiratory system have also been discussed. The air quality during COVID-2019 lockdown in Indian cities with its health impacts has also been reviewed. Finally, the correlation between climate change, air pollution, and urbanizations has been presented as air pollutants (such as aerosols) affect the climate of Earth both directly (by absorption and scattering) and indirectly (by altering the cloud properties and radiation transfer processes). So, the present review will serve as a baseline data for policy makers in analyzing vulnerable regions and implementing mitigation plans for tackling air pollution. The adaptation and mitigation measures can be taken based on the review in Indian cities to reciprocate human health impacts by regular air pollution monitoring and addressing climate change as well.

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“…There is no doubt that research on land-use-related carbon emissions has achieved fruitful results and these results have acted as a valuable reference for this study. There remain some knowledge gaps that can be studied: (1) The resolution of land-use data is generally not high enough, and many studies have focused on low-resolution remote sensing data, which has constrained the accuracy of the research results, especially in relation to large-scale areas [27,28]; (2) The existing studies have mainly focused on single city or country study areas [17,29,30], neglecting cross-city regions such as urban agglomerations. This is significant, as urban agglomerations represent the areas where land use changes drastically, making them key carbon emission areas, and hence, it is very important to study such regions [22]; (3) Some factors in the agricultural production process have been ignored, including the issue of agricultural machinery use [31].…”

Section: Introductionmentioning

confidence: 99%

Examining the Effects of Land Use on Carbon Emissions: Evidence from Pearl River Delta

Zhao

Jian-hong

et al. 2021

IJERPH

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Land-use change accounts for a large proportion of the carbon emissions produced each year, especially in highly developed urban agglomerations. In this study, we combined remote sensing data and socioeconomic data to estimate land-use-related carbon emissions, and applied the logarithmic mean Divisia index (LMDI) method to analyze its influencing factors, in the Pearl River Delta (PRD) of China in 1990–2015. The main conclusions are as follows: (1) The total amount of land-use-related carbon emissions increased from 684.84 × 104 t C in 1990 to 11,444.98 × 104 t C in 2015, resulting in a net increase of 10,760.14 × 104 t (16.71 times). (2) Land-use-related carbon emissions presented a “higher in the middle and lower on both sides” spatial distribution. Guangzhou had the highest levels of carbon emissions, and Zhaoqing had the lowest; Shenzhen experienced the greatest net increase, and Jiangmen experienced the least. (3) The land-use-related carbon emissions intensity increased from 4795.76 × 104 Yuan/t C to 12,143.05 × 104 Yuan/t C in 1990–2015, with the greatest increase seen in Huizhou and the lowest in Zhongshan. Differences were also found in the spatial distribution, with higher intensities located in the south, lower intensities in the east and west, and medium intensities in the central region. (4) Land-use change, energy structure, energy efficiency, economic development, and population all contributed to increases in land-use-related carbon emissions. Land-use change, economic development and population made positive contributions, while energy efficiency and energy structure made negative contributions. At last, we put forward several suggestions for promoting low-carbon development, including development of a low-carbon and circular economy, rationally planning land-use structure, promoting reasonable population growth, improving energy efficiency and the energy consumption structure, and advocating low-carbon lifestyles. Our findings are useful in the tasks related to assessing carbon emissions from the perspective of land-use change and analyzing the associated influencing factors, as well as providing a reference for realizing low-carbon and sustainable development in the PRD.

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Tracing land use and land cover change in peri-urban Delhi, India, over 1973–2017 period

Cited by 37 publications

References 53 publications

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

Air Pollution, Climate Change, and Human Health in Indian Cities: A Brief Review

Examining the Effects of Land Use on Carbon Emissions: Evidence from Pearl River Delta

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