Geospatial Assessment of Urban Growth Dynamics and Land Surface Temperature in Ajmer Region, India

Lakra, Kanchan; Sharma, Devesh

doi:10.1007/s12524-019-00968-w

Cited by 14 publications

(9 citation statements)

References 33 publications

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“…Overall accuracy is the generally used metric to assess classification results, which correspond to the percentage of correctly predicted samples of all classes. Two other metrics reflecting the accuracies of different classes have also been calculated: the user's accuracy and the producer's accuracy [31,41,80,[86][87][88][89][90][91][92][93][94][95][96][97][98][99]. Another measure used in tandem with the aforementioned accuracy metrics or alone is the kappa statistic [100] For most studies, the minimum required for the overall classification accuracy was 85%, following the recommendations of [102,103].…”

Section: Extraction Methodsmentioning

confidence: 99%

“…Samples for validation purposes have been collected through field surveys using GPS [54,93,107,108] and aerial photos [51,72], as well as being extracted from the commercial imagery of high-resolution satellites such as IKONOS [58,109], SPOT [51,52,63,72,107,110,111], QuickBird [20,58], GeoEye [111], and GaoFen-1 [112] provided by private companies. However, researchers are moving away from these methods for several reasons.…”

Section: Extraction Methodsmentioning

confidence: 99%

“…In an investigation of the Chinese city of Wuhan, in which water accounts for over 25% of the total area of the central district-including two rivers (the Yangtze River and Han River), East Lake (the largest urban lake in China), and dozens of other lakes-Wu et al found that both the area and the spatial distribution of water bodies contributed to significant distributions in the effects caused by SUHIs [70]. Moreover, the findings reported in multiple studies have affirmed that water bodies exhibit the lowest LSTs [11,20,41,45,47,60,73,78,79,88,90,93,97,98,111,112,115,138,139,[151][152][153], along with vegetation cover. Furthermore, water has generally been found to have a negative correlation with LST [20,111,154], except for a few circumstances due to various reasons primarily related to the climate characteristics of cities.…”

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confidence: 99%

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Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

et al. 2021

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An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It is projected that UHI intensity will rise to record levels in the following decades due to rapid urban expansion, as two-thirds of the world population is expected to live in urban areas by 2050. Nevertheless, the last two decades have seen a considerable increase in the number of studies on surface UHI (SUHI)—a form of UHI quantified based on land surface temperature (LST) derived from satellite imagery—and its relationship with the land use/cover (LULC) changes. This surge has been facilitated by the availability of freely accessible five-decade archived remotely sensed data, the use of state-of-art analysis methods, and advancements in computing capabilities. The authors of this systematic review aimed to summarize, compare, and critically analyze multiple case studies—carried out from 2001 to 2020—in terms of various aspects: study area characteristics, data sources, methods for LULC classification and SUHI quantification, mechanisms of interaction coupled with linking techniques between SUHI intensity with LULC spatial and temporal changes, and proposed alleviation actions. The review could support decision-makers and pave the way for scholars to conduct future research, especially in vulnerable cities that have not been well studied.

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Section: Extraction Methodsmentioning

confidence: 99%

Section: Extraction Methodsmentioning

confidence: 99%

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confidence: 99%

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Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

et al. 2021

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“…Gautam and Singh ( 2018 ) hypothesized that the excessive heat emanating from the urban areas either does allow for the temperature to fall enough for the formation of fog or it causes the fog formed to dissipate a lot sooner. This might be the explanation for fog holes to form specifically over the urban areas., The impervious surface of town and cities and lack of vegetation are responsible for exaggerated nocturnal remittance of heat along with low evapotranspiration rates along with, both of which causes the temperature to rise (Lakra and Sharma 2019 ), decreasing the dew point depression bringing down the relative humidity (Baldocchi and Waller 2014 ), which again causes the condensation level to rise, thus increasing the cloud base height and reducing fog (Williams et al 2015 ). UHI, UDI (Urban Dry Island) quell and delay low-level fog, but upper-level fog formation is promoted by vertical updraft (Yan et al 2020 ).…”

Section: Factors Influencing Fog Formationmentioning

confidence: 99%

A review on factors influencing fog formation, classification, forecasting, detection and impacts

Lakra

Avishek

2022

Rend. Fis. Acc. Lincei

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With the changing climate and environment, the nature of fog has also changed and because of its impact on humans and other systems, study of fog becomes essential. Hence, the study of its controlling factors such as the characteristics of condensation nuclei, microphysics, air–surface interaction, moisture, heat fluxes and synoptic conditions also become crucial, along with research in the field of prediction and detection. The current review expands for the period between 1976 to 2021, however, especially focused on the research articles published in the last two decades. It considers 250 research papers/research letters, 24 review papers, four book chapters/manuals, five news articles, 15 reports, six conference papers and five other online readings. This review is a compilation of the pros and cons of the techniques used to determine the factors influencing fog formation, its classification, tools and techniques available for its detection and forecast. Some recent advanced are also discussed in this review: role of soil properties on fogs, application of microwave communication links in the detection of fog, new class of smog, and how the cognitive abilities of humans are affected by fog. Recently India and China are facing an emergence and repetitions of fog haze/smog and thus their policies initiatives are also briefly discussed. It is concluded that the complexity in fog forecasting is high due to multiple factors playing a role at multiple levels. Most of the researchers have worked upon the role of humidity, temperature, wind, and boundary layer to predict fogs. However, the role of global wind circulations, soil properties, and anthropogenic heat requires further investigations. Literature shows that fog is being harnessed to address water insecurity in various countries, however, coastal areas of Angola, Namibia and South Africa, Kenya, Eastern Yemen, Oman, China, India, Sri Lanka, Mexico, along with the mountainous regions of Peru, Chile, and Ecuador, are some of the potential sites that can benefit from the installation of fog water harvesting systems.

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“…Unfortunately, in recent years, soil quality has deteriorated overall due to excessive organic (Kato et al 2009;CPCB 2013;OS and WA 2017), inorganic, and heavy metal pollution (Tang et al 2010;Delang 2018;Haque et al 2019). These pollutants are influxed in the ecosystem by the growing urbanization (Lakra and Sharma 2019) and industrialization processes (Banerjee et al 2016). Among all forms of metals, heavy metals act as a major environmental contaminant when present in excess.…”

Section: Introductionmentioning

confidence: 99%

Soil quality and pollution assessment around Jumar watershed of Jharkhand, India

2021

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This work aimed to analyze the soil quality and heavy metal contamination in the Jumar watershed of Jharkhand and suggests possible measures that could be taken to guarantee suitable management practices. Therefore, in the present work, the soil sampling was done at 8 locations, selected based on site visit. The GPS coordinates of the sampling sites were noted. The soil samples were collected during pre-monsoon and post-monsoon seasons for 2017 and 2018 at a depth of 20 cm from the top. Water samples were collected from the river flowing closer to the sampling points. Then, soil quality parameters such as pH; electrical conductivity; bulk density; moisture content; and surface water quality parameters such as pH, EC, TDS, DO, COD, BOD, alkalinity, turbidity, and nutrients (N & P) were studied. For heavy metal analysis (Pb, Zn, Cu, As, Ni), both soil and water samples were analyzed using an inductive coupled plasma-optical emission spectrophotometer (ICP-OES). Statistical tools like descriptive statistics, multivariate analysis, principal component analysis, correlation coefficient, and ANOVA were applied using SPSS version 21. The data indicate that the values of moisture content varied between 22.06 and 45.7%, soil bulk density varied between 1.20 and 1.55 g/cm 3 , pH varied between 7.79 and 8.17, and EC varied between 116.66 and 207.08 μS/cm. The concentrations of heavy metals in the pre-monsoon season were higher (Zn 0.183 mg/l, Cu 0.09 mg/l, Ni 0.061 mg/l) than those in the post-monsoon season (Zn 0.17 mg/l, Cu 0.076 mg/l, Ni 0.138 mg/l). The water quality index found using pH, EC, TDS, DO, COD, BOD, alkalinity, turbidity, BOD, COD, nutrients, and heavy metals indicated that most sites had excellent water quality, especially in the pre-monsoon period. But in the post-monsoon period of 2017, the water quality index showed that water was in poor condition. Overall water quality was found to be good. It was found that the soil had very slight traces of heavy metals and was slightly alkaline indicating the need for better watershed management practices for the future. PCA showed that the component variables like pH and EC were principal components specially for both pre-monsoon seasons, whereas through ANOVA, it was found that the variable has strong relationship among themselves.

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Geospatial Assessment of Urban Growth Dynamics and Land Surface Temperature in Ajmer Region, India

Cited by 14 publications

References 33 publications

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

A review on factors influencing fog formation, classification, forecasting, detection and impacts

Soil quality and pollution assessment around Jumar watershed of Jharkhand, India

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