Effect of land-use intensity on surface temperature: A study on Chittagong city corporation area

Ara, Shamim; Islam, Ashraful; Showkat, Sanjida

doi:10.1109/iciev.2016.7759971

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…The rise of LST in Dhaka city has observed 3.2 0 C during the year 2002 to 2014 [18] and in case of Chittagong metropolitan area it is observed 3.81 0 C [20]. The change of average LST at the research study area was 3.43 0 C which is found almost same in the case of Dhaka, Chittagong, and Rajshahi City [21].…”

Section: Introductionmentioning

confidence: 53%

“…During the same time frame, a comparison was made between the 400 random sampling points and their corresponding point on Google Earth images to verify the LULC classifications [20]. The most likelihood supervised image classification method used for this research is of good accuracy as the validity rates are more than 90% for the study area.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…Among all these researches, [16] calculated all the factors and indexes related to the change of the LST. Sharmin [20] showed the relationship between NDVI and LST. Kafy et.…”

Section: Introductionmentioning

confidence: 99%

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Surface Temperature Dynamics in Response to Land Cover Transformation

Morshed

Fattah

Rimi

et al. 2020

JCEST

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This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

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Section: Introductionmentioning

confidence: 53%

Section: Accuracy Assessmentmentioning

confidence: 99%

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Surface Temperature Dynamics in Response to Land Cover Transformation

Morshed

Fattah

Rimi

et al. 2020

JCEST

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“…Roni [30] studied the relation between LST and Normalized Difference Vegetation Index (NDVI) using Landsat TM data. Ara et al [32] used Landsat 8, along with Landsat TM and ETM+ data, to study the effects of land use intensity on LST in Chittagong city corporation area. Study of LST with Landsat 8 images in the capital city or its surrounding areas in Bangladesh is not known as of the time of writing this paper.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms

Sajib

Tao

2020

Sensors

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The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (A ρ ρ EEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of 1.19 ° C . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of 1.50 ° C . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of 2.47 ° C and 4.11 ° C , respectively. For Du et al.’s algorithm, the w range of 0.0 to 6.3 g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from A ρ ρ EEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

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“…For example the value of GDP exposed to tropical cyclones alone more than tripled from US$526 billion in the 1970s to US$1.6 trillion in the first decade of the 2000s [9]. Ultimately if climate change is not being solved, then poverty eradication, food security and other major related problem cannot be solved either [10]. Secretary-General of UN, Ban Ki-moon affirmed that climate change was an "unholy brew" that could create perilous security vacuums, and that we must address a clear danger that not only exacerbated the threats but was itself a threat to international peace and security [9].…”

Section: Introductionmentioning

confidence: 99%

Climate Change and Its Impact on Sustainable Development in Bangladesh

Islam¹,

Islam²

2017

IJEEE

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There has been a large scientific evidences on climate change and its direct as well as indirect influences. Every year around 2.5 million people die from non-infectious diseases, which are directly attributable to environmental factors and these are related to climate changes. So Climate change is one of the most important issues in present senario. Changes in conditions and climate variability affect temperature, sea level rise, poverty, rising salinity, greenhouse effect and it can also affect human health both directly and indirectly. Though Bangladesh is a very low energy consuming country, Bangladesh is one of the top 10 nations that are mostly vulnerable to climate changes. This study was carried out by employing a general review of literature on climate change, focusing on its effects in Bangladesh and sustainable development. The effects would be as Crop production will decrease, floods are contaminating water that Increasing water borne diseases such as cholera, diarrhea etc. If the global temperature rises by 2°C, 30% of all land species will be threatened by an increased risk of extinction. About 75% area of mangrove forest Sundarban will submerse if the sea level will increases 45 cm. The southwestern coastal districts of Bangladesh will increase 16% in 2050 and 18% in 2100, which will make people homeless and bring social instability.

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Effect of land-use intensity on surface temperature: A study on Chittagong city corporation area

Cited by 8 publications

References 6 publications

Surface Temperature Dynamics in Response to Land Cover Transformation

Surface Temperature Dynamics in Response to Land Cover Transformation

Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms

Climate Change and Its Impact on Sustainable Development in Bangladesh

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