2018
DOI: 10.1016/j.apgeog.2018.07.012
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Analysis of impervious land-cover expansion using remote sensing and GIS: A case study of Sylhet sadar upazila

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Cited by 34 publications
(18 citation statements)
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“…Analyses of the processes and dynamic characteristics of urban expansion are important, so as to understand urban agglomeration and metropolitan development under the guidance of integrated development strategies. Hitherto, studies of the dynamic characteristics of urban expansion have mainly been based on high-resolution remote sensing data in the form of Landsat TM images [3,[11][12][13][14][15][16], socioeconomic data [17][18][19], and observed regional temperature data [2]. In recent years, nighttime light remote sensing has become a particular focus.…”
Section: Introductionmentioning
confidence: 99%
“…Analyses of the processes and dynamic characteristics of urban expansion are important, so as to understand urban agglomeration and metropolitan development under the guidance of integrated development strategies. Hitherto, studies of the dynamic characteristics of urban expansion have mainly been based on high-resolution remote sensing data in the form of Landsat TM images [3,[11][12][13][14][15][16], socioeconomic data [17][18][19], and observed regional temperature data [2]. In recent years, nighttime light remote sensing has become a particular focus.…”
Section: Introductionmentioning
confidence: 99%
“…A frequent practice since the origins of landscape planning has been to compile inventories of documentation containing data in different formats, depending on the discipline contributing them and on the nature of the information. This involves painstaking fieldwork to obtain this documentation followed by the laborious task of compiling the information, whereas non-intrusive geomatic techniques currently have the potential to perform most of these tasks in a more economical, detailed and integrated way [32][33][34].…”
Section: Motivationmentioning
confidence: 99%
“…When identifying the training sites, the spectral signatures separability of all the eight land use classes presented in Table 1 were verified including control fields in situ that were also set for validation of each classified image [38]. Land use types were classified by supervised classification maximum likelihood method since it's among the broadly used methods in the scientific literature in addition to it being the fastest and easy to use and giving a perfect interpretation of the outcomes [38][39][40][41][42][43][44]. In addition, the method is able to accommodate covarying data which is common with satellite image data [41,45].…”
Section: Land Use Classificationmentioning
confidence: 99%