Abstract
The environmental ecology of the coastal area is very sensitive; especially the part of the river estuary, due to its special geographical location and the functional attributes that can provide humans with more agricultural and fishery resources, generally in this area, human activities are relatively intensive. There are also more environmental problems. As far as China’s southeast coastal areas are concerned, the environmental ecology and coastlines of coastal areas have a series of impacts due to human activities. For example, land reclamation and other activities will cause a series of problems such as changes in coastlines, changes in coastal land areas, changes in regional land types, decreases in the diversity of animals and plants, and changes in surface environmental temperature. Due to the rapid development of information technology in the new era, it has also promoted the rapid development of the Internet. In the process of modern network development, new media technology as a high-tech information carrier has been widely popularized. Weibo is the leader in new information dissemination, and its user base is very large. Companies can use the Weibo platform to promote their products and services and to spread corporate brands, realizing the convenience of dialogue between companies and users. Weibo, with its own characteristics, provides technical support for corporate brand communication and has gradually become the first choice for many companies to promote. This article uses remote sensing images to conduct an in-depth study of coastal atmospheric climate, as well as an in-depth study of how to promote corporate brands through Weibo.
Similar content being viewed by others
Change history
11 November 2021
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12517-021-08896-1
28 September 2021
An Editorial Expression of Concern to this paper has been published: https://doi.org/10.1007/s12517-021-08471-8
References
Abrams MJ, Ashley RP, Rowan LC, Goetz AFH, Kahle AB (1977) Mapping of hydrothermal alteration in the Cuprite mining district, Nevada using aircraft scanner images for the spectral region 0.46 to 2.36 μm. Geology 5(12):713–718. https://doi.org/10.1130/0091-7613(1977)5<713:MOHAIT>2.0.CO;2
Bojinski S, Schaepman M, Schlaper D, Itten K (2003) SPECCHIO: a spectrum database for remote sensing applications. Comput Geosci 29:27–38. https://doi.org/10.1016/S0098-3004(02)00107-3
Boori MS, Paringer RA, Choudhary K, Kupriyanov AV (2018) Comparison of hyperspectral and multi-spectral imagery to building a spectral library and landcover classification performance. Comput Opt 42(6):1035–1045. https://doi.org/10.18287/2412-6179-2018-42-6-1035-1045
Chattoraj SL, Prasad G, Sharma RU, van der Meer FD, Guha A, Pour AB (2020) Integration of remote sensing, gravity and geochemical data for exploration of cu-mineralization in Alwar basin, Rajasthan, India. Int J Appl Earth Obs Geoinf 91:102162. https://doi.org/10.1016/j.jag.2020.102162
Clark RN (1999) Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual of Remote Sensing 3(3–58):2–2
Clark RN, Swayze GA, Livo KE, Kokaly RF, Sutley SJ, Dalton JB, McDougal RR, Gent CA (2003) Imaging spectroscopy: earth and planetary remote sensing with the USGS Tetracorder and exper systems. J Geophys Res 108(E12):1–44. https://doi.org/10.1029/2002JE001847
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46. https://doi.org/10.1016/0034-4257(91)90048-B
De Carvalho OA, Meneses PR (2000) Spectral correlation mapper (SCM): an improvement on the spectral angle mapper (SAM). Ninth JPL airborne earth science workshop. Pasadena, California
Ding JG, Li XB, Huang LQ (2015) A novel method for spectral similarity measure by fusing shape and amplitude features. J Eng Sci Technol Rev 8(5):172–179
Du Y, Chang CI, Ren H, Chang CC, Jensen JO, D'Amico FM (2004) New hyperspectral discrimination measure for spectral characterization. Opt Eng 43(8):1777–1787. https://doi.org/10.1117/1.1766301
Fasnacht L, Vogt ML, Renard P, Brunner P (2019) A 2D hyperspectral library of mineral reflectance, from 900 to 2500 nm. Sci Data 6(1):1–7. https://doi.org/10.6084/m9.figshare.9963167
Flaash UG (2009) Atmospheric correction module: QUAC and FLAASH user guide, 4.7 edn. ITT visual information solutions Inc, Boulder
Galal A, Hasan H, Imam IF (2012) Learnable hyperspectral measures. Egypt Inform J 13(2):85–94. https://doi.org/10.1016/j.eij.2012.04.004
Goetz AFH, Srivastava V (1985) Mineralogical mapping in the Cuprite mining district. AIS Data Analysis Workshop, Pasadena, California, Nevada
Hunt GR, Salisbury JW (1971) Visible and near infrared spectra of minerals and rocks. II. Carbonates. Mod Geol 2:23–30
Jain R, Sharma RU (2019) Airborne hyperspectral data for mineral mapping in Southeastern Rajasthan, India. Int J Earth Obs Geoinformation 81:137–145. https://doi.org/10.1016/j.jag.2019.05.007
Kavzoglu T, Mather PM (2000) The use of feature selection techniques in the context of artificial neural networks. Twenty sixth annual conference of the remote sensing society, Leicester
Khan SD, Jacobson S (2008) Remote sensing and geochemistry for detecting hydrocarbon microseepages. Geol Soc Am Bull 120(1–2):96–105. https://doi.org/10.1130/0016-7606(2008)120[96:RSAGFD]2.0.CO;2
Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH (1993) The spectral image processing system (SIPS)- interactive visualization and analysis of imaging spectrometer data. AIP Conf Proc 283(1):192–201
Kumar C, Chatterjee S, Oommen T (2020) Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India. Int J Remote Sens 41(2):794–812
Magendran T, Sanjeevi S (2014) Hyperion image analysis and linear spectral unmixing to evaluate the grades of iron ores in parts of Noamundi, Eastern India. Int J Appl Earth Obs Geoinf 26:413–426. https://doi.org/10.1016/j.jag.2013.09.004
Mistrik R, Lutisan J, Huang Y, Suchy M, Wang J, Raab M (2013) mzCloud: a key conceptual shift to understand ‘Who’s who’ in untargeted metabolomics. Metabolomics Society Conference, Glasgow
Muwanguzi AJ, Karasev AV, Byaruhanga JK, Jonsson PG (2012) Characterization of chemical composition and microstructure of natural iron ore from Muko deposits. Int Sch Res Notices 2012:1–9. https://doi.org/10.5402/2012/174803
Naresh Kumar M, Seshasai MVR, Vara Prasad KS, Kamala V, Ramana KV, Dwivedi RS, Roy PS (2011) A new hybrid spectral similarity measure for discrimination among Vigna species. Int J Remote Sens 32(14):4041–4053. https://doi.org/10.1080/01431161.2010.484431
Nidamanuri RR, Zbell B (2011) Normalized spectral similarity score. IEEE J Sel Top Appl Earth Obs Remote Sens 4:226–240. https://doi.org/10.1109/JSTARS.2010.2086435
Padma S, Sanjeevi S (2014) Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis. Int J Appl Earth Obs Geoinf 32:138–151. https://doi.org/10.1016/j.jag.2014.04.001
Padma S, Sanjeevi S (2016) Spectral correlation and Jeffries-Matusita based matching algorithm for improved information extraction from hyperspectral images. Thirty seventh Asian conference on remote sensing, Colombo
Panda S, Jain MK, Jeyaseelan AT (2018) A study and implications on the potential of satellite image spectral to assess the iron ore grades of Noamundi iron deposits area. J Geol Soc India 91(2):227–231. https://doi.org/10.1007/s12594-018-0840-y
Rejith RG, Sundararajan M, Gnanappazham L, Loveson VJ (2020) Satellite-based spectral mapping (ASTER and Landsat data) of mineralogical signatures of beach sediments: a precursor insight. Geocarto Int:1–24. https://doi.org/10.1080/10106049.2020.1750061
Ren Z, Sun L, Zhai Q (2020) Improved k-means and spectral matching for hyperspectral mineral mapping. Int J Appl Earth Obs Geoinf 91:102154. https://doi.org/10.1016/j.jag.2020.102154
Sabins FF (1999) Remote sensing for mineral exploration. Ore Geol Rev 14(3–4):157–183. https://doi.org/10.1016/S0169-1368(99)00007-4
Sanjeevi S (2008) Targeting limestone and bauxite deposits in southern India by spectral unmixing of hyperspectral image data. Int Arch Photogramm Remote Sens Spat Inf Sci 37(B8):1189–1194
Shanmugam S, SrinivasaPerumal P (2014) Spectral matching approaches in hyperspectral image processing. Int J Remote Sens 35(24):8217–8251. https://doi.org/10.1080/01431161.2014.980922
Thangavelu M, Shanmugam S, Bhattacharya AK (2011) Hyperspectral radiometry to quantify the grades of iron ores of Noamundi and Joda mines, Eastern India. J Indian Soc Remote Sens 39:473–483. https://doi.org/10.1007/s12524-011-0109-z
Van Der Meer F (2006) The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. Int J Appl Earth Obs Geoinf 8(1):3–17. https://doi.org/10.1016/j.jag.2005.06.001
Vishnu S, Nidamanuri RR, Bremananth R (2013) Spectral material mapping using hyperspectral imagery: a review of spectral matching and library search methods. Geocarto Int 28(2):171–190. https://doi.org/10.1080/10106049.2012.665498
Williams NR, Holtzhausen S (2001) The impact of ore characterization and blending on metallurgical plant performance. J S Afr I Min Metall 101(8):437–446
Funding
The study was supported by the “National Social Science Foundation of China (Grant No. 14CTQ030)”.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Additional information
Responsible Editor: Sheldon Williamson
This article is part of the Topical Collection on Environment and Low Carbon Transportation.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12517-021-08896-1
About this article
Cite this article
Du, X. RETRACTED ARTICLE: Coastal atmospheric climate based on remote sensing images and corporate Weibo brand marketing. Arab J Geosci 14, 974 (2021). https://doi.org/10.1007/s12517-021-07343-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-021-07343-5