Literatura científica selecionada sobre o tema "Zhong gong Dali Zhou wei"

Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos

Selecione um tipo de fonte:

Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Zhong gong Dali Zhou wei".

Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.

Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.

Artigos de revistas sobre o assunto "Zhong gong Dali Zhou wei"

1

Yakubu, Bashir Ishaku, Shua’ib Musa Hassan e Sallau Osisiemo Asiribo. "AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES". Geosfera Indonesia 3, n.º 2 (28 de agosto de 2018): 27. http://dx.doi.org/10.19184/geosi.v3i2.7934.

Texto completo da fonte
Resumo:
Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). Geological Investigation of Tagwai Dams using Remote Sensing Technique, Minna Niger State, Nigeria. Journal of Environment, 1(01), pp. 26-32. Amadi, A., & Olasehinde, P. (2010). Application of remote sensing techniques in hydrogeological mapping of parts of Bosso Area, Minna, North-Central Nigeria. International Journal of Physical Sciences, 5(9), pp. 1465-1474. Aplin, P., & Smith, G. (2008). Advances in object-based image classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), pp. 725-728. Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., . . . Teshale, E. Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p 1178622117751603. Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heat island in Birmingham, UK: a comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), p 153. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, pp. 180-191. Bukata, R. P., Jerome, J. H., Kondratyev, A. S., & Pozdnyakov, D. V. (2018). Optical properties and remote sensing of inland and coastal waters: CRC press. Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, 31(1), pp. 45-54. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., . . . Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp. 7-27. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), pp. 171-209. Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE transactions on geoscience and remote sensing, 53(8), pp. 4238-4249. Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, pp. 119-132. Coale, A. J., & Hoover, E. M. (2015). Population growth and economic development: Princeton University Press. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press. Corner, R. J., Dewan, A. M., & Chakma, S. (2014). Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651. Debnath, A., Debnath, J., Ahmed, I., & Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309. Desheng, L., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401. Dronova, I., Gong, P., Wang, L., & Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272. Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1) Farhani, S., & Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676. Feng, L., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328. Fensham, R., & Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429. Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52. Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents. Government, N. S. (2007). Niger state (The Power State). Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337. Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734. Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261. Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016. Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124. Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., & Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406. Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120. Jiang, L., Wu, F., Liu, Y., & Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600. Jin, S., Yang, L., Zhu, Z., & Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70. Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380. Koop, S. H., & van Leeuwen, C. J. (2015). Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670. Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234. Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity Object-based image analysis (pp. 3-27): Springer. Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35(3), pp. 286-304. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons. Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., & Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285. Luo, M., & Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720. Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., & Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250. Mas, J.-F., Velázquez, A., Díaz-Gallegos, J. R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., . . . Pérez-Vega, A. (2004). Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5(4), pp. 249-261. Mathew, A., Chaudhary, R., Gupta, N., Khandelwal, S., & Kaul, N. (2015). Study of Urban Heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters. International Journal of Computer & Mathematical Science, 4, pp. 2347-2357. Megahed, Y., Cabral, P., Silva, J., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), pp. 1750-1769. Metternicht, G. (2001). Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological modelling, 144(2-3), pp. 163-179. Miller, R. B., & Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy, 6(2), pp. 129-137. Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. Mohammed, I., Aboh, H., & Emenike, E. (2007). A regional geoelectric investigation for groundwater exploration in Minna area, north west Nigeria. Science World Journal, 2(4) Morenikeji, G., Umaru, E., Liman, S., & Ajagbe, M. (2015). Application of Remote Sensing and Geographic Information System in Monitoring the Dynamics of Landuse in Minna, Nigeria. International Journal of Academic Research in Business and Social Sciences, 5(6), pp. 320-337. Mukherjee, A. B., Krishna, A. P., & Patel, N. (2018). Application of Remote Sensing Technology, GIS and AHP-TOPSIS Model to Quantify Urban Landscape Vulnerability to Land Use Transformation Information and Communication Technology for Sustainable Development (pp. 31-40): Springer. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), pp. 1145-1161. Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), pp. 125-133. Niu, X., & Ban, Y. (2013). Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), pp. 1-26. Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854. Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88. Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84. Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234. Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611. Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, p 16. Shrivastava, L., & Nag, S. (2017). MONITORING OF LAND USE/LAND COVER CHANGE USING GIS AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF SAGAR RIVER WATERSHED, TRIBUTARY OF WAINGANGA RIVER OF MADHYA PRADESH, INDIA. Shuaibu, M., & Sulaiman, I. (2012). Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res, 5, pp. 43-55. Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J. M., . . . Chanussot, J. (2014). Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE transactions on geoscience and remote sensing, 52(8), pp. 5122-5136. Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, pp. 1-13. Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, pp. 459-469. Teodoro, A. C., Gutierres, F., Gomes, P., & Rocha, J. (2018). Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 579-587): Springer. Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36. Tuholske, C., Tane, Z., López-Carr, D., Roberts, D., & Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Applied Geography, 88, pp. 84-93. Tuia, D., Flamary, R., & Courty, N. (2015). Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions. ISPRS Journal of Photogrammetry and Remote Sensing, 105, pp. 272-285. Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer. Wang, L., Sousa, W., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International journal of remote sensing, 25(24), pp. 5655-5668. Wang, Q., Zeng, Y.-e., & Wu, B.-w. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews, 54, pp. 1563-1579. Wang, S., Ma, H., & Zhao, Y. (2014). Exploring the relationship between urbanization and the eco-environment—A case study of Beijing–Tianjin–Hebei region. Ecological Indicators, 45, pp. 171-183. Weitkamp, C. (2006). Lidar: range-resolved optical remote sensing of the atmosphere: Springer Science & Business. Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., & Lausch, A. (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, pp. 190-203. Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), pp. 884-893. Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS congress. Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., . . . Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), pp. 2310-2323. Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations: Springer. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), pp. 799-811. Zhou, D., Zhao, S., Zhang, L., & Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. Remote Sensing of Environment, 176, pp. 272-281. Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., . . . Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, pp. 243-257.
Estilos ABNT, Harvard, Vancouver, APA, etc.
2

Thanh Huyen, Le, Dao Sy Duc, Nguyen Xuan Hoan, Nguyen Huu Tho e Nguyen Xuan Viet. "Synthesis of Fe3O4-Reduced Graphene Oxide Modified Tissue-Paper and Application in the Treatment of Methylene Blue". VNU Journal of Science: Natural Sciences and Technology 35, n.º 3 (20 de setembro de 2019). http://dx.doi.org/10.25073/2588-1140/vnunst.4883.

Texto completo da fonte
Resumo:
Graphene-based composites have received a great deal of attention in recent year because the presence of graphene can enhance the conductivity, strength of bulk materials and help create composites with superior qualities. Moreover, the incorporation of metal oxide nanoparticles such as Fe3O4 can improve the catalytic efficiency of composite material. In this work, we have synthesized a composite material with the combination of reduced graphene oxide (rGO), and Fe3O4 modified tissue-paper (mGO-PP) via a simple hydrothermal method, which improved the removal efficiency of the of methylene blue (MB) in water. MB blue is used as the model of contaminant to evaluate the catalytic efficiency of synthesized material by using a Fenton-like reaction. The obtained materials were characterized by SEM, XRD. The removal of materials with methylene blue is investigated by UV-VIS spectroscopy, and the result shows that mGO-PP composite is the potential composite for the color removed which has the removal efficiency reaching 65% in acetate buffer pH = 3 with the optimal time is 7 h. Keywords Graphene-based composite, methylene blue, Fenton-like reaction. References [1] Ma Joshi, Rue Bansal, Reng Purwar, Colour removal from textile effluents, Indian Journal of Fibre & Textile Research, 29 (2004) 239-259 http://nopr.niscair.res.in/handle/123456789/24631.[2] Kannan Nagar, Sundaram Mariappan, Kinetics and mechanism of removal of methylene blue by adsorption on various carbons-a comparative study, Dyes and pigments, 51 (2001) 25-40 https://doi.org/10.1016/S0143-7208(01)00056-0.[3] K Rastogi, J. N Sahu, B. C Meikap, M. N Biswas, Removal of methylene blue from wastewater using fly ash as an adsorbent by hydrocyclone, Journal of hazardous materials, 158 (2008) 531-540.https://doi.org/10.1016/j.jhazmat.2008.01. 105.[4] Qin Qingdong, Ma Jun, Liu Ke, Adsorption of anionic dyes on ammonium-functionalized MCM-41, Journal of Hazardous Materials, 162 (2009) 133-139 https://doi.org/10.1016/j.jhazmat. 2008.05.016.[5] Mui Muruganandham, Rps Suri, Sh Jafari, Mao Sillanpää, Lee Gang-Juan, Jaj Wu, Muo Swaminathan, Recent developments in homogeneous advanced oxidation processes for water and wastewater treatment, International Journal of Photoenergy, 2014 (2014). http://dx. doi.org/10.1155/2014/821674.[6] Herney Ramirez, Vicente Miguel , Madeira Luis Heterogeneous photo-Fenton oxidation with pillared clay-based catalysts for wastewater treatment: a review, Applied Catalysis B: Environmental, 98 (2010) 10-26 https://doi.org/ 10.1016/j.apcatb.2010.05.004.[7] Guo Rong, Jiao Tifeng, Li Ruifei, Chen Yan, Guo Wanchun, Zhang Lexin, Zhou Jingxin, Zhang Qingrui, Peng Qiuming, Sandwiched Fe3O4/carboxylate graphene oxide nanostructures constructed by layer-by-layer assembly for highly efficient and magnetically recyclable dye removal, ACS Sustainable Chemistry & Engineering, 6 (2017) 1279-1288 https://doi.org/10.1021/acssuschemeng.7b03635.[8] Sun Chao, Yang Sheng-Tao, Gao Zhenjie, Yang Shengnan, Yilihamu Ailimire, Ma Qiang, Zhao Ru-Song, Xue Fumin, Fe3O4/TiO2/reduced graphene oxide composites as highly efficient Fenton-like catalyst for the decoloration of methylene blue, Materials Chemistry and Physics, 223 (2019) 751-757 https://doi.org/ 10.1016/j.matchemphys.2018.11.056.[9] Guo Hui, Ma Xinfeng, Wang Chubei, Zhou Jianwei, Huang Jianxin, Wang Zijin, Sulfhydryl-Functionalized Reduced Graphene Oxide and Adsorption of Methylene Blue, Environmental Engineering Science, 36 (2019) 81-89 https://doi. org/10.1089/ees.2018.0157.[10] Zhao Lianqin, Yang Sheng-Tao, Feng Shicheng, Ma Qiang, Peng Xiaoling, Wu Deyi, Preparation and application of carboxylated graphene oxide sponge in dye removal, International journal of environmental research and public health, 14 (2017) 1301 https://doi.org/10.3390/ijerph14111301.[11] Yu Dandan, Wang Hua, Yang Jie, Niu Zhiqiang, Lu Huiting, Yang Yun, Cheng Liwei, Guo Lin, Dye wastewater cleanup by graphene composite paper for tailorable supercapacitors, ACS applied materials & interfaces, 9 (2017) 21298-21306 https://doi.org/10.1021/acsami.7b05318.[12] Wang Hou, Yuan Xingzhong, Wu Yan, Huang Huajun, Peng Xin, Zeng Guangming, Zhong Hua, Liang Jie, Ren MiaoMiao, Graphene-based materials: fabrication, characterization and application for the decontamination of wastewater and wastegas and hydrogen storage/generation, Advances in Colloid and Interface Science, 195 (2013) 19-40 https://doi. org/10.1016/j.cis.2013.03.009.[13] Marcano Daniela C, Kosynkin Dmitry V, Berlin Jacob M, Sinitskii Alexander, Sun Zhengzong, Slesarev Alexander, Alemany Lawrence B, Lu Wei, Tour James M, Improved synthesis of graphene oxide, ACS nano, 4 (2010) 4806-4814 https://doi.org/10.1021/nn1006368.[14] Zhang Jiali, Yang Haijun, Shen Guangxia, Cheng Ping, Zhang Jingyan, Guo Shouwu, Reduction of graphene oxide via L-ascorbic acid, Chemical Communications, 46 (2010) 1112-1114 http://doi. org/10.1039/B917705A [15] Gong Ming, Zhou Wu, Tsai Mon-Che, Zhou Jigang, Guan Mingyun, Lin Meng-Chang, Zhang Bo, Hu Yongfeng, Wang Di-Yan, Yang Jiang, Nanoscale nickel oxide/nickel heterostructures for active hydrogen evolution electrocatalysis, Nature communications, 5 (2014) 4695 https:// doi.org/10.1038/ncomms5695.[16] Wu Zhong-Shuai, Yang Shubin, Sun Yi, Parvez Khaled, Feng Xinliang, Müllen Klaus, 3D nitrogen-doped graphene aerogel-supported Fe3O4 nanoparticles as efficient electrocatalysts for the oxygen reduction reaction, Journal of the American Chemical Society, 134 (2012) 9082-9085 https://doi.org/10.1021/ja3030565.[17] Nguyen Son Truong, Nguyen Hoa Tien, Rinaldi Ali, Nguyen Nam Van, Fan Zeng, Duong Hai Minh, Morphology control and thermal stability of binderless-graphene aerogels from graphite for energy storage applications, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 414 (2012) 352-358 https://doi.org/ 10.1016/j.colsurfa.2012.08.048.[18] Deng Yang, Englehardt James D, Treatment of landfill leachate by the Fenton process, Water research, 40 (2006) 3683-3694 https://doi.org/ 10.1016/j.watres.2006.08.009.
Estilos ABNT, Harvard, Vancouver, APA, etc.

Teses / dissertações sobre o assunto "Zhong gong Dali Zhou wei"

1

Mei, Yunting. "Zhong yi yao zhou qi zhi liao zi gong nei mo yi wei zheng de yan jiu gai kuang /". click here to view the abstract and table of contents, 2006. http://net3.hkbu.edu.hk/~libres/cgi-bin/thesisab.pl?pdf=b19987535a.pdf.

Texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.

Livros sobre o assunto "Zhong gong Dali Zhou wei"

1

"Dali Zhou wei dang xiao xiao zhi" bian zuan wei yuan hui. Zhong gong Dali Zhou wei dang xiao xiao zhi: (xiu ding ban). [Dali Zhou: Dali Zhou wei dang xiao xiao zhi bian zuan wei yuan hui], 2011.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
2

Zhong gong Dali Zhou wei dang xiao xiao zhi bian xie zu. Zhong gong Dali Zhou wei dang xiao xiao zhi (1951-2011). [Dali Zhou: Zhong gong Dali Zhou wei dang xiao xiao zhi bian xie zu], 2011.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
3

Zhong gong Aba Zhou wei. Dang xiao. Zhong gong Aba Zhou wei dang xiao zhi. [Aba Zhou: Zhong gong Aba Zhou wei dang xiao], 2009.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
4

"Zhong gong Aba Zhou wei dang xiao han shou fen yuan zhi" bian zhuan wei yuan hui. Zhong gong Aba Zhou wei dang xiao han shou fen yuan zhi (1993 nian-2013 nian). [Aba Zhou]: [Zhong gong Aba Zhou wei dang xiao han shou fen yuan], 2013.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
5

finisher, Qiu Jiahui text, ed. Jing dian bu bai Tai shi mian bao: 1 zhong mian tuan + 30 kuan kou wei + 12 kuan zheng xing shou fa + 700 zhang ju xi mi yi bu zhou tu, jie he fu gu yu chuang xin, cheng gong lü gao, jue dui hao chi! Taibei Shi: Zhu que wen hua shi ye you xian gong si, 2017.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
6

Zhong gong Changsha Shi wei dang xiao wu shi zhou nian xiao zhi. [Changsha Shi: Zhong gong Changsha Shi wei dang xiao, 1999.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
7

Chun hua qiu shi: Ji nian Zhong gong Yulin shi wei dang xiao jian xiao 60 zhou nian. Nanning Shi: Guangxi ren min chu ban she, 2010.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
8

Bolin de 100 zhong sheng huo: Qian wei yi lang x gong yuan ye can x bai nian ka fei guan x dian ying chang jing, zai Ou Zhou de zhong xin guo ri zi = Die 100 Leben eines Berliners. Taibei Shi: Chuang yi shi ji, 2016.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
9

Mei zhou 2 tian qing duan shi, 2 ge yue shou 8 gong jin!: Gao yi jian zhong ban mei nü ying ye shi no Taiwan wei 500 ka cai dan, zai jia chi, wai shi zu dou neng shou! Xinbei Shi Zhonghe Qu: Ping guo wu chu ban she you xian gong si, 2014.

Encontre o texto completo da fonte
Estilos ABNT, Harvard, Vancouver, APA, etc.
Oferecemos descontos em todos os planos premium para autores cujas obras estão incluídas em seleções literárias temáticas. Contate-nos para obter um código promocional único!

Vá para a bibliografia