Otwarty dostęp

Quantifying Urban Vegetation Coverage Change with a Linear Spectral Mixing Model: A Case Study in Xi’an, China

Ecological Chemistry and Engineering S's Cover Image
Ecological Chemistry and Engineering S
Special Issue: ECO-TECHNOLOGY AND ECO-INNOVATION FOR GREEN SUSTAINABLE GROWTH

Zacytuj

[1] Li X, Li T, Li H, Qi J, Hu L. Research on the online consumption effect of China’s urbanization under population aging background. Sustainability. 2019;11:4349. DOI: 10.3390/su11164349.10.3390/su11164349 Search in Google Scholar

[2] Elhoseny H, Elhoseny M, Riad AM, Hassanien AE. A framework for big data analysis in smart cities. Int Conf Adv Machine Learning Technol Appl. 2018;723:405-14. DOI: 10.1007/978-3-319-74690-6_40.10.1007/978-3-319-74690-6_40 Search in Google Scholar

[3] Lv Z, Hu B, Lv H. Infrastructure monitoring and operation for smart cities based on IoT system. IEEE Trans Industrial Informatics. 2019;16:1957-62. DOI: 10.1109/TII.2019.2913535.10.1109/TII.2019.2913535 Search in Google Scholar

[4] Richards DR, Belcher RN. Global changes in urban vegetation cover. Remote Sens. 2020;12(1):23. DOI: 10.3390/rs12010023.10.3390/rs12010023 Search in Google Scholar

[5] Deska I, Mrowiec M, Ociepa E, Michniewski M. Impact of the hydrogel amendment and the dry period duration on the green roof retention capacity. Ecol Chem Eng S. 2020;27:357-71. DOI: 10.2478/eces-2020-0023.10.2478/eces-2020-0023 Search in Google Scholar

[6] Gdela M, Widomski MK, Musz-Pomorska A. Hydraulic efficency of selected intensive green roof substrates. Ecol Chem Eng A. 2019;26(1-2):37-45. DOI: 10.2428/ecea.2019.26(1-2)4. Search in Google Scholar

[7] Tsai SB. Wang K. Using a novel method to evaluate the performance of human resources in green logistics enterprises. Ecol Chem Eng S. 2019;26(4):629-40. DOI: 10.1515/eces-2019-0045.10.1515/eces-2019-0045 Search in Google Scholar

[8] Oh RRY, Richards DR, Yee ATK. Community-driven skyrise greenery in a dense tropical city provides biodiversity and ecosystem service benefits. Landscape Urban Planning. 2018;169:115-23. DOI: 10.1016/j.landurbplan.2017.08.014.10.1016/j.landurbplan.2017.08.014 Search in Google Scholar

[9] Meng XY, Gao X, Li SY, Lei JQ. Spatial and temporal characteristics of vegetation NDVI changes and the driving forces in Mongolia during 1982-2015. Remote Sens. 2020;12:603-28. DOI: 10.3390/rs12040603.10.3390/rs12040603 Search in Google Scholar

[10] Voorde TVD. Spatially explicit urban green indicators for characterizing vegetation cover and public green space proximity: a case study on Brussels, Belgium. Int J Digital Earth. 2017;10:798-813. DOI:10.1080/17538947.2016.1252434.10.1080/17538947.2016.1252434 Search in Google Scholar

[11] Elhoseny M. Multi-object detection and tracking (MODT) machine learning model for real-time video surveillance systems. Circuits Systems Signal Processing. 2020;39:611-30. DOI: 10.1007/s00034-019-01234-7.10.1007/s00034-019-01234-7 Search in Google Scholar

[12] Du JQ, Zhao CX, Shu JM, Jiaerheng A, Yuan XJ, Yin JQ, et al. Spatiotemporal changes of vegetation on the Tibetan Plateau and relationship to climatic variables during multiyear periods from 1982-2012. Environ Earth Sci. 2016;75:77. DOI:10.1007/s12665-015-4818-4.10.1007/s12665-015-4818-4 Search in Google Scholar

[13] Li ZH, Gao W, Gao ZQ, Shi RH, Liu CH. A study on assessment of urbanization and ecosystem changes based on MODIS time series in Shanghai municipality from 2000 to 2009. Proc SPIE. 2010;7809:78090R. DOI: 10.1117/12.858605.10.1117/12.858605 Search in Google Scholar

[14] Liu T, Yang XJ. Monitoring Urban Growth and Land Changes in Beijing, China’s Capital City by Remote Sensing: Progress and Challenges: An Interdisciplinary Perspective. In: Challenges Towards Ecological Sustainability in China. 2019:55-67. DOI: 10.1007/978-3-030-03484-9_4.10.1007/978-3-030-03484-9_4 Search in Google Scholar

[15] Hussein SO. Monitoring urban greenness evolution using multitemporal Landsat imagery in the city of Erbil (Iraq). Central European Geol. 2018;62:1-12. DOI: 10.1556/24.61.2018.10.10.1556/24.61.2018.10 Search in Google Scholar

[16] Lv Z. The security of Internet of drones. Computer Commun. 2019;148:208-14. DOI: 10.1016/j.comcom.2019.09.018.10.1016/j.comcom.2019.09.018 Search in Google Scholar

[17] Liang HL, Li WZ, Zhang QP, Zhu W, Chen D, Liu J, et al. Using unmanned aerial vehicle data to assess the three-dimension green quantity of urban green space: A case study in Shanghai, China. Landscape Urban Planning. 2017;164:81-90. DOI: 10.1016/j.landurbplan.2017.04.006.10.1016/j.landurbplan.2017.04.006 Search in Google Scholar

[18] Hashim H, Abd Latif Z, Adnan NA. Urban vegetation classification with NDVI thresold value method with very high resolution (VHR) PLEIADES Imagery. Int Arch Photogramm. Remote Sens Spat Inf Sci. 2019;237-40. DOI: 10.5194/isprs-archives-XLII-4-W16-237-2019.10.5194/isprs-archives-XLII-4-W16-237-2019 Search in Google Scholar

[19] Rwanga S, Ndambuki J. Accuracy assessment of land use/land cover classification using remote sensing and GIS. Int J Geosci. 2017;8:611-22. DOI: 10.4236/ijg.2017.84033.10.4236/ijg.2017.84033 Search in Google Scholar

[20] Kussul N, Lavreniuk M, Skakun S, Shelestov A. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sensing Lett. 2017;14:778-82. DOI: 10.1109/LGRS.2017.2681128.10.1109/LGRS.2017.2681128 Search in Google Scholar

[21] Ramasamy B, Yeung MCH. China’s one belt one road initiative: The impact of trade facilitation versus physical infrastructure on exports. World Econ. 2019;42:1673-94. DOI: 10.1111/twec.12808.10.1111/twec.12808 Search in Google Scholar

[22] National Development and Reform Commission of the People’s Republic of China. Notice of the National Development and Reform Commission and the Ministry of Housing and Urban-Rural Development on Printing and Distributing the Development Plan of the Guanzhong Plain Urban Agglomeration. 2018. No. 220. Available from: http://www.ndrc.gov.cn/zcfb/zcfbtz/201802/t20180207_876904.html. Search in Google Scholar

[23] John AR. Remote Sensing Digital Image Analysis. 2013. ISBN: 9783642300615. DOI: 10.1007/978-3-642-30062-2.10.1007/978-3-642-30062-2 Search in Google Scholar

[24] Xie YC, Sha ZY, Yu M. Remote sensing imagery in vegetation mapping: a review. J Plant Ecol. 2008;1:6240-52. DOI: 10.1093/jpe/rtm005.10.1093/jpe/rtm005 Search in Google Scholar

[25] Quast R, Albergel C, Calvet JC, Wagner W. A generic first-order radiative transfer modelling approach for the inversion of soil and vegetation parameters from scatterometer observations. Remote Sens. 2019;11:285. DOI: 10.3390/rs11030285.10.3390/rs11030285 Search in Google Scholar

[26] Wang J, Du Y, Liu Z, Cheng H. Determining diagnostic indicators for fine-scale short vegetation aboveground biomass inversion using a HVRU-based analysis approach. Ecol Indicators. 2020;111:106033. DOI: 10.1016/j.ecolind.2019.106033.10.1016/j.ecolind.2019.106033 Search in Google Scholar

[27] Zanotta DC, Haertel V, Shimabukuro YE, Renno CD. Linear spectral mixing model for identifying potential missing endmembers in spectral mixture analysis. IEEE Trans Geosci Remote Sens. 2013;52:3005-12. DOI: 10.1109/TGRS.2013.2268539.10.1109/TGRS.2013.2268539 Search in Google Scholar

[28] Li H, Lei J, Wu J. Evolution analysis of vegetation cover under the disturbance of rare earth mining: a case in Lingbei mining area. J Appl Sci Eng. 2017;20:393-400. DOI: 10.6180/jase.2017.20.3.14. Search in Google Scholar

[29] Adami M, Bernardes S, Arai E, Freitas RM, Shimabukuro YE, Espírito-Santo FDB, et al. Seasonality of vegetation types of South America depicted by moderate resolution imaging spectroradiometer (MODIS) time series. Int J Appl Earth Observation Geoinform. 2018;69:148-63. DOI: 10.1016/j.jag.2018.02.010.10.1016/j.jag.2018.02.010 Search in Google Scholar

[30] Fassoni-Andrade AC, Zanotta DC, Guasselli LA, Andrade AM. Linear spectral mixing model for estimating optically active components in estuarine waters of Patos Lagoon, Brazil. Int J Remote Sens. 2017;38:4767-81. DOI: 10.1080/01431161.2017.1323281.10.1080/01431161.2017.1323281 Search in Google Scholar

[31] Li C, Liu P, Zou C, Sun F, Cioffi JM. Yang L. Spectral-efficient cellular communications with coexistent one-and two-hop transmissions. IEEE Trans Vehicular Technol. 2015;65:6765-72. DOI: 10.1109/TVT.2015.2472456.10.1109/TVT.2015.2472456 Search in Google Scholar

[32] Dong Di, Wang Di. Comparisons of ERDAS and ENVI in thematic mapping. 2011 IEEE 3rd Int Conf Communication Software Networks. 2011. DOI: 10.1109/ICCSN.2011.6014623.10.1109/ICCSN.2011.6014623 Search in Google Scholar

[33] Ran P, Eyal BD. Assessing the detection limit of petroleum hydrocarbon in soils using hyperspectral remote-sensing. Remote Sensing Environ. 2019;224:145-53. DOI: 10.1016/j.rse.2019.01.026.10.1016/j.rse.2019.01.026 Search in Google Scholar

[34] Kumar C, Chatterjee S, Oommen T. Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area, India. Int J Remote Sens. 2020;41:794-812. DOI: 10.1080/01431161.2019.1648906.10.1080/01431161.2019.1648906 Search in Google Scholar

[35] Zhao Y, Yang C. Information fusion robust guaranteed cost Kalman estimators with uncertain noise variances and missing measurements. Int J Systems Sci. 2019;50:2853-69. DOI: 10.1080/00207721.2019.1690719.10.1080/00207721.2019.1690719 Search in Google Scholar

[36] Bian JH, Li AN, Zhang ZJ, Zhao W, Lei GB, Yin GF, et al. Monitoring fractional green vegetation cover dynamics over a seasonally inundated alpine wetland using dense time series HJ-1A/B constellation images and an adaptive endmember selection LSMM model. Remote Sensing Environ. 2017;197:98-114. DOI: 10.1016/j.rse.2017.05.031.10.1016/j.rse.2017.05.031 Search in Google Scholar

[37] Zhang J, Liu Y, Zhou DM, Zhang Q, Chen WN. Spatial-temporal character of vegetation cover and its influence factors in the Shule River Basin China, during 1985-2011. Human Ecol Risk Assess: Int J. 2020;26:608-20. DOI: 10.1080/10807039.2018.1528437.10.1080/10807039.2018.1528437 Search in Google Scholar

[38] Peng J, Liu YH, Shen H, Han YN, Pan YJ. Vegetation coverage change and associated driving forces in mountain areas of Northwestern Yunnan, China using RS and GIS. Environ Monit Assess. 2012;184:4787-98. DOI: 10.1007/s10661-011-2302-5.10.1007/s10661-011-2302-521912871 Search in Google Scholar

[39] Liu X, Zhou W, Bai Z. Vegetation coverage change and stability in large open-pit coal mine dumps in China during 1990-2015. Ecol Eng. 2016;95:447-51. DOI: 10.1016/J.ECOLENG.2016.06.051.10.1016/j.ecoleng.2016.06.051 Search in Google Scholar

[40] Zhao K, Jin BX, Fan H, Song WW, Zhou SY, Jiang YY. High-performance overlay analysis of massive geographic polygons that considers shape complexity in a cloud environment. Int J Geo-Information. 2019;8:290. DOI: 10.3390/ijgi8070290.10.3390/ijgi8070290 Search in Google Scholar

[41] Yan LB, He RX, Kašanin-Grubin M, Luo G, Peng H, Qiu JX. The dynamic change of vegetation cover and associated driving forces in Nanxiong Basin, China. Sustainability. 2017;9:443. DOI: 10.3390/su9030443.10.3390/su9030443 Search in Google Scholar

[42] Liu SY, Huang SZ, Xie YY, Wang H, Huang Q, Leng GY, et al. Spatial-temporal changes in vegetation cover in a typical semi-humid and semi-arid region in China: Changing patterns, causes and implications. Ecol Indicators. 2019;98:462-75. DOI: 10.1016/j.ecolind.2018.11.037.10.1016/j.ecolind.2018.11.037 Search in Google Scholar

[43] Du JQ, Fu Q, Fang SF, Wu JH, He P, Quan ZJ. Effects of rapid urbanization on vegetation cover in the metropolises of China over the last four decades. Ecol Indicators. 2019;107:105458. DOI: 10.1016/j.ecolind.2019.105458.10.1016/j.ecolind.2019.105458 Search in Google Scholar

[44] Yang Z, Song J, Cheng D, Xia J, Li Q, Ahamad MI. Comprehensive evaluation and scenario simulation for the water resources carrying capacity in Xi’an city, China. J Environ Manage. 2019;230:221-33. DOI: 10.1016/j.jenvman.2018.09.085.10.1016/j.jenvman.2018.09.08530290309 Search in Google Scholar

eISSN:
2084-4549
Język:
Angielski