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Changes in urban vegetation cover and analysis of the influencing factors: a case study of Harbin, Heilongjiang Province, China

  • GMGDA 2019
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Abstract

Urban vegetation has an important role in ensuring the ecological security of cities. Rapid urbanization in China has radically changed the urban vegetation cover. Isolation of the contributions of human activities from observed vegetation can aid in understanding the effects of human activities on urban ecosystems. In this study, the Google Earth engine platform was employed, and the 1986–2017 archived Landsat data were used to derive the annual normalized difference vegetation index (NDVI), i.e., the observed NDVI. This was combined with annual mean temperature and annual precipitation data; then, Theil-Sen median and Mann-Kendall trend analysis was used to study the spatiotemporal variation characteristics of NDVI. Residual analysis and relative effect analysis were used to analyze the contributions of human activities to vegetation cover changes in Harbin. The results of the study showed the following: (1) In 1986-2017, the NDVI of Harbin ranged from 0.36 to 0.67, and NDVI fluctuations are large. From a temporal scale, there were no clear trends in the observed annual mean NDVI values, but NDVI showed an increasing trend before 2000 and after 2010. (2) From a spatial perspective, the mean NDVI value of 32 years showed spatial variation, which was low in the center and high in the periphery, and the area of the vegetation improvement regions (38.04%) was greater than the area of the vegetation degradation regions (21.33%). (3) Regions in which human activities played a dominant role in vegetation degradation (relative effect of more than 50%) accounted for 67.14% of the entire degeneration regions, while regions in which human activities played a dominant role in vegetation improvement (relative effect of more than 50%) accounted for 84.64% of the entire improvement regions. Our study shows that over 32 years, an overall greening trend was observed in Harbin, but urban vegetation cover changes showed spatial variability, and human activities had a dominant effect on vegetation changes. Vegetation degradation was mainly caused by urban expansion. Greening projects in recent years such as tree planting, greening of the old city area, building of urban parks, and greening of city roads in Harbin can improve urban vegetation cover and are important measures for improving the urban ecological environment.

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References

  • Cai B, Yu R (2009) Advance and evaluation in the long time series vegetation trends research based on remote sensing. J Remote Sens 13(6):1170–1186

    Google Scholar 

  • Cai D, Fraedrich K, Sielmann F, Guan Y, Guo S, Zhang L, Zhu X (2014) Climate and vegetation: an ERA-interim and GIMMS NDVI analysis. J Clim 27(13):5111–5118

    Google Scholar 

  • Duarte DHS, Shinzato P, Gusson CDS, Alves CA (2015) The impact of vegetation on urban microclimate to counterbalance built density in a subtropical changing climate. Urban Clim 14:224–239

    Google Scholar 

  • Erasmi S, Schucknecht A, Barbosa M, Matschullat J (2014) Vegetation greenness in northeastern brazil and its relation to ENSO warm events. Remote Sens 6:3041–3058

    Google Scholar 

  • Estel S, Kuemmerle T, Alcántara C, Levers C, Prishchepov A, Hostert P (2015) Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sens Environ 163:312–325

    Google Scholar 

  • Evans J, Geerken R (2004) Discrimination between climate and human-induced dryland degradation. J Arid Environ 57(4):535–554

    Google Scholar 

  • Feng X, Cheng W, Fu B, Lü Y (2016a) The role of climatic and anthropogenic stresses on long-term runoff reduction from the Loess Plateau, China. Sci Total Environ 571:688–698

    Google Scholar 

  • Feng X, Fu B, Piao S, Wang S, Ciais P, Zeng Z, Lü Y, Zeng Y, Li Y, Jiang X, Wu B (2016b) Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat Clim Chang 6(11):1019–1022

    Google Scholar 

  • Fraser RH, Olthof I, Carrière M, Deschamps A, Pouliot D (2011) Detecting long-term changes to vegetation in northern Canada using the Landsat satellite image archive. Environ Res Lett 6(4):045502

    Google Scholar 

  • Fu B, Wang S, Liu Y, Liu J, Liang W, Miao C (2017) Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annu Rev Earth Planet Sci 45:223–243

    Google Scholar 

  • Gandhi GM, Parthiban S, Thummalu N, Christy A (2015) NDVI: Vegetation change detection using remote sensing and GIS—a case study of Vellore district. Procedia Comput Sci 57:1199–1210

    Google Scholar 

  • Geerken R, Ilaiwi M (2004) Assessment of rangeland degradation and development of a strategy for rehabilitation. Remote Sens Environ 90(4):490–504

    Google Scholar 

  • Gelfand I, Zenone T, Jasrotia P, Chen J, HamiltonS K, Robertson GP (2011) Carbon debt of conservation reserve program (CRP) grasslands converted to bioenergy production. Proc Natl Acad Sci 108(33):13864–13869

    Google Scholar 

  • Gong P (2012) Remote sensing of environmental change over China: a review. Chin Sci Bull 57(22):2793–2801

    Google Scholar 

  • Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27

    Google Scholar 

  • Griffiths P, Kuemmerle T, Baumann M, Radeloff VC, Abrudan IV, Lieskovsky J, Munteanu C, Ostapowicz K, Hostert P (2014) Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sens Environ 151:72–88

    Google Scholar 

  • Guan X, Shen H, Li X, Gan W, Zhang L (2019) A long-term and comprehensive assessment of the urbanization-induced impacts on vegetation net primary productivity. Sci Total Environ 669:342–352

    Google Scholar 

  • Harbin Municipal People’s Government. 2010 government work report in Harbin. Availabe online: http://www.harbin.gov.cn/art/2010/1/27/art_397_27444.html (accessed on 29 August, 2019).

  • Harbin Municipal People’s Government. Harbin Statistical Yearbook (2013, 2014, 2015, 2016, 2017, 2018). Availabe online: http://www.harbin.gov.cn/col/col39/index.html (accessed on 29 August, 2019).

  • Huang K, Zhang Y, Zhu J, Liu Y, Zu J, Zhang J (2016) The influences of climate change and human activities on vegetation dynamics in the Qinghai-Tibet Plateau. Remote Sens 8(10):876

    Google Scholar 

  • Hutchinson MF, McKenney DW, Lawrence K, Pedlar JH, Hopkinson RF, Milewska E, Papadopol P (2009) Development and testing of Canada-wide interpolated spatial models of daily minimum–maximum temperature and precipitation for 1961–2003. J Appl Meteorol Climatol 48(4):725–741

    Google Scholar 

  • Ibrahim Y, Balzter H, Kaduk J, Tucker C (2015) Land degradation assessment using residual trend analysis of GIMMS NDVI3g, soil moisture and rainfall in sub-Saharan West Africa from 1982 to 2012. Remote Sens 7(5):5471–5494

    Google Scholar 

  • Ichii K, Kawabata A, Yamaguchi Y (2002) Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990. Int J Remote Sens 23(18):3873–3878

    Google Scholar 

  • Jeong SJ, Ho CH, Jeong JH (2009) Increase in vegetation greenness and decrease in springtime warming over east Asia. Geophys Res Lett 36(2)

  • Johansen K, Phinn S, Taylor M (2015) Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth engine. Remote Sensing Applications: Society and Environment 1:36–49

    Google Scholar 

  • Kendall M (1975) Multivariate Analysis. London, Charles Griffin Company

    Google Scholar 

  • Kim DH, Sexton JO, Noojipady P, Huang C, Anand A, Channan S, Feng M, Townshend JR (2014) Global, Landsat-based forest-cover change from 1990 to 2000. Remote Sens Environ 155:178–193

    Google Scholar 

  • Lee E, Kastens JH, Egbert SL (2016) Investigating collection 4 versus collection 5 MODIS 250 m NDVI time-series data for crop separability in Kansas, USA. Int J Remote Sens 37(2):341–355

    Google Scholar 

  • Leroux L, Bégué A, Seen DL, Jolivot A, Kayitakire F (2017) Driving forces of recent vegetation changes in the Sahel: lessons learned from regional and local level analyses. Remote Sens Environ 191:38–54

    Google Scholar 

  • Li A, Wu J, Huang J (2012) Distinguishing between human-induced and climate-driven vegetation changes: a critical application of RESTREND in inner Mongolia. Landsc Ecol 27(7):969–982

    Google Scholar 

  • Li S, Liang W, Fu B, Lü Y, Fu S, Wang S, Su H (2016) Vegetation changes in recent large-scale ecological restoration projects and subsequent impact on water resources in China’s Loess Plateau. Sci Total Environ 569:1032–1039

    Google Scholar 

  • Liu D, Yu C (2018) Urban expansion and its influence on spatio-temporal variation of thermal environment: a case study of Harbin city. Ecol Environ Sci 27(03):509–517

    Google Scholar 

  • Liu J, Kuang W, Zhang Z, Xu X, Qin Y, Ning J, Zhou W, Zhang S, Li R, Yan C, Wu S, Shi X, Jiang N, Yu D, Pan X, Chi W (2014) Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J Geogr Sci 24(2):195–210

    Google Scholar 

  • Liu X, Hu G, Chen Y, Li X, Xu X, Li S, Pei F, Wang S (2018) High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth engine platform. Remote Sens Environ 209:227–239

    Google Scholar 

  • Livesley SJ, McPherson GM, Calfapietra C (2016) The urban forest and ecosystem services: impacts on urban water, heat, and pollution cycles at the tree, street, and city scale. J Environ Qual 45(1):119–124

    Google Scholar 

  • Luo P, Yang X, Wan L, Wu X, Zhou J (2017) Study of coordination of population urbanization with land urbanization in Harbin, a cold northern city. J Glaciol Geocryol 39(5):1150–1156

    Google Scholar 

  • Luo H, Dai S, Xie Z, Fang J (2018) NDVI-based analysis on the influence of human activities on vegetation variation on Hainan Island. IOP Conf Ser Earth Environ Sci 121:032045

    Google Scholar 

  • Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259

    Google Scholar 

  • Masek JG, Huang C, Wolfe R, Cohen W, Hall F, Kutler J, Nelson P (2008) North American forest disturbance mapped from a decadal Landsat record. Remote Sens Environ 112(6):2914–2926

    Google Scholar 

  • Matin S, Ghosh S, Behera MD (2019) Assessing land transformation and associated degradation of the west part of Ganga River basin using forest cover land use mapping and residual trend analysis. J Arid Land 11(1):29–42

    Google Scholar 

  • Milich L, Weiss E (2000) GAC NDVI interannual coefficient of variation (CoV) images: ground truth sampling of the Sahel along north-south transects. Int J Remote Sens 21(2):235–260

    Google Scholar 

  • Ning J, Liu J, Kuang W, Xu X, Zhang S, Yan C, Li R, Wu S, Hu Y, Du G, Chi W, Pan T, Ning J (2018) Spatiotemporal patterns and characteristics of land-use change in China during 2010–2015. J Geogr Sci 28(5):547–562

    Google Scholar 

  • Olthof I, Fraser RH, Schmitt C (2015) Landsat-based mapping of thermokarst lake dynamics on the Tuktoyaktuk Coastal Plain, Northwest Territories, Canada since 1985. Remote Sens Environ 168:194–204

    Google Scholar 

  • Patel NN, Angiuli E, Gamba P, Gaughan A, Lisini G, Stevens FR, Tatem AJ, Trianni G (2015) Multitemporal settlement and population mapping from Landsat using Google Earth engine. Int J Appl Earth Obs Geoinf 35:199–208

    Google Scholar 

  • Pei J, Yang W, Cai Y, Yi Y, Li X (2018) Relationship between vegetation and environment in an arid-hot valley in southwestern China. Sustainability 10(12):4774

    Google Scholar 

  • Peng S, Chen A, Xu L, Cao C, Fang J, Myneni RB, Pinzon JE, Tucker CJ, Piao S (2011) Recent change of vegetation growth trend in China. Environ Res Lett 6(4):044027

    Google Scholar 

  • Rani M, Kumar P, Pandey PC, Srivastava PK, Chaudhary BS, Tomar V, Mandal VP (2018) Multi-temporal NDVI and surface temperature analysis for inbuilt surrounding of sub-humid region: a case study of two geographical regions. Remote Sens Appl Soc Environ 10:163–172

    Google Scholar 

  • Rasmussen MS (1998) Developing simple, operational, consistent NDVI-vegetation models by applying environmental and climatic information: Part I. Assessment of net primary production. Int J Remote Sens 19(1):97–117

    Google Scholar 

  • Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173

    Google Scholar 

  • Soulard C, Albano C, Villarreal M, Walker J (2016) Continuous 1985-2012 Landsat monitoring to assess fire effects on meadows in Yosemite National Park, California. Remote Sens 8(5):371

    Google Scholar 

  • Sun J, Qin X (2016) Precipitation and temperature regulate the seasonal changes of NDVI across the Tibetan Plateau. Environ Earth Sci 75(4):291

    Google Scholar 

  • Teferi E, Bewket W, Uhlenbrook S, Wenninger J (2013) Understanding recent land use and land cover dynamics in the source region of the upper Blue Nile, Ethiopia: spatially explicit statistical modeling of systematic transitions. Agric Ecosyst Environ 165:98–117

    Google Scholar 

  • Wang J, Wang K, Zhang M, Zhang C (2015) Impacts of climate change and human activities on vegetation cover in hilly southern China. Ecol Eng 81:451–461

    Google Scholar 

  • Wang S, Pan T, Lei G (2019) Land use pattern and NDVI response characteristics based on Landsat TM in Harbin city. Jiangsu Agric Sci 47(06):221–225

    Google Scholar 

  • Xiong J, Thenkabail PS, Gumma MK, Teluguntla P, Poehnelt J, Congalton RG, Yadav K, Thau D (2017) Automated cropland mapping of continental Africa using Google Earth engine cloud computing. ISPRS J Photogramm Remote Sens 126:225–244

    Google Scholar 

  • Xu D, Kang X, Liu Z, Zhuang D, Pan J (2009) Assessing the relative role of climate change and human activities in sandy desertification of Ordos region, China. Sci China Ser D Earth Sci 39(4):516–528

    Google Scholar 

  • Zhao H, Liu S, Dong S, Su X, Wang X, Wu X, Wu L, Zhang X (2015) Analysis of vegetation change associated with human disturbance using MODIS data on the rangelands of the Qinghai-Tibet Plateau. Rangeland J 37(1):77–87

    Google Scholar 

  • Zhao Y, Feng D, Yu L, Cheng Y, Zhang M, Liu X, Xu Y, Fang L, Zhu Z, Gong P (2019) Long-term land cover dynamics (1986–2016) of Northeast China derived from a multi-temporal Landsat archive. Remote Sens 11(5):599

    Google Scholar 

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Correspondence to Wei Gao.

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This article is part of the Topical Collection on Geological Modeling and Geospatial Data Analysis

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Wan, Z., Gao, W. Changes in urban vegetation cover and analysis of the influencing factors: a case study of Harbin, Heilongjiang Province, China. Arab J Geosci 13, 1053 (2020). https://doi.org/10.1007/s12517-020-05931-5

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