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Quantifying highly dynamic urban landscapes: Integrating object-based image analysis with Landsat time series data

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Abstract

Context

Urban landscapes are highly dynamic with changes frequently occurring at short time intervals. Although the Landsat data archive allows the use of high-density time-series data to quantify such dynamics, the approaches that can fully address the spatial and temporal complexity of the urban landscape are still lacking.

Objectives

A new approach is presented for accurately quantifying urban landscape dynamics. Information regarding when and where a change occurs, what type of change exists, and how often it happens are incorporated.

Methods

The new approach integrates object-based image analysis and time-series change detection techniques by using all available Landsat images for several decades. This approach was tested on the rapidly urbanizing city of Shenzhen, China from 1986 to 2017.

Results

Land cover changes in both long- and short-time intervals can be proficiently detected with an overall accuracy of 90.65% and a user’s accuracy of 92.18% and 82.40% for “No change” and “Change”, respectively. The frequency and time of change can be explicitly displayed while incorporating the advantages of object-based image analysis and time-series change detection. The efficiency of the change analysis can be greatly increased because the object-based analysis greatly reduces the number of analyzed units.

Conclusion

The new approach can accurately and efficiently detect the land cover change for quantifying urban landscape dynamics. Integrating the object and the remotely sensed time-series data has the potential to link the physical and socio-economic properties together for facilitating sustainable landscape planning.

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References

  • Bai J, Perron P (2003) Computation and analysis of multiple structural change models. J Appl Econom 18:1–22

    Google Scholar 

  • Baker F, Smith C (2019) A GIS and object based image analysis approach to mapping the greenspace composition of domestic gardens in Leicester, UK. Landsc Urban Plan 183:133–146

    Google Scholar 

  • Bechtel B, Alexander PJ, Böhner J, Ching J, Conrad O, Feddema J, Mills G, See L, Stewart I (2015) Mapping local climate zones for a worldwide database of the form and function of cities. ISPRS Int J Geo-Inf 4:199–219

    Google Scholar 

  • Bishop-Taylor R, Tulbure MG, Broich M (2018) Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series. Landsc Ecol 33:625–640

    Google Scholar 

  • Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm 65:2–16

    Google Scholar 

  • Blaschke T, Hay GJ, Kelly M, Lang S, Hofmann P, Addink E, Feitosa RQ, van der Meer F, van der Werff H, van Coillie F, Tiede D (2014) Geographic object-based image analysis - towards a new paradigm. ISPRS J Photogramm 87:180–191

    Google Scholar 

  • Blaschke T, Lang S, Lorup E, Strobl J, Zeil P (2000) Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environ Inf Plan Polit Public 2:555–570

    Google Scholar 

  • Boeing G (2019) Urban spatial order: street network orientation, configuration, and entropy. Appl Netw Sci 4:67

    Google Scholar 

  • Cadenasso ML, Pickett STA, Schwarz K (2007) Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification. Front Ecol Environ 5:80–88

    Google Scholar 

  • Chen G, Zhao KG, Powers R (2014) Assessment of the image misregistration effects on object-based change detection. ISPRS J Photogramm 87:19–27

    Google Scholar 

  • Cleve C, Kelly M, Kearns F, Moritz M (2008) Classification of the wildland-urban interface: a comparison of pixel- and object-based classification using high-resolution aerial photography. Comput Environ Urban Syst 32:10

    Google Scholar 

  • Deng JS, Wang K, Hong Y, Qi JG (2009) Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landsc Urban Plan 92:187–198

    Google Scholar 

  • DeVries B, Verbesselt J, Kooistra L, Herold M (2015) Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sens Environ 161:107–121

    Google Scholar 

  • Dou P, Chen YB (2017) Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015. Int J Remote Sens 38:5388–5407

    Google Scholar 

  • Ewing R, Meakins G, Hamidi S, Nelson AC (2014) Relationship between urban sprawl and physical activity, obesity and morbidity. Health Place 26:118

    PubMed  Google Scholar 

  • Fan C, Myint S (2014) A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation. Landsc Urban Plan 121:117–128

    Google Scholar 

  • Guindon B, Zhang Y, Dillabaugh C (2004) Landsat urban mapping based on a combined spectral-spatial methodology. Remote Sens Environ 92:15

    Google Scholar 

  • Huang HB, Chen YL, Clinton N, Wang J, Wang XY, Liu CX, Gong P, Yang J, Bai YQ, Zheng YM, Zhu ZL (2017) Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens Environ 202:166–176

    Google Scholar 

  • Hussain M, Chen DM, Cheng A, Wei H, Stanley D (2013) Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm 80:91–106

    Google Scholar 

  • Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens Environ 110:370–386

    Google Scholar 

  • Kennedy RE, Yang ZG, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sens Environ 114:2897–2910

    Google Scholar 

  • Li WF, Zhou WQ, Bai Y, Pickett STA, Han LJ (2018a) The smart growth of Chinese cities: opportunities offered by vacant land. Land Degrad Dev 29:3512–3520

    Google Scholar 

  • Li X, Zhou Y, Zhu Z, Liang L, Yu B, Cao W (2018b) Mapping annual urban dynamics (1985–2015) using time series of Landsat data. Remote Sens Environ 216:674–683

    Google Scholar 

  • Li X, Myint SW, Zhang Y, Galletti C, Zhang X, Turner BL (2014) Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography. Int J Appl Earth Observ Geoinf 33:321–330

    CAS  Google Scholar 

  • Li XC, Gong P, Liang L (2015) A 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data. Remote Sens Environ 166:78–90

    Google Scholar 

  • Liu D, Chen N, Zhang X, Wang C, Du W (2020) Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: a case study in the middle Yangtze River basin. ISPRS Int J Photogramm Remote Sens 159:15

    Google Scholar 

  • Liu F, Zhang Z, Wang A (2016) Forms of urban expansion of Chinese municipalities and provincial capital, 1970s–2013. Remote Sens-Basel 8:19

    Google Scholar 

  • Long Y, Shen Y, Jin X (2015) Mapping block-level urban area for all Chinese cities. Ann Assoc Am Geogr 106:1–18

    Google Scholar 

  • Lu Y, Coops N, Hermosilla T (2017) Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data. ISPRS J Photogramm 126:13

    Google Scholar 

  • McDermid GJ, Linke J, Pape AD, Laskin DN, McLane AJ, Franklin SE (2008) Object-based approaches to change analysis and thematic map update: challenges and limitations. Can J Remote Sens 34:462–466

    Google Scholar 

  • Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng QH (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ 115:1145–1161

    Google Scholar 

  • Nesbitt L, Meitner MJ, Girling C, Sheppard SRJ, Lu Y (2019) Who has access to urban vegetation? A spatial analysis of distributional green equity in 10 US cities. Landsc Urban Plan 181:51–79

    Google Scholar 

  • Peng J, Xie P, Liu Y, Ma J (2016a) Urban thermal environment dynamics and associated landscape pattern factors: a case study in the Beijing metropolitan region. Remote Sens Environ 173:145–155

    Google Scholar 

  • Peng Y, Qian J, Ren F, Zhang W, Du Q (2016b) Sustainability of land use promoted by construction-to-ecological land conversion: a case study of Shenzhen city China. Sustainability-Basel 8:16

    Google Scholar 

  • Pickett STA, Cadenasso ML, Rosi-Marshall EJ, Belt KT, Groffman PM, Grove JM, Irwin EG, Kaushal SS, LaDeau SL, Nilon CH, Swan CM, Warren PS (2017) Dynamic heterogeneity: a framework to promote ecologcial intergration and hypothesis generation in urban systems. Urban Ecosyst 20:1–14

    Google Scholar 

  • Qian YG, Zhou WQ, Li WF, Han LJ (2015) Understanding the dynamic of greenspace in the urbanized area of Beijing based on high resolution satellite images. Urban For Urban Green 14:39–47

    Google Scholar 

  • Schneider A, Woodcock CE (2008) Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Stud 45:659–692

    Google Scholar 

  • Seto KC, Fragkias M, Güneralp B, Reilly MK (2011) A meta-analysis of global urban land expansion. PLoS ONE 6:e23777

    CAS  PubMed  PubMed Central  Google Scholar 

  • Sexton JO, Song XP, Huang CQ, Channan S, Baker ME, Townshend JR (2013) Urban growth of the Washington, D.C.-Baltimore, MD metropolitan region from 1984 to 2010 by annual Landsat-based estimates of impervious cover. Remote Sens Environ 155:379–379

    Google Scholar 

  • Siksna A (1997) The effects of block size and form in North American and Australian city centres. Urban Morphol 1:19–33

    Google Scholar 

  • Shao G, Tang L, Liao J (2019) Overselling overall map accuracy misinforms about research reliability. Landsc Ecol 34:2487–2492

    Google Scholar 

  • Smith JP, Li X, Turner BL (2017) Lots for greening: Identification of metropolitan vacant land and its potential use for cooling and agriculture in Phoenix, AZ, USA. Appl Geogr 85:139–151

    Google Scholar 

  • Song XP, Sexton JO, Huang CQ, Channan S, Townshend JR (2016) Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sens Environ 175:1–13

    Google Scholar 

  • Stefanov WL, Ramsey MS, Christensen PR (2001) Monitoring urban land cover change: an expert system approach to land cover classification of semiarid to arid urban centers. Remote sens of Environ 77:173–185

    Google Scholar 

  • Stehman SV, Foody GM (2019) Key issues in rigorous accuracy assessment of land cover products. Remote Sens Environ 231:111199

    Google Scholar 

  • Stehman SV, Wickham J (2020) A guide for evaluating and reporting map data quality: affirming Shao et al. “Overselling overall accuracy misinforms about research reliability”. Landsc Ecol 35:1263–1267

    CAS  PubMed  PubMed Central  Google Scholar 

  • Tulbure MG, Broich M (2013) Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011. ISPRS J Photogramm 79:44–52

    Google Scholar 

  • Turner BL (2010) Vulnerability and resilience: coalescing or paralleling approaches for sustainability sciences? Glob Environ Change Hum Policy Dimens 20:570–576

    Google Scholar 

  • Uhl JH, Leyk S (2019) Towards a novel backdating strategy for creating built-up land time series data using contemporary spatial constraints. Remote Sens Environ 238:111197

    Google Scholar 

  • Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010a) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 114:106–115

    Google Scholar 

  • Verbesselt J, Hyndman R, Zeileis A, Culvenor D (2010b) Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens Environ 114:2970–2980

    Google Scholar 

  • Wang XX, Xiao XM, Zou ZH, Chen BQ, Ma J, Dong JW, Doughty RB, Zhong QY, Qin YW, Dai SQ, Li XP, Zhao B, Li B (2018) Tracking annual changes of coastal tidal flats in China through analyses of Landsat images with Google Earth Engine. Remote Sens Environ 38:110987

    Google Scholar 

  • Wu J, Jenerette GD, Buyantuyev A, Redman CL (2011) Quantifying spatiotemporal patterns of urbanization: the case of the two fastest growing metropolitan regions in the United States. Ecol Complex 8:1–8

    Google Scholar 

  • Wu JG (2013) Landscape sustainability science: ecosystem services and human well-being in changing landscapes. Landsc Ecol 28:999–1023

    Google Scholar 

  • Wu JG (2014) Urban ecology and sustainability: the state-of-the-science and future directions. Landsc Urban Plan 125:209–221

    Google Scholar 

  • Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122:2–10

    Google Scholar 

  • Xia C, Gar-On Yeh A, Zhang A (2020) Analyzing spatial relationships between urban land use intensity and urban vitality at street block level: a case study of five Chinese megacities. Landsc Urban Plan 193:18

    Google Scholar 

  • Yin H, Prishchepov AV, Kuemmerle T, Bleyhl B, Buchner J, Radeloff VC (2018) Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sens Environ 210:13

    Google Scholar 

  • Yu W, Zhang Y, Zhou W, Wang W, Tang R (2019) Urban expansion in Shenzhen since 1970s: a retrospect of change from a village to a megacity from the space. Phys Chem Earth Parts A/B/C 110:21

    Google Scholar 

  • Yuan NJ, Zheng Y, Xie X, Wang Y, Zheng K, Xiong H (2015) Discovering urban functional zones using Latent Activity Trajectories. IEEE Trans Knowl Data Eng 27:14

    Google Scholar 

  • Zeileis A, Leisch F, Hornik K, Kleiber C (2002) strucchange: an R package for testing for structural change in linear regression models. J Stat Softw 7:38

    Google Scholar 

  • Zhang T, Huang X (2018) Monitoring of urban impervious surfaces using time series of high-resolution remote sensing images in rapidly urbanized areas: a case study of Shenzhen. IEEE J-Stars 11:17

    Google Scholar 

  • Zhou W, Fisher B, Pickett STA (2019) Cities are hungry for actionable ecological knowledge. Front Ecol Environ 17:135

    Google Scholar 

  • Zhou W, Pickett ST, Cadenasso ML (2017) Shifting concepts of urban spatial heterogeneity and their implications for sustainability. Landsc Ecol 32:16

    Google Scholar 

  • Zhou W, Wang J, Qian Y, Pickett STA, Li W, Han L (2018) The rapid but “invisible” changes in urban greenspace: a comparative study of nine Chinese cities. Sci Total Environ 627:1572–1584

    CAS  PubMed  Google Scholar 

  • Zhou WQ, Troy A, Grove M (2008) Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors 8:1613–1636

    PubMed  Google Scholar 

  • Zhu Z, Fu Y, Woodcock CE, Olofsson P, Vogelmann JE, Holden C, Wang M, Dai S, 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 Sens Environ 185:243–257

    Google Scholar 

  • Zhu Z, Woodcock CE (2014) Continuous change detection and classification of land cover using all available Landsat data. Remote Sens Environ 144:152–171

    Google Scholar 

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Acknowledgements

This research was supported by funding from the National Natural Science Foundation of China (Grant No. 41801178 and 41771203), Chinese Academy of Sciences (Grant No. XDA23030102) and Shenzhen Municipal Ecology and Environment Bureau (Grant No. SZCG2018161498).

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Authors and Affiliations

  1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China

    Wenjuan Yu, Weiqi Zhou, Chuanbao Jing & Yuguo Qian

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Weiqi Zhou & Chuanbao Jing

  3. Beijing Urban Ecosystem Research Station, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China

    Weiqi Zhou

  4. School of Geographical Sciences and Urban Planning, Arizona States University, Tempe, AZ, 85287, USA

    Yujia Zhang

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  1. Wenjuan Yu
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  2. Weiqi Zhou
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  3. Chuanbao Jing
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  4. Yujia Zhang
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  5. Yuguo Qian
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Correspondence to Weiqi Zhou.

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Yu, W., Zhou, W., Jing, C. et al. Quantifying highly dynamic urban landscapes: Integrating object-based image analysis with Landsat time series data. Landscape Ecol 36, 1845–1861 (2021). https://doi.org/10.1007/s10980-020-01104-7

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