183
Views
2
CrossRef citations to date
0
Altmetric
Original Articles

Detecting grassland cover changes through spatiotemporal outlier analysis using remotely sensed time-series data: a case study from Xilingol, China

&
Pages 1236-1252 | Received 15 Apr 2019, Accepted 08 Jun 2019, Published online: 10 Jul 2019

References

  • Amici V, Marcantonio M, La Porta N, Rocchini D. 2017. A multi-temporal approach in MaxEnt modelling: a new frontier for land use/land cover change detection. Ecol Inform. 40:40–49.
  • Anselin L. 1996. The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In: Fischer M, Scholten HJ, Unwin D, editors. Spatial analytical perspectives on GIS. London: Taylor & Francis; p. 111–125.
  • Anselin L. 2010. Local Indicators of Spatial Association—LISA. Geogr Anal. 27(2):93–115.
  • Carlson TC, Ripley DA. 1997. On the relationship between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ. 62(3):241–252.
  • Chen T, Christensen M, Nan Z, Hou F. 2017. The effects of different intensities of longterm grazing on the direction and strength of plant-soil feedback in a semiarid grassland of Northwest China. Plant Soil. 413:303–317.
  • Chi W, Zhao Y, Kuang W, He H. 2019. Impacts of anthropogenic land use/cover changes on soil wind erosion in China. Sci Total Environ. 668:204–215.
  • 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.
  • Fan C, Myint S, Zheng B. 2015. Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures. Progr Phys Geogr. 39(2):199–219.
  • Fenta AA, Yasuda H, Haregeweyn N, Belay AS, Hadush Z, Gebremedhin MA, Mekonnen G. 2017. The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: The case of Mekelle city of northern Ethiopia. Int J Remote Sens. 38(14):4107–4129.
  • Fu X, Ma M, Jiang P, et al. 2017. Spatiotemporal vegetation dynamics and their influence factors at a large coal-fired power plant in Xilinhot, Inner Mongolia. Int J Sust DevWorld Ecol. 24:433–438.
  • Gal L, Grippa M, Hiernaux P, Pons, L, Kergoat, L. 2017. The paradoxical evolution of runoff in the pastoral Sahel: analysis of the hydrological changes over the Agoufou watershed (Mali) using the KINEROS-2 model. Hydrol. Earth Syst. Sci. 21: 4591–4613.
  • Gillanders SN, Coops NC, Wulder MA, Gergel SE, Nelson T. 2008. Multitemporal remote sensing of landscape dynamics and pattern change: Describing natural and anthropogenic trends. Prog Phys Geogr. 32(5):503–528.
  • Gong C, Yu S, Joesting H, Chen J. 2013. Determining socioeconomic drivers of urban forest fragmentation with historical remote sensing images. Landsc Urban Plan. 117:57–65.
  • Hauser LT, Nguyen Vu G, Nguyen BA, Dade E, Nguyen HM, Nguyen TTQ, Le TQ, Vu LH, Tong ATH, Pham HV, et al. 2017. Uncovering the spatiotemporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004–2013). Appl Geogr. 86:197–207.,
  • He C, Zhang Q, Li Y, Li X, Shi P. 2005. Zoning grassland protection area using remote sensing and cellular automata modelling: a case study in Xilingol steppe grassland in northern China. J Arid Environ. 63(4):814–826.
  • Jiang L, Jiapaer G, Bao A, et al. 2017. Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci Total Environ. 599–600:967–980.
  • 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 Sens Environ. 195:44–55.
  • Kadavi P, Lee C. 2018. Land cover classification analysis of volcanic island in Aleutian Arc using an artificial neural network (ANN) and a support vector machine (SVM) from Landsat imagery. Geosci J. 22(4):653–665.
  • Lai S, Leone F, Zoppi C. 2017. Land cover changes and environmental protection: A study based on transition matrices concerning Sardinia (Italy). Land Use Policy. 67:126–150.
  • Lawler JJ, Lewis DJ, Nelson E, Plantinga AJ, Polasky S, Withey JC, Helmers DP, Martinuzzi S, Pennington D, Radeloff VC, et al. 2014. Projected land-use change impacts on ecosystem services in the United States. Proc Natl Acad Sci. 111(20):7492–7497.
  • Lee S, Cho M, Lee C. 2016. An effective gap filtering method for Landsat ETM + SLC-Off data. Terr Atmos Ocean Sci. 27:921–932.
  • Liu Y, Lü Y, Fu B, Harris P, Wu L. 2019. Quantifying the spatio-temporal drivers of planned vegetation restoration on ecosystem services at a regional scale. Sci Tot Environ. 650:1029–1040.
  • 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 Sens. 7(2):2067–2088.
  • Mancino G, Nolè A, Ripullone F, Ferrara A. 2014. Landsat TM imagery and NDVI differencing to detect vegetation change: Assessing natural forest expansion in Basilicata, southern Italy. iForest. 7(2):75–84.
  • Ming D, Ci T, Cai H. 2012. Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift algorithm. IEEE Geosci Remote Sens Lett. 9:813–817.
  • Ord JK, Getis A. 2010. Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal. 27(4):286–306.
  • Pagliarella MC, Sallustio L, Capobianco G, Conte E, Corona P, Fattorini L, Marchetti M. 2016. From one- to two-phase sampling to reduce costs of remote sensing-based estimation of land-cover and land-use proportions and their changes. Remote Sens Environ. 184:410–417.
  • Pérez-Hugalde C, Romero-Calcerrada R, Delgado-Pérez P, Novillo CJ. 2011. Understanding land cover change in a special protection area in central Spain through the enhanced land cover transition matrix and a related new approach. J Environ Manag. 92(4):1128–1137.
  • Pérez-Hugalde C, Romero-Calcerrada R, Delgado-Pérez P, et al. 2014. Land fragmentation and variation of ecosystem services in the context of rapid urbanization: the case of Taizhou city, China. Stoch Environ Res Risk Assess. 28:843–855.
  • Pu R, Gong P, Tian Y, Miao X, Carruthers RI, Anderson GL. 2008. Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA. Int J Remote Sens. 29:3987–4011.
  • Rodríguez-González PM, Albuquerque A, Martínez-Almarza M, Díaz-Delgado R. 2017. Long-term monitoring for conservation management: Lessons from a case study integrating remote sensing and field approaches in floodplain forests. J Environ Manag. 202:392–402.
  • Singh S, Reddy CS, Pasha SV, Dutta K, Saranya KRL, Satish KV. 2017. Modeling the spatial dynamics of deforestation and fragmentation using Multi-Layer Perceptron neural network and landscape fragmentation tool. Ecol Eng. 99:543–551.
  • Song W, Deng X. 2017. Land-use/land-cover change and ecosystem service provision in China. Sci Tot Environ. 576:705–719.
  • Su S, Hu Y, Luo F, Mai G, Wang Y. 2014. Farmland fragmentation due to anthropogenic activity in rapidly developing region. Agric Syst. 131:87–93.
  • Tan M. 2017. An intensity gradient/vegetation fractional coverage approach to mapping urban areas from DMSP/OLS nighttime light data. IEEE J Sel Top Appl Earth Observations Remote Sens. 10(1):95–103.
  • Tsai W-L, Floyd MF, Leung Y-F, McHale MR, Reich BJ. 2016. Urban vegetative cover fragmentation in the U.S.: Associations with physical activity and BMI. Am J Prev Med. 50(4):509–517.
  • USGS. 2018. West Africa: land use and land cover dynamics [accessed 2018 July 27]. https://eros.usgs.gov/westafrica/case-study/land-cover-modification.
  • Usman M, Liedl R, Shahid MA, Abbas A. 2015. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. J Geogr Sci. 25(12):1479–1506.
  • Verburg PH, Neumann K, Nol L. 2011. Challenges in using land use and land cover data for global change studies. Glob Chang Biol. 17(2):974–989.
  • Wang Z, Gaole, Dai, Yiru L, et al. 2015., Assessment of spatio-temporal vegetation productivity pattern based on MODIS-NDVI and geo-correlation analysis. Proceedings of 3rd International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem; Oct 16–18; Wuhan, China; p. 673–681.
  • Xie Y, Sha Z, Bai Y. 2010. Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model. ISPRS J Photogramm Remote Sens. 65(2):182–190.
  • Xu G, Kang M, Metzger M, et al. 2014. Vulnerability of the human-environment system in arid regions: the case of Xilingol grassland in northern China. Pol J Environ Stud. 23:1773–1785.
  • Yang X. T, Liu H, Gao X. 2015. Land cover changed object detection in remote sensing data with medium spatial resolution. Int J Appl Earth Observ Geoinform. 38:129–137.
  • Zhang HK, Roy DP. 2017. Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification. Remote Sens Environ. 197:15–34.
  • Zhou Y, Wang Y, Gold AJ, August PV. 2010. Modeling watershed rainfall–runoff relations using impervious surface-area data with high spatial resolution. Hydrogeology Journal. 18(6): 1413–1423.
  • Zhao Y, Wang S, Ge Y, Liu Q, Liu X. 2017. The spatial differentiation of the coupling relationship between urbanization and the eco-environment in countries globally: a comprehensive assessment. Ecol Model. 360:313–327.
  • Zhou W, Yang H, Huang L, Chen C, Lin X, Hu Z, Li J. 2017. Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010. Ecol Indic. 83:303–313.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.