Abstract
Based on multi-temporal Landsat images from 1996 to 2014, a hierarchical strategy for land use classification has been presented. The land use dynamic index, conversion matrix and spatial center have been used to quantitatively analyze the spatial–temporal dynamics of land cover in Binzhou urban area. Per results, the overall accuracy and kappa coefficient of hierarchical classification method have been found to be 93.82% and 0.92, respectively, indicating that the method used for land cover classification was feasible. Per findings from the urban area under study, during 1996 to 2014, the cropland area decreased constantly, forestland and water body area decreased at first and then increased, while the built-up land area increased constantly. The major pattern emerging from the land cover change was represented by the conversions of cropland to built-up land. As a result of hydraulic and landscape engineering, gains in forestland and water body were more than the corresponding losses. The area gained by forestland reached peak in the period of 2005 to 2009, while the area gained by water body peaked in the period of 2001 to 2005. Moreover, spatial centers of cropland and built-up land had been located on the southwest side and the east side of the city square, respectively. Per findings, spatial center of cropland was moving away from the City Square in southwestern direction by about 634.30 m, while the built-up land extended to the west, and the spatial center of the built-up land moved towards the southwest as well by about 1575.84 m. The movement of built-up land spatial center was closely related to the development and construction of west urban area.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of Data and Material
The data are transparent.
References
Aburas, M. M., Abdullah, S. H., Ramli, M. F., et al. (2015). Measuring land cover change in Seremban, Malaysia using NDVI index. Procedia Environmental Sciences, 30, 238–243. https://doi.org/10.1016/j.proenv.2015.10.043.
Beykaei, S. A., Zhong, M., Shiravi, S., et al. (2014). A hierarchical rule-based land use extraction system using geographic and remotely sensed data: A case study for residential uses. Transportation Research Part C: Emerging Technologies, 47, 155–167. https://doi.org/10.1016/j.trc.2014.06.012.
Broadbent, E. N., Zambrano, A. M. A., Dirzo, R., et al. (2012). The effect of land use change and ecotourism on biodiversity: A case study of Manuel Antonio, Costa Rica, from 1985 to 2008. Landscape Ecology, 27(5), 731–744. https://doi.org/10.1007/s10980-012-9722-7.
Chen, Y., Lu, D., Moran, E., et al. (2018). Mapping croplands, cropping patterns, and crop types using MODIS time-series data. International Journal of Applied Earth Observation and Geoinformation, 69, 133–147. https://doi.org/10.1016/j.jag.2018.03.005.
Cui, X., & Wang, X. (2015). Urban land use change and its effect on social metabolism: An empirical study in Shanghai. Habitat International, 49, 251–259. https://doi.org/10.1016/j.habitatint.2015.05.018.
Du, P., Li, X. L., Cao, W., et al. (2010). Monitoring urban land cover and vegetation change by multi-temporal remote sensing information. Mining Science and Technology (China), 20(6), 922–932. https://doi.org/10.1016/S1674-5264(09)60308-2.
Eitelberg, D. A., van Vliet, J., Doelman, J. C., et al. (2016). Demand for biodiversity protection and carbon storage as drivers of global land change scenarios. Global Environmental Change, 40, 101–111. https://doi.org/10.1016/j.gloenvcha.2016.06.014.
Estoque, R. C., & Murayama, Y. (2015a). Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological Indicators, 56, 205–217. https://doi.org/10.1016/j.ecolind.2015.03.037.
Estoque, R. C., & Murayama, Y. (2015b). Intensity and spatial pattern of urban land changes in the megacities of Southeast Asia. Land Use Policy, 48, 213–222. https://doi.org/10.1016/j.landusepol.2015.05.017.
Fan, Q., & Ding, S. (2016). Landscape pattern changes at a county scale: A case study in Fengqiu, Henan Province, China from 1990 to 2013. CATENA, 137, 152–160. https://doi.org/10.1016/j.catena.2015.09.012.
Guo, Y. Q., Wu, Y. B., Ju, Z. S., et al. (2010). Remote sensing image classification by the Chaos Genetic Algorithm in monitoring land use changes. Mathematical and Computer Modelling, 51(11–12), 1408–1416. https://doi.org/10.1016/j.mcm.2009.10.023.
Gupta, R., Prasad, T., & Vijayan, D. (2000). Relationship between LAI and NDVI for IRS LISS and LANDSAT TM bands. Advance in Space Research, 26(7), 1047–1050. https://doi.org/10.1016/S0273-1177(99)01115-1.
Hamedianfar, A., & Shafri, H. Z. M. (2016). Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data. Journal of applied remote sensing, 10(2), 25001. https://doi.org/10.1117/1.JRS.10.025001.
He, Y., Chen, Y., Tang, H., et al. (2011). Exploring spatial change and gravity center movement for ecosystem services value using a spatially explicit ecosystem services value index and gravity model. Environmental Monitoring & Assessment., 175(1–4), 563–571. https://doi.org/10.1007/s10661-010-1551-z.
Hossain Bhuiyan, M. M., Islam, K., Islam, K. N., et al. (2018). Monitoring dynamic land-use change in rural–urban transition: A case study from Hathazari Upazila, Bangladesh. Geology, Ecology, and Landscapes, 3(4), 247–257. https://doi.org/10.1080/24749508.2018.1556034.
Hu, Q., Wu, W., Song, Q., et al. (2017). How do temporal and spectral features matter in crop classification in Heilongjiang Province, China? Journal of Integrative Agriculture, 16(2), 324–336. https://doi.org/10.1016/S2095-3119(15)61321-1.
Jia, K., Liang, S., Zhang, L., et al. (2014). Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data. International Journal of Applied Earth Observation and Geoinformation, 33, 32–38. https://doi.org/10.1016/j.jag.2014.04.015.
Jiao, M., Hu, M., & Xia, B. (2019). Spatiotemporal dynamic simulation of land-use and landscape-pattern in the Pearl River Delta. China. Sustainable Cities and Society, 49, 101581. https://doi.org/10.1016/j.scs.2019.101581.
Jin, Y., Liu, X., Chen, Y., et al. (2018). Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong. International journal of remote sensing, 39(23), 8703–8723. https://doi.org/10.1080/01431161.2018.1490976.
Julien, Y., Sobrino, J. A., & Jiménez-Muñoz, J. C. (2011). Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method. International Journal of Applied Earth Observation and Geoinformation, 13(5), 711–720. https://doi.org/10.1016/j.jag.2010.04.007.
Kalther, J., & Itaya, A. (2020). Coastline changes and its effect on land cover and use in Subang, Indonesia. Journal of Coastal Conservation, 24(2), 2. https://doi.org/10.1007/s11852-020-00736-w.
Li, Z., Shi, W., Wang, Q., et al. (2015). Extracting man-made objects from high spatial resolution remote sensing images via fast level set evolutions. IEEE Transactions on Geoscience and Remote Sensing, 53(2), 883–899. https://doi.org/10.1109/TGRS.2015.2454251.
Liu, T., & Yang, X. (2015). Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Applied Geography, 56, 42–54. https://doi.org/10.1016/j.apgeog.2014.10.002.
Liu, X., Chen, X., Hua, K., et al. (2018). Effects of land use change on ecosystem services in arid area ecological migration. Chinese Geographical Science, 28(5), 894–906. https://doi.org/10.1007/s11769-018-0971-5.
Lizarazo, I. (2012). Quantitative land cover change analysis using fuzzy segmentation. International Journal of Applied Earth Observation and Geoinformation, 15, 16–27. https://doi.org/10.1016/j.jag.2011.05.012.
Lucas, R., Rowlands, A., Brown, A., et al. (2007). Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 62(3), 165–185. https://doi.org/10.1016/j.isprsjprs.2007.03.003.
Maimaiti, B., Ding, J., Simayi, Z., et al. (2017). Characterizing urban expansion of Korla City and its spatial-temporal patterns using remote sensing and GIS methods. Journal of Arid Land, 9(3), 458–470. https://doi.org/10.1007/s40333-017-0099-y.
Qiu, L., Pan, Y., Zhu, J., et al. (2019). Integrated analysis of urbanization-triggered land use change trajectory and implications for ecological land management: A case study in Fuyang, China. Science of The Total Environment, 660, 209–217. https://doi.org/10.1016/j.scitotenv.2018.12.320.
Quarmby, N. A., Milnes, M., Hindle, T. L., et al. (1993). The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International journal of remote sensing, 14(2), 199–210. https://doi.org/10.1080/01431169308904332.
Rawat, J. S., & 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), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002.
Roy, D. P., Kovalskyy, V., Zhang, H. K., et al. (2016). Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185, 57–70. https://doi.org/10.1016/j.rse.2015.12.024.
Shifaw, E., Sha, J. M., & Li, X. M. (2020). Detection of spatiotemporal dynamics of land cover and its drivers using remote sensing and landscape metrics (Pingtan Island, china). Environment, Development and Sustainability, 22, 1269–1298. https://doi.org/10.1007/s10668-018-0248-2.
Sulla-Menashe, D., Friedl, M. A., Krankina, O. N., et al. (2011). Hierarchical mapping of Northern Eurasian land cover using MODIS data. Remote Sensing of Environment, 115(2), 392–403. https://doi.org/10.1016/j.rse.2010.09.010.
Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences of United States of America, 104(52), 20666–20671. https://doi.org/10.1073/pnas.0704119104.
Valor, E., & Caselles, V. (1996). Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote Sensing of Environment, 57(3), 167–184. https://doi.org/10.1016/0034-4257(96)00039-9.
Were, K. O., Dick, Ø. B., & Singh, B. R. (2013). Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Kenya. Applied Geography, 41, 75–86. https://doi.org/10.1016/j.apgeog.2013.03.017.
Yang, Y., Liu, Y., Li, Y., et al. (2018). Quantifying spatio-temporal patterns of urban expansion in beijing during 1985–2013 with rural-urban development transformation. Land Use Policy, 74, 220–230. https://doi.org/10.1016/j.landusepol.2017.07.004.
Zhang, Q. Q., Xu, H. L., Fu, J. Y., et al. (2012). Spatial Analysis of land use and land cover changes in recent 30 years in Manas River Basin. Procedia Environmental Sciences, 12, 906–916. https://doi.org/10.1016/j.landusepol.2017.07.004.
Zhao, Y., Feng, D., Yu, L., et al. (2016). Detailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal data. Remote Sensing of Environment, 183, 170–185. https://doi.org/10.1016/j.rse.2016.05.016.
Zheng, B., Myint, S. W., Thenkabail, P. S., et al. (2015). A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. International Journal of Applied Earth Observation and Geoinformation, 34, 103–112. https://doi.org/10.1016/j.jag.2014.07.002.
Zhu, Z., Woodcock, C. E., Rogan, J., et al. (2012). Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data. Remote Sensing of Environment, 117, 72–82. https://doi.org/10.1016/j.rse.2011.07.020.
Acknowledgements
Great thanks to the editor and anonymous reviewers for their valuable comments to improve our manuscript and many thanks to the National Aeronautics and Space Administration (NASA) for providing requisite data sets (Landsat TM/ETM) for the analysis.
Funding
This study was supported by The National Key Research and Development Program of China (Nos. 2018YFC0408000, 2018YFC0408004); The Project of China’s Ministry of Housing and Urban–Rural Development-The Exploring Research on the Sponge City: A Case Study of Core Area in West Coast of Qingdao (2015R2026); Shandong Provincial Water Conservancy Scientific Research and Technology Promotion Projects (SDSLKY201312).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares no known competing financial interests or personal relationships that could have appeared to influence the work reported in the paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Wang, H., Wei, B. & Wang, L. Analyzing Land Cover Dynamics Using Hierarchical Classification in Binzhou City, China. J Indian Soc Remote Sens 49, 1393–1405 (2021). https://doi.org/10.1007/s12524-021-01327-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-021-01327-4