Environmental Quality Assessment of Urban Ecology based on Spatial Heterogeneity and Remote Sensing Imagery
Abstract
The phenomenon of urban ecology is very comprehensive, for example, rapid land-use changes, decrease in vegetation cover, dynamic urban climate, high population density, and lack of urban green space. Temporal resolution and spatial resolution of remote sensing data are fundamental requirements for spatial heterogeneity research. Remote sensing data is very effective and efficient for measuring, mapping, monitoring, and modeling spatial heterogeneity in urban areas. The advantage of remote sensing data is that it can be processed by visual and digital analysis, index transformation, image enhancement, and digital classification. Therefore, various information related to the quality of urban ecology can be processed quickly and accurately. This study integrates urban ecological, environmental data such as vegetation, built-up land, climate, and soil moisture based on spectral image response. The combination of various indices obtained from spatial data, thematic data, and spatial heterogeneity analysis can provide information related to urban ecological status. The results of this study can measure the pressure of environment caused by human activities such as urbanization, vegetation cover and agriculture land decreases, and urban micro-climate phenomenon. Using the same data source indicators, this method is comparable at different spatiotemporal scales and can avoid the variations or errors in weight definitions caused by individual characteristics. Land use changes can be seen from the results of the ecological index. Change is influenced by human behavior in the environment. In 2002, the ecological index illustrated that regions with low ecology still spread. Whereas in 2017, good and bad ecological indices are clustered.
Keywords: spatial heterogeneity, urban ecology, urban remote sensing
References
[1] Weng, Q., & Fu, P. (2014). Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data. Remote Sensing of Environment, 140, 267-278
[2] Fuglsang, M., Münier, B., & Hansen, H. S. (2013). Modelling land-use effects of future urbanization using cellular automata: An Eastern Danish case. Environmental modelling & software, 50, 1-11.
[3] Zhang, X., Estoque, R. C., & Murayama, Y. (2017). An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustainable cities and society, 32, 557-568..
[4] Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in ecology & evolution, 20(9), 503-510.
[5] Aubrecht, C., Steinnocher, K., Hollaus, M., & Wagner, W. (2009). Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use. Computers, Environment and Urban Systems, 33(1), 15-25.
[6] Nwogugu, M. (2007). Correlation, covariance, variance and semi-variance are irrelevant in risk analysis, portfolio management and mechanics. Working paper). Available at www. ssrn. com.
[7] Sindeaux, R., de Souza Figueiredo, P. T., de Melo, N. S., Guimarães, A. T. B., Lazarte, L., Pereira, F. B.,... & Leite, A. F. (2014). Fractal dimension and mandibular cortical width in normal and osteoporotic men and women. Maturitas, 77(2), 142-148.
[8] Gangulya, K., & Shankar, G. R. (2014). Geo-environmental appraisal for studying urban environment and its associated biophysical parameters using remote sensing and GIS technique. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 8.
[9] Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295-309.
[10] Prabhakara, K., Hively, W. D., & McCarty, G. W. (2015). Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. International journal of applied earth observation and geoinformation, 39, 88-102.
[11] Sharma, R., Ghosh, A., & Joshi, P. K. (2013). Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990–2009). Environmental monitoring and assessment, 185(4), 3313-3325.
[12] Xu, W., Wooster, M. J., & Grimmond, C. S. B. (2008). Modelling of urban sensible heat flux at multiple spatial scales: A demonstration using airborne hyperspectral imagery of Shanghai and a temperature–emissivity separation approach. Remote Sensing of Environment, 112(9), 3493-3510.
[13] Al-Hamdan, M., Quattrochi, D., Bounoua, L., Lachir, A., & Zhang, P. (2016). Using Landsat, MODIS, and a Biophysical Model to Evaluate LST in Urban Centers. Remote Sensing, 8(11), 952.
[14] Rodarmel, C., & Shan, J. (2002). Principal component analysis for hyperspectral image classification. Surveying and Land Information Science, 62(2), 115-122.
[15] Barbosa, D. S., Belo, V. S., Rangel, M. E. S., & Werneck, G. L. (2014). Spatial analysis for identification of priority areas for surveillance and control in a visceral leishmaniasis endemic area in Brazil. Acta Tropica, 131, 56-62.
[16] Lanorte, A., Danese, M., Lasaponara, R., & Murgante, B. (2013). Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis. International Journal of Applied Earth Observation and Geoinformation, 20, 42-51.
[17] Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C.,... & Scambos, T. A. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote sensing of Environment, 145, 154-172.
[18] Hoffer, R. M., & Johannsen, C. J. (1969). Ecological potentials in spectral signature analysis. Remote sensing in ecology, 1-16.
[19] Houborg, R., Anderson, M. C., Daughtry, C. S. T., Kustas, W. P., & Rodell, M. (2011). Using leaf chlorophyll to parameterize light-use-efficiency within a thermal-based carbon, water and energy exchange model. Remote Sensing of Environment, 115(7), 1694-1705.
[20] Jawak, S. D., & Luis, A. J. (2013). A spectral index ratio-based Antarctic land-cover mapping using hyperspatial 8-band WorldView-2 imagery. Polar Science, 7(1), 18-38.
[21] Amani, M., Salehi, B., Mahdavi, S., Masjedi, A., & Dehnavi, S. (2017). Temperaturevegetation-soil moisture dryness index (tvmdi). Remote sensing of environment, 197, 1-14.