219
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Impacts of eco-environmental quality, spatial configuration, and landscape connectivity of urban vegetation patterns on seasonal land surface temperature in Harare metropolitan city, Zimbabwe

, , &
Pages 125-143 | Received 02 Feb 2022, Accepted 18 Aug 2022, Published online: 02 Sep 2022

References

  • Akbari, H., Pomerantz, M., & Taha, H. (2001). Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Solar Energy, 70(3), 295–310. https://doi.org/10.1016/S0038-092X(00)00089-X
  • Anderson, G. B., & Bell, M. L. (2010). Heat waves in the United States: Mortality risk during heat waves and effect modification by heat wave characteristics in 43 US communities. Environmental Health Perspectives, 119(2), 210–218. https://doi.org/10.1289/ehp.1002313
  • Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
  • Bao, T., Li, X., Zhang, J., Zhang, Y., & Tian, S. (2016). Assessing the distribution of urban green spaces and its anisotropic cooling distance on urban heat island pattern in Baotou, China. ISPRS International Journal of Geo-Information, 5(2), 12. https://doi.org/10.3390/ijgi5020012
  • Cao, X., Onishi, A., Chen, J., & Imura, H. (2010). Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landscape and Urban Panning, 96(4), 224–231. https://doi.org/10.1016/j.landurbplan.2010.03.008
  • Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews, 9(1–2), 161–173. https://doi.org/10.1080/02757259409532220
  • Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
  • Chander, G., & Markham, B. (2003). Revised Landsat-5 TM radiometric calibration procedures and post calibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2674–2677. https://doi.org/10.1109/TGRS.2003.818464
  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903. https://doi.org/10.1016/j.rse.2009.01.007
  • Chen, A., Sun, R., & Chen, L. (2012). Studies on urban heat island from a landscape pattern view: A review. Shengtai Xuebao/Acta Ecologica Sinica, 32(14), 4553–4565. https://doi.org/10.5846/stxb201106280965
  • Chen, A., Zhao, X., Yao, L., & Chen, L. (2016). Application of a new integrated landscape index to predict potential urban heat islands. Ecological Indicators, 69, 828–835. https://doi.org/10.1016/j.ecolind.2016.05.045
  • Connors, J. P., Galletti, C. S., & Chow, W. T. (2013). Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landscape Ecology, 28(2), 271–283. https://doi.org/10.1007/s10980-012-9833-1
  • Dos Santos, A. R., de Oliveira, F. S., da Silva, A. G., Gleriani, J. M., Gonçalves, W., Moreira, G. L., Silva, F. G., Branco, E. R. F., Moura, M. M., da Silva, R. G., Juvanhol, R. S., de Souza, K. B., Ribeiro, C. A. A. S., de Queiroz, V. T., Costa, A. V., Lorenzon, A. S., Domingues, G. F., Marcatti, G. E., de Castro, N. L. M., … Mota, P. H. S. (2017). Spatial and temporal distribution of urban heat islands. Science of the Total Environment, 605, 946–956. https://doi.org/10.1016/j.scitotenv.2017.05.275
  • Fan, C., & Myint, S. (2014). A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation. Landscape and Urban Planning, 121, 117–128. https://doi.org/10.1016/j.landurbplan.2013.10.002
  • Fan, C., Myint, S. W., & Zheng, B. (2015). Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures. Progress in Physical Geography, 39(2), 199–219. https://doi.org/10.1177/0309133314567583
  • Fischer, E. M., Seneviratne, S. I., Lüthi, D., & Schär, C. (2007). Contribution of land‐atmosphere coupling to recent European summer heat waves. Geophysical Research Letters, 34(6). https://doi.org/10.1029/2006GL029068
  • Guha, S. (2021). Dynamic seasonal analysis on LST-NDVI relationship and ecological health of Raipur City, India. Ecosystem Health and Sustainability, 7(1), 1927852. https://doi.org/10.1080/20964129.2021.1927852
  • Guha, S., & Govil, H. (2021a). Annual assessment on the relationship between land surface temperature and six remote sensing indices using Landsat data from 1988 to 2019. Geocarto International 37(15), 1–20. doi:10.1080/10106049.2021.1886339.
  • Guha, S., Govil, H., Dey, A., & Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1), 667–678. https://doi.org/10.1080/22797254.2018.1474494
  • Hwang, Y. H., Lum, Q. J. G., & Chan, Y. K. D. (2015). Micro-scale thermal performance of tropical urban parks in Singapore. Building and Environment, 94, 467–476. https://doi.org/10.1016/j.buildenv.2015.10.003
  • Jenerette, G. D., Harlan, S. L., Brazel, A., Jones, N., Larsen, L., & Stefanov, W. L. (2007). Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landscape Ecology, 22(3), 353–365. https://doi.org/10.1007/s10980-006-9032-z
  • Kamusoko, C., Gamba, J., & Murakami, H. (2013). Monitoring urban spatial growth in Harare metropolitan province, Zimbabwe. Advances in Remote Sensing, 2(4), 322–331. https://doi.org/10.4236/ars.2013.24035
  • Kim, J. H., Gu, D., Sohn, W., Kil, S. H., Kim, H., & Lee, D. K. (2016). Neighborhood landscape spatial patterns and land surface temperature: An empirical study on single-family residential areas in Austin, Texas. International Journal of Environmental Research and Public Health, 13(9), 880. https://doi.org/10.3390/ijerph13090880
  • Kong, F., Yin, H., James, P., Hutyra, L. R., & He, H. S. (2014). Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of eastern China. Landscape and Urban Planning, 128, 35–47. https://doi.org/10.1016/j.landurbplan.2014.04.018
  • Li, W. F., Cao, Q. W., Lang, K., & Wu, J. S. (2017). Linking potential heat source and sink to urban heat island: Heterogeneous effects of landscape pattern on land surface temperature. Science of the Total Environment, 586, 457–465. https://doi.org/10.1016/j.scitotenv.2017.01.191
  • Li, X., Li, W., Middel, A., Harlan, S. L., Brazel, A. J., & Turner Ii, B. L. (2016). Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors. Remote Sensing of Environment, 174, 233–243. https://doi.org/10.1016/j.rse.2015.12.022
  • Li, J., Song, C., Cao, L., Zhu, F., Meng, X., & Wu, J. (2011). Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sensing of Environment, 115(12), 3249–3263. https://doi.org/10.1016/j.rse.2011.07.008
  • Li, H., & Wu, J. (2004). Use and misuse of landscape indices. Landscape Ecology, 19(4), 389–399. https://doi.org/10.1023/B:LAND.0000030441.15628.d6
  • Li, X., Zhou, W., Ouyang, Z., Xu, W., & Zheng, H. (2012). Spatial pattern of greenspace affects land surface temperature: Evidence from the heavily urbanized Beijing metropolitan area, China. Landscape Ecology, 27(6), 887–898. https://doi.org/10.1007/s10980-012-9731-6
  • Liu, L., & Zhang, Y. (2011). Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, 3(7), 1535–1552. https://doi.org/10.3390/rs3071535
  • Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys. Union, 12(3), 131–140.
  • McGarigal, K., Cushman, SA, Neel, MC, Ene, E. 2002. Fragstats: spatial pattern analysis program for categorical maps. http://www.umass.edu/landeco/research/fragstats/fragstats.Html
  • Memon, R. A., Leung, D. Y., & Liu, C. H. (2009). An investigation of urban heat island intensity (UHII) as an indicator of urban heating. Atmospheric Research, 94(3), 491–500. https://doi.org/10.1016/j.atmosres.2009.07.006
  • Mushore, T. D., Mutanga, O., Odindi, J., & Dube, T. (2017). Linking major shifts in land surface temperatures to long term land use and land cover changes: A case of Harare, Zimbabwe. Urban Climate, 20, 120–134. https://doi.org/10.1016/j.uclim.2017.04.005
  • Myint, S. W., Wentz, E. A., Brazel, A. J., & Quattrochi, D. A. (2013). The impact of distinct anthropogenic and vegetation features on urban warming. Landscape Ecology, 28(5), 959–978. https://doi.org/10.1007/s10980-013-9868-y
  • Naeem, S., Cao, C., Waqar, M. M., Wei, C., & Acharya, B. K. (2018). Vegetation role in controlling the ecoenvironmental conditions for sustainable urban environments: A comparison of Beijing and Islamabad. Journal of Applied Remote Sensing, 12(1), 016013. https://doi.org/10.1117/1.JRS.12.016013
  • Nor, A. N. M., Corstanje, R., Harris, J. A., & Brewer, T. (2017). Impact of rapid urban expansion on green space structure. Ecological Indicators, 81, 274–284. https://doi.org/10.1016/j.ecolind.2017.05.031
  • Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1–24.
  • Peng, S., Piao, S., Ciais, P., Friedlingstein, P., Ottle, C., Bréon, F. M., Nan, H., Zhou, L., & Myneni, R. B. (2011). Surface urban heat island across 419 global big cities. Environmental Science and Technology, 46(2), 696–703. https://doi.org/10.1021/es2030438
  • Peng, J., Xie, P., Liu, Y., & Ma, J. (2016). Urban thermal environment dynamics and associated landscape pattern factors: A case study in the Beijing metropolitan region. Remote Sensing of Environment, 173, 145–155. https://doi.org/10.1016/j.rse.2015.11.027
  • Schumaker, N. H. (1996). Using landscape indices to predict habitat connectivity. Ecology, 77(4), 1210–1225. https://doi.org/10.2307/2265590
  • Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4), 434–440. https://doi.org/10.1016/j.rse.2004.02.003
  • Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 316–327. https://doi.org/10.1109/TGRS.2007.904834
  • Tan, Z., Lau, K. K. L., & Ng, E. (2016). Urban tree design approaches for mitigating daytime urban heat island effects in a high-density urban environment. Energy and Buildings, 114, 265–274. https://doi.org/10.1016/j.enbuild.2015.06.031
  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
  • Uuemaa, E., Mander, Ü., & Marja, R. (2013). Trends in the use of landscape spatial metrics as landscape indicators: A review. Ecological Indicators, 28, 100–106. https://doi.org/10.1016/j.ecolind.2012.07.018
  • Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384. https://doi.org/10.1016/S0034-4257(03)00079-8
  • Wang, C., Li, Y., Myint, S. W., Zhao, Q., & Wentz, E. A. (2019). Impacts of spatial clustering of urban land cover on land surface temperature across Köppen climate zones in the contiguous United States. Landscape and Urban Planning, 192, 103668. https://doi.org/10.1016/j.landurbplan.2019.103668
  • Wang, J. K., Wang, K. C., & Wang, P. C. (2007). Urban heat (or cool) island over Beijing from MODIS land surface temperature. Journal of Remote Sensing-Beijing-, 11(3), 330.
  • Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483. https://doi.org/10.1016/j.rse.2003.11.005
  • Yang, F., Lau, S. S., & Qian, F. (2010). Summertime heat island intensities in three high-rise housing quarters in inner-city Shanghai China: Building layout, density and greenery. Building and Environment, 45(1), 115–134. https://doi.org/10.1016/j.buildenv.2009.05.010
  • Yao, L., Sun, S., song, C., Li, J., Xu, W., & Xu, Y. (2021). Understanding the spatiotemporal pattern of the urban heat island footprint in the context of urbanization, a case study in Beijing, China. Applied Geography, 133, 102496. https://doi.org/10.1016/j.apgeog.2021.102496
  • Zhang, Y., Balzter, H., Zou, C., Xu, H., & Tang, F. (2015). Characterizing bi-temporal patterns of land surface temperature using landscape metrics based on sub-pixel classifications from Landsat TM/ETM+. International Journal of Applied Earth Observation and Geoinformation, 42, 87–96. https://doi.org/10.1016/j.jag.2015.06.007
  • Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C., Gao, F., Reed, B. C., & Huete, A. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3), 471–475. https://doi.org/10.1016/S0034-4257(02)00135-9
  • Zhang, Y., Odeh, I. O., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11(4), 256–264. https://doi.org/10.1016/j.jag.2009.03.001
  • Zhang, J., Wang, Y., & Li, Y. (2006). A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6. Computers & Geosciences, 32(10), 1796–1805. https://doi.org/10.1016/j.cageo.2006.05.001
  • Zhang, X., Zhong, T., Feng, X., & Wang, K. (2009). Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. International Journal of Remote Sensing, 30(8), 2105–2118. https://doi.org/10.1080/01431160802549252
  • Zheng, B., Myint, S. W., & Fan, C. (2014). Spatial configuration of anthropogenic land cover impacts on urban warming. Landscape and Urban Planning, 130, 104–111. doi: 10.1016/.j.landurbplan.2014.07.001
  • Zhibin, R., Haifeng, Z., Xingyuan, H., Dan, Z., & Xingyang, Y. (2015). Estimation of the relationship between urban vegetation configuration and land surface temperature with remote sensing. Journal of the Indian Society of Remote Sensing, 43(1), 89–100. https://doi.org/10.1007/s12524-014-0373-9
  • Zhou, W., Huang, G., & Cadenasso, M. L. (2011). Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and Urban Planning, 102(1), 54–63. https://doi.org/10.1016/j.landurbplan.2011.03.009
  • Zhou, W., Qian, Y., Li, X., Li, W., & Han, L. (2014). Relationships between land cover and the surface urban heat island: Seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures. Landscape Ecology, 29(1), 153–167. https://doi.org/10.1007/s10980-013-9950-5
  • Zimstat. (2022). ZIMBABWE POPULATION CENSUS 2022, National Report. http://www.zimstat.co.zw/sites/wp-content/uploads/2022/07/Census2022_Preliminary_Report.pdf

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.