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- W3204356863 abstract "This chapter presents spatiotemporal changes in an urban sprawl, land surface temperature, and urban agglomeration of Lahore district in Pakistan using Landsat satellite images from 1990 to 2017. Using the Google Earth Engine (GEE) geospatial cloud computing platform, Classification And Regression Trees (CART) classifier was applied on Landsat 30 m spatial resolution satellite images to classify five land cover classes: “Greenery/Agricultural land,” “Waterbody,” “Urban area,” “Tree Cover” and “Barren area.” In four developed land cover maps, > 80% overall accuracy was achieved, which were further used for the landscape metrics analysis to assess and quantify changes in the shape of urban patches. Overall, the findings of this study revealed that in Lahore district, “Urban area” increased at rate of 23 km2/year while in “Greenery/Agricultural land” ~ 9 km2/year decreased with “Barren area” loss of ~ 12 km2/year and “Tree Cover” reduced ~ 2 km2/year from 1990 to 2017. Spatiotemporal variations and statistics of land surface temperature revealed that the overall mean temperature in the coldest month (January) increased from 16.4°C in 1990 to 19.7°C in 2017, even in the hottest month (June) mean temperature climbed from 28.7°C in 1990 to 35.6°C in 2017. Landscape metrics analysis showed that the urban patches of the Lahore district became more complex with increasing trends. The findings of this study could help for decisions to prevent tree loss and encroachment in the urban landscape of Lahore, and also plan future urban growth keeping with the use of remote sensing imagery and geospatial analysis." @default.
- W3204356863 created "2021-10-11" @default.
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- W3204356863 date "2022-01-01" @default.
- W3204356863 modified "2023-09-23" @default.
- W3204356863 title "Spatiotemporal urban sprawl and land resource assessment using Google Earth Engine platform in Lahore district, Pakistan" @default.
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- W3204356863 doi "https://doi.org/10.1016/b978-0-323-89861-4.00023-3" @default.
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