马拉维利隆圭市土地利用、土地覆盖和地表温度变化监测

S. Gondwe, Richard Muchena, Jerome Boys
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引用次数: 4

摘要

在非洲东南部,利隆圭市在过去15年里人口增长迅速。同一城市最近也观察到气温升高和不利的天气条件,如偶尔的暴雨,导致2017年1月和12月的严重洪水。因此,明智的做法是对城市进行土地利用和土地覆盖(LULC)研究,以检测土地覆盖的变化及其对地表温度(LST)的影响。获取了2008年、2013年和2017年的陆地卫星图像,并对其进行了分类,以检测这些年份的LULC变化。2008 - 2013年,城市扩张显著(P<0.05)。通过模拟ndvi得到地表温度,并以此计算发射率,进而估算地表温度。NDVI与LST呈负相关(r2=0.65),相关性高(r=0.806)。随着城市化进程的推进,自然用地和农业用地被转化为聚落,导致ndvi降低,地表温度升高。由此得出的结论是,除其他因素外,城市化肯定会导致全球变暖。关键词:马拉维,土地覆盖,土地利用,辐射率,归一化植被指数,地表温度马拉维利隆圭市土地利用、土地覆盖和地表温度变化监测遥感与地理信息系统学报。2018;9 (2): 17-26p。
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Detecting Land Use and Land Cover and Land Surface Temperature Change in Lilongwe City, Malawi
In south east Africa, Lilongwe city had an observed rapid population growth over the past decade and a half. The same city also had recently observed increased temperatures and adverse weather conditions such as occassional heavy storms which caused severe flooding in January 2017 and December 2017. It was therefore thought wise to do a land use and land cover (LULC) study of the city over time to detect land cover changes and its effect on land surface temperatures (LST). Landsat imagery was acquired for the year(s) 2008, 2013 and 2017 and it was classified to detect LULC changes for these given years. A significant (P<0.05) expansion of the city was detected especially between 2008 and 2013. The LST was derived from modelling NDVIs, from which emmisivity was calculated and then the LST was estimated. There was an inverse regression (r2=0.65) with a high correlation (r=0.806) between NDVI and LST. With urbanization, the natural and agricultural land was converted into settlements resulting in lower NDVIs and higher land surface temperatures. It was concluded that urbanization, amongst others can therefore definitely contribute to global warming. Keywords: Malawi, land cover, land use, emissivity, normalized vegetation index, land surface temperature Cite this Article Gondwe Steven VC, Richard Muchena, Jerome Boys. Detecting Land Use and Land Cover and Land Surface Temperature Change in Lilongwe City, Malawi. Journal of Remote Sensing & GIS. 2018; 9(2):    17–26p.
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