The relationship between land surface temperature and land use/land cover in Guangzhou, China

Sun, Qing; Wu, Zhifeng; Tan, Jianjun

doi:10.1007/s12665-011-1145-2

Cited by 181 publications

(82 citation statements)

References 27 publications

(29 reference statements)

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“…The result of this study is similar to the findings of Baguio City (mountain city) in the Philippines [7]. Additionally, the results of this study are comparable to tropical low land coastal cities like Jakarta (2.9 • C), Bangkok (2.2 • C) [11], and Guangzhou (2.8 • C) [44]. These changes in ∆ mean LST will intensify the impact of SUHI on the population of Kandy City.…”

Section: Suhi Intensity and Its Effectssupporting

confidence: 86%

Quantifying Surface Urban Heat Island Formation in the World Heritage Tropical Mountain City of Sri Lanka

Ranagalage

Dissanayake

Murayama

et al. 2018

IJGI

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Presently, the urban heat island (UHI) phenomenon, and its adverse impacts, are becoming major research foci in various interrelated fields due to rapid changes in urban ecological environments. Various cities have been investigated in previous studies, and most of the findings have facilitated the introduction of proper mitigation measures to overcome the negative impact of UHI. At present, most of the mountain cities of the world have undergone rapid urban development, and this has resulted in the increasing surface UHI (SUHI) phenomenon. Hence, this study focuses on quantifying SUHI in Kandy City, the world heritage tropical mountain city of Sri Lanka, using Landsat data (1996 and 2017) based on the mean land surface temperature (LST), the difference between the fraction of impervious surfaces (IS), and the fraction of green space (GS). Additionally, we examined the relationship of LST to the green space/impervious surface fraction ratio (GS/IS fraction ratio) and the magnitude of the GS/IS fraction ratio. The SUHI intensity (SUHII) was calculated based on the temperature difference between main land use/cover categories and the temperature difference between urban-rural zones. We demarcated the rural zone based on the fraction of IS recorded, <10%, along with the urban-rural gradient zone. The result shows a SUHII increase from 3.9 • C in 1996 to 6.2 • C in 2017 along the urban-rural gradient between the urban and rural zones (10 < IS). These results relate to the rapid urban expansion of the study areas from 1996 to 2017. Most of the natural surfaces have changed to impervious surfaces, causing an increase of SUHI in Kandy City. The mean LST has a positive relationship with the fraction of IS and a negative relationship with the fraction of GS. Additionally, the GS/IS fraction ratio shows a rapid decline. Thus, the findings of this study can be considered as a proxy indicator for introducing proper landscape and urban planning for the World Heritage tropical mountain city of Kandy in Sri Lanka.

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Section: Suhi Intensity and Its Effectssupporting

confidence: 86%

Quantifying Surface Urban Heat Island Formation in the World Heritage Tropical Mountain City of Sri Lanka

Ranagalage

Dissanayake

Murayama

et al. 2018

IJGI

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“…This could be due to the improvement in (2015) and Sun et al (2012) revealed that areas with rich vegetation cover have characterized by lowest LST. In Fig.…”

Section: Interpretation Of Lst and Savimentioning

confidence: 98%

Effects of soil and water conservation on vegetation cover: a remote sensing based study in the Middle Suluh River Basin, northern Ethiopia

2017

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Background: Soil and water conservation (SWC) has been implemented in the Tigray Region of Ethiopia since 1985. Besides this, the agricultural development strategy of the region which was derived from the national agricultural development led industrialization strategy formulated in 1993 was focused on natural resources rehabilitation and conservations. Accordingly, each year a 20-days free labor work on SWC activities were contributed by the rural communities. Other programmes such as productive safety net programmes, and sustainable land management project were deploying their resources aiming to reverse the degraded landscape in the region. Method:Multi-temporal remote sensing data of landsat imageries were used for estimating the normalized difference vegetation index, soil adjusted vegetation index (SAVI) and land surface temperature (LST) for the years 1985, 2000 and 2015. Long-term station based data on daily precipitation started from 1973 was aggregated to derive average annual precipitation (AAP) into three sections to correspond with the processed image data. The precipitation data then converted into raster format using the inverse distance weight interpolation method. The analysis was done using ENVI 5.3 software and results were mapped in ArcGIS 10.3 package. The correlation between AAP and SAVI; LST and SAVI was evaluated on village polygon based as well as pixel-by-pixel. Results:The results based on village polygons show that there is statistical significant inverse relationship between SAVI and LST in all the study periods. The correlations between AAP and SAVI pixel-by-pixel were r = − 0.14 in 2015 and r = 0.06, r = 0.25 for 2000 and 1985 respectively. In 1985, the total area with SAVI ≥ 0.2 was 23.57 km2. After 15 years (from 1985 to 2000), the total area with SAVI ≥ 0.2 increased to 64.94 km2. In 2015, the total area of SAVI with values ≥ 0.2 reached 67.11 km2, which is a 3.3% increment from year 2000. Conclusion:Based on the field observation and the remote sensing analysis results, noticeable gain in vegetation cover improvement have been observed in the 30 years period. These improvements are attributable to the implementation of integrated SWC measures particularly in areas where exclosure areas were defined and protected by the local community. Therefore, this study concludes by providing a theoretical bases and an indicator data support for further research on vegetation restoration for the entire region.

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“…Both NDVI and NDBI were considered as relating closely to UHI by Chen et al [2] who proposed two bivariate regression models between the NDVI, NDBI and the brightness temperature. Although Sun et al [58] revealed that the NDVI and NDBI were effective indicators for quantifying the impacts of LULC on the LST, it is necessary to further analyze the correlation among the LST, NDBI and NDVI to better understand the impacts of urban expansion and vegetation change on the urban thermal environment. As shown by Figure 10, higher LSTs are usually found in the areas of lower NDVI values and higher NDBI values because of the absence of vegetation cover or the predominance of high-density urban areas, including commercial, industrial, and some residential developments.…”

Section: Correlation Analysis Of Lst and Ndbi Ndvimentioning

confidence: 99%

The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China

et al. 2012

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Abstract:The effect of urbanization on the urban thermal environment (UTE) has attracted increasing research attention for its significant relationship to local climatic change and habitat comfort. Using quantitative thermal remote sensing and spatial statistics methods, here we analyze four Landsat TM/ETM+ images of Guangzhou in South China acquired respectively on 13 October 1990, 2 January 2000, 23 November 2005, and 2 January 2009, to investigate the spatiotemporal variations in the land surface temperature (LST) over five land use/land cover (LULC) types and over different urban/rural zones. The emphases of this study are placed on the urban heat island (UHI) intensity and the relationships among LST, the normalized difference built-up index (NDBI), and the normalized difference vegetation index (NDVI). Results show that: (1) the UHI effect existed obviously over the period from 1990 to 2009 and high temperature anomalies were closely associated with built-up land and densely populated and heavily industrialized districts; (2) the UHI intensities represented by the mean LST difference between the urban OPEN ACCESSRemote Sens. 2012, 4 2034 downtown area and the suburban area were on average 0.88, 0.49, 0.90 and 1.16 K on the four dates, at the 99.99% confidence level; and (3) LST is related positively with NDBI and negatively with NDVI. The spatiotemporal variation of UTE of Guangzhou could be attributed to rapid urbanization, especially to the expanding built-up and developing land, declining vegetation coverage, and strengthening of anthropogenic and industrial activities which generate increasing amounts of waste heat. This study provides useful information for understanding the local climatic and environment changes that occur during rapid urbanization.

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The relationship between land surface temperature and land use/land cover in Guangzhou, China

Cited by 181 publications

References 27 publications

Quantifying Surface Urban Heat Island Formation in the World Heritage Tropical Mountain City of Sri Lanka

Quantifying Surface Urban Heat Island Formation in the World Heritage Tropical Mountain City of Sri Lanka

Effects of soil and water conservation on vegetation cover: a remote sensing based study in the Middle Suluh River Basin, northern Ethiopia

The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China

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