{"title":"Spatial patterns of urban blue-green landscapes on land surface temperature: A case of Addis Ababa, Ethiopia","authors":"Neway Kifle Bekele, Binyam Tesfaw Hailu, Karuturi Venkata Suryabhagavan","doi":"10.1016/j.crsust.2022.100146","DOIUrl":null,"url":null,"abstract":"<div><p>Drastic changes in the urban landscape can lead to irreversible changes in the spatiotemporal pattern of the land surface temperature (LST). The present study was aimed to map the effects of blue-green urban landscapes on LST using geospatial techniques in Addis Ababa during 2006–2021. Object-based image analysis (OBIA) was used to produce land-use/land-cover (LULC) maps using high-resolution imagery from SPOT 5 and Sentinel 2A. Land surface temperature was retrieved from thermal imageries of Landsat 7 ETM<sup>+</sup> (band 6) and Landsat 8 TIRS (band 10) using the Mono-Window Algorithm (MWA). Built-up area was the most dominant LULC in the city with expanding trend with an annual growth of 4.4% at the expense of farmland, vegetation, and bare land. In contrast, 53.7% of farmland, 48.1% of vegetation, and 59.4% of bare land were transformed into built-up class during 2006–2021. Mean LST showed an increasing trend from 25.8 °C in 2006 to 27.2 °C and 28.2 °C during 2016 and 2021, respectively. Highest mean LST was observed at bare land having average values of 26.9 °C, 28.7 °C, and 30.1 °C in 2006, 2016 and 2021, respectively. Regression analysis has revealed a strong negative correlation between NDVI and LST, a strong positive correlation between NDBI and LST, and a weak negative correlation between MNDWI and LST. Built-up areas and vegetation cover play a decisive role in the variation of LST compared to surface water. These findings are helpful for understanding urban green as well as land-use planning to minimize the potential impacts of urbanization.</p></div>","PeriodicalId":34472,"journal":{"name":"Current Research in Environmental Sustainability","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266604902200024X/pdfft?md5=512382cf8d9d73950898b6736fd9ca48&pid=1-s2.0-S266604902200024X-main.pdf","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Research in Environmental Sustainability","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266604902200024X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 9
Abstract
Drastic changes in the urban landscape can lead to irreversible changes in the spatiotemporal pattern of the land surface temperature (LST). The present study was aimed to map the effects of blue-green urban landscapes on LST using geospatial techniques in Addis Ababa during 2006–2021. Object-based image analysis (OBIA) was used to produce land-use/land-cover (LULC) maps using high-resolution imagery from SPOT 5 and Sentinel 2A. Land surface temperature was retrieved from thermal imageries of Landsat 7 ETM+ (band 6) and Landsat 8 TIRS (band 10) using the Mono-Window Algorithm (MWA). Built-up area was the most dominant LULC in the city with expanding trend with an annual growth of 4.4% at the expense of farmland, vegetation, and bare land. In contrast, 53.7% of farmland, 48.1% of vegetation, and 59.4% of bare land were transformed into built-up class during 2006–2021. Mean LST showed an increasing trend from 25.8 °C in 2006 to 27.2 °C and 28.2 °C during 2016 and 2021, respectively. Highest mean LST was observed at bare land having average values of 26.9 °C, 28.7 °C, and 30.1 °C in 2006, 2016 and 2021, respectively. Regression analysis has revealed a strong negative correlation between NDVI and LST, a strong positive correlation between NDBI and LST, and a weak negative correlation between MNDWI and LST. Built-up areas and vegetation cover play a decisive role in the variation of LST compared to surface water. These findings are helpful for understanding urban green as well as land-use planning to minimize the potential impacts of urbanization.