Mirsanjari Mir Mehrdad, Mohammadyari Fatemeh, Visockiene Jurate Suziedelyte, Zarandian Ardavan
Department of Environmental Sciences, Malayer University, Malayer, Iran.
Faculty of Natural Resources, Malayer University, Malayer, Iran.
Environ Monit Assess. 2021 Jul 6;193(8):472. doi: 10.1007/s10661-021-09209-5.
The present study aims to evaluate the effect of vegetation on land surface temperature (LST) in different land uses and covers in Vilnius district in 1999 and 2019. To that end, in addition to mono-window and split-window algorithms that help estimate the LST, the variables digital elevation model (DEM), slope, heat load index (HLI), distances from the road and the water, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) affecting the surface temperature were used. Furthermore, the random forest regression (RFR) method was applied to evaluate the effect of the mentioned variables on the LST. The performance model was also assessed by using the mean absolute (MAE), mean squared (MSE), and root mean square error (RMSE). Based on the results, NDVI and NDWI indexes had the greatest impact on the temperature of Vilnius city, respectively. The study area images were categorized as built-up area, cropland, semi-forest land, dense forest land, water bodies, pastures, and green urban areas. It was found that the pastures in 1999 and the built-up class in 2019 received the highest temperature from the land surface and that the classes characterized by natural land cover such as forest land and agricultural and water bodies had a relatively low surface temperature. NDVI response curves in both 1999 and 2019 indicated that the higher the density of vegetation on the land surface, the lower the surface temperature. A lower rate of urbanization, a higher density of vegetation and consequently, a lower the temperature of the land surface were recorded for 1999 in comparison with 2019. Therefore, urbanization was demonstrated to play a significant role in changes in LULC and the increase in LST.
本研究旨在评估1999年和2019年植被对维尔纽斯地区不同土地利用和覆盖类型下地表温度（LST）的影响。为此，除了有助于估算地表温度的单窗算法和劈窗算法外，还使用了数字高程模型（DEM）、坡度、热负荷指数（HLI）、距道路和水体的距离、归一化植被指数（NDVI）以及影响地表温度的归一化水体指数（NDWI）等变量。此外，应用随机森林回归（RFR）方法来评估上述变量对地表温度的影响。还使用平均绝对误差（MAE）、均方误差（MSE）和均方根误差（RMSE）对性能模型进行了评估。结果表明，NDVI和NDWI指数分别对维尔纽斯市的温度影响最大。研究区域图像被分类为建成区、农田、半林地、茂密林地、水体、牧场和城市绿地。研究发现，1999年的牧场和2019年的建成区地表温度最高，而以林地、农业用地和水体等自然土地覆盖为特征的区域地表温度相对较低。1999年和2019年的NDVI响应曲线均表明，地表植被密度越高，地表温度越低。与2019年相比，1999年的城市化率较低，植被密度较高，因此地表温度较低。因此，城市化在土地利用/土地覆盖变化和地表温度升高方面发挥了重要作用。
