基于遥感数据的济南市旅游景区通用热气候指数(UTCI)研究  

Universal Thermal Climate Index in tourist attractions of Jinan City based on remote sensing data

作　　者：张博[1] 顾方哲 徐涛[2] 范雅静 Zhang Bo;Gu Fangzhe;Xu Tao;Fan Yajing(Cultural&Tourism College,University of Jinan,Jinan 250022,Shandong,China;School of Information Science&Engineering,University of Jinan,Jinan 250022,Shandong,China;China-Asean Institute of Statistics,Guangxi University of Finance and Economics,Nanning 530003,Guangxi,China)

机构地区：[1]济南大学文化和旅游学院,山东济南250022 [2]济南大学信息科学与工程学院,山东济南250022 [3]广西财经学院中国−东盟统计学院,广西南宁530003

出　　处：《地理科学》2024年第12期2195-2204,共10页Scientia Geographica Sinica

基　　金：山东省文化和旅游研究课题[22WL(Z)02];山东省社会科学规划研究项目(21CLYJ12);广西高校中青年教师科研基础能力提升项目(2022KY0648)资助。

摘　　要：本研究构建了一个基于遥感数据的济南市旅游景区通用热气候指数(UTCI)模型。研究整合了地面气象站数据和卫星遥感数据,运用定量反演技术进行分析。在深入回顾定量遥感反演技术在气温、地表温度、风速和相对湿度等气象要素研究的基础上,针对UTCI指数计算所需的关键气象参数,制定了系统的定量反演方案。同时,利用地面气象站实测数据对模型精度进行了验证和评估。结果显示,在以2021年Landsat 8及MODIS卫星遥感数据为反演数据集、济南及附近城市的22个自动气象站为验证集的122组数据中,由随机森林算法建模后的UTCI估测值与站点实测值之间的平均绝对误差(MAE)为0.89℃,均方根误差(RMSE)为1.37℃,精度较为理想。进一步,对济南市旅游景区进行应用研究发现,第一,基于遥感数据对旅游景区UTCI进行反演是可行且具有一定可靠性;第二,旅游景区UTCI分布存在时间和空间差异;第三,UTCI与气象预报(气温)之间存在较大差异;第四,济南地区适合出游的季节和区域为春季全部地区、夏季高海拔景区的背阴坡及山谷地区、秋季的向阳坡地区。本文还对提升模型精度、提高数据质量、识别景区地物特征、甄别游客个体特征、控制游客主观差异等方面进行了讨论。Tourism is highly susceptible to climate and weather variations,with favorable climatic conditions being recognized as a crucial tourism resource.In climate comfort research,the Universal Thermal Climate Index(UTCI)has emerged as a widely applied and highly validated evaluation metric in recent years.This study aims to estimate UTCI temperatures for tourist attractions of Jinan City using satellite remote sensing data,providing tourists with more accurate thermal comfort information.This research integrates ground meteorological station data and satellite remote sensing data using quantitative inversion techniques.The methodology first reviews quantitative remote sensing applications for retrieving key meteorological parameters(air temperature,land surface temperature,wind speed,and relative humidity),followed by the development and validation of a UTCI quantitative retrieval model.Validation using 122 data pairs from 2021 Landsat 8 and MODIS satellite imagery against 22 automatic weather stations in the Jinan metropolitan area demonstrates the efficacy of the Random Forest-based UTCI model,achieving a Mean Absolute Error(MAE)of 0.89℃ and Root Mean Square Error(RMSE)of 1.37℃.Application to Jinan’s tourist attractions reveals that:1)Remote sensing-based UTCI retrieval is feasible and reliable for tourist destinations;2)UTCI distributions show distinct temporal and spatial patterns;3)Substantial disparities exist between UTCI and conventional temperature forecasts;and 4)Optimal tourism conditions in Jinan vary seasonally—encompassing all regions in spring,high-altitude shaded slopes and valleys in summer,and sun-facing slopes in autumn.The study concludes by addressing improvements in model accuracy,data quality,landscape feature identification,tourist characteristic differentiation,and mitigation of subjective variations in tourist responses.

关 键 词：气象遥感 定量反演 随机森林(RF) 旅游景区 通用热气候指数(UTCI) 

分 类 号：P407.1[天文地球—大气科学及气象学]