东北林业大学学报2026，Vol.54Issue(3)：115-124,10.
协同空地数据的城市森林降温潜力预测
Prediction of Cooling Potential of Urban Forests Based on Collaborative Airborne and Ground Data
摘要
Abstract
Canopy structure and vegetation coverage characteristics jointly affect the cooling effect of urban forests,and exploring their cooling potential is of great significance for improving the urban thermal environment.From 08:00 to 18:00 in sum-mer daytime,UAV thermal infrared and measured ground temperature data were collected in 5 time periods,and canopy structure parameters and vegetation indices were extracted using UAV lidar and multispectral data,respectively,to evalu-ate the contribution of canopy structure and vegetation coverage characteristics to the cooling effect in different time periods.To accurately predict the daytime cooling effect of urban forests in summer,the optimal feature combination was screened,and the cooling prediction potential of multiple machine learning models was compared.The results showed that:(1)There were significant differences in the dominant cooling factors in different time periods.The height index showed the strongest predictive ability(0.27＜R2＜0.35)during 10:00-12:00,while the coverage and openness index,stand structure and heterogeneity index showed the best predictive performance(0.43＜R2＜0.70)during 16:00-18:00.In addition,there was a significant negative correlation between vegetation index and cooling.(2)The model combining canopy structure and vegetation index performed better in summer cooling prediction(0.47＜R2＜0.66),with the R2value increased by 2％-14％and the root mean square error(ERMS)value decreased by 0.02-0.23 compared with the optimal single variable mod-el.The machine learning models based on the optimal feature variables were ranked from best to worst as Random Forest,Support Vector Machine,Multiple Linear Regression,and k-Nearest Neighbor.(3)The prediction results showed that the high temperature period in the study area was concentrated in 10:00-14:00(average temperature 25.96 ℃,standard de-viation 3.59℃).This study revealed the influence and prediction potential of canopy structure and vegetation index on daytime cooling in summer,and provided data support for the optimization of urban forest management under the back-ground of summer high temperature.关键词
城市森林/冠层结构/植被指数/降温潜力/预测模型/空地数据Key words
Urban forest/Canopy structure/Vegetation index/Cooling potential/Predictive model/Airborne and ground data分类
农业科技引用本文复制引用
王蕾,马小婷,张心语,丁梦思,姚允龙..协同空地数据的城市森林降温潜力预测[J].东北林业大学学报,2026,54(3):115-124,10.基金项目
国家自然科学基金项目(42171246). （42171246）
