The monitoring method was developed by using remote sensing, very high-resolution (VHR) satellite imagery and machine learning. Three classification models (Decision Tree, Random Forest and XGBoost) were used to classify urban land cover in Amsterdam. All the models achieved an overall accuracy of 90%, but the Random Forest classifier achieved the highest overall accuracy with 99%. Correctly distinguishing urban green space from other land cover classes was also achieved, because green space was classified correctly in all cases by the Random Forest classifier. The quality was assessed with a case study that measured the difference in temperature and air quality between parks and a built-up area in Amsterdam. The results show that urban parks can function as urban cool islands during a heatwave. While the definition of a heatwave applied to the built-up area, it did not apply to the parks. Temperatures were between 3.8 °C and 5.8 °C lower in the parks than in the built-up area during the heatwave. Air pollution was worse during the heatwave than during average weather conditions, but was not worse in parks than in the built-up area. The traffic intensity of nearby roads most likely was the source of air pollution. The parks were not able to function as islands of clean air at the scale of this study.
Author: Florence van der Hoven