A novel framework for dynamic and quantitative mapping of damage severity due to compound Drought–Heatwave impacts on tea Plantations, integrating Sentinel-2 and UAV images

Abstract

In 2022, China experienced a historically rare compound drought–heatwave (CDH) event, which had more severe impacts on vegetation compared with individual extreme events. However, quantitatively mapping the damage severity of CDH on tea tree using satellite data remains a significant challenge. Here we proposed a novel framework for dynamic and quantitative mapping of tea trees damage severity caused by CDH in 2022 using Sentinel-2 and Unmanned Aerial Vehicle (UAV) data. The Extreme Gradient Boosting (XGBoost) was selected as the optimal machine learning algorithm to extract tea plantations using Sentinel-2 data from XGBoost, Random Forest (RF), Logistic regression (LR), and Naive Bayes. The User’s Accuracy and Producer’s Accuracy for the extraction of tea plantations are 92.20 % and 93.51 %, respectively. UAV images with 2.5 cm spatial resolution were utilized to detect the tea trees damaged caused by the CDH in 2022. A new index, named the CDH damage severity index (CDH_DSI), was proposed to quantitatively evaluate the damage severity of CDH on tea trees at pixel level, with a spatial resolution of 10 m x 10 m. Based on the results of tea plantations and damaged tea trees detection, UAV-derived CDH_DSI was calculated and used as ground truth data. Then, The XGBoost was selected as the optimal CDH_DSI prediction model from XGBoost, RF, and LR with the Sentnel-2 derived vegetation indices and spectral reflectance as predictors. The coefficient of determination was 0.81 and root mean squared error was 7.61 %. Finally, dynamic and quantitative CDH_DSI maps were generated with the optimal CDH_DSI prediction model. The results show that 50 percent of tea plantations in Wuyi were damaged by the prolonged CDH event in 2022. These results can be attributed to precipitation deficits and heatwaves. Given that more severe CDH events are projected for the future, quantifying their impacts can provide decision-making support for disaster mitigation and prevention.