Time-series simulation of alpine grassland cover using transferable stacking deep learning and multisource remote sensing data in the Google Earth Engine

Abstract

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high-precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for the production of high-precision AGC products and the formulation of ecological conservation policies. In this study, a transferable stacking deep learning (Stacking-DL) model is proposed based on a CNN, a DNN, and a GRU for AGC time series simulation. The applicability of deep learning models for AGC simulation is evaluated based on long time series of measured data, MODIS data, and environmental factors. Finally, the AGC spatiotemporal changes and controlling environmental factors in the alpine region were analyzed based on Sen’s slope and structural equation modeling (SEM). The results showed that feature selection and parameter optimization improved the applicability of the deep learning models in AGC simulations, and the DNN (R2 = 0.899, RMSE = 0.078) model performed best among the base deep learning models. The Stacking-DL model combines the advantages of multiple models and achieves high transfer accuracy. In the YRSR, the AGC increase area (20.34 %) is greater than the AGC decrease area (3.34 %), the increase area is mainly located in the northeast, and the decrease area is mainly located in the southwest. AGC changes in the YRSR are mainly controlled by permafrost and climate. This study provides a high-precision and transferable vegetation monitoring model for alpine mountain regions based on advanced deep learning models and clarifies the response mechanism of AGC under climate change.