Spatiotemporal Vegetation Degradation Simulation and Inversion in Inner Mongolia Autonomous Region

Engineering notes

Key topics: autonomous driving, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract. Under climate and human pressures, vegetation in Inner Mongolia exhibits complex fragmentation and degradation. Scientifically inverting its spatiotemporal dynamics is crucial for regional ecological restoration. To address the challenges faced by traditional cellular automata (CA) models in large-scale complex ecological transition zones—such as computing power bottlenecks and subjective transition rules—this study proposes a cloud-based vegetation degradation simulation and inversion framework (CA-VDS) via Google Earth Engine. By coupling Random Forest (RF) and an Improved Genetic Algorithm (IGA) with CA, the framework extracts nonlinear driving potentials and automates the optimization of bidirectional transition thresholds. Validation against the 2020 baseline shows CA-VDS effectively resolves manual parameter tuning limitations. Furthermore, it smooths the spectral fluctuations caused by short-term sporadic disturbances through the underlying spatial neighborhood mechanism, demonstrating its value in simulating potential ecological degradation risks and developmental trajectories. This work not only verifies the reliability of CA-VDS in analyzing complex nonlinear ecological processes, but also establishes a reliable parameter baseline and model paradigm for subsequent integration with CMIP6 and other multi-scenario data to conduct long-term future ecological predictions.

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