Time series prediction (TSP) of land use/land cover (LULC) is an important scientific issue, but forecasting LULC changes at lead times of multiple time steps at fine time scales remains problematic. Especially in the context of current rapid economic and social development, the traditional one-step prediction models with a five-year or ten-year cycle cannot meet the application needs of land management departments. Temporal convolutional networks (TCNs) outperform other traditional TSP approaches. Therefore, we have proposed a pixel-level multistep TSP (pMTSP) approach that employs TCNs to carry out multistep prediction of land cover from dense time series remote sensing images, making up for the shortcomings of low accuracy, coarse time granularity, and labor-consuming of the current LULC prediction approaches. The results of comparative experiments with seasonal-trend decomposition procedure based on LOcally wEighted regreSsion Smoother and autoregression (STL-AR), seasonal autoregressive integrated moving average, and dynamic harmonics regression using single enhanced vegetation index time series, as well as the comparative experiment with the cellular automata-Markov model using real moderate resolution imaging spectroradiometer image time series, showed that the pMTSP can accurately extrapolate the change trend of the time series in fine-scale and obtain highly consistent prediction results with actual data, performing better than the other four contrasting algorithms in 23-step LULC prediction. The pMTSP can be used for multistep, fine-time-scale, and long time-series land cover prediction, which is of great guiding significance for the sustainable development and utilization of land resources.

中文翻译：

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基于时间卷积网络的密集时间序列遥感图像土地覆盖多步预测

土地利用/土地覆盖 (LULC) 的时间序列预测 (TSP) 是一个重要的科学问题，但在精细时间尺度的多个时间步长的提前期预测 LULC 变化仍然存在问题。尤其是在当前经济社会快速发展的背景下，传统的五年或十年周期的一步预测模型已经不能满足土地管理部门的应用需求。时间卷积网络 (TCN) 优于其他传统的 TSP 方法。因此，我们提出了一种像素级多步TSP（pMTSP）方法，利用TCNs对密集时间序列遥感图像中的土地覆盖进行多步预测，弥补了精度低、时间粒度粗、费时费力的缺点。消耗当前的 LULC 预测方法。基于局部加权回归平滑和自回归 (STL-AR)、季节性自回归综合移动平均和使用单一增强植被指数时间序列的动态谐波回归的季节性趋势分解程序的比较实验结果，以及与元胞自动机-马尔可夫模型使用真实中分辨率成像光谱仪图像时间序列，表明pMTSP可以精确外推时间序列的精细尺度变化趋势，并获得与实际数据高度一致的预测结果，表现优于其他四种23 步 LULC 预测中的对比算法。pMTSP 可用于多步、精细时间尺度和长时间序列的土地覆盖预测，