Pan Rong, Sun Jian-Guo, Hu Bo-Yang, Liu Rong
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China.
National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China.
Ying Yong Sheng Tai Xue Bao. 2025 Aug;36(8):2420-2428. doi: 10.13287/j.1001-9332.202508.028.
Residual trend method is an important method for attributing vegetation changes. The performance of this method depends on the ability of vegetation-climate relationship model to avoid the disturbance from signals of human activities effects (referred to as human disturbance). The fundamental way to suppress human disturbance is to seek modeling reference, and to ensure the degree of freedom of spatial reference is far greater than that of the temporal reference. Previous vegetation-climate relationship model was limited by the fact that only temporal reference could be used in the traditional pixel-by-pixel modeling approach. We broke through the pixel-by-pixel vegetation-climate relationship model and constructed a spatially integrated vegetation-climate relationship model. Within the new model, we developed an iterative scheme for selecting spatial reference, which help improve residual trend method. We further analyzed the vegetation changes in China from 2003 to 2022 with this new model. Results showed that the enhanced vegetation index in China showed an overall increasing trend from 2003 to 2022, with a growth rate 0.002·a. Vegetation distribution showed significant spatial differences, which was bounded by the Heihe-Tengchong line. The eastern region showed significant and extremely significant improvement, accounting for 61.5% of the area covered by vegetation. Vegetation in the western region showed insignificant improvement and degradation, accounting for 36.5%. The remaining 2% area showed significant and extremely significant vegetation degradation. Human factors dominated such vegetation changes in China, with an average contribution of 87.9%. The contribution rates of human factors to both vegetation improvement and degradation areas exceeded 85%. The implementation of ecological protection policies, the improvement of agricultural management and the transformation of socio and economic development patterns were the main reasons for promoting vegetation improvement in most regions of China. Overgrazing and rapid urbanization led to the vegetation degradation in parts of the northern, eastern and central regions. The vegetation-climate relationship model constructed by residual trend method based on spatial reference outperformed the traditional residual trend method in prediction accuracy, which was more precise in quantifying the relative roles of climate and human factors. Moreover, the new model effectively avoided overestimation of the influence of climate factors and reduced human disturbance to a certain extent.
残差趋势法是归因植被变化的重要方法。该方法的性能取决于植被-气候关系模型避免人类活动影响信号（简称人类干扰）干扰的能力。抑制人类干扰的根本途径是寻求建模参考，并确保空间参考的自由度远大于时间参考的自由度。以往的植被-气候关系模型受限于传统逐像素建模方法只能使用时间参考这一事实。我们突破了逐像素植被-气候关系模型，构建了空间集成植被-气候关系模型。在新模型中，我们开发了一种选择空间参考的迭代方案，有助于改进残差趋势法。我们用这个新模型进一步分析了2003年至2022年中国的植被变化。结果表明，2003年至2022年中国增强植被指数呈总体上升趋势，增长率为0.002·a⁻¹。植被分布存在显著空间差异，以黑河-腾冲线为界。东部地区呈现显著和极显著改善，占植被覆盖面积的61.5%。西部地区植被改善不显著且有退化，占36.5%。其余2%的区域呈现显著和极显著植被退化。人为因素主导了中国的此类植被变化，平均贡献率为87.9%。人为因素对植被改善和退化区域的贡献率均超过85%。生态保护政策的实施、农业管理的改善以及社会经济发展模式的转变是中国大部分地区促进植被改善的主要原因。过度放牧和快速城市化导致中国北部、东部和中部部分地区植被退化。基于空间参考的残差趋势法构建的植被-气候关系模型在预测精度上优于传统残差趋势法，在量化气候和人为因素的相对作用方面更精确。此外，新模型有效避免了对气候因素影响的高估，在一定程度上减少了人类干扰。
