Guilin University of Technology, Guilin 541000, China.
Guilin University of Technology, Guilin 541000, China.
Sci Total Environ. 2022 Oct 15;843:156990. doi: 10.1016/j.scitotenv.2022.156990. Epub 2022 Jun 25.
Vegetation phenology is a sensitive indicator which can comprehensively reflect the response of wetland vegetation to external environment changes. However, the time-series monitoring wetland vegetation phenological changes and clarifying its response to hydrology and meteorology still face great challenges. To fill these research gaps, this paper proposed a novel time-series approach for monitoring phenological change of marsh vegetation in Honghe National Nature Reserve (HNNR), Northeast China, using continuous change detection and classification (CCDC) algorithm and Landsat and Sentinel-1 SAR images from 1985 to 2021. We evaluated the spatio-temporal response relationship of phenological characteristics to hydro-meteorological factors by combining CCDC algorithm with partial least squares regression (PLSR). Finally, this study further explored the intra-annual loss and restoration of marsh vegetation in response to hydro-meteorological factors using the transfer entropy (TE) and CCDC-MLSR model constructed by CCDC and Multiple Linear Stepwise Regression (MLSR) algorithms. We found that the bimodal trajectory of phenology reflects two growth processes of marsh vegetation in one year, and high-frequency and high-amplitude loss occurred in shallow-water and deep-water marsh vegetation from April to October, resulting in the loss area within the year was significantly greater than the recovery area. We confirmed that the CCDC algorithm could track the evolution trajectory of time-series phenology of marsh vegetation. We further revealed that precipitation, temperature and frequency of water-level changes are the main driving factors for the spatio-temporal phenological evolution of different marsh vegetation. This study verified the effect of alternative changes of hydrology and climate on loss and recovery of marsh vegetation in each growth stage. The results of this study provide a scientific basis for wetland protection, ecological restoration, and sustainable development.
植被物候是一种敏感的指示物，可以综合反映湿地植被对外部环境变化的响应。然而，对湿地植被物候变化进行时间序列监测并阐明其对水文学和气象学的响应仍然面临巨大挑战。为了填补这些研究空白，本文提出了一种利用连续变化检测和分类（CCDC）算法以及 1985 年至 2021 年的 Landsat 和 Sentinel-1 SAR 图像监测中国东北红河国家自然保护区（HNNR）湿地植被物候变化的新时间序列方法。我们通过结合 CCDC 算法与偏最小二乘回归（PLSR），评估了物候特征对水文学气象因素的时空响应关系。最后，本研究利用 CCDC 和多元线性逐步回归（MLSR）算法构建的转移熵（TE）和 CCDC-MLSR 模型，进一步探讨了湿地植被对水文学气象因素的年内损失和恢复情况。结果表明，物候的双峰轨迹反映了湿地植被在一年内的两个生长过程，浅水和深水湿地植被在 4 月至 10 月期间高频、大幅减少，导致年内损失面积明显大于恢复面积。我们证实了 CCDC 算法可以跟踪湿地植被时间序列物候的演变轨迹。我们进一步揭示了降水、温度和水位变化频率是不同湿地植被时空物候演变的主要驱动因素。本研究验证了水文学和气候的替代变化对湿地植被各生长阶段损失和恢复的影响。本研究的结果为湿地保护、生态恢复和可持续发展提供了科学依据。
