Spatiotemporal characteristics and variation tendency of vegetation resilience over China during 2000-2022
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摘要:
在全球气候变暖背景下，极端干旱热浪事件频发，植被抵御极端事件并从中恢复的能力遭遇严重挑战。该研究利用在轨道碳观测卫星-2（orbiting carbon observatory 2，OCO-2）的日光诱导叶绿素荧光（solar induced chlorophyll fluorescence，SIF）数据基础上优化生成的Global Orbiting Carbon Observatory-2 based Solar Induced chlorophyll Fluorescence（GOSIF）数据，分析了2000—2022年中国植被恢复力的时空特征及演变趋势，研究结果表明：1）中国植被恢复力整体呈现南方好、北方差的空间格局；2）结合一阶滞后自相关系数（lag-one autocorrelation，AC1）和方差的趋势分析结果显示，全国植被恢复力总体呈减弱趋势，与2000—2010年相比，2011—2022年恢复力呈减弱趋势的网格增加了24.28%，主要集中在黄河流域、长江中上游流域及珠江流域；3）就全国平均而言，植被恢复力发生变化的转折点出现在2013年，西南地区、东南沿海及珠江流域的植被恢复力转折出现时间较早，川渝贵鄂的山区、黄河流域、内蒙古中东部及新疆北部植被恢复力的变化与全国同步，内蒙古与黑龙江北部交界处植被恢复力出现转折时间较晚。该研究成果可为中国植被生态系统的修复与保护提供理论基础。
Abstract:Vegetation is an essential component of the terrestrial ecosystem, which plays a crucial role in facilitating material and energy exchanges among the soil, water, and atmosphere. In the context of global warming, climate extremes such as drought and heatwave events become more frequent. These extreme events threaten vegetation growth, leading to vegetation degradation and causing serious impacts on the structure and function of the terrestrial ecosystem. With the weakening of vegetation resistance to extreme dry and hot events, it has also been challenging for it to recover to its original state. To better cope with the risks of ecosystem degradation in a warming environment, it is necessary to evaluate the spatiotemporal patterns of vegetation resilience and its variation tendency. In this study, we used the Global Orbiting Carbon Observatory-2 based Solar Induced chlorophyll Fluorescence (GOSIF) satellite gridded data at a spatial resolution of 0.05° and eight day-interval during 2000-2022 to analyze the trend and spatial patterns of vegetation resilience over China. Based on the concept of the critical slowing down, the lag-one autocorrelation (AC1) and variance were employed as two indicators of the early-warning signals for the critical transitions of vegetation state. On this basis, the Kendall test was used to examine the trend of vegetation resilience, and the k-means clustering method based on the elbow method was employed to classify the different types of vegetation resilience. The results showed that spatially the vegetation resilience over China generally presents a decreasing pattern from south to north. Spatially, the lowest vegetation resilience was found in the Yellow River Basin, while vegetation resilience was generally high in Southwest China such as Sichuan and Yunnan provinces, and eastern parts of North China. Except for the northern parts of the Heilongjiang province and Inner Mongolia, the trends indicated by the AC1 and variance series were generally similar, where the vegetation resilience in most regions over China presented a decreasing pattern during 2000-2022. Compared with the period of 2000-2010, grid cells with enhanced vegetation resilience decreased by 31.04%, and grid cells with weakened vegetation resilience increased by 24.28% during 2011-2022, mainly distributed in the Yellow River Basin, upper and middle reaches of the Yangtze River Basin, Pearl River Basin. A tipping point of the vegetation resilience over China on average occurred in 2013. According to the k-means clustering method, the variation patterns of vegetation resilience were classified into seven groups. The tipping points of vegetation resilience in southwestern China, southeastern coastal areas, and the Pearl River Basin occurred earlier than the national average, while the tipping points in mountain areas in southern China, the Yellow River Basin, central Inner Mongolia and northern Xinjiang were the same as the national average. In contrast, the tipping points of vegetation resilience at the junction of Inner Mongolia and northern Heilongjiang were later than the national average. The results have implications for understanding the temporal characteristics and spatial heterogeneity of vegetation resilience over China, which are also promising to provide some references for the vegetation ecological restoration and protection strategies in China.
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Keywords:
- vegetation resilience /
- SIF /
- spatiotemporal characteristics /
- variation tendency
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图 1 2000—2022年AC1的空间分布
Figure 1. Spatial distribution of AC1 (lag-one autocorrelation) during 2000-2022
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图 2 2000—2022年、2000—2010年、2011—2022年植被AC1与方差的Kendall τ趋势（P＜0.05）的空间分布
Figure 2. Spatial distribution of resilience trends (with P< 0.05) indicated by Kendall τ of AC1 and variance by using SIF(solar induced chlorophyll fluorescence) data of 2000-2022, 2000-2010, and 2011-2022, respectively
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图 3 基于AC1和方差序列的2000—2010年、2011—2022年中国植被恢复力变化趋势的空间分布
Figure 3. Spatial distribution of resilience trends based on five-year rolling-window AC1 and variance estimates by using SIF data for 2000-2010, and 2011-2022
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图 5 2000—2022年中国植被恢复力空间聚类图
Figure 5. A clustering distribution map for vegetation resilience during 2000-2022 over China
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