Nonlinear relationship of vegetation greening with nature and human factors and its forecast – A case study of Southwest China
Nonlinear relationship of vegetation greening with nature and human factors and its forecast – A case study of Southwest China
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DOI：
10.1016/j.ecolind.2019.106009
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发表时间：
2020-04
影响因子：
6.9
通讯作者：
Huiyu Liu;F. Jiao;Jingqiu Yin;Tingyou Li;H. Gong;Zhaoyue Wang;Zhen-shan Lin
中科院分区：
文献类型：
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作者：
Huiyu Liu;F. Jiao;Jingqiu Yin;Tingyou Li;H. Gong;Zhaoyue Wang;Zhen-shan Lin
西南大部分地区植被呈现绿化趋势。然而，植被绿化与自然和人为因素的非线性关系尚不清楚。本研究利用增强回归树（boosting Regression Tree， BRT）方法研究绿化的非线性关系，预测绿化的未来变化。结果表明：(1)最干旱月降水（Bio14, 20.64%）、土地利用变化（10.39%）和人口密度（8%）是限制植被绿化的三个最重要因素。气候变化（42.655%）和人类活动（33.163%）是最重要的两个变量类型，但地形也是不可忽视的重要变量类型（18.481%）；(2) Bio14与海拔的变量交互作用最强。气候变化与人类活动和海拔高度的相互作用均较强，但人类活动与海拔高度的相互作用较小；(3) Bio14、距居民区距离、年温差（Bio7）的增加促进了植被的绿化，而GDP和最冷季降水量（Bio19）的增加则随速率变化而抑制了植被的绿化。当接近阈值时，这些因素不会产生进一步的影响。在人口密度较低时，人口密度的增加对植被的绿化有较大的促进作用；在人口密度较高时，人口密度的增加对植被的绿化有较强的抑制作用。当海拔<300 m时，海拔升高促进植被绿化，然后抑制植被绿化。在土地利用变化方面，退耕还林提高了植被的绿化率，但城市化降低了植被的绿化率；(4)在当前环境条件下，植被褐变面积百分比将从13.88%大幅增加到37.69%，且主要是植被变化不明显，且主要分布在西部。然而，未来气候变化将促进除西北地区外的大部分地区的植被绿化。本文的研究结果强调了非线性分析对于确定植被绿化的驱动因素和预测未来变化，以及制定气候变化和人类活动在脆弱生态系统中的适应和缓解策略的重要性。
Vegetation showed a greening trend in most Southwest China. However, the nonlinear relationship of vegetation greening with nature and human factors remains unclear. In this study, we studied the nonlinear relationship and predicted the future changes of the greening with Boosted Regression Tree (BRT) method. Results showed that: (1) Precipitation of Driest Month (Bio14, 20.64%), land use changes (10.39%) and population density (8%) were the three most important factors limiting vegetation greening. Climate changes (42.655%) and human activities (33.163%) were the two most important variable types, but topography was also important (18.481%), which cannot be ignored; (2) Bio14 and elevation had the strongest variable interactions. Climate changes had strong interactions with both human activities and elevation, but human activities and elevation had less interactions; (3) vegetation greening was facilitated by the increasing of Bio14, distance to residential area, temperature annual range (Bio7), but inhibited by the increasing of GDP and precipitation of coldest quarter (Bio19) with changing rates. These factors would have no further impacts when approaching threshold values. The increase of population density improved vegetation greening greatly when it was low, while inhibited the greening strongly when it was high. Elevation increase promoted vegetation greening when elevation <300 m, then inhibited it. For land use changes, ‘Grain for Green’ improved vegetation greening, but urbanization decreased it; (4) Under current environmental condition, area percentage of vegetation browning will greatly increase from 13.88% to 37.69%, which is mostly from insignificant vegetation changes and located in the west. However, future climate changes will facilitate vegetation greening in the most area except the northwest. Our results highlight the importance of nonlinear analysis for determining the drivers and predicting future changes of vegetation greening, and developing adaptation and alleviation strategies for climate changes and human activities in fragile ecosystem.