Dynamics of natural vegetation on the Tibetan Plateau from past to future using a comprehensive and sequential classification system and remote sensing data

Abstract

Long‐term series of vegetation change is one of the key study contents for evaluating terrestrial ecosystems and plays an important role in global change study. The CSCS (Comprehensive and Sequential Classification System) model was used in the paper to analyze the change of Potential Natural Vegetation (PNV) types during the period 1951–2000 and for the three future years 2020, 2050 and 2080. The growth conditions for each PNV type were analyzed based on MODIS Normalized Difference Vegetation Index (NDVI) datasets on the Tibetan Plateau (TP) from 2001 to 2010. The results showed that: (i) The mean annual precipitation (MAP) and mean annual temperature (MAT) had increasing trends (1.72 mm year−1 and 0.025°C year−1, respectively) over the past 50 years from 1951 to 2000. (ii) Based on the three general circulation models (CCCMA, CSRIO and HadCM3), the MAT and MAP on TP would increase in the future, with magnitudes 3.54 to 6.08°C and 28.71 mm to 126.84 mm from 2000 to 2080, respectively; it is also predicted that two new vegetation categories: warm desert and savanna would appear in the future and that the area of forest would increase significantly, while the area of grassland would have an obvious decreasing trend. And (iii) Overall, the ecological environment of vegetation in the recent 10 years has been improved in most of the areas in central and southern regions of Qinghai Province, but degraded in some areas of Sichuan Province, and the southwest and south of Tibet.

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of Grassland and Its Application

“…As one of the most widespread ecosystem types, natural grassland plays a significant but poorly recognized role in the global carbon cycle [1,2]. In order to effectively manage grassland ecosystems and maintain their sustainability, large-scale analysis and modelling of grassland NPP are needed to develop a better grasp of the spatial distribution of grasslands and their productivity [3,4,13,20,66]. Continuous monitoring of global grassland productivity has never been possible because of technological limitations.…”

“…In order to effectively manage grassland ecosystems and maintain their sustainability, a deeper understanding of how these ecosystems will respond to growing pressures is needed. Large-scale analysis and modelling is needed to develop a better grasp of the spatial distribution of grasslands, their productivity, and potential variations in response to climate changes [3,4,13,20,23,66].…”

Spatio-temporal distribution pattern of potential natural vegetation and its response to climate change from Last Interglacial to future 2070s in China

“…任正超 等：最后间冰期至未来 2070s 中国潜在自然植被时空分布格局及其对气候变化的响应 行了模拟研究。Yuan 等 [20] 和赵茂盛等 [21] 利用植被和气候的关系以及 IBIS (Integrated Biosphere Simulator) 和 MAPSS (Mapped Atmosphere-Plant-Soil System) 模型对当前和未来 气候变化下的我国 PNV 类型进行模拟分析。Wang 等 [6] 利用 BIOME 4 (生物地理耦合) 模 型对我国的 PNV 进行模拟，并重点进行 PNV 分布格局对温度、降水量和 CO2的敏感性分 析。以上研究从 PNV 的概念、时空分布格局和对自然条件的响应等方面进行深入的剖 析，但是研究的时间序列较短，无法了解历史气候变化对 PNV 的演替作用，并且采用的 模型对区域 PNV 划分的类型较少，无法对全球范围内的 PNV 进行有效的时空模拟。 综合顺序分类系统 (Comprehensive and Sequential Classification System，CSCS) 以 生物气候特征为基础，用年降水量 r 和≥0 ℃年积温Σθ的比值划分湿润度 K，且将具有同 一热量级和湿润度级相结合的自然植被划分为类。将全球自然植被划分为 42 类，包括草 原、荒漠、森林和冻原等陆地自然景观 [22] 。李飞等 [23][24][25][26] 依据地貌和植被分布特点，利用 GIS (Geographic Information System) 技术和地统计学方法，结合生态信息图谱，实现 西北干旱半干旱区和中国 PNV 的划分。柳小妮等 [27] 将地形特征如海拔高度、坡度和坡向 等与气候要素 (温度和降水量) 之间建立回归模型，并应用于气象要素空间插值法 中，较高精度地划分中国 PNV 类型，其空间分布较好地体现了 PNV 的地带性分布规 律。Liang 等 [22] 、任继周等 [28] 和 Feng 等 [29,30] 利用 CSCS 理论和气候数据划分青藏高原和全球 的 PNV，并分析当前和未来气候变化该地区 PNV 的演替方向、碳动态及其对气候变化的 响应。上述学者不仅利用 CSCS 划分了区域和全球的 PNV，而且也从应用的视角利用信 息技术和数学模型对 CSCS 理论进行验证和扩展 [31] [27]…”

“…任正超 等：最后间冰期至未来 2070s 中国潜在自然植被时空分布格局及其对气候变化的响应 增加导致冻原和高山草地的面积大幅度缩小。但并未出现热荒漠和萨王纳两个类组，与 Liang 等[30] 的研究结果并不一致，可能原因为本文中采用的 RCP 2.6 情景假定 CO2浓度的 增加在 2020 年就达到峰值，随后逐年降低。所以本研究中至未来 2070s 时期，青藏高原 地区温度的增加值不足 3.54 ℃，未达到热荒漠和萨王纳的立地条件。 PNV 类型进行划分以及模拟 PNV 类型的地理分布格局[38,39] 。PNV 预测…”

A new temporal prediction method of grazing pressure based on normalized difference vegetation index and precipitation using nonlinear autoregressive with exogenous input networks

“…The vegetation productivity can be approximately represented by the normalized difference vegetation index (NDVI) with the development of the remote sensing technology (Kawamura et al, 2005;Liang et al, 2012;Reiche et al, 2012). Because the temporal variations of the NDVI is highly correlated with the precipitation in arid and semi-arid grasslands (Richard et al, 2008), the NDVI variations can be predicted through the precipitation (Dulamsuren & Hauck, 2008;Huang, Ming, Huang, Leng, & Hou, 2017;Lin & Zhang, 2013;Wu, Fu, Jin, Wu, & Bai, 2019).…”