Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Qinghai-Tibetan Plateau from 2001 to 2017

Nian

Liu

et al. 2022

IJERPH

In recent years, ecological concerns such as vegetation destruction, permafrost deterioration, and river drying have been paid much more attention to on the Yellow River Basin in China. Soil pH is regarded to be the fundamental variable among soil properties for vegetation growth, while net primary productivity (NPP) is also an essential indicator to reflect the healthy growth of vegetation. Due to the limitation of on-site samples, the spatial–temporal variations in soil pH and NPP, as well as their intrinsic mechanisms, remain unknown, especially in the Yellow River source area, China. Therefore, it is imperative to investigate the coupling relationship between soil pH and NPP of the area. The study coupled MODIS reflectance data (MOD09A1) with on-site soil pH to estimate spatial–temporal variations in soil pH, explore the response of NPP to soil pH, and assess the extent to which they contribute to grassland ecosystems, thus helping to fill knowledge gaps. Results indicated that the surface spectral reflectance for seven bands could express the geographic pattern of soil pH by applying a multiple linear regression equation; NPP exhibited an increasing trend while soil pH was the contrary in summer from 2000 to 2021. In summer, NPP was negatively correlated with soil pH and there was a lag effect in the response of NPP to soil pH, revealing a correlation between temperate steppes > montane meadows > alpine meadows > swamps in different grassland ecosystems. In addition, contribution indices for temperate steppes and montane meadows were positive whereas they were negative for swamps and alpine meadows, which are apparent findings. The contribution index of montane and alpine meadows was greater than that of temperate steppes and swamps. The approach of the study can enable managers to easily identify and rehabilitate alkaline soil and provides an important reference and practical value for ecological restoration and sustainable development of grassland ecosystems in alpine regions.

Section: Discussionmentioning

confidence: 99%

Spatial–Temporal Correlations between Soil pH and NPP of Grassland Ecosystems in the Yellow River Source Area, China

Nian

Liu

et al. 2022

IJERPH

“…Precipitation and temperature are fundamental driving forces for ecosystem process change and vegetation distribution as well as productivity in QTP (Huang et al, 2016;Liu et al, 2021;yin Zhang et al, 2021). To analyze the climatic controls on each ES indicator, spatial multiple regression analysis is a better choice than spatial correlation analysis owing to the former's causal explanatory power.…”

Section: The Influence Factors On the Variations Of Ecosystem Service...mentioning

confidence: 99%

Spatiotemporal Variations of Ecosystem Service Indicators and the Driving Factors Under Climate Change in the Qinghai–Tibet Highway Corridor

Yang

Zhu

et al. 2022

Front. Environ. Sci.

In recent decades, the influence of climate change and human activities on the ecosystem services (ES) in the Qinghai–Tibet Plateau (QTP) has been extensively investigated. However, few studies focus on linear traffic corridor area, which is heavily affected by human activities. Taking the Golmud–Lhasa national highway corridor as a case, this study investigated the land-use and land-cover change (LUCC) and spatiotemporal variations of ES indicators using ecosystem indices of fractional vegetation cover (FVC), leaf area index (LAI), evapotranspiration (ET), and net primary productivity (NPP) from 2000 to 2020. The results indicated that LUCC was faster in the last decade, mostly characterized by the conversion from grassland to unused land. In buffer within 3000 m, the proportions of productive areas represented the increased trends with distance. In terms of ES variations, the improved areas outweighed the degraded areas in terms of FVC, LAI, and NPP from 2000 to 2020, mostly positioned in the Qinghai Province. In addition, FVC, LAI, and NPP peaked at approximately 6000 m over time. With regard to influencing factors, precipitation (20.54%) and temperature (14.19%) both positively influenced the spatiotemporal variation of FVC. Nearly 60% of the area exhibited an increased NPP over time, especially in the Qinghai Province, which could be attributed to the temperature increase over the last two decades. In addition, the distance effects of climatic factors on ES indicators exhibited that the coincident effects almost showed an opposite trend, while the reverse effects showed a similar trend. The findings of this study could provide a reference for the ecological recovery of traffic corridors in alpine fragile areas.

“…However, we found that topographic factors (elevation and slope) and geomorphic type dominated the vegetation growth in the Sanjiang Plain, thus dominating the spatial differentiation of CI. Topographic factors (elevation and slope), which were of great importance in redistributing the solar energy and precipitation [30], generally controlled the soil condition through different processes [66], determining the spatial characteristics of CI. Notably, climatic factors (average annual temperature and annual precipitation) and aspect had the smallest q values, but they still had a notable impact on the spatial differentiation of CI when they interacted with other driving factors.…”

Section: Driving Factors Of the Spatial Distribution Of CImentioning

confidence: 99%

“…Notably, climatic factors (average annual temperature and annual precipitation) and aspect had the smallest q values, but they still had a notable impact on the spatial differentiation of CI when they interacted with other driving factors. Related studies have also shown that the interactions between two driving forces could enhance the impact of a single factor [30,59,66]. Therefore, the government should use the land rationally according to the characteristics of topographic factors (elevation and slope) and geomorphic type in the Sanjiang Plain to improve grain production and ensure regional ecological security.…”

Section: Driving Factors Of the Spatial Distribution Of CImentioning

confidence: 99%

Spatio-Temporal Characteristics and Driving Factors of the Foliage Clumping Index in the Sanjiang Plain from 2001 to 2015

Fang

et al. 2021

Remote Sensing

The Sanjiang Plain is the largest agricultural reclamation area and the biggest marsh area in China. The regional vegetation coverage in this area is vital to local ecological systems, and vegetation growth is affected by natural and anthropogenic factors. The clumping index (CI) is of great significance for land surface models and obtaining information on other vegetation structures. However, most existing ecological models and the retrieval of other vegetation structures do not consider the spatial and temporal variations of CI, and few studies have focused on detecting factors that influence the spatial differentiation of CI. To address these issues, this study investigated the spatial and temporal characteristics of foliage CI in the Sanjiang Plain, analysing the correlation between CI and leaf area index (LAI) through multiple methods (such as Theil−Sen trend analysis, the Mann−Kendall test, and the correlation coefficient) based on the 2001−2015 Chinese Academy of Sciences Clumping Index (CAS CI) and Global LAnd Surface Satellite Leaf Area Index (GLASS LAI). The driving factors of the spatial differentiation of CI were also investigated based on the geographical detector model (GDM) with natural data (including the average annual temperature, annual precipitation, elevation, slope, aspect, vegetation type, soil type, and geomorphic type) and anthropogenic data (the land use type). The results showed that (1) the interannual variation of foliage CI was not obvious, but the seasonal variation was obvious in the Sanjiang Plain from 2001 to 2015; (2) the spatial distribution of the multiyear mean CI of each season in the Sanjiang Plain was similar to the spatial distribution of the land use type, and the CI decreased slightly with increases in elevation; (3) the correlation between the growing season mean CI (CIGS) and the growing season mean LAI (LAIGS) time series was not significant, but their spatial distributions were negatively correlated; (4) topographic factors (elevation and slope) and geomorphic type dominated the spatial differentiation of foliage CI in the Sanjiang Plain, and the interactions between driving factors enhanced their explanatory power in terms of the spatial distribution of foliage CI. This study can help improve the accuracy of the retrieval of other vegetation structures and the simulation of land surface models in the Sanjiang Plain, providing invaluable insight for the analysis of the spatial and temporal variations of vegetation based on CI. Moreover, the results of this study support a theoretical basis for understanding the explanatory power of natural and anthropogenic factors in the spatial distribution of CI, along with its driving mechanism.