Characterizing Multiscale Effects of Climatic Factors on the Temporal Variation of Vegetation in Different Climatic Regions of China

Vegetation dynamic is sensitive to climatic warming, and is affected by individual or combined climatic factors at different temporal scale with different intensity. Previous studies have unraveled the relationships between vegetation condition and individual climatic factors; however, it is unclear whether the effects of single or combined climatic factors on vegetation dynamic was dominant for different temporal scales, vegetation types, and climatic regions. The objective of this study was to explore the scale-specific univariate and multivariate controls on vegetation over the period 1982–2015 using bivariate wavelet coherency (BWC), multiple wavelet coherence (MWC), and multiple empirical model decomposition (MEMD). The results indicated that the significant vegetation dynamics were mainly located at scales of 1, 0.5, and 0.3 years. The combined explanatory power of the seven climatic factors on the vegetation were greater at the short-term and long-term scales, while the individual climatic factor might affect vegetation dynamic in the seasonal and medium-term scales at some climatic regions. The combined effect of climatic factors in grassland of Tibetan Plateau (TP) and Tempera grassland of Inner Mongolia (TGIM) regions were the greatest, which were 65.06% and 59.53%, respectively. The explanatory powers of climate for crop dynamics between temperate humid & subhumid Northeast China (THSNC) and TP, warm-temperate humid & subhumid North China (WHSNC) and subtropical humid Central & South China (SHCSC), and TGIM and temperate & warm-temperate desert of Northwest China (TWDNC) were equivalent, which were around 47%, 45%, and 39%, respectively. Farming practices in cropland could alleviate the spatial variation of the relationships between climate and vegetation, while enhance the temporal difference of their relationships. Additionally, the dominant influencing factor among different regions varied greatly in the medium-term scale. Collectively, the results might provide alternative perspective for understanding vegetation evolution in response to climatic changes in China.

Climate Change and Ecological Engineering Jointly Induced Vegetation Greening in Global Karst Regions From 2001 to 2020

Backgrounds Vegetation dynamic plays a dominant role in the global carbon cycle and climate, especially in vulnerable karst ecosystem. Many studies have examined past several decades changes in vegetation greenness and the associated with climate drivers. Yet, few studies have analyzed the vegetation change in global karst regions particularly in the last decades when climate change and anthropogenic disturbance widely occurred. Methods In this study, we investigated the spatio-temporal variations of vegetation dynamic using the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and examined their relationship to climate changes by correlation analysis, the ordinary least squares method investigate the variation trends and the Mann-Kendal test to detect the turning point from 2001 to 2020. Results As expected, there have greening trend in global karst SINDVI from 2001 to 2020, with significant increasing trend in China (range = 0.836, P < 0.05), Europe (range = 0.456, P < 0.05) and many other regions. According to correlation analyses, SINDVI is water-limited in arid and semi-arid regions, such as Middle East and central Asia, and temperature-limited in northern high-latitude. Conclusions consistent with previous studies, our results suggest that anthropogenic activities are mainly responsible for increasing vegetation greenness in tailoring management measures (e.g., Ecological Engineering, the Grain to Green Project) of China and Europe, intensive farmed in Middle East. Coupling warming temperature and increasing precipitation, southeastern Asia and Russia showed an increasing trend in SINDVI. In general, climate factors were the dominant drivers of the variation in vegetation greenness in globally karst regions during research period.

Vegetation Dynamic Assessment by NDVI and Field Observations for Sustainability of China’s Wulagai River Basin

Accurate monitoring of grassland vegetation dynamics is essential for ecosystem restoration and the implementation of integrated management policies. A lack of information on vegetation changes in the Wulagai River Basin restricts regional development. Therefore, in this study, we integrated remote sensing, meteorological, and field plant community survey data in order to characterize vegetation and ecosystem changes from 1997 to 2018. The residual trend (RESTREND) method was utilized to detect vegetation changes caused by human factors, as well as to evaluate the impact of the management of pastures. Our results reveal that the normalized difference vegetation index (NDVI) of each examined ecosystem type showed an increasing trend, in which anthropogenic impact was the primary driving force of vegetation change. Our field survey confirmed that the meadow steppe ecosystem increased in species diversity and aboveground biomass; however, the typical steppe and riparian wet meadow ecosystems experienced species diversity and biomass degradation, therefore suggesting that an increase in NDVI may not directly reflect ecosystem improvement. Selecting an optimal indicator or indicator system is necessary in order to formulate reasonable grassland management policies for increasing the sustainability of grassland ecosystems.

Short-term effect of oil-mulch on vegetation dynamic; Integration of ecological and remote sensing-based approaches

Wind erosion is one of the desertification results and is among the natural processes that mostly occur under dry conditions and high wind velocity. Using oil-mulches is one of the common methods to stabilize sand dunes. The current study aimed to investigate the short-term effects of oil-mulch on vegetation attributes (i.e., cover and diversity) and rangeland condition score (RCS) using integrated ecological and remote sensing-based approaches in arid regions of Southwestern Iran. A vegetation survey was carried out in 2019 in the oil-mulched and control area, and a remotely sensed vegetation index (MSAVI) was calculated for 2017 and 2019. The results indicate that one year after treatment, compared to the control area, vegetation cover (30 ± 17.11 vs. 17 ± 5.44 %) and litter (4.6 ± 2.18 vs. 0.94 ± 1.55 %) increased significantly in the oil-mulched area, while bare soil (65.20 ± 17.34 vs. 82.31 ± 5.84 %) decreased. Further, diversity indices (Species evenness, Shannon, and Simpson indices) declined by applying oil-mulch (88%, 63%, and 71%, respectively). The rangeland condition score was significantly higher in the oil-mulched area than in the control area (22 ± 1.86 vs. 12 ± 0.88; P < 0.001). Comparing MSAVI between 2017 and 2019 showed that vegetation cover increased 44.8%. Based on the results, it can be suggested that planting native palatable species in an oil-mulched area with the exclusion of livestock grazing is likely to increase the benefits of oil-mulch treatment and will lead to better rangeland condition score.

Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.