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Land-cover map of the Mongolian Plateau (source: MODIS land cover product 2004). https://doi.org/10.1371/journal.pone.0190313.g001
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Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for th...
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Using 19 years of remotely sensed Enhanced Vegetation Index (EVI), we examined the effects of climatic variability on terrestrial vegetation of six protected areas along southwestern South America, from the semiarid edge of the Atacama desert to southern Patagonia (30°S-51°S). The relationship between satellite phenology and climate indices, namely...
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... Temperature and precipitation are widely acknowledged as primary natural factors influencing vegetation phenology [20][21][22]. Many studies have shown that the SOS in the Northern Hemisphere advanced with climate warming, including in Xinjiang [14,23], the Mongolian Plateau [24,25], and other regions. Moreover, recent studies have found asymmetric effects of daytime and nighttime warming on the SOS [26,27]. ...
Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY–170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP.
... In general, trends towards a certain delay and advancement in the end of the vegetative season have been evidenced, although in a weaker magnitude than the change in the start of the season. In this sense, and although there are fewer local, regional, and global studies focused on this, it is important to note that most of them agree in showing a dominant delay in wide regions of temperate climates (Miao, et al., 2017;Piao et al., 2019), which aligns with the delay seen in this study in the Spanish Eurosiberian region. On the other hand, the advancement of the end of the season could be associated with the previously described advancement of the start. ...
En este estudio se analiza espacialmente la fenología vegetal y sus variaciones a lo largo del tiempo en la España peninsular e Islas Baleares. Para realizar el análisis se ha generado una serie temporal de casi 40 años (1983-2020) a partir de la fusión de valores del índice de vegetación NDVI de imágenes de satélite procedentes de los sensores NOAA-AVHRR y MODIS. El cálculo de las variables fenológicas se ha realizado con TIMESAT 3.3. que ha extraído 13 fenométricas cuya tendencia se ha evaluado a partir del modelo Theil-Sen y la significación de esta con el test de Mann Kendal. Los resultados muestran diferencias regionales entre la España eurosiberiana y la mediterránea respecto a las fenofases de inicio y final de temporada. Las zonas eurosiberianas de media han visto retrasadas sus fechas de inicio y final de temporada, en torno a 0,35 y 0,22 días cada año a lo largo del periodo de estudio respectivamente, mientras que la región mediterránea ha adelantado las fechas de salida de las hojas y la senescencia de media alrededor de 0,07 y 0,05 días al año. También se ha observado una tendencia al reverdecimiento de toda el área de estudio e importantes contrastes entre las cubiertas del suelo que abren la puerta a futuros estudios que profundicen en estas diferencias de comportamiento y en sus interacciones con los cambios en el clima y en la gestión del territorio.
... For example, shrub-grassland vegetation typically has a short phenological period that is concentrated during optimal grassland hydrothermal conditions (Lei et al., 2015). The tolerance strategy of these plants involves phenological dormancy under drought stress to slow metabolism and reduce sensitivity to drought (He et al., 2018;Miao et al., 2017). However, these plants are actually more vulnerable after dormancy because the newly germinated shoots or flowers are fragile, especially when exposed to thermal stress caused by heatwaves (Liu et al., 2021;Shao et al., 2013). ...
Extreme heatwaves have become more frequent and severe in recent decades, and are expected to significantly influence carbon fluxes at regional scales across global terrestrial ecosystems. Nevertheless, accurate prediction of future heatwave impacts remains challenging due to a lack of a consistent comprehension of intrinsic and extrinsic mechanisms. We approached this knowledge gap by analyzing the complexity factors in heatwave studies, including the methodology for determining heatwave events, divergent responses of individual ecosystem components at multiple ecological and temporal scales, and ecosystem vegetation status and hydrothermal environment, among other factors. We found that heatwaves essentially are continuously changing compound environmental stress that can unfold into multiple chronological stages, and plant physiology and carbon flux responses differs in each of these stages. This approach offers a holistic perspective, recognizing that the impacts of heatwaves on ecosystems can be better understood when evaluated over time. These stages include instantaneous, post-heatwave, legacy, and cumulative effects, each contributing uniquely to the overall impact on the ecosystem carbon cycle. Next, we investigated the importance of the timing of heatwaves and the possible divergent consequences caused by different annual heatwave patterns. Finally, a conceptual framework is proposed to establish a united foundation for the study and comprehension of the consequences of heatwaves on the ecosystem carbon cycle. This instrumental framework will assist in guiding regional assessments of heatwave impacts, shedding light on the underlying mechanisms responsible for the varied responses of terrestrial ecosystems to specific heatwave events, which are imperative for devising efficient adaptation and mitigation approaches.
... Some studies have shown that EVI is more suitable for vegetation phenology research in arid zones than other vegetation indices that characterise canopy morphology [13,14]. Moreover, traditional vegetation indices are limited due to their correlation with the morphological characteristics of vegetation canopies, resulting in errors when characterising vegetation phenology based solely on canopy morphology, as observed in the study by Miao et al. (2017) [15]. Gross primary productivity (GPP), the amount of organic carbon fixed by photosynthesis, is the most directly relevant factor for vegetation phenology, but it is commonly used as a validation dataset as it contains flux site data, which are spatially discontinuous and difficult for large-scale plant phenology monitoring [16,17]. ...
... Some studies have shown that EVI is more suitable for vegetation phenology research in arid zones than other vegetation indices that characterise canopy morphology [13,14]. Moreover, traditional vegetation indices are limited due to their correlation with the morphological characteristics of vegetation canopies, resulting in errors when characterising vegetation phenology based solely on canopy morphology, as observed in the study by Miao et al. (2017) [15]. Gross primary productivity (GPP), the amount of organic carbon fixed by photosynthesis, is the most directly relevant factor for vegetation phenology, but it is commonly used as a validation dataset as it contains flux site data, which are spatially discontinuous and difficult for large-scale plant phenology monitoring [16,17]. ...
The accurate monitoring of vegetation phenology is critical for carbon sequestration and sink enhancement. Vegetation phenology in arid zones is more sensitive to climate responses; therefore, it is important to conduct research on phenology in arid zones in response to global climate change. This study compared the applicability of the enhanced vegetation index (EVI), which is superior in arid zones, and global solar-induced chlorophyll fluorescence (GOSIF), which has a high spatial resolution, in extracting vegetation phenology in arid zones, and explored the mechanism of the differences in the effects of environmental factors on the phenology of different vegetation types. Therefore, this study employed a global solar-induced chlorophyll fluorescence (GOSIF) dataset to determine the start and end of the vegetation growth season (SOSSIF and EOSSIF, respectively) in the arid zone of Northwest China from 2001 to 2019. The results were compared with those from the EVI-based MODIS climate product MCD12Q2 (SOSEVI and EOSEVI). Variations in the sensitivity of these climatic datasets concerning temperature, precipitation, and standardised precipitation evapotranspiration index (SPEI) were assessed through partial correlation analysis. Results: Compared to the MCD12Q2 climatic products, SOSSIF and EOSSIF closely matched the observed climate data in the study area. Spring onset was delayed at higher altitudes and latitudes, and the end of the growing season occurred earlier in these areas. Both SOSSIF and EOSSIF significantly advanced from 2001 to 2019 (trend degrees −0.22 and −0.48, respectively). Spring vegetation phenology was chiefly influenced by precipitation while autumn vegetation phenology was driven by both precipitation and SPEI. GOSIF-based climate data provides a more accurate representation of vegetation phenology compared to traditional vegetation indices. The findings of this study contribute to a deeper understanding of the potential ability of EVI and SIF to reveal the influence of vegetation phenology on the carbon cycle.
... Precipitation has been reported to affect spring phenology (C. Li et al., 2021), particularly in arid areas (Miao et al., 2017). Moreover, recent studies have revealed that sunlight hours and/or illumination intensity are strongly correlated with spring phenology (Gao & Zhao, 2022;Q. ...
Exploring spring phenology dynamics driven by climate change is crucial for investigating land–climate feedback. However, a thorough assessment of the influence of climatic drivers on spring phenology remains lacking. Here, we investigated spatiotemporal variations at the start of the growing season (SOS) in the Greater Khingan Mountains, Northeast China, during 1982–2015 using the normalized difference vegetation index third‐generation dataset from Global Inventory Modelling and Mapping Studies. To investigate the influence of preseason climatic factors on the SOS, partial correlation analysis was employed to assess correlations as well as to identify dominant regional drivers. Overall, the SOS dates in the study area showed obvious regional characteristics from 1982 to 2015, with a gradual delay from north to south, with more than 90% of the SOS dates occurring from Julian days 125–140. Advancement trends were observed in all ecogeographical regions, thus accounting for 94.6% of the entire research area, with an average of 7.14 days in advance. The partial correlation analysis revealed spring phenology was most significantly influenced by preseason temperature, and temperature promoted the advancement of SOS in almost the entire study area, but for semi‐humid/semi‐arid regions, the effects of total precipitation was greatest. Interestingly, preseason radiation also dominated earlier SOS dates by a few pixels, albeit in a fragmented distribution. Generally, the preseason temperature had a prominent influence on the SOS dates across all ecogeographical regions, whereas the roles of preseason precipitation and radiation varied by region. In addition to analysing a single climatic factor, our study aimed to analyse the comprehensive impacts of climatic factors on spring phenological processes, with a focus on the composite effects, helping us to improve predictions of spring phenology and provide a better understanding of land–climate interactions.
... It is high in the west and low in the east, rising from 89 m above the sea level in eastern Inner Mongolia to 4,140 m above the sea level in western Mongolia, with an average elevation of 1,288 m. The Mongolian Plateau is dominated by an arid and semi-arid continental monsoon climate, with cold and dry winters and warm summers (Miao et al., 2017). In Mongolia, the average annual temperature from 1980 to 1999 ranged from −4.3°C in the northern high-altitude forest to 6.8°C in the eastern low-latitude forest. ...
Taking the Mongolian Plateau as the research area, this paper studied the vegetation growth from 2001 to 2018. We quantified the vegetation growth changes based on changes in gross primary productivity (GPP) and leaf area index (LAI) and their relationships to climate variables using correlation analysis, partial correlation analysis and multiple correlation analysis. The results showed that from 2001 to 2018 both GPP and LAI showed an increasing trend, with great heterogeneities among different areas and land cover types. The largest increase of GPP and LAI occurred in the northeast plateau with the land cover types of forest and cropland. The main driving factor of vegetation growth was precipitation, while temperature was significantly negatively correlated with vegetation growth. The CO2 concentration had a significant impact on the GPP in farmland, and the increase of solar radiation had a significant impact on tundra. Our study highlights the importance of precipitation in regulating vegetation growth in the Mongolian Plateau, challenging the prevailing views that the temperature dominates the vegetation growth in the northern ecosystems.
... Remote sensing has its own limitations in terms of data errors and a tendency to be influenced by the surroundings (i.e., atmosphere, sensors, and soil). The SOSNPP data of this study varied slightly from those of some previous studies [56,57], which might be explained by the resolution, quality, and smoothing method used in the vegetation index [58]. The different smoothing models for remote sensing time series data differ significantly [59]. ...
Snow is one of the important water sources for vegetation growth in the Mongolian Plateau, and temporal and spatial changes to it have a profound impact on terrestrial vegetation phenology. In recent years, due to global climate change, the snow associated with the different vegetation types of the Mongolian Plateau has changed substantially, and the mechanism of the resulting change in the vegetation growth date needs to be studied. To address this issue, we used the modified Carnegie Ames Stanford Approach (CASA) model was to estimate the start of growing season net primary productivity (SOSNPP) for different types of vegetation over the Mongolian Plateau from 2001 to 2019. An extensive study of the spatial changes in the SOSNPP and the responses reflected by the winter snow cover fraction (SCFWinter), spring snow melting date (SMDSpring), and SOSNPP to influencing factors is of great significance for ecosystem maintenance. We observed: (1) Different vegetation types exhibited similar changes; SCFWinter underwent a significant decrease of −0.2%, and SMDSpring followed a slow downward trend of −0.59 day of the year (DOY)/year for the whole study area. (2) In the Mongolia Plateau, SOSNPP showed a trend of significant decrease of −0.53 DOY/year. (3) The local hydrothermal condition coupling relationship effect on different vegetation types. Spring temperature (TEMSpring) has a direct effect on vegetation SOSNPP, with a path coefficient of −0.09 in the Mongolian Plateau. SCFWinter and SMDSpring were shown through a path analysis to employ different effects on vegetation SOSNPP. SMDSpring has a direct effect on vegetation SOSNPP, with a path coefficient of 0.53. (4) The SMDSpring and PRESpring factors have a significant impact on vegetation SOSNPP, and they account for 21.11% and 21.26% of the whole study area SOSNPP, respectively. This study is expected to promote the examination of the snow phonological parameters of different related vegetation types and theoretical research on SOSNPP.
... Previous studies have shown that the normalized difference vegetation index (NDVI) can be used to effectively monitor vegetation phenology [7]. Therefore, NDVI has frequently been used to estimate vegetation phenological parameters in studies of Mongolian Plateau phenology [1,2,8,9]. However, limitations may exist to using only NDVI when retrieving vegetation phenology information for evergreen forests [10,11]. ...
The phenological parameters estimated from different data may vary, especially in response to climatic factors. Therefore, we estimated the start of the growing season (SOS) and the end of the growing season (EOS) based on sunlight-induced chlorophyll fluorescence (SIF), the normalized difference vegetation index (NDVI) and the near-infrared reflectance of vegetation (NIRv). The SIF, NDVI and NIRv breakpoints were detected, and the trends and change-points of phenological parameters based on these data were analyzed. The correlations between the phenological parameters and snow-related factors, precipitation, temperature, soil moisture and population density were also analyzed. The results showed that SIF and NIRv could identify breakpoints early. SIF could estimate the latest SOS and the earliest EOS. NDVI could estimate the earliest SOS and the latest EOS. The change-points of SOSSIF were mostly concentrated from 2001 to 2003, and those of SOSNDVI and SOSNIRv occurred later. The change-points of EOSSIF and EOSNIRv were mostly concentrated from 2001 to 2007, and those of EOSSIF occurred later. Differently from the weak correlation with SOSSIF, SOSNDVI and SOSNIRv were significantly correlated with snow-related factors. The correlation between the meteorological factors in the summer and autumn and EOSSIF was the most significant. The population density showed the highest degree of interpretation for SOSNIRv and EOSNDVI. The results reveal the differences and potentials of different remote-sensing parameters in estimating phenological indicators, which is helpful for better understanding the dynamic changes in phenology and the response to changes in various influencing factors.
... Desertification has had a disastrous impact on the global environment, especially in developing countries in arid and semi-arid regions [5,6]. The Mongolian Plateau is a major component of the global grassland ecosystem and plays a key role in the East Asian and global carbon cycle [7][8][9]. Inner Mongolia, China, and Mongolia are the main areas of the Mongolian Plateau and are important agricultural and livestock production areas. ...
Desertification is one of the most harmful ecological disasters on the Mongolian Plateau, placing the grassland ecological environment under great pressure. Remote-sensing monitoring of desertification and exploration of the drivers behind it are important for effectively combating this issue. In this study, four banners/counties on the border of China and Mongolia on the Mongolian Plateau were selected as the target areas. We explored desertification dynamics and their drivers by using remote sensing imagery and a product dataset for the East Ujimqin Banner and three counties in Mongolia during the period 2000–2015. First, remote sensing information on desertification in the fourth phase of the study area was extracted using the visual interpretation method. Second, the dynamic change characteristics of desertification were analyzed using the intensity analysis method. Finally, the drivers of desertification and their explanatory powers were identified using the geographical detector method. The results show that the desertification of the East Ujimqin Banner has undergone a process of reversion, development, and mild development, with the main transition occurring between slight (SL) and non-desertified land (N), very serious desertified land (VS), and water areas. The dynamics of desertification in this region are influenced by a combination of natural and anthropogenic factors. Desertification in the three counties of Mongolia has undergone processes of development, mild development and mild development with SL and vs. as the main types. Desertification in Mongolia is mainly concentrated in Matad County, which is greatly affected by natural conditions and has little impact from anthropogenic activities. In addition, the change intensity of desertification dynamics in the study area showed a decreasing trend, and the interaction between natural and anthropogenic drivers could enhance the explanatory power of desertification dynamics. The research results provide a scientific basis for desertification control, ecological protection, and ecological restoration on the Mongolian Plateau.
... Climatic factors such as wind speed, temperature and precipitation change with the seasons (Dulamsuren and Hauck, 2008). Vegetation also undergoes weather-related processes such as regreening during the growing season and wilting season during the year due to climate influences, and vegetation cover changes accordingly (Miao et al., 2017). Due to the seasonal change in these factors, the amount of wind erosion also changes with the seasons. ...
Wind erosion can cause desertification and sandstorms in arid and semiarid areas. However, quantitative studies of the dynamic changes in wind erosion over long time periods are relatively rare, and this knowledge gap hinders our understanding of desertification under the conditions of a changing climate. Here, we selected the Mongolian Plateau as the study area. Using the revised wind erosion equation (RWEQ) model, we assessed the spatial and temporal dynamics of wind erosion on the Mongolian Plateau from 1982 to 2018. Our results showed that the wind erosion intensity on the Mongolian Plateau increased from northeast to southwest. The annual mean wind erosion modulus was 46.5 t·ha-1 in 1982-2008, with a significant decline at a rate of -5.1 t·ha-1·10 yr-1. The intensity of wind erosion was the strongest in spring, followed by autumn and summer, and was weakest in winter. During 1982-2018, wind erosion showed a significant decreasing trend in all seasons except winter, but the wind erosion contribution of spring to the total annual wind erosion significantly increased, while that of summer significantly decreased. These results can help decision-makers identify high-risk areas for soil erosion on the Mongolian Plateau and take effective measures to adapt to climate change.
