scholarly journals Specific Drivers and Responses to Land Surface Phenology of Different Vegetation Types in the Qinling Mountains, Central China

2021 ◽  
Vol 13 (22) ◽  
pp. 4538
Author(s):  
Jiaqi Guo ◽  
Xiaohong Liu ◽  
Wensen Ge ◽  
Xiaofeng Ni ◽  
Wenyuan Ma ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Meteorological Factors ◽  
Growing Season ◽  
Central China ◽  
Shortwave Radiation ◽  
Qinling Mountains ◽  
Vegetation Types ◽  
Land Surface Phenology ◽  
Vegetation Growth

Land surface phenology (LSP), as a precise bio-indicator that responds to climate change, has received much attention in fields concerned with climate change and ecology. Yet, the dynamics of LSP changes in the Qinling Mountains (QMs)—A transition zone between warm-temperate and north subtropical climates with complex vegetation structure—under significant climatic environmental evolution are unclear. Here, we analyzed the spatiotemporal dynamics of LSP for different vegetation types in the QMs from 2001 to 2019 and quantified the degree of influence of meteorological factors (temperature, precipitation, and shortwave radiation), and soil (temperature and moisture), and biological factors (maximum of NDVI and middle date during the growing season) on LSP changes using random forest models. The results show that there is an advanced trend (0.15 days/year) for the start of the growing season (SOS), a delayed trend (0.24 days/year) for the end of the growing season (EOS), and an overall extended trend (0.39 days/year) for the length of the growing season (LOS) in the QMs over the past two decades. Advanced SOS and delayed EOS were the dominant patterns leading to a lengthened vegetation growing season, followed by a joint delay of SOS and EOS, and the latter was particularly common in shrub and evergreen broadleaved forests. The growth season length increased significantly in western QMs. Furthermore, we confirmed that meteorological factors are the main factors affecting the interannual variations in SOS and EOS, especially the meteorological factor of preseason mean shortwave radiation (SWP). The grass and crop are most influenced by SWP. The soil condition has, overall, a minor influence the regional LSP. This study highlighted the specificity of different vegetation growth in the QMs under warming, which should be considered in the accurate prediction of vegetation growth in the future.

scholarly journals Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 1007 ◽  
Author(s):  
Haoming Xia ◽  
Yaochen Qin ◽  
Gary Feng ◽  
Qingmin Meng ◽  
Yaoping Cui ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Climate Change Impacts ◽  
Growing Season ◽  
Biological Indicators ◽  
Central China ◽  
Qinling Mountains ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Climate Transition

Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year−1, EOS was delayed by 0.22 days year−1, and LOS increased by 0.35 day year−1. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.

scholarly journals Asymmetric Behavior of Vegetation Seasonal Growth and the Climatic Cause: Evidence from Long-Term NDVI Dataset in Northeast China

2019 ◽  
Vol 11 (18) ◽  
pp. 2107 ◽  
Author(s):  
Yuke Zhou
Keyword(s):  
Climate Change ◽  
Northeast China ◽  
Growing Season ◽  
Relative Importance ◽  
Vegetation Types ◽  
Vegetation Growth ◽  
Temperature And Precipitation ◽  
Importance Analysis ◽  
Relative Importance Analysis

Land surface phenology is a response of vegetation to local climate and to climate change, leading to crucial impacts on plant growth rhythm and productivity. Differences in vegetation growth activities in earlier and latter parts of the growing season are tightly correlated to phenological changes and the temporal distribution of plant productivity. However, its spatiotemporal pattern and climatic constraints are poorly understood. For Northeast China (NEC), long-term remotely-sensed vegetation greenness records (NDVI) were employed to quantify seasonally asymmetrical characteristics of vegetation growth in detail, which consists of asymmetry in growing rate (AsyR), mean vegetation greenness (AsyV), and growing period length (AsyL) during vegetation green up and senescence stages (simply termed as spring and autumn). Furthermore, the impact of temperature and precipitation on these indices were examined using relative importance analysis. The results indicate these asymmetric metrics present a pronounced interannual variability profile with a potential cycle of ten years (significant in AsyV and AsyR) for the entire NEC. AsyV is changing synchronously with AsyL but asynchronously with AsyR. The geographical distribution of asymmetric indices shows a similar pattern to identified vegetation cover types, especially in distinguishing crops from natural vegetation. Spatial-averaged asymmetric indices indicate spring production is greater than autumn production (reflected by negative AsyV) across most vegetation types in NEC, yet autumn is longer than spring in all vegetation types, which is identified by positive AsyL. Negative AsyR is mainly found in forests implying there is rapid green up and slow senescence in trees. From a temporal perspective, AsyV decreases with time in forested regions but increases in cropland and grassland, which is similar to the pattern for AsyL. AsyR primarily exhibits a positive trend in forest and a negative trend in cropland and grassland. A relative importance analysis indicates that asymmetries of temperature (AsyTemp) and precipitation (AsyPrcp) play an equal role in significantly affecting vegetation asymmetries in greenness and growth rate but are insignificant to growing season length. AsyTemp mainly presents an obvious contribution to changes in AsyR and AsyV over cropland and grassland. AsyPrcp shows a more widespread controlling effect on AsyR and AsyV over the NEC, except in eastern broad-leaved forest. For the entire NEC, asymmetries of temperature and precipitation are negatively correlated with AsyR but are positively correlated with AsyV and AsyL. This finding may imply that a warmer (positive AsyTemp) autumn tends to improve the length and intensity of vegetation activity. Thus, the long-term change in vegetation growth asymmetries may provide insights for the altering functions of ecosystems and provide information to more accurately build plant growth models in the context of global climate change. Additionally, when combined with other information, asymmetric indices can serve as a supporting tool in classification of vegetation types.

scholarly journals Impact of Climate Change on Potential Distribution of Chinese White Pine Beetle Dendroctonus armandi in China

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 544
Author(s):  
Hang Ning ◽  
Ming Tang ◽  
Hui Chen
Keyword(s):  
Climate Change ◽  
Potential Distribution ◽  
Greenhouse Gas Emission ◽  
Central China ◽  
Qinling Mountains ◽  
Climate Scenarios ◽  
Impact Of Climate Change ◽  
Precipitation Seasonality ◽  
Suitable Area ◽  
The Impact

Dendroctonus armandi (Coleoptera: Curculionidae: Scolytidae) is a bark beetle native to China and is the most destructive forest pest in the Pinus armandii woodlands of central China. Due to ongoing climate warming, D. armandi outbreaks have become more frequent and severe. Here, we used Maxent to model its current and future potential distribution in China. Minimum temperature of the coldest month and precipitation seasonality are the two major factors constraining the current distribution of D. armandi. Currently, the suitable area of D. armandi falls within the Qinling Mountains and Daba Mountains. The total suitable area is 15.83 × 104 km2. Under future climate scenarios, the total suitable area is projected to increase slightly, while remaining within the Qinling Mountains and Daba Mountains. Among the climate scenarios, the distribution expanded the most under the maximum greenhouse gas emission scenario (representative concentration pathway (RCP) 8.5). Under all assumptions, the highly suitable area is expected to increase over time; the increase will occur in southern Shaanxi, northwest Hubei, and northeast Sichuan Provinces. By the 2050s, the highly suitable area is projected to increase by 0.82 × 104 km2. By the 2050s, the suitable climatic niche for D. armandi will increase along the Qinling Mountains and Daba Mountains, posing a major challenge for forest managers. Our findings provide information that can be used to monitor D. armandi populations, host health, and the impact of climate change, shedding light on the effectiveness of management responses.

scholarly journals Spatial and Temporal Differences in Alpine Meadow, Alpine Steppe and All Vegetation of the Qinghai-Tibetan Plateau and Their Responses to Climate Change

2021 ◽  
Vol 13 (4) ◽  
pp. 669
Author(s):  
Hanchen Duan ◽  
Xian Xue ◽  
Tao Wang ◽  
Wenping Kang ◽  
Jie Liao ◽  
...  
Keyword(s):  
Climate Change ◽  
Alpine Meadow ◽  
Global Climate ◽  
Growing Season ◽  
Tibet Plateau ◽  
Vegetation Types ◽  
Analysis Method ◽  
Alpine Steppe ◽  
Qinghai Tibet Plateau ◽  
Variation Trends

Alpine meadow and alpine steppe are the two most widely distributed nonzonal vegetation types in the Qinghai-Tibet Plateau. In the context of global climate change, the differences in spatial-temporal variation trends and their responses to climate change are discussed. It is of great significance to reveal the response of the Qinghai-Tibet Plateau to global climate change and the construction of ecological security barriers. This study takes alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau as the research objects. The normalized difference vegetation index (NDVI) data and meteorological data were used as the data sources between 2000 and 2018. By using the mean value method, threshold method, trend analysis method and correlation analysis method, the spatial and temporal variation trends in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau were compared and analyzed, and their differences in the responses to climate change were discussed. The results showed the following: (1) The growing season length of alpine meadow was 145~289 d, while that of alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau was 161~273 d, and their growing season lengths were significantly shorter than that of alpine meadow. (2) The annual variation trends of the growing season NDVI for the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau increased obviously, but their fluctuation range and change rate were significantly different. (3) The overall vegetation improvement in the Qinghai-Tibet Plateau was primarily dominated by alpine steppe and alpine meadow, while the degradation was primarily dominated by alpine meadow. (4) The responses between the growing season NDVI and climatic factors in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau had great spatial heterogeneity in the Qinghai-Tibet Plateau. These findings provide evidence towards understanding the characteristics of the different vegetation types in the Qinghai-Tibet Plateau and their spatial differences in response to climate change.

Land surface phenology and greenness in Alpine grasslands driven by seasonal snow and meteorological factors

2020 ◽  
Vol 725 ◽  
pp. 138380
Author(s):  
Jing Xie ◽  
Tobias Jonas ◽  
Christian Rixen ◽  
Rogier de Jong ◽  
Irene Garonna ◽  
...  
Keyword(s):  
Land Surface ◽  
Meteorological Factors ◽  
Alpine Grasslands ◽  
Land Surface Phenology ◽  
Seasonal Snow

Trend Analysis and ARIMA Modeling to Assess Meteorological and Surface Parameters In Ranchi, India During Pre-Monsoon Months

GIS Business ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 69-87
Author(s):  
Poulomi Chakravarty ◽  
Manoj Kumar
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Land Surface ◽  
Regional Climate ◽  
Arima Model ◽  
Sensible Heat Flux ◽  
Shortwave Radiation ◽  
Maximum Temperature ◽  
Positive Temperature ◽  
Surface Parameters

The assessment of the human-induced climate change on a global level can be carried out only after the study of local and regional climate change patterns. This study was an attempt to establish a link between regional climate and the surface parameters. The study was carried out for Ranchi, India to assess the changes in climatic pattern over the years (1901-2016) and applied Mann-Kendall Trend analysis test. The pre-monsoon period was chosen due to high intensity and number of thunderstorms taking place in the study area. Maximum temperature (Tmax), minimum temperature (Tmin), rainfall (P) &diurnal temperature range (DTR) for the months (March, April & May) were studied, and a significant negative trend in Tmax and DTR was observed. Autoregressive Integrated Moving Average (ARIMA) model was applied to fit the datasets and predict 5 values for the meteorological parameters, and the model depicted positive temperature trends and negative rainfall and DTR trends in the future. Land surface process parameters such as sensible heat flux, momentum flux, frictional velocity, shortwave radiation, longwave radiation, and net radiation for Ranchi were also fit into the ARIMA model, and the fitness of the model and predictions were determined.

scholarly journals Satellite-Observed Effects from Ozone Pollution and Climate Change on Growing-Season Vegetation Activity over China during 1982–2020

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1390
Author(s):  
Zhaosheng Wang
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Growing Season ◽  
Ozone Pollution ◽  
Vegetation Growth ◽  
Vegetation Activity ◽  
The Impact

Remote sensing vegetation index data contain important information about the effects of ozone pollution, climate change and other factors on vegetation growth. However, the absence of long-term observational data on surface ozone pollution and neglected air pollution-induced effects on vegetation growth have made it difficult to conduct in-depth studies on the long-term, large-scale ozone pollution effects on vegetation health. In this study, a multiple linear regression model was developed, based on normalized difference vegetation index (NDVI) data, ozone mass mixing ratio (OMR) data at 1000 hPa, and temperature (T), precipitation (P) and surface net radiation (SSR) data during 1982–2020 to quantitatively assess the impact of ozone pollution and climate change on vegetation growth in China on growing season. The OMR data showed an increasing trend in 99.9% of regions in China over the last 39 years, and both NDVI values showed increasing trends on a spatial basis with different ozone pollution levels. Additionally, the significant correlations between NDVI and OMR, temperature and SSR indicate that vegetation activity is closely related to ozone pollution and climate change. Ozone pollution affected 12.5% of NDVI, and climate change affected 26.7% of NDVI. Furthermore, the effects from ozone pollution and climate change on forest, shrub, grass and crop vegetation were evaluated. Notably, the impact of ozone pollution on vegetation growth was 0.47 times that of climate change, indicating that the impact of ozone pollution on vegetation growth cannot be ignored. This study not only deepens the understanding of the effects of ozone pollution and climate change on vegetation growth but also provides a research framework for the large-scale monitoring of air pollution on vegetation health using remote sensing vegetation data.

Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations

2010 ◽  
Vol 23 (3) ◽  
pp. 618-633 ◽  
Author(s):  
Arnon Karnieli ◽  
Nurit Agam ◽  
Rachel T. Pinker ◽  
Martha Anderson ◽  
Marc L. Imhoff ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Growing Season ◽  
Primary Factor ◽  
Drought Monitoring ◽  
Negative Slope ◽  
Limiting Factor ◽  
Vegetation Growth ◽  
Wide Range

Abstract A large number of water- and climate-related applications, such as drought monitoring, are based on spaceborne-derived relationships between land surface temperature (LST) and the normalized difference vegetation index (NDVI). The majority of these applications rely on the existence of a negative slope between the two variables, as identified in site- and time-specific studies. The current paper investigates the generality of the LST–NDVI relationship over a wide range of moisture and climatic/radiation regimes encountered over the North American continent (up to 60°N) during the summer growing season (April–September). Information on LST and NDVI was obtained from long-term (21 years) datasets acquired with the Advanced Very High Resolution Radiometer (AVHRR). It was found that when water is the limiting factor for vegetation growth (the typical situation for low latitudes of the study area and during the midseason), the LST–NDVI correlation is negative. However, when energy is the limiting factor for vegetation growth (in higher latitudes and elevations, especially at the beginning of the growing season), a positive correlation exists between LST and NDVI. Multiple regression analysis revealed that during the beginning and the end of the growing season, solar radiation is the predominant factor driving the correlation between LST and NDVI, whereas other biophysical variables play a lesser role. Air temperature is the primary factor in midsummer. It is concluded that there is a need to use empirical LST–NDVI relationships with caution and to restrict their application to drought monitoring to areas and periods where negative correlations are observed, namely, to conditions when water—not energy—is the primary factor limiting vegetation growth.

scholarly journals Examining Fractional Vegetation Cover Dynamics in Response to Climate from 1982 to 2015 in the Amur River Basin for SDG 13

2020 ◽  
Vol 12 (14) ◽  
pp. 5866
Author(s):  
Ran Yang ◽  
Xiaoyan Li ◽  
Dehua Mao ◽  
Zongming Wang ◽  
Yanlin Tian ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Cover ◽  
Growing Season ◽  
Amur River Basin ◽  
Mountainous Area ◽  
High Latitudes ◽  
Vegetation Types ◽  
Amur River ◽  
The Amur River ◽  
Increasing Trends

The impacts of climate and the need to improve resilience to current and possible future climate are highlighted in the UN’s Sustainable Development Goal (SDG) 13. Vegetation in the Amur River Basin (ARB), lying in the middle and high latitudes and being one of the 10 largest basins worldwide, plays an important role in the regional carbon cycle but is vulnerable to climate change. Based on GIMMS NDVI3g and CRU TS4.01 climate data, this study investigated the spatiotemporal patterns of fractional vegetation cover (FVC) in the ARB and their relationships with climatic changes from 1982 to 2015 varying over different seasons, vegetation types, geographical gradients, and countries. The results reveal that the FVC presented significant increasing trends (P < 0.05) in growing season (May to September) and autumn (September to October), but insignificant increasing trends in spring (April to May) and summer (June to August), with the largest annual FVC increase occurring in autumn. However, some areas showed significant decreases of FVC in growing season, mainly located on the China side of the ARB, such as the Changbai mountainous area, the Sanjiang plain, and the Lesser Khingan mountainous area. The FVC changes and their relationships varied among different vegetation types in various seasons. Specifically, grassland FVC experienced the largest increase in growing season, spring, and summer, while woodland FVC changed more dramatically in autumn. FVC correlated positively with air temperature in spring, especially for grassland, and correlated negatively with precipitation, especially for woodland. The correlations between FVC and climatic factors in growing season were zonal in latitude and longitude, while 120° E and 50° N were the approximate boundaries at which the values of mean correlation coefficients changed from positive to negative, respectively. These findings are beneficial to a better understanding the responses of vegetation in the middle and high latitudes to climate change and could provide fundamental information for sustainable ecosystem management in the ARB and the northern hemisphere.

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