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

Xia, Haoming; Qin, Yaochen; Feng, Gary; Meng, Qingmin; Cui, Yaoping; Song, Hongquan; Ouyang, Ying; Liu, Gangjun

doi:10.3390/f10111007

Cited by 40 publications

(25 citation statements)

References 80 publications

(135 reference statements)

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“…It has been used to analyze and interpolate multivariate data using a smooth spline function. It can perform reasonable statistical analysis and data diagnosis on data, and can analyze the spatial distribution of data to realize spatial interpolation (Hutchinson, 1998;Xia et al, 2019).…”

Section: Climatic Datamentioning

confidence: 99%

“…Qu et al (2018) showed that temperature is a controlling factor determining the vegetation greenness in the Yangtze River Basin, and the response of vegetation to precipitation is relatively lower because of the abundant water. Xia et al (2019) suggested that the variation in vegetation responses to diurnal temperature changes is important for understanding the changes in vegetation photosynthetic activity in a warming world. Meanwhile, land use changes caused by ecological restoration project is the major driving factor for improving vegetation conditions in YRB, and the spatial distributions between human-induced GSN increasing trends and areas with increased forest have a strong consistency in the north of YRB.…”

Section: The Effects Of Climate Change On Ndvimentioning

confidence: 99%

“…The cultivated plants, shrubs, and deserts are significantly and positively related to both the increasing maximum temperature and minimum temperature, meanwhile different vegetation types have different responses to asymmetric warming (Ma et al, 2019a;Ma, Xia & Meng, 2019b). The influence of elevation gradients on the start of the growing season, end of the growing season, and the length of growing season on a spatiotemporal scale has been evaluated (Xia et al, 2019). Therefore, in addition to temperature and precipitation, some other climatic factors such as soil moisture, net radiation, and surface specific humidity will be discussed to form a more complete ecohydrothermal system in this study.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Zhuang

Feng

et al. 2020

PeerJ

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Background Vegetation dynamics is defined as a significant indictor in regulating terrestrial carbon balance and climate change, and this issue is important for the evaluation of climate change. Though much work has been done concerning the correlations among vegetation dynamics, precipitation and temperature, the related questions about relationships between vegetation dynamics and other climatic factors (e.g., specific humidity, net radiation, soil moisture) have not been thoroughly considered. Understanding these questions is of primary importance in developing policies to address climate change. Methods In this study, the least squares regression analysis method was used to simulate the trend of vegetation dynamics based on the normalized difference vegetation index (NDVI) from 1981 to 2018. A partial correlation analysis method was used to explore the relationship between vegetation dynamics and climate change; and further,the revised greyscale model was applied to predict the future growth trend of natural vegetation. Results The Mann-Kendall test results showed that th e air temperature rose sharply in 1997 and had been in a state of high fluctuations since then. Strong changes in hydrothermal conditions had major impact on vegetation dynamics in the area. Specifically, the NDVI value of natural vegetation showed an increasing trend from 1981 to 2018, and the same changes occurred in the precipitation. From 1981 to 1997, the values of natural vegetation increased at a rate of 0.0016 per year. From 1999 to 2009, the NDVI value decreased by an average rate of 0.0025 per year. From 2010 to 2018, the values began an increasing trend and reached a peak in 2017, with an average annual rate of 0.0033. The high vegetation dynamics areas were mainly concentrated in the north and south slopes of the Tianshan Mountains, the Ili River Valley and the Altay area. The greyscale prediction results showed that the annual average NDVI values of natural vegetation may present a fluctuating increasing trend. The NDVI value in 2030 is 0.0196 higher than that in 2018, with an increase of 6.18%. Conclusions Our results indicate that: (i) the variations of climatic factors have caused a huge change in the hydrothermal conditions in Xinjiang; (ii) the vegetation dynamics in Xinjiang showed obvious volatility, and then in the end stage of the study were higher than the initial stage the vegetation dynamics in Xinjiang showed a staged increasing trend; (iii) the vegetation dynamics were affected by many factors,of which precipitation was the main reason; (iv) in the next decade, the vegetation dynamics in Xinjiang will show an increasing trend.

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Section: Climatic Datamentioning

confidence: 99%

Section: The Effects Of Climate Change On Ndvimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Zhuang

Feng

et al. 2020

PeerJ

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“…Damage to the ecological environment is a major global issue that needs to be studied and resolved [ 1 , 2 ]. However, with an increase in the scope and intensity of human social and economic activities, human disturbance has produced a series of significant negative problems on different regional scales related to land cover, biodiversity, hydrological systems and local climate [ 1 , 3 , 4 ], such as ecosystem degradation, environmental pollution and global warming [ 5 , 6 , 7 ]. At the same time, the ecological environment limits human social and economic development to a certain extent, resulting in a gradual realization that the maintenance of ecosystem health is very important.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characteristics and Driving Force of Ecosystem Health in an Important Ecological Function Region in China

Shen

Zheng

Qin

et al. 2020

IJERPH

Self Cite

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Quantitative assessment can scientifically determine the health status of a regional ecosystem, identify regional eco-environmental problems, and assist in promoting regional sustainable development and environmental management. Taking China’s important ecological function region, the Yellow River affected area as an example, this study constructed an extended evaluation index system based on the pressure-state-response framework, and remote sensing and GIS techniques were used to dynamically evaluate the spatial and temporal characteristics of ecosystem health in the study area. Furthermore, influencing factors on ecosystem health in the study area were extensively analyzed using the GeoDetector model. The results show that the ecosystem health level in the study area shows significant spatial heterogeneity from 1995–2015, and showed a fluctuating change process. Areas with large fluctuations in health level were mainly distributed in extreme climate areas, ecologically fragile areas, on plains and in hilly areas. Spatial differences of ecosystem health were well explained by using the biological abundance index, relief degree of land surface, soil type, annual average precipitation, elevation, annual average temperature, and population density. Influencing factors have significant interactive effects on ecosystem health.

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“…ARIATIONS in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity [1]- [2]. Hence, the ability to detect and monitor soil moisture affects almost every aspect of society (e.g., social, economic, humanitarian and environmental segments of society).…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China

Liu

Xia

Chen

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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There have been some limitations in acquiring an accurate representation of remotely sensed data-derived soil moisture. Here, we propose a simplified thermal inertia model (STIM). This model requires only the albedo and surface maximum temperature easily obtained from satellite imagery such as that of the Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, we defined a soil moisture index (SMI) using MODIS imagery to obtain a simplified thermal inertia (STI)-ratio vegetation index (RVI) spectral feature space. SMI results from STIM were validated at several locations in the study area of western Inner Mongolia and compared with those from the apparent thermal inertia (ATI) model. Our results showed that our SMI model could explain 71% of the variance in surface soil moisture, approximately 5% higher than that of the ATI model. In a comparison of field-measured soil moisture data with data simulated using two methods, the SMI and ATI, the SMI showed better retrieval accuracy by lessening the effective error due to vegetation by 4.2-10.8%, whereas soil moisture data simulated with ATI showed an effective error of 4.5-17.0%. The SMI model was also used to map soil moisture; relative root mean square error (RRMSE) of 7.67% was recorded for the region, implying the ability of the model to map soil moisture over large areas. Here, the proposed SMI model was proven to be more suitable for estimating soil moisture in locations in which the vegetation index values ranged from 0 to 3. Thus, the proposed SMI model provides a new approach using remote sensing thermal inertia methods to quantify soil moisture at the regional-scale. Index Terms-Albedo, apparent thermal inertia (ATI), MODIS, simplified thermal inertial model (STIM), soil moisture, STI-ratio vegetation index (RVI).

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

Cited by 40 publications

References 80 publications

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Spatiotemporal Characteristics and Driving Force of Ecosystem Health in an Important Ecological Function Region in China

Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China

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