Normalized Difference Vegetation Index‐based assessment of climate change impact on vegetation growth in the humid‐arid transition zone in northern China during 1982–2013

2019 ◽  
Vol 39 (15) ◽  
pp. 5583-5598
Author(s):  
Wei Yuan ◽  
Shuang‐Ye Wu ◽  
Shugui Hou ◽  
Zhiwei Xu ◽  
Huayu Lu
Keyword(s):  
Climate Change ◽  
Transition Zone ◽  
Climate Change Impact ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Northern China ◽  
Vegetation Growth ◽  
Change Impact

scholarly journals Vegetation Dynamic Changes and Their Response to Ecological Engineering in the Sanjiangyuan Region of China

2020 ◽  
Vol 12 (24) ◽  
pp. 4035
Author(s):  
Xiaohui Zhai ◽  
Xiaolei Liang ◽  
Changzhen Yan ◽  
Xuegang Xing ◽  
Haowei Jia ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Positive Impact ◽  
Ecological Engineering ◽  
Scientific Basis ◽  
Vegetation Dynamic ◽  
Vegetation Growth ◽  
The Impact

In recent decades, the vegetation of the Sanjiangyuan region has undergone a series of changes under the influence of climate change, and ecological restoration projects have been implemented. In this paper, we analyze the spatiotemporal dynamics of vegetation in this region using the satellite-retrieved normalized difference vegetation index (NDVI) from the global inventory modeling and mapping studies (GIMMS) and moderate resolution imaging and spectroradiometer (MODIS) datasets during the past 34 years. Specifically, the characteristics of vegetation changes were analyzed according to the stage of implementation of different ecological engineering programs. The results are as follows. (1) The vegetation in 65.6% of the study area exhibited an upward trend, and in 53.0% of the area, it displayed a large increase, which was mainly distributed in the eastern part of the study area. (2) The vegetation NDVI increased to differing degrees during stages of ecological engineering. (3) The NDVI in the western part of the Sanjiangyuan region is mainly affected by temperature, while in the northeastern part, the NDVI is affected more by precipitation. In the southern part, however, vegetation growth is affected neither by temperature nor by precipitation. On the whole region, vegetation growing is more affected by temperature than by precipitation. (4) The impacts of human activities on vegetation change are both positive and negative. In recent years, ecological engineering projects have had a positive impact on vegetation growth. This study can help us to correctly understand the impact of climate change on vegetation growth, so as to provide a scientific basis for the evaluation of regional ecological engineering effectiveness and the formulation of ecological protection policies.

scholarly journals Relating historical vegetation cover to aridity index patterns in the greater desert region of northern China: Implications to planned and existing restoration projects

2016 ◽  
Author(s):  
Yanying Shao ◽  
Yuqing Zhang ◽  
Xiuqin Wu ◽  
Charles P.-A. Bourque ◽  
Jutao Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Ecological Integrity ◽  
Aridity Index ◽  
Northern China ◽  
Management Planning ◽  
Desert Region

Abstract. Desert regions of northern China have always been the most severely affected by climate change, especially in terms of their ecological integrity and social sustainable development. Assessments of dryness in both space and time are central to the development of adaptation strategies to climate change. Earlier studies have identified long-term patterns of dryness in northern China, but these studies have usually been of limited value to land-management planning as they ignore local-to-regional-scale climate features. To identify potential cause-and-effect relationship between aridity and vegetation cover, changes in aridity index (AI) and vegetation cover were tracked with the assistance of a chronological series of surfaces based on the mapping of AI and normalized difference vegetation index (NDVI) and convergent cross mapping. By tracking regional-scale variation in precipitation, air temperature, AI from 1961–2013 (53 years), and vegetation cover dynamics from 1982–2013 (32 years), we show that precipitation increased in approximately 70 % of the greater desert region, including in the Ulanbuh, Tengger, Badain Jaran, Qaidam, Kumtag, Gurbantunggut, and Taklimakan Deserts. This increase was statistically strongest for the Gurbantunggut (p 

scholarly journals Spatial and Temporal Changes in the Normalized Difference Vegetation Index and Their Driving Factors in the Desert/Grassland Biome Transition Zone of the Sahel Region of Africa

2020 ◽  
Vol 12 (24) ◽  
pp. 4119
Author(s):  
Shupu Wu ◽  
Xin Gao ◽  
Jiaqiang Lei ◽  
Na Zhou ◽  
Yongdong Wang
Keyword(s):  
Climate Change ◽  
Transition Zone ◽  
Human Activities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climate Factors ◽  
Desert Grassland ◽  
Temperature And Precipitation ◽  
Grassland Biome ◽  
Sahel Region

The ecological system of the desert/grassland biome transition zone is fragile and extremely sensitive to climate change and human activities. Analyzing the relationships between vegetation, climate factors (precipitation and temperature), and human activities in this zone can inform us about vegetation succession rules and driving mechanisms. Here, we used Landsat series images to study changes in the normalized difference vegetation index (NDVI) over this zone in the Sahel region of Africa. We selected 6315 sampling points for machine-learning training, across four types: desert, desert/grassland biome transition zone, grassland, and water bodies. We then extracted the range of the desert/grassland biome transition zone using the random forest method. We used Global Inventory Monitoring and Modelling Studies (GIMMS) data and the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5) meteorological assimilation data to explore the spatiotemporal characteristics of NDVI and climatic factors (temperature and precipitation). We used the multiple regression residual method to analyze the contributions of human activities and climate change to NDVI. The cellular automation (CA)-Markov model was used to predict the spatial position of the desert/grassland biome transition zone. From 1982 to 2015, the NDVI and temperature increased; no distinct trend was found for precipitation. The climate change and NDVI change trends both showed spatial stratified heterogeneity. Temperature and precipitation had a significant impact on NDVI in the desert/grassland biome transition zone; precipitation and NDVI were positively correlated, and temperature and NDVI were negatively correlated. Both human activities and climate factors influenced vegetation changes. The contribution rates of human activities and climate factors to the increase in vegetation were 97.7% and 48.1%, respectively. Human activities and climate factors together contributed 47.5% to this increase. The CA-Markov model predicted that the area of the desert/grassland biome transition zone in the Sahel region will expand northward and southward in the next 30 years.

scholarly journals Phenology Response to Climatic Dynamic across China’s Grasslands from 1985 to 2010

2018 ◽  
Vol 7 (8) ◽  
pp. 290 ◽  
Author(s):  
Jun Wang ◽  
Tiancai Zhou ◽  
Peihao Peng
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Northern China ◽  
Plant Phenology ◽  
Meadow Steppe ◽  
Typical Steppe ◽  
Alpine Steppe

Because the dynamics of phenology in response to climate change may be diverse in different grasslands, quantifying how climate change influences plant growth in different grasslands across northern China should be particularly informative. In this study, we explored the spatiotemporal variation of the phenology (start of the growing season [SOS], peak of the growing season [POS], end of the growing season [EOS], and length of the growing season [LOS]) across China’s grasslands using a dataset of the GIMMS3g normalized difference vegetation index (NDVI, 1985–2010), and determined the effects of the annual mean temperature (AMT) and annual mean precipitation (AMP) on the significantly changed phenology. We found that the SOS, POS, and EOS advanced at the rates of 0.54 days/year, 0.64 days/year, and 0.65 days/year, respectively; the LOS was shortened at a rate of 0.62 days/year across China’s grasslands. Additionally, the AMT combined with the AMP explained the different rates (ER) for the significantly dynamic SOS in the meadow steppe (R2 = 0.26, p = 0.007, ER = 12.65%) and typical steppe (R2 = 0.28, p = 0.005, ER = 32.52%); the EOS in the alpine steppe (R2 = 0.16, p < 0.05, ER = 6.22%); and the LOS in the alpine (R2 = 0.20, p < 0.05, ER = 6.06%), meadow (R2 = 0.18, p < 0.05, ER = 16.69%) and typical (R2 = 0.18, p < 0.05, ER = 19.58%) steppes. Our findings demonstrated that the plant phenology in different grasslands presented discrepant dynamic patterns, highlighting the fact that climate change has played an important role in the variation of the plant phenology across China’s grasslands, and suggested that the variation and relationships between the climatic factors and phenology in different grasslands should be explored further in the future.

scholarly journals Why does a conceptual hydrological model fail to predict discharge changes in response to climate change?

2020 ◽  
Author(s):  
Doris Duethmann ◽  
Günter Blöschl ◽  
Juraj Parajka
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Wide Spectrum ◽  
Precipitation Data ◽  
Discharge Data ◽  
Climate Conditions ◽  
Impact Studies ◽  
Change Impact

Abstract. Several studies have shown that hydrological models do not perform well when applied to periods with climate conditions that differ from those during model calibration. This has important implications for the application of these models in climate change impact studies. The causes of the low transferability to changed climate conditions have, however, only been investigated in a few studies. Here we revisit a study in Austria that demonstrated the inability of a conceptual model to simulate the discharge response to increases in precipitation and air temperature. The aim of the paper is to shed light on the reasons of these model problems. We set up hypotheses for the possible causes of the mismatch between the observed and simulated changes in discharge and evaluate these using simulations with modifications of the model. In the baseline model, trends of simulated and observed discharge over 1978–2013 differ, on average over all 156 catchments, by 92 ± 50 mm yr−1 per 35 yrs. Accounting for variations in vegetation dynamics, as derived from a satellite-based vegetation index, in the calculation of reference evaporation explains 35 ± 9 mm yr−1 per 35 yrs of the differences between the trends in simulated and observed discharge. Inhomogeneities in the precipitation data, caused by a variable number of stations explain 44 ± 28 mm yr−1 per 35 yrs of this difference. Extending the calibration period from 5 to 25 yrs, varying the objective function by including annually aggregated discharge data, or estimating evaporation with the Penman–Monteith instead of the Blaney–Criddle approach has little influence on the simulated discharge trends. The precipitation data problem highlights the importance of using precipitation data based on a stationary input station network when studying hydrologic changes. The model structure problem with respect to vegetation dynamics is likely relevant for a wide spectrum of regions in a transient climate and has important implications for climate change impact studies.

scholarly journals Response of Natural Vegetation to Climate in Dryland Ecosystems: A Comparative Study between Xinjiang and Arizona

2020 ◽  
Vol 12 (21) ◽  
pp. 3567
Author(s):  
Fang Zhang ◽  
Chenghao Wang ◽  
Zhi-Hua Wang
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Natural Vegetation ◽  
Vegetation Growth ◽  
Climate Change And Variability ◽  
Changing Climate ◽  
Dryland Ecosystems ◽  
Background Climate

As one of the most sensitive areas to climate change, drylands cover ~40% of the Earth’s terrestrial land surface and host more than 38% of the global population. Meanwhile, their response to climate change and variability carries large uncertainties as induced by background climate, topography, and land cover composition; but there is a lack of intercomparison of different dryland ecosystems. In this study, we compare the changing climate and corresponding responses of major natural vegetation cover types in Xinjiang and Arizona, two typical drylands with similar landscapes in Asia and North America. Long-term (2002–2019) quasi-8-day datasets of daily precipitation, daily mean temperature, and Normalized Difference Vegetation Index (NDVI) were constructed based on station observations and remote sensing products. We found that much of Xinjiang experienced warming and wetting trends (although not co-located) over the past 18 years. In contrast, Arizona was dominated by warming with insignificant wetting or drying trends. Significant greening trends were observed in most parts of both study areas, while the increasing rate of NDVI anomalies was relatively higher in Xinjiang, jointly contributed by its colder and drier conditions. Significant degradation of vegetation growth (especially for shrubland) was observed over 18.8% of Arizona due to warming. Our results suggest that responses of similar natural vegetation types under changing climate can be diversified, as controlled by temperature and moisture in areas with different aridity.

scholarly journals Why does a conceptual hydrological model fail to correctly predict discharge changes in response to climate change?

2020 ◽  
Vol 24 (7) ◽  
pp. 3493-3511 ◽  
Author(s):  
Doris Duethmann ◽  
Günter Blöschl ◽  
Juraj Parajka
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Type Model ◽  
Precipitation Data ◽  
Discharge Data ◽  
Climate Conditions ◽  
Impact Studies ◽  
Change Impact

Abstract. Several studies have shown that hydrological models do not perform well when applied to periods with climate conditions that differ from those during model calibration. This has important implications for the application of these models in climate change impact studies. The causes of the low transferability to changed climate conditions have, however, only been investigated in a few studies. Here we revisit a study in Austria that demonstrated the inability of a conceptual semi-distributed HBV-type model to simulate the observed discharge response to increases in precipitation and air temperature. The aim of the paper is to shed light on the reasons for these model problems. We set up hypotheses for the possible causes of the mismatch between the observed and simulated changes in discharge and evaluate these using simulations with modifications of the model. In the baseline model, trends of simulated and observed discharge over 1978–2013 differ, on average over all 156 catchments, by 95±50 mm yr−1 per 35 years. Accounting for variations in vegetation dynamics, as derived from a satellite-based vegetation index, in the calculation of reference evaporation explains 36±9 mm yr−1 per 35 years of the differences between the trends in simulated and observed discharge. Inhomogeneities in the precipitation data, caused by a variable number of stations, explain 39±26 mm yr−1 per 35 years of this difference. Extending the calibration period from 5 to 25 years, including annually aggregated discharge data or snow cover data in the objective function, or estimating evaporation with the Penman–Monteith instead of the Blaney–Criddle approach has little influence on the simulated discharge trends (5 mm yr−1 per 35 years or less). The precipitation data problem highlights the importance of using precipitation data based on a stationary input station network when studying hydrologic changes. The model structure problem with respect to vegetation dynamics is likely relevant for a wide spectrum of regions in a transient climate and has important implications for climate change impact studies.

Climate-forced changes of bio-productivity of Belorussian terrestrial ecosystems

2019 ◽  
pp. 77-88
Author(s):  
S. A. Lysenko
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Terrestrial Ecosystems ◽  
Vegetation Period ◽  
Climatic Trend ◽  
Vegetation Growth ◽  
Current Century ◽  
The Impact

The spatial and temporal particularities of Normalized Differential Vegetation Index (NDVI) changes over territory of Belarus in the current century and their relationship with climate change were investigated. The rise of NDVI is observed at approximately 84% of the Belarus area. The statistically significant growth of NDVI has exhibited at nearly 35% of the studied area (t-test at 95% confidence interval), which are mainly forests and undeveloped areas. Croplands vegetation index is largely descending. The main factor of croplands bio-productivity interannual variability is precipitation amount in vegetation period. This factor determines more than 60% of the croplands NDVI dispersion. The long-term changes of NDVI could be explained by combination of two factors: photosynthesis intensifying action of carbon dioxide and vegetation growth suppressing action of air warming with almost unchanged precipitation amount. If the observed climatic trend continues the croplands bio-productivity in many Belarus regions could be decreased at more than 20% in comparison with 2000 year. The impact of climate change on the bio-productivity of undeveloped lands is only slightly noticed on the background of its growth in conditions of rising level of carbon dioxide in the atmosphere.

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