Quantifying influences of physiographic factors on temperate dryland vegetation, Northwest China

Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models

Seasonality and volatility of vegetation in the ecosystem are associated with climatic sensitivity, which can have severe consequences for the environment as well as on the social and economic well-being of the nation. Monitoring and forecasting vegetation growth patterns in ecosystems significantly rely on remotely sensed vegetation indices, such as Normalized Difference Vegetation Index (NDVI). A novel integration of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and the Holt-Winters (H-W) models was used to simulate the seasonality and volatility of the three different agro-climatic zones in Jharkhand, India: the central north-eastern, eastern, and south-eastern agro-climatic zones. MODIS Terra Vegetation Indices NDVI data MOD13Q1, from 2001 to 2021, was used to create NDVI time series volatility and seasonality modeled by the GARCH and the H-W models, respectively. GARCH-based Exponential GARCH (EGARCH) [1,1] and Standard GARCH (SGARCH) [1,1] models were used to check the volatility of vegetation growth in three different agro-climatic zones of Jharkhand. The SGARCH [1,1] and EGARCH [1,1] models for the western agro-climatic zone experienced the best indicator as it has maximum likelihood and minimal Schwarz-Bayesian criterion and Akaike information criterion. The seasonality results showed that the additive H-W model showed better results in the eastern agro-climatic zone with the optimized values of MAE (16.49), MAPE (0.49), NSE (0.86), RMSE (0.49), and R2 (0.82) followed by the south-eastern and central north-eastern agro-climatic zones. By utilizing the H-W and GARCH models, the finding demonstrates that vegetation orientation and monitoring seasonality can be predicted using NDVI.

Distinguishing the Impacts of Human Activities and Climate Change on the Livelihood Environment of Pastoralists in the Qinghai Lake Basin

Grassland vegetation is the largest terrestrial ecosystem in the Qinghai Lake Basin (QLB), and it is also the most important means of production for herders’ livelihoods. Quantifying the impact of climate change and human activities on grassland vegetation changes is an essential task for ensuring the sustainable livelihood of pastoralists. To this end, we investigated vegetation cover changes in the QLB from 2000 to 2020 using the normalized difference vegetation index (NDVI), meteorological raster data, and digital elevation and used residual analysis of multiple linear regression to evaluate the residuals of human activities. The residual analysis of partial derivatives was used to quantify the contribution of climate change and human activities to changes in vegetation cover. The results showed that: (1) The vegetation coverage of the QLB increased significantly (0.002/a, p < 0.01), with 91.38% of the area showing a greening trend, and 8.62% of the area suffering a degrading trend. The NDVI decreased substantially along the altitude gradient (−0.02/a, p < 0.01), with the highest vegetation coverage at 3600–3700 m (0.37/a). The vegetation degraded from 3200–3300 m, vegetation greening accelerated from 3300–3500 m, and vegetation greening slowed above 3500 m. (2) The contribution of climate change, temperature (T), and precipitation (P) to vegetation cover change were 1.62/a, 0.005/a, and 1.615/a, respectively. Below 3500 m, the vegetation greening was more limited by P. Above 3500 m, the vegetation greening was mainly limited by T. (3) Residual analysis showed that the contribution of human activities to vegetation cover was −1.618/a. Regarding the altitude gradient, at 3300–3500 m, human activities had the highest negative contribution to vegetation coverage (−2.389/a), and at 3200–3300 m, they had the highest positive contribution (0.389/a). In the past 21 years, the impact of human activities on vegetation coverage changed from negative to positive. Before 2009, the annual average NDVIres value was negative; after 2010, the average yearly NDVIres value turned positive. In general, the vegetation greening of the QLB depends on climate warming and humidification. The positive impact of human activities over the past decade was also essential for vegetation greening. These findings deepen our understanding of the QLB vegetation changes under climate change and human activities.

Spatio-temporal evolution and driving factors of carbon storage in the Western Sichuan Plateau

The carbon sequestration function of the ecosystem is one of the most important functions of ecosystem service, and it of great significance to study the spatio-temporal differentiation of carbon storage for promoting regional sustainable development. Ecosystems on the Western Sichuan Plateau are highly variable, but its spatio-temporal differentiation and driving factors are not yet clear. In this study, on the basis of land use monitoring data, meteorological and demographic data interpreted from Landsat remote sensing image, and through GIS analysis tools, the carbon storage module of InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model was used to estimate carbon storage and geodetector was used to detect the driving factors of carbon storage spatial differentiation. The results show that: (1) The carbon storage increased to 1.2455 × 10¹⁰ t from 1.2438 × 10¹⁰ t in the past 20 years, the ecosystem developed in a healthy way overall. (2) Carbon storage show High-High and Low-Low aggregation characteristics, but the area decreased by 1481.81 km² and 311.11 km² respectively, and the spatial cluster effect gradually weakened. (3) HAI is the leading factor causing the spatio-temporal differentiation of regional carbon storage, followed by temperature and NDVI; the interaction between factors significantly enhances the spatial differentiation of carbon storage, indicating that the change of carbon storage is the result of the joint action of natural and socioeconomic factors. The results of the study provide some theoretical basis for the development of differentiated ecological regulation models and strategies, and help to promote high-quality regional development.

Changes in Landscape Pattern and an Ecological Risk Assessment of the Changshagongma Wetland Nature Reserve

The Changshagongma wetlands is the Chinese National Nature Reserve were listed as a Ramsar Wetland of International Importance in 2018. Here, we examined four periods (1992, 2002, 2013, and 2020) of remote sensing image data to analyze the changes in wetland landscape patterns and the ecological risk in Changshagongma Wetland Nature Reserve over the past 30 years. The results showed that wetlands account for approximately 30% of the study area, and swamp meadows were the main type of wetland, accounting for approximately 95% of the total wetland area. In terms of landscape patterns, wetland fragmentation declined, wetland patch shapes became less complicated, and spatial connectivity increased. The landscape fragmentation of non-wetland alpine meadows was reduced. The patches of sandy grasslands tended to be regular, and their spatial connectivity was reduced. The wetland regions of high ecological risk are concentrated in the central and southern parts of the Changshagongma Wetland Nature Reserve. Low-risk regions are mainly concentrated in the contiguous swamp meadows in the northwest and wetlands in the southwest. From 1992 to 2020, the level of ecological risk of the Changshagongma Wetland Nature Reserve showed a “∧”-shaped trend, with the highest risk in 2002 and the lowest risk in 2020. Among the selected indicators, climate conditions constituted the main factor affecting the ecological risk of the Changshagongma Wetland Nature Reserve, followed by topographical conditions, and human activities were the least influential. Over the past 30 years, the temperature and precipitation in the study area increased significantly. The climate in the study area can be roughly divided into two periods bounding 2002, and the climate has been changing from cold and dry to warm and wet. The ecological environment of the study area is affected by natural and human activities. Cold and dry climatic conditions and uncontrolled grazing accelerate the destruction of the wetland ecological environment, and warm and wet climatic conditions and ecological conservation policies are conducive to the ecological restoration of wetlands. In general, the wetland landscape structure in the study area has become less complex, landscape heterogeneity has decreased, and ecological quality has improved.

Spatial–Temporal Evolution of Vegetation NDVI in Association with Climatic, Environmental and Anthropogenic Factors in the Loess Plateau, China during 2000–2015: Quantitative Analysis Based on Geographical Detector Model

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP.

Spatio-Temporal Characteristics and Driving Factors of the Foliage Clumping Index in the Sanjiang Plain from 2001 to 2015

The Sanjiang Plain is the largest agricultural reclamation area and the biggest marsh area in China. The regional vegetation coverage in this area is vital to local ecological systems, and vegetation growth is affected by natural and anthropogenic factors. The clumping index (CI) is of great significance for land surface models and obtaining information on other vegetation structures. However, most existing ecological models and the retrieval of other vegetation structures do not consider the spatial and temporal variations of CI, and few studies have focused on detecting factors that influence the spatial differentiation of CI. To address these issues, this study investigated the spatial and temporal characteristics of foliage CI in the Sanjiang Plain, analysing the correlation between CI and leaf area index (LAI) through multiple methods (such as Theil−Sen trend analysis, the Mann−Kendall test, and the correlation coefficient) based on the 2001−2015 Chinese Academy of Sciences Clumping Index (CAS CI) and Global LAnd Surface Satellite Leaf Area Index (GLASS LAI). The driving factors of the spatial differentiation of CI were also investigated based on the geographical detector model (GDM) with natural data (including the average annual temperature, annual precipitation, elevation, slope, aspect, vegetation type, soil type, and geomorphic type) and anthropogenic data (the land use type). The results showed that (1) the interannual variation of foliage CI was not obvious, but the seasonal variation was obvious in the Sanjiang Plain from 2001 to 2015; (2) the spatial distribution of the multiyear mean CI of each season in the Sanjiang Plain was similar to the spatial distribution of the land use type, and the CI decreased slightly with increases in elevation; (3) the correlation between the growing season mean CI (CIGS) and the growing season mean LAI (LAIGS) time series was not significant, but their spatial distributions were negatively correlated; (4) topographic factors (elevation and slope) and geomorphic type dominated the spatial differentiation of foliage CI in the Sanjiang Plain, and the interactions between driving factors enhanced their explanatory power in terms of the spatial distribution of foliage CI. This study can help improve the accuracy of the retrieval of other vegetation structures and the simulation of land surface models in the Sanjiang Plain, providing invaluable insight for the analysis of the spatial and temporal variations of vegetation based on CI. Moreover, the results of this study support a theoretical basis for understanding the explanatory power of natural and anthropogenic factors in the spatial distribution of CI, along with its driving mechanism.

Response of net primary productivity to grassland phenological changes in Xinjiang, China

Determining the relationship between net primary productivity (NPP) and grassland phenology is important for an in-depth understanding of the impact of climate change on ecosystems. In this study, the NPP of grassland in Xinjiang, China, was simulated using the Carnegie-Ames-Stanford approach (CASA) model with Moderate Resolution Imaging Spectroradiometer (MODIS) grassland phenological (MCD12Q2) data to study trends in phenological metrics, grassland NPP, and the relations between these factors from 2001-2014. The results revealed advancement of the start of the growing season (SOS) for grassland in most regions (55.2%) in Xinjiang. The percentage of grassland area in which the end of the growing season (EOS) was delayed (50.9%) was generally the same as that in which the EOS was advanced (49.1%). The percentage of grassland area with an increase in the length of the growing season (LOS) for the grassland area (54.6%) was greater than that with a decrease in the LOS (45.4%). The percentage of grassland area with an increase in NPP (61.6%) was greater than that with a decrease in NPP (38.4%). Warmer regions featured an earlier SOS and a later EOS and thus a longer LOS. Regions with higher precipitation exhibited a later SOS and an earlier EOS and thus a shorter LOS. In most regions, the SOS was earlier, and spring NPP was higher. A linear statistical analysis showed that at various humidity (K) levels, grassland NPP in all regions initially increased but then decreased with increasing LOS. At higher levels of K, when NPP gradually increased, the LOS gradually decreased.

Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI

Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) can detect an abrupt change that was undetected by Residual Trend analysis (RESTREND), but it is usually combined with the Global Inventory for Mapping and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI), which cannot detect detailed vegetation changes in small areas. Hence, we used Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD-TR) to analyze the vegetation dynamic of the Pearl River Delta region (PRD) in this study. To choose the most suitable MODIS NDVI from MOD13Q1 (250 m), MOD13A1 (500 m), and MOD13A2 (1 km), whole and local comparison of results of the break year and MOD-TR were used. Meanwhile, a comparison of vegetation change at the city-scale was also implemented. Moreover, to reduce insignificant trend pixels in TSS-RESTREND, a combination method of TSS-RESTREND and RESTREND (CTSS-RESTREND) was proposed. We found that: (1) MOD13Q1 and MOD13A1 two NDVI were suitable for combination with TSS-RESTREND to detect vegetation change in PRD, but MOD13Q1 was a better choice when considering the accuracy of local detailed vegetation change; (2) CTSS-RESTREND could detect more pixels with a significant change (i.e., significant increase and significant decrease) than those of TSS-RESTREND and RESTREND. Also, its effectiveness could be verified by Landsat data; (3) at the city-scale, the CTSS-RESTREND detected that only vegetation decreases in Shenzhen, Foshan, Dongguan, and Zhongshan were higher than vegetation increases, but, significant vegetation changes (i.e., decreases and increases) were mainly concentrated in Huizhou, Jiangmen, Zhaoqing, and Guangzhou.

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

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.

Spatio-Temporal Relationship between Land Cover and Land Surface Temperature in Urban Areas: A Case Study in Geneva and Paris

Currently, more than half of the world’s population lives in cities, which leads to major changes in land use and land surface temperature (LST). The associated urban heat island (UHI) effects have multiple impacts on energy consumption and human health. A better understanding of how different land covers affect LST is necessary for mitigating adverse impacts, and supporting urban planning and public health management. This study explores a distance-based, a grid-based and a point-based analysis to investigate the influence of impervious surfaces, green area and waterbodies on LST, from large (distance and grid based analysis with 400 m grids) to smaller (point based analysis with 30 m grids) scale in the two mid-latitude cities of Paris and Geneva. The results at large scale confirm that the highest LST was observed in the city centers. A significantly positive correlation was observed between LST and impervious surface density. An anticorrelation between LST and green area density was observed in Paris. The spatial lag model was used to explore the spatial correlation among LST, NDBI, NDVI and MNDWI on a smaller scale. Inverse correlations between LST and NDVI and MNDWI, respectively, were observed. We conclude that waterbodies display the greatest mitigation on LST and UHI effects both on the large and smaller scale. Green areas play an important role in cooling effects on the smaller scale. An increase of evenly distributed green area and waterbodies in urban areas is suggested to lower LST and mitigate UHI effects.

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen’s slope, Mann–Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.

Comparative analysis of drivers of energy consumption in China, the USA and India - A perspective from stratified heterogeneity

With the limited amount of resources, developing effective strategies to make full use of them and decrease the energy consumption without too much sacrifice of economic output requires identifying key drivers of energy consumption growth rate as a prerequisite. Meanwhile, as top three consumers of primary energy of the world, China, the United States of America, and India burn over 45% of global fuels in 2016. Conducting an empirically comparative analysis of them can also set up pilot scheme for other economies to develop more efficient strategies for energy consumption. The paper modified the original Geographical Detector model with a different sampling method to detect the key driver of energy consumption growth rate, which filling the gap that there are possible interactions of potential factors. The results show that coal intensity is the biggest driver to change overall energy consumption growth rate in China and India. In comparison, for the United States, the leading drivers of energy use are the factors of individual incomes and oil intensity. In addition, all factors have interactions and enhance each other when influencing total energy consumption growth rate. India has the strongest factor interactions when influencing the energy consumption growth rate among the three economies, all interactions between factors in US is not significant as those in China and India. Besides providing outcomes that can contribute towards developing new strategies to use energy more efficiently, this research offers a pilot example of analyzing energy issues from the perspective of stratified heterogeneity in consideration the characteristic differences of each factor.

Responses of vegetation activity to the daytime and nighttime warming in Northwest China

Though temperature over the past three decades has shown an asynchronous warming trend between daytime and nighttime, the response of vegetation activity to such non-uniform warming is still not very clear. In this study, the least squares linear trend analysis and geographic information system spatial analysis were conducted to analyze the spatiotemporal patterns of the daytime and nighttime warming based on the daily temperature data from 1982 to 2015 in Northwest China. The normalized difference vegetation index (NDVI) from Global Inventory Monitoring and Modeling System and vegetation type data were used to investigate the responses of vegetation activity to the daytime and nighttime warming using the partial correlation analysis. Our results suggested that (1) there was a very significant increasing trend in both daytime and nighttime temperatures in Northwest China from 1982 to 2015; night temperatures increased about 1.2 times faster than daytime temperatures, showing diurnal asymmetric warming; (2) the responses of vegetation activity to daytime and nighttime warming in Northwest China showed a distinct spatial pattern; the change in night temperatures had a more significant (positive in most regions) effect on vegetation; (3) various types of vegetation responded differently to asymmetric daytime and nighttime warming. Grassland NDVI, broad-leaved, and coniferous forest NDVI significantly responded to daytime warming. Shrub NDVI and desert NDVI significantly responded to night warming. These findings can deepen the understanding of the effects of the daytime and nighttime warming on vegetation activities in arid regions in the context of the current asymmetric warming.