Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China

Enhanced carbon use efficiency and warming resistance of soil microorganisms under organic amendment

The frequency and intensity of extreme weather events, including rapid temperature fluctuations, are increasing because of climate change. Long-term fertilization practices have been observed to alter microbial physiology and community structure, thereby affecting soil carbon sequestration. However, the effects of warming on the carbon sequestration potential of soil microbes adapted to long-term fertilization remain poorly understood. In this study, we utilized 18O isotope labeling to assess microbial carbon use efficiency (CUE) and employed stable isotope probing (SIP) with 18O-H2O to identify growing taxa in response to temperature changes (5-35 °C). Organic amendment with manure or straw residue significantly increased microbial CUE by 86-181 % compared to unfertilized soils. The microorganisms inhabiting organic amended soils displayed greater resistance of microbial CUE to high temperatures (25-35 °C) compared to those inhabiting soils fertilized only with minerals. Microbial growth patterns determined by the classification of taxa into incorporators or non-incorporators based on 18O incorporation into DNA exhibited limited phylogenetic conservation in response to temperature changes. Microbial clusters were identified by grouping taxa with similar growth patterns across different temperatures. Organic amendments enriched microbial clusters associated with increased CUE, whereas clusters in unfertilized or mineral-only fertilized soils were linked to decreased CUE. Specifically, shifts in the composition of growing bacteria were correlated with enhanced microbial CUE, whereas modifications in the composition of growing fungi were associated with diminished CUE. Notably, the responses of microbial CUE to temperature fluctuations were primarily driven by changes in the bacterial composition. Overall, our findings demonstrate that organic amendments enhance soil microbial CUE and promote the enrichment of specific microbial clusters that are better equipped to cope with temperature changes. This study establishes a theoretical foundation for manipulating soil microbes to enhance carbon sequestration under global climate scenarios.

Temporal variation characteristics in the association between climate and vegetation in Northwest China

Northwest China has undergone notable alterations in climate and vegetation growth in recent decades. Nevertheless, uncertainties persist concerning the response of different vegetation types to climate change and the underlying mechanisms. This study utilized the Normalized Difference Vegetation Index (NDVI) and three sets of meteorological data to investigate the interannual variations in the association between vegetation and climate (specifically precipitation and temperature) from 1982 to 2015. Several conclusions were drawn. (1) RNDVI-GP (relationship between Growing Season NDVI and precipitation) decreased significantly across all vegetation, while RNDVI-GT (relationship between Growing Season NDVI and temperature) showed an insignificant increase. (2) Trends of RNDVI-GP and RNDVI-GT exhibited great variations across various types of vegetation, with forests displaying notable downward trends in both indices. The grassland exhibited a declining trend in RNDVI-GP but an insignificant increase in RNDVI-GT, while no significant temporal changes in RNDVI-GP or RNDVI-GT were observed in the barren land. (3) The fluctuations in RNDVI-GP and RNDVI-GT closely aligned with variations in drought conditions. Specifically, in regions characterized by VPD (vapor pressure deficit) trends less than 0.02 hpa/yr, which are predominantly grasslands, a rise in SWV (soil water volume) tended to cause a reduction in RNDVI-GP but an increase in RNDVI-GT. However, a more negative trend in SWV was associated with a more negative trend in both RNDVI-GP and RNDVI-GT when the VPD trend exceeded 0.02 hPa/yr, primarily in forests. Our results underscore the variability in the relationship between climate change and vegetation across different vegetation types, as well as the role of drought in modulating these associations.

Analysis of spatial and temporal changes in vegetation cover and its drivers in the Aksu River Basin, China

Exploring vegetation dynamics in arid areas and their responses to different natural and anthropogenic factors is critical for understanding ecosystems. Based on the monthly MOD13Q1 (250 m) remote sensing data from 2000 to 2019, this study analyzed spatio-temporal changes in vegetation cover in the Aksu River Basin and predicted future change trends using one-dimensional linear regression, the Mann-Kendall test, and the Hurst index. Quantitative assessment of the magnitude of anthropogenic and natural drivers was performed using the Geodetector model. Eleven natural and anthropogenic factors were quantified and analyzed within five time periods. The influence of the driving factors on the changes in the normalized difference vegetation index (NDVI) in each period was calculated and analyzed. Four main results were found. (1) The overall vegetation cover in the region significantly grew from 2000 to 2019. The vegetation cover changes were dominated by expected future improvements, with a Hurst index average of 0.45. (2) Land use type, soil moisture, surface temperature, and potential vapor dispersion were the main drivers of NDVI changes, with annual average q-values above 0.2. (3) The driving effect of two-factor interactions was significantly greater than that of single factors, especially land use type interacts with other factors to a greater extent on vegetation cover. (4) The magnitude of the interaction between soil moisture and potential vapor dispersion and the magnitude of the interaction between anthropogenic factors and other factors showed an obvious increasing trend. Current soil moisture and human activities had a positive influence on the growth of vegetation in the area. The findings of this study are important for ecological monitoring and security as well as land desertification control.

Vegetation cover change and its response to climate extremes in the Yellow River Basin

Extreme climate events have increased in frequency and severity under the background of climate change, with vegetation growth exhibiting a sensitive response to them. By assimilating GIMMS NDVI and MODIS NDVI using the Residual Network, we obtained a long time series and high resolution NDVI dataset of the Yellow River Basin (YRB). The dataset was utilized for examining the spatiotemporal variability of NDVI and analyzing the response of vegetation cover to climate extremes with meteorological data. Our findings reveal the following: (1) A significant rise in NDVI was seen in the YRB, displaying a mean growth rate of 0.019/10a (p < 0.001). However, seasonal differences exist. The mean NDVI of multi-year declines from southeast to northwest, while the overall trend of vegetation cover improves. (2) The NDVI response to extreme temperature exhibits noticeable spatiotemporal differences. Daytime extreme high temperature in the northern YRB is negatively correlated with NDVI, while they are positively correlated in the lower YRB and the southern part of the middle YRB. Nighttime extreme high temperature exhibits a positive correlation with NDVI. Overall, NDVI displays a stronger response to extreme precipitation than to extreme temperature, with a negative correlation with CWD and a positive correlation with PRCPTOT. (3) The NDVI demonstrates a lagged response to climate extremes in the YRB, with a greater lag in response to extreme temperature than extreme precipitation. The research findings can provide scientific support for the future management and planning of vegetation in the YRB, as well as contribute to the promotion of ecological environment regulation and sustainable development.

Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains' Northern Slopes, Xinjiang, China

Accurately capturing the changing patterns of ecological quality in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) and researching its significant impacts responds to the requirements of high-quality sustainable urban development. In this study, the spatial and temporal distribution patterns of remote sensing ecological index (RSEI) were obtained by normalization and PCA transformation of four basic indicators based on Landsat images. It then employed geographic detectors to analyze the factors that influence ecological change. The result demonstrates that: (1) In the distribution of land use conversions and degrees of human disturbance, built-up land, principally urban land, and agricultural land, represented by dry land, are rising, while the shrinkage of grassland is the most substantial. The degree of human disturbance is increasing overall for glaciers. (2) The overall ecological environment of the northern slopes of Tianshan is relatively poor. Temporally, the ecological quality changes and fluctuates, with an overall rising trend. Spatially, ecological quality is low in the north and south and high in the center, with high values concentrated in the mountains and agriculture and low values in the Gobi and desert. However, on a large scale, the ecological quality of the Urumqi-Changji-Shihezi metropolitan area has worsened dramatically compared to other regions. (3) Driving factor detection showed that LST and NDVI were the most critical influencing factors, with an upward trend in the influence of WET. Typically, LST has the biggest influence on RSEI when interacting with NDVI. In terms of the broader region, the influence of social factors is smaller, but the role of human interference in the built-up area of the oasis city can be found to be more significant at large scales. The study shows that it is necessary to strengthen ecological conservation efforts in the UANSTM region, focusing on the impact of urban and agricultural land expansion on surface temperature and vegetation.

Vegetation Dynamics and Food Security against the Background of Ecological Restoration in Hubei Province, China

A series of ecological restoration projects have been proposed to solve ecological problems resulting from human activities. The project of returning farmlands to forests, initiated in 1999, was the most widely implemented ecological restoration project in China. Large amounts of cropland with steep slopes have been converted to forests or grasslands to promote vegetation restoration, reduce soil erosion, and control nonpoint source pollution. Therefore, identifying the dynamics of vegetation and food security is crucial for further decision making. Based on the mean normalized difference vegetation index (NDVI) and grain yield data, this study explored the vegetation dynamics and food security of Hubei Province against the background of ecological restoration. The results show that, on a whole, the NDVI significantly increased from 2000 to 2018. The spatial agglomeration of the NDVI decreased between 2000 and 2008 and then increased from 2009 onwards. High-high NDVI agglomerations were more concentrated in mountainous areas. Food security was not threatened, and the grain yield in Hubei Province and most of the cities exhibited significant upward trends, as a whole. The change trend of the grain yield was not stable during the period from 2000 to 2018. The grain yield for Hubei Province and almost all of the cities decreased during the first 5 to 11 years, probably due to the sharp decrease in the sloping cropland areas against the background of ecological restoration. Grain yield was more sensitive and had a longer downward trend in regions with steeper slopes. Increasing trends in grain yield were detected during the last 6 to 10 years for most of the cities, and this can mainly be attributed to the newly added croplands that were created from land with other kinds of land uses, the increase in grain productivity, and strict cropland protection policies. The project of returning farmlands to forests is suggested as a long-term policy from the perspective of ecological restoration, and effective measures should also be continuously taken to maintain grain production and food security.

The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

Spatial-temporal evolution of ESV and its response to land use change in the Yellow River Basin, China

The value of ecosystem services, as well as their temporal and spatial characteristics, can be used to help areas develop focused and localized sustainable ecological management plans. Thus, this study conducted in the Yellow River Basin (YRB) of China, analyzed the ecosystem service value (ESV) and its spatial–temporal variation characteristics. This study used the equivalent factor and geospatial exploration methods, introduced the elasticity coefficient, and explored the response of ESV change to land-use change, based on the land use cover data from 1990 to 2020. The results showed that from 1990 to 2020, YRB ecosystem service value showed an overall increasing trend, mainly because the ecological construction project increased forest and grasslands in this region. In the past 30 years, spatial characteristics of ESV in YRB was relatively stable. The high-value areas were mainly distributed in the upper Yellow River Basin, while the low-value areas were mainly distributed in the lower Yellow River Basin, as the cold and hot spots were reduced. The ESV barycenter coordinates showed the direction of the transfer trajectory, which is first to southwest, northeast, and then to southwest. From 2000 to 2010, YRB land-use change had greater impact on ESV. Since 2010, the disturbance of ecosystem services by land-use change has decreased. Consequently, the elastic index of the upstream and Loess Plateau regions were significantly higher than that of other regions, and the impact of land-use change on ecosystem services was more obvious, due to improved large-scale ecological construction projects implementation. Conclusively, this study recommends the use of comprehensive spatial–temporal assessment of ESV for sustainable development and ecological protection in the YRB.

Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency

Water Use Efficiency (WUE) is an important indicator of the carbon cycle in the hydrological and ecological system. It is of great significance to study the response of different hydrological processes to climate and to understand ecosystem carbon sink. However, little is known about the effects and mechanisms of precipitation and temperature on the WUE of different hydrological processes. Thus, three kinds of WUEs (GPP/E (eWUE), GPP/Et (tWUE), and GPP/P (pWUE)) are defined for three different hydrological indicators in semi-arid areas in this study in order to reveal the variation pattern of WUEs based on hydrological indicators and their response to climate. We found that in the past 15 years, the seasonal fluctuation of evapotranspiration in arid areas was large, and the spatial difference of WUE of different hydrological processes was obvious. In semi-arid areas, temperature had a significant effect on WUE (about 68–81%). However, precipitation had a lag effect on WUEs, and the negative impact of precipitation has a great influence (about 84–100%). Secondly, the threshold values of precipitation to WUEs (200 or 300 mm) and temperature to WUEs (2 or 7 °C) are also different from previous studies. This study advances our understanding of the influence of different hydrological processes on ecosystem carbon and climate.

China's deserts greening and response to climate variability and human activities

Vegetation, which is a good indicator of the impacts of climate variability and human activities, can reflect desert ecosystem dynamics. To reveal the vegetation variations in China's deserts, trends in the monthly, seasonal, and annual normalized difference vegetation index (NDVI) from 2000 to 2017 were measured both temporally and spatially by the Theil-Sen estimator and Mann-Kendall test. Additionally, correlation coefficients and residual analysis were employed to evaluate the correlations between the NDVI and climatic factors and to distinguish the impacts of climate variability and human activities. The results showed that China's deserts underwent greening. The annual NDVI showed a significant increasing trend at a rate of 0.0018/yr, with values of 0.094 in 2000 and 0.126 in 2017. Significant increasing trends in NDVI were observed in all four seasons. The NDVI were higher in summer and autumn than in spring and winter. Both the monthly NDVI and its trends showed an inverted U-shaped curve during the year. Spatially, the greening trends were mainly distributed on the southern edge of the Gurbantunggut Desert, in the northwestern part of the Taklimakan Desert, and in the Kubuqi Desert. The correlations between the NDVI and climatic factors at the monthly and seasonal scales were stronger than those at the annual scale. Temperature and precipitation had positive effects on NDVI at the monthly and seasonal scales, but only precipitation had a positive effect at the annual scale. Human activities, especially oasis expansion and sand stabilization measures, were two major causes of large increasing areas of desert greening in China indicated by the NDVI.

Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning

Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.

Assessment of Landscape Change of Lesser Himalayan Road Corridor of Uttarakhand, India

The spatio-temporal monitoring and understanding of the pattern of land-use and land-cover (LULC) change in the Himalayas are essential for sustainable development, especially from environmental planning and management perspective. The increasing anthropogenic activities and climate change in the Siwalik and Lesser Himalayas have substantially experienced rapid change in the natural landscape; however, detailed investigation and documentation of such observed changes are limited. This study aims to assess the LULC changes along the Kalsi-Chakrata road corridor located in the Lesser Himalayan region of Uttarakhand state of India using remote sensing and geographic information system (GIS) for the periods 2000-2010 and 2010-2019. The LULC maps were generated from multi-temporal satellite images of the Landsat -7 Enhanced Thematic Mapper Plus (ETM+) series for 2000 and 2010, and the Linear Imaging Self-Scanning System IV (LISS IV) images from Resourcesat-1 for 2019. The extent of spatial landscape changes occurred in different LULC classes was performed through the cross-tabulation change matrix in the GIS module up to the individual village level. The results indicate that the forest cover of the area was intensively converted to open areas, sparse vegetation, and different land-use categories. These included agricultural land, built-up areas, and decreased from 47.27 % in 2000 to 36.66 % in 2019. During the same period, the open areas and agricultural areas were increased by 15.86 % and 4.49 %, respectively. Moreover, the built-up areas (both urban and rural settlements) were progressively increased from 0.33% in 2000 to 0.56 % in 2019. The conversion of forests and sparsely vegetative areas to agricultural land and rural settlements is closely associated with the increasing anthropogenic activities due to population growth, tourism, movement of heavy vehicles for mining and other economic activities. The changes in land-cover to land use classes are more prominent in Samalta Dadauli, Nithala, Bhugtari, and Udapalta villages located between Kalsi and Sahiya town. The reported maximum transition of forest areas into the open area, agricultural land, and sparse vegetation indicates the possible scarcity of water, which could link with the incidence of climatic or seasonal variation in the Lesser Himalayan terrain to the hydro-geomorphic and anthropogenic processes. The trend in LULC change at the village level gave the insight to help to prioritize future mitigation planning and sustainable development that are exceedingly convenient for the planners, policymakers, and local authorities for comprehensive forest management, biodiversity strategies, and necessary conservation

Spatio-Temporal Evolution of Land Use Transition and Its Eco-Environmental Effects: A Case Study of the Yellow River Basin, China

Human activities and environmental deterioration have resulted in land use transition (LUT), which seriously affects the ecosystem service value (ESV) of its region. Therefore, relevant policy measures are urgently needed. Nevertheless, research on the relationships between LUTs and ESVs from the overall watershed scale is lacking. Thus, the geo-information Tupu method was applied to analyze the dynamic patterns of LUT based on land use data from 1990, 2000, 2010, and 2018 of the Yellow River Basin (YRB). Then, a newly revised ecosystem services calculation method was utilized to the responses of ESV to LUTs. The results indicated that the Tupu units of the LUT were mainly based on the mutual transformation of grassland and unused land, and cultivated land and forestland, which were widely distributed in the upper and middle reaches of the basin. The spatial distribution was concentrated, and the expansion’s trend was also obvious. Moreover, the conversion of cultivated land into construction land was mainly distributed in the lower reaches of the basin. During 1990–2018, the total ESV fluctuated and increased (+10.47 × 108 USD) in the YRB. Thereinto, the ESV of grassland (45%) and forestland (30%) made the greatest contribution to the total ESV. As for different reaches, the ESV increased in the upstream, but decreased in the midstream and the downstream. In terms of contribution rate, the conversion of unused land into grassland (12.477%) and grassland into forestland (9.856%) were the main types to enhance the ESV in the YRB, while the conversion of forestland into grassland (−8.047%) and grassland to unused land (−7.358%) were the main types to reduce the ESV. Furthermore, the range of ecological appreciation zones was widely distributed and scattered, while the range of ecological impairment zones was gradually expanded. These findings could have theoretical support and policy implications for land use planning and environmental services in the YRB.