Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Assessing the effects of climate and human activity on vegetation change in Northern China

Fractional vegetation cover (FVC) has changed significantly under various disturbances over northern China in recent decades. This research examines the dynamics of FVC and how it is affected by climate and human activity during the period of 1990-2018 in northern China. The effects of climate change (i.e., temperature, precipitation, solar radiation, and soil moisture) and human activity (socioeconomic data and land use) on vegetation coverage change in northern China from 1990 to 2018 were quantified using the Sen + Mann-Kendall test, partial correlation analysis, and structural equation modelling (SEM) methods. The findings of this research indicate the following: (1) From 1990 to 2018, the overall trend in FVC in northern China was increased. The areas with obvious increases were mainly situated on the northern slope of Tianshan Mountains, Xinjiang, the Loess Plateau, the Northeast China Plain, and the Sanjiang Plain, while the areas with distinct degradation were located in the Inner Mongolia Plateau, the Changbai Mountain and the eastern part of north China. (2) In the past 29 years, the FVC in northern China has been mainly affected by precipitation and soil moisture. (3) Based on structural equation modelling, we discovered that certain variables impacted the main factors influencing the amount of FVC in northern China. Human activity has had a larger impact on FVC than climate change. Our findings can accelerate the comprehension of vegetation dynamics and their underlying mechanisms and provide a theoretical basis for regional ecological environmental protection.

Quantifying impacts of climate dynamics and land-use changes on water yield service in the agro-pastoral ecotone of northern China

Large-scale revegetation practices have lasted approximately two decades in the agro-pastoral ecotone of northern China (AENC), and their impacts on hydrological and ecological effects remain poorly understood. Previous studies largely focused on assessing water yield service (WYs) based on several fixed time points, whereas time series information-continuous WYs dynamics were more reliable and valuable in decision-making about water sustainability goals. This study analyzed the interannual WYs trend and relative roles of its drivers in the last 20 years based on a newly proposed approach, and revealed the past, present and future impacts of revegetation on WYs. The final results indicated that the annual WYs averaged approximately 97 mm and exhibited an increasing trend of 1.96 mm year^-1 (p = 0.086) during 2000-2019, in which climate and land-use changes were responsible for 88% and 12% of WYs variations, respectively. From 2000 to 2019, WYs was pronouncedly 1.47 mm year^-1 (p = 0.119) lower in the afforestation area than in the nonafforestation area, but the precipitation in the two regions had a statistically insignificant difference (p = 0.97). Future revegetation scenarios showed great potential for the shrinkage of WYs provision, even approaching a maximum of 50 mm at a local scale. Even so, the afforestation-induced reductions in blue water and benefits in green water both should receive equal attention. Specifically, any attempts to assess WYs or other climate-driven ecosystem services using discontinuous years as the study period must be taken with extreme caution.

Tracking Sustainable Restoration in Agro-Pastoral Ecotone of Northwest China

Large-scale ecological restoration (ER) projects have been implemented in northwest China in recent decades as a means to prevent desertification and improve ecosystem services. However, previous studies have demonstrated adverse impacts in the form of widespread soil water deficit caused by intensive ER activities. Understanding the role of climate change and ER efforts in vegetation dynamics and soil moisture consumption is essential for sustainable ecosystem management. Here, we used the break for additive season and trend (BFAST) method to analyse spatial patterns in the normalized difference vegetation index (NDVI) variation over the agro-pastoral ecotone of northwest China (APENC) for 2000–2015. From the combined use of generalized additive modelling (GAM) and residual-trend analysis (RESTREND), we distinguished and quantified the effects of climate and human management on vegetation and soil water dynamics. Approximately 78% of the area showed vegetation variations representing a significant change in NDVI, of which more than 68% were categorized as abrupt changes. Large areas of the abrupt change type, interrupted increase and monotonic increase in NDVI were observed before 2006, and small areas of the change type of negative reversals were observed after 2012. Anthropogenic activity was found to be the major driving factor of variation in vegetation (contribution rate of 56%) and soil moisture (contribution rate of 78%). The vegetation expansion, which was mainly related to the large number of ER programs that started in 2000, was found to increase soil moisture depletion. By comparing areas where anthropogenic activities had a high contribution rate to vegetation increase and areas where soil moisture consumption was severely increased, we identify and discuss hotspot areas of soil moisture consumption caused by the ER programs. The current methodological workflow and results represent a novel foundation to inform and support water resource management and ecological-restoration-related policy making.

Revegetation projects significantly improved ecosystem service values in the agro-pastoral ecotone of northern China in recent 20 years

Revegetation is a conventional approach used for ecological protection and restoration projects, especially in the agro-pastoral ecotone of northern China (AENC). However, for this ecologically vulnerable area, the changes in land use and ecosystem service values (ESV) resulting from revegetation projects have received little attention and have not been fully elucidated. In this study, based on a rapid valuation tool-the benefit transfer method modified by the biomass factor (net primary productivity, NPP)-we assessed the ESV of the AENC at multiple-time scales by designing land scenarios related to local revegetation projects. The results showed that forestland expansion (+697 thousand ha) and grassland shrinkage (-650 thousand ha) dominated the land use change in the AENC in 2000-2018 and indirectly resulted in a net increase of US$8.18 billion in total ESV, in which revegetation projects generated nearly 1.5 million ha of new vegetated land and a corresponding US$6.86 billion in ESV (83.83% of the total increase). For future revegetation, the returning-farmland-to-forestland scenario exhibited a greater potential with increases of 5.66 million ha of forestland and US$65.20 billion in ESV to be provided. Overall, revegetation projects improved the ESV of the AENC in the last two decades because of the pronounced expansion in forestland of high ESV at the expense of the reductions in farmland and grassland, and this trend will be led continually by the Grain for Green Project in the future through the rapid assessment based on the modified benefit transfer method. Specifically, more investments and attention must be directed to the protection and restoration of grassland and wetland ecosystems.

Assessing the Effects of Spatial Scales on Regional Evapotranspiration Estimation by the SEBAL Model and Multiple Satellite Datasets: A Case Study in the Agro-Pastoral Ecotone, Northwestern China

Evapotranspiration (ET) estimation is important for understanding energy exchanges and water cycles. Remote sensing (RS) is the main method used to obtain ET data over large scales. However, owing to surface heterogeneities and different model algorithms, ET estimated from RS products with different spatial resolutions can cause significant uncertainties, whose causes need to be thoroughly analyzed. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) model was selected to explore spatial resolution influences on ET simulations. Three satellite datasets (Landsat Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High-Resolution Radiometer (AVHRR)) were selected to independently estimate ET in SEBAL model to identify the influence of the spatial scale on ET estimation, and analyze the effects and causes of scale aggregation. Results indicated that: (1) the spatial distributions of ET estimated from the three satellite datasets were similar, with the MODIS-based ET having the largest uncertainty; and (2) aggregating input parameters had limited changes in the net radiation and soil heat fluxes. However, errors in the sensible heat and latent heat fluxes were relatively larger, which were caused by changes in the selection of hot and cold pixels and the NDVI and surface albedo parameters during scale aggregation. The scale errors caused by the model mechanisms were larger than those caused by the land use/cover pattern in the SEBAL model. Overall, this study highlights the impact of spatial scale on ET and provides a better understanding of the scale aggregation effect on ET estimation by RS.

Climate Dynamics of the Spatiotemporal Changes of Vegetation NDVI in Northern China from 1982 to 2015

As an important part of a terrestrial ecosystem, vegetation plays an important role in the global carbon-water cycle and energy flow. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological station data, climate reanalysis data, and land cover data, this study analyzed the climate dynamics of the spatiotemporal variations of vegetation NDVI in northern China from 1982 to 2015. The results showed that growth season NDVI (NDVIgs) increased significantly at 0.006/10a (p < 0.01) in 1982–2015 on the regional scale. The period from 1982 to 2015 was divided into three periods: the NDVIgs increased by 0.026/10a (p < 0.01) in 1982–1990, decreased by −0.002/10a (p > 0.1) in 1990–2006, and then increased by 0.021/10a (p < 0.01) during 2006–2015. On the pixel scale, the increases in NDVIgs during 1982–2015, 1982–1990, 1990–2006, and 2006–2015 accounted for 74.64%, 85.34%, 48.14%, and 68.78% of the total area, respectively. In general, the dominant climate drivers of vegetation growth had gradually switched from solar radiation, temperature, and precipitation (1982–1990) to precipitation and temperature (1990–2015). For woodland, high coverage grassland, medium coverage grassland, low coverage grassland, the dominant climate drivers had changed from temperature and solar radiation, solar radiation and precipitation, precipitation and solar radiation, solar radiation to precipitation and solar radiation, precipitation, precipitation and temperature, temperature and precipitation. The areas controlled by precipitation increased significantly, mainly distributed in arid, sub-arid, and sub-humid areas. The dominant climate drivers for vegetation growth in the plateau climate zone or high-altitude area changed from solar radiation to temperature and precipitation, and then to temperature, while in cold temperate zone, changed from temperature to solar radiation. These results are helpful to understand the climate dynamics of vegetation growth, and have important guiding significance for vegetation protection and restoration in the context of global climate change.

A remote sensing study of spatiotemporal variations in drought conditions in northern Asir, Saudi Arabia

Changes in vegetation land cover are influenced by, and therefore an indicator of, climatic conditions. The aim of this study is to investigate the relationship between vegetation cover changes and drought events in a small-scale area. Six Landsat images during 1987-2019 were used to extract information about the vegetation land cover changes using the normalized difference vegetation index (NDVI) and the fractional vegetation cover (FVC) in Balqarn Governorate in the northern mountains of Asir, Saudi Arabia. Two climatic parameters, temperature and precipitation, were used as time series for the same period and were decomposed to investigate the seasonal and trend changes for each parameter. The two parameters were also used to calculate the standardized precipitation evapotranspiration index (SPEI) to conduct an in-depth analysis of the drought events influencing vegetation cover. The results showed that the state of the vegetation coverage of the study area remained at a medium level with an average NDVI value, but the FVC values showed evidence of dynamic variability associated with drought and moisture events. The SPEI showed that the study area has been undergoing a long-duration drought event since 2004, ranging from light to severe drought, which was consistent with the time series decomposition results. This investigation has revealed that drought drives changes in vegetation cover and is expressed on small geographic scales as changes in the vegetation cover structure. The framework described here is simple and can be used to evaluate and manage drought risks.

Unraveling the relative impacts of climate change and human activities on grassland productivity in Central Asia over last three decades

Climate change (CC) and human activities (HA) have severely influenced grassland productivity in Central Asia since the 1980s. However, the relative impacts of CC and HA on grassland productivity are not adequately documented, especially over the past three decades. In this study, we adapted the Ensemble Empirical Mode Decomposition (EEMD) to reveal potential timescales at which grassland productivity varied in Central Asia and to investigate the spatiotemporal variations of grassland productivity during 1982-2015. We developed a quantitative method that incorporated the EEMD, along with six scenarios, to disentangle the effects of CC and HA on grassland productivity in Central Asia. Results showed that grassland productivity in Central Asia trended upward significantly at a rate of 0.66 gC m^-2 yr^-1 and was dominated by a 3-year time scale oscillation. The impacts of CC and HA on grassland productivity varied significantly over space and time. CC mainly facilitated grassland productivity restoration, whereas HA decreased grassland productivity in Central Asia. Besides, varied HA in six regions of Central Asia were due to different policy implementations across these regions. In particular, HA in Xinjiang significantly promoted grassland restoration, accounting for 22.5% of the total human-affected area, mostly because of the implementation of the Grazing Withdrawal Program (GWP), while HA significantly accelerated grassland productivity degradation in Uzbekistan and Turkmenistan over last three decades. Additionally, HA promoted the restoration of grassland productivity in Kazakhstan in a short period due to the disintegration of the Soviet Union, but degraded it at long-term scale. Further, precipitation was found to be the main climatic factor while grazing be the main human factor for controlling grassland productivity variations in Central Asia, respectively. Overall, our study provides not only a novel way of quantifying the impacts of CC and HA on vegetation variations but also new insights into mechanisms mediating grassland productivity in Central Asia.