Assessing the impact of drought-land cover change on global vegetation greenness and productivity

Exploring the impact of natural and human activities on vegetation changes: An integrated analysis framework based on trend analysis and machine learning

Climate, human activities and terrain are crucial factors influencing vegetation changes. Despite their crucial role, there is a notable lack of research exploring the nonlinear relationships between them and vegetation changes, especially over extended time series. This research integrates trend analysis with machine learning and SHAP technology, proposing a methodological analysis framework named Theil-Sen - Mann-Kendall - XGBoost - SHAP (TMXS), aiming to explore the nonlinear relationships between vegetation changes and their influencing factors. Taking vegetation changes in the Chengdu-Chongqing urban agglomeration from 2003 to 2022 as an example. The results indicate that the TMXS analytical framework can effectively quantify the nonlinear impact of assessment factors on vegetation changes. During the specified period, there was a general increase in LAI across the study area, with an annual growth rate of 0.0245/a. Notably, a significant decrease in LAI was observed in urban cores and areas undergoing rapid urbanization. The combined contribution of climatic and human activity factors to vegetation changes exceeded 65% in all regions, with temperature distribution being more critical than precipitation. Human activities accounted for 45.73% of the contribution to vegetation degradation in the study area, and vegetation degradation was more prone to occur in densely populated regions. Among the topographic factors, alterations in slope gradient have a relatively significant effect on changes in vegetation cover. The research findings demonstrate that the TMXS method is reliable in elucidating the nonlinear relationships between vegetation changes and influencing factors, which can aid in guiding regional vegetation restoration projects and ecological environment policies.

Assessing terrestrial water storage dynamics and multiple factors driving forces in China from 2005 to 2020

In the context of global warming, comprehending the dynamics of terrestrial water storage (TWS) and its responses to natural and anthropogenic factors is paramount for hydrological research and the management of water resources in China. This study utilized GRACE (Gravity Recovery and Climate Experiment)/GRACE-Follow On (GRACE-FO) satellite data to analyze terrestrial water storage across nine basins in China from 2005 to 2020 at multiple temporal and spatial scales. Subsequently, employing a Geographic detector model, potential influencing factors were identified, and an enhanced Geographically Weighted Regression (GWR) method was proposed for attributing changes in TWS in China. The findings reveal a consistent declining trend in TWS based on GRACE/GRACE-FO data across different temporal scales, with the most pronounced decreases observed in August and September. Geographic Detector analysis unveils significant interactions among various environmental factors, with climate variables playing a pivotal role in modulating hydrological characteristics of major river basins, where rising temperatures can exacerbate the severity of precipitation events, thus increasing the risk of floods and droughts. Moreover, analysis of the primary influencing factors indicates significant impacts of population density and topography on water resources in the southeastern and southwestern regions, particularly amidst increasing human activities and urbanization expansion. The results of this study are crucial for comprehending the dynamic changes and mechanisms of TWS in China, as well as for formulating water resource management strategies.

β-diketone accumulation in response to drought stress is weakened in modern bread wheat varieties (Triticum aestivum L.)

Cuticular waxes coating leaf surfaces can help plants tolerate drought events by reducing non-stomatal water loss. Despite their role in drought tolerance, little is known about how cuticular wax composition has changed during breeding in Canadian bread wheat (Triticum aestivum L.) varieties. To fill in this gap, flag leaves of the Canadian Heritage Bread Wheat Panel, which include 30 varieties released between 1842 and 2018, were surveyed to determine if and how cuticular wax composition in wheat has changed at two breeding ecozones over this period. Following this, a subset of varieties was subjected to drought conditions to compare their responses. As expected, modern varieties outperformed old varieties with a significantly larger head length and reaching maturity earlier. Yet, when challenged with drought, old varieties were able to significantly increase the accumulation of β-diketones to a higher extent than modern varieties. Furthermore, RNAseq was performed on the flag leaf of four modern varieties to identify potential markers that could be used for selection of higher accumulation of cuticular waxes. This analysis revealed that the W1 locus is a good candidate for selecting higher accumulation of β-diketones. These findings indicate that the variation in cuticular waxes upon drought could be further incorporated in breeding of future bread wheat varieties.

Assessment of vegetation net primary productivity variation and influencing factors in the Beijing-Tianjin-Hebei region

Exploring the spatiotemporal variations of vegetation net primary productivity (NPP) and analyzing the relationships between NPP and its influencing factors are vital for ecological protection in the Beijing-Tianjin-Hebei (BTH) region. In this study, we employed the CASA model in conjunction with spatiotemporal analysis techniques to estimate and analyze the spatiotemporal variations of NPP in BTH and different ecological function sub-regions over the past two decades. Subsequently, we established three scenarios (actual, climate-driven and land cover-driven) to assess the influencing factors and quantify their relative contributions. The results indicated that the overall NPP in BTH exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 3.83 gC·m-2a-1. Furthermore, all six sub-regions exhibited an increase. The Bashang Plateau Ecological Protection Zone (BP) exhibited the highest growth rate (5.03 gC·m-2a-1), while the Low Plains Ecological Restoration Zone (LP) exhibited the lowest (2.07 gC·m-2a-1). Geographically, the stability of NPP exhibited a spatial pattern of gradual increase from west to east. Climate and land cover changes collectively increased NPP by 0.04 TgC·a-1 and 0.07 TgC·a-1, respectively, in the BTH region. Climate factors were found to have the greatest influence on NPP variations, contributing 40.49% across the BTH region. This influence exhibited a decreasing trend from northwest to southeast, with precipitation identified as the most influential climatic factor compared to temperature and solar radiation. Land cover change has profound effects on ecosystems, which is an important factor on NPP. From 2000 to 2020, 15.45% area of the BTH region underwent land cover type change, resulting in a total increase in NPP of 1.33 TgC. The conversion of grass into forest brought about the 0.89 TgC increase in NPP, which is the largest of all change types. In the area where land cover had undergone change, the land cover factor has been found to be the dominant factor influencing variations in NPP, with an average contribution of 49.37%. In contrast, in the south-central area where there has been no change in land cover, the residual factor has been identified as the most influential factor influencing variations in NPP. Our study highlights the important role of land cover change in influencing NPP variations in BTH. It also offers a novel approach to elucidating the influences of diverse factors on NPP, which is crucial for the scientific assessment of vegetation productivity and carbon sequestration capacity.

Challenges for sustainable development goal of land degradation neutrality in drylands: Evidence from the Northern Slope of the Tianshan Mountains, China

The SDG 15.3.1 target of Land Degradation Neutrality (LDN) only has 15 years from conception (in 2015) to realization (in 2030). Therefore, investigating the effectiveness and challenges of LDN has become a priority, especially in drylands, where fragile ecosystems intersect with multiple disturbances. In this study, solutions are proposed and validated based on the challenges of LDN. We chose the Northern Slope of the Tianshan Mountains as a case study and set baselines in 2005 and 2010. The region and degree of land change (including degraded, stable, and improved) were depicted at the pixel scale (100 × 100 m), and LDN realization was assessed at the regional scale (including administrative districts and 5000 × 5000 m grids). The results showed a significant disparity between the two baselines. The number of areas that realized the LDN target was rare, regardless of the scale of the administrative districts or grids. Chord plots, Spearman's correlation, and curve estimation were employed to reveal the relationship between LDN and seven natural or socioeconomic factors. We found that substantial degradation was closely related to the expansion of unused, urban, and mining land and reduction in water, glaciers, and forests. Further evidence suggests that agricultural development both positively and negatively affects LDN, whereas urbanization and mining activities are undesirable for LDN. Notably, the adverse effects of glacier melting require additional attention. Therefore, we consider the easy-to-achieve and hard-to-achieve baselines as the mandatory and desirable targets of LDN, respectively, and focus further efforts in three aspects: preventing agricultural exploitation from occupying ecological resources, defining reasonable zones for urbanization and mining, and reducing greenhouse gas emissions to mitigate warming. Overall, this study is expected to be a beneficial addition to existing LDN theoretical systems and serve as a case validation of the challenges of LDN in drylands.

Spatial-temporal evolution characteristics of PM2.5 and its driving mechanism: spatially explicit insights from Shanxi Province, China

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM2.5 concentration within Shanxi Province, with PM2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m3 overall. The Hurst exponent indicated a projected decrease in PM2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM2.5 and provided a reference for similar studies.

Assessing the responses of ecosystem patterns, structures and functions to drought under climate change in the Yellow River Basin, China

Understanding how ecosystems respond and adapt to drought has become an urgent issue as drought stress intensifies under climate change, yet this topic is not fully understood. Currently, conclusions on the response of ecosystems in different regions to drought disturbance are inconsistent. Based on long MODIS data and observed data, this study systematically explored the relationships between ecosystem patterns, structures and functions and drought, taking a typical climate change-sensitive area and an ecologically fragile area-the Yellow River Basin-as a case study. Drought assessment results revealed that the Yellow River Basin has experienced meteorological and hydrological drought during most of the last two decades, predominantly characterized by medium and slight droughts. The ecosystem patterns and structures changed dramatically as the grassland decreased and the landscape fragmentation index (F) increased with increasing wetness. The annual gross primary productivity (GPP) increased, the water use efficiency (WUE) declined and ecosystem service value (ESV) exhibited a W-shaped increase at the watershed scale, but there were significant regional differences. There were positive correlations between F, GPP, ESV and drought indices, while there was a negative correlation between WUE and drought indices at the watershed scale. Under drought stress, the ecosystem structure in the basin was disrupted, the GPP and ESV decreased, but the WUE increased. Notably, approximately 106 %, 20 %, and 1 % of the maximum reductions in F, GPP, and ESV, respectively, were caused by drought, while the maximum 4 % of WUE increased. Responses of some functions in the wetland and grassland to drought vary from those in other ecosystems. The mechanisms underlying ecosystem responses to drought were further investigated. This study enhances the understanding of these responses and will help stakeholders formulate drought mitigation policies and protect ecosystem health.

Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolution

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

Unraveling the enigma of NPP variation in Chinese vegetation ecosystems: The interplay of climate change and land use change

Global carbon emissions have exacerbated the greenhouse effect, exerting a profound impact on ecosystems worldwide. Gaining an understanding of the fluctuations in vegetation net primary productivity (NPP) is pivotal in the assessment of environmental quality, estimation of carbon source/sink potential, and facilitation of ecological restoration. Employing MODIS and meteorological data, we conducted a comprehensive analysis of NPP evolution in Chinese vegetation ecosystems (VESs), employing Theil-Sen median trend analysis and the Mann-Kendall test. Furthermore, utilizing scenario-based analysis, we quantitatively determined the respective contributions of climate change and land use change to NPP variations across various scales. The overall NPP exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 5.83 gC·m-2·year-1. Forestland ecosystem (FES) displayed the highest rate of increase (9.40 gC·m-2·year-1), followed by cropland ecosystem (CES) (4.00 gC·m-2·year-1) and grassland ecosystem (GES) (3.40 gC·m-2·year-1). Geographically, NPP exhibited a spatial pattern characterized by elevated values in the southeast and diminished values in the northwest. In addition, climate change had elevated 76.39 % of CES NPP, 90.62 % of FES NPP, and 71.78 % of GES NPP. At the national level, climate change accounted for 83.14 % of the NPP changes, while land use change contributed 14.14 %. Notably, climate change emerged as the primary driving force behind NPP variations across all VEGs, with land use change exerting the most pronounced influence on CES. At the grid scale (2 km × 2 km), land use change played a substantial role in all VEGs, contributing 60.01 % in CES, 54.20 % in FES, and 55.61 % in GES of the NPP variations.

What drives the spatial heterogeneity of cropping patterns in the Northeast China: The natural environment, the agricultural economy, or policy?

Understanding the spatiotemporal dynamic of crop cover types and the driving forces of cropping patterns in the Northeast China (NEC) is essential for establishing suitable and sustainable cropping patterns that are adapted to local conditions, and for promoting the optimal use of black soil resources. Here, we classified the major grain crop cover types and investigated their spatiotemporal dynamic in the NEC by combining multi-source remote sensing imagery and phenological information based on the Google Earth Engine (GEE) platform. A number of typical cropping patterns from 2017 to 2021 were defined and extracted, and the characteristics of their spatial heterogeneity were analyzed. Driving mechanisms for the spatial heterogeneity of cropping patterns were revealed using Geodetector. The results concluded that over the past five years (2017-2021), there has been a shift from soybean to maize in the NEC, while rice has remained stable in terms of spatiotemporal dynamics. Seven dominant cropping patterns showed high spatial heterogeneity and positive spatial agglomeration. The center of gravity of the cropping pattern shifted southwards as the frequency of maize planting increased, while the center of gravity shifted northwards as the frequency of soybean planting increased, while the rice cropping pattern remained stable. The interaction between black-soil productivity index (BPI) and total grain income trend (TGIT) exhibits the most pronounced impact on the spatial heterogeneity of cropping patterns, with a q statistic of 0.523. Following closely are the interactions of soybean subsidies trend (SST), rice subsidies trend (RST), and maize subsidies trend (MST) with TGIT, with q statistics of 0.481, 0.472, and 0.452, respectively. Among the seven dominant cropping patterns, the soybean-based cropping pattern had the highest level of TGIT and BPI, followed by the maize-based cropping pattern, while the rice-based cropping pattern had the lowest level. All of the natural environmental, agri-economic and policy factors have a synergistic effect in contributing to the spatial heterogeneity of cropping patterns. Natural environmental factors determine the overall spatial distribution of cropping patterns in the NEC, while economic and policy factors combine to influence farmers' decisions, resulting in diverse regional cropping patterns. It is recommended that maize-soybean rotations such as Maize-Soybean Alternate Cropping (MSAC) and Maize-Soybean Rotational Cropping (MSRC) should be promoted, especially in the central and southern regions of the NEC, to meet agricultural market demand and stabilize soil productivity.

Vegetation as the catalyst for water circulation on global terrestrial ecosystem

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water‑carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

Spatiotemporal change in ecological quality and its influencing factors in the Dongjiangyuan region, China

It is of great significance for regional ecological protection and sustainable development to quickly and effectively assess and monitor regional ecological quality and identify the factors that affect ecological quality. This paper constructs the Remote Sensing Ecological Index (RSEI) based on the Google Earth Engine (GEE) platform to analyze the spatial and temporal evolution of ecological quality in the Dongjiangyuan region from 2000 to 2020. An ecological quality trend analysis was conducted through the Theil-Sen median and Mann-Kendall tests, and the influencing factors were analyzed by using a geographically weighted regression (GWR) model. The results show that (1) the RSEI distribution can be divided into the spatiotemporal characteristics of "three highs and two lows," and the proportion of good and excellent RSEIs reached 70.78% in 2020. (2) The area with improved ecological quality covered 17.26% of the study area, while the area of degradation spanned 6.81%. The area with improved ecological quality was larger than that with degraded ecological quality because of the implementation of ecological restoration measures. (3) The global Moran's I index gradually decreased from 0.638 in 2000 to 0.478 in 2020, showing that the spatial aggregation of the RSEI became fragmented in the central and northern regions. (4) Both slope and distance from roads had positive effects on the RSEI, while population density and night-time light had negative effects on the RSEI. Precipitation and temperature had negative effects in most areas, especially in the southeastern study area. The long-term spatiotemporal assessment of ecological quality can not only help the construction and sustainable development of the region but also have reference significance for regional ecological management in China.

Effects of drought and climate factors on vegetation dynamics in Central Asia from 1982 to 2020

Ecological security and ecosystem stability in Central Asia depend heavily on the local vegetation. Vegetation dynamics and the response and hysteresis relationships to climate factors and drought on multiple scales over long time series in the region still need to be further explored. Using the net primary productivity (NPP) values as the vegetation change index of interest, in this study, we analyzed vegetation dynamics in Central Asia from 1982 to 2020 and assessed the responses and time lags of vegetation to climate factors and drought. The results showed that NPP gradually decreased from north to south and from east to west. Vegetation was distributed along both sides of the mountains. The temperatures rose from northeast to southwest, while precipitation gradually increased from southwest to northeast. The proportion of dry and wet years was as follows: normal (56.41%) > slightly dry (28.2%) > slightly humid (15.39%). Precipitation and drought conditions were positively correlated with NPP during the growing season, while temperature was negatively correlated with NPP. Increased spring temperature, precipitation, and drought conditions positively affected vegetation, while sustained summer temperature resulted in suppressed vegetation growth. Autumn vegetation was positively affected by temperature and drought, and precipitation was negatively correlated with autumn vegetation. Increasing winter temperatures promoted vegetation growth. The time lag between NPP and temperature gradually increased from northeast to southwest, and the time lag between NPP and precipitation gradually increased from south to north. Spring temperatures had the greatest beneficial impact on forestlands; summer climatic factors and drought had little effect on shrublands; the autumn climate exhibited small differences in its influence of each plant type; and winter temperatures had the greatest positive effect on grasslands. No time lag effect was found between any of the four vegetation types and precipitation. A one-month lag was found between cultivated lands and temperature; a two-month lag was found between forestlands and temperature; and a one-month lag was found between forestlands and drought and between shrublands and drought. The results can provide a scientific foundation for the sustainable development and management of ecosystems.