Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan

Driving mechanism of natural vegetation response to climate change in China from 2001 to 2022

Understanding driving mechanism of natural vegetation response to climate change is crucial for maintaining vegetation stability. In this study, driving mechanism of natural vegetation sensitivity to precipitation (SVP) and temperature (SVT) changes in China were analyzed based on Normalized Difference Vegetation Index (NDVI), Solar-induced Chlorophyll Fluorescence (SIF), Dead Fuel Index (DFI), and climate, hydrological, and CO2 data. Results showed that NDVI and SIF significantly increased but DFI significantly decreased from 2001 to 2022, with proportion of over 67 % of natural vegetation area. The SVP of NDVI (SVPN) and DFI (SVPD) of natural vegetation decreased while SVP of SIF (SVPS) increased during 2001-2022, with average of -6.8 × 10-5/mm, -9.9 × 10-3/mm, and 2.3 × 10-5/mm, respectively. The SVPN and SVPD decreased from arid to humid regions, SVPS was high in semi-arid and semi-humid regions. The SVP was correlated with precipitation, runoff, CO2 and surface soil moisture (SSM), and their correlation was higher in drier regions. The SVT of NDVI (SVTN) of natural vegetation increased while SVT of SIF (SVTS) and DFI (SVTD) decreased during 2001-2022, with average of 13.3 × 10-3/°C, 7 × 10-3/°C, and -1.2/°C, respectively. And there was no significant spatial variation of SVT in different climate regions. The SVT was correlated with aridity index (AI), potential evapotranspiration (PET), temperature and SSM. The explanation of climate, hydrological, and CO2 for SVP and SVT was over 64 %, especially for SVTD at 76.2 %. The influencing factors had great explanations for alpine vegetation, desert, needle-leaf forest, and shrubland, and small explanations for broadleaf forest, mixed forest, and wetland. Overall, natural vegetation of China greened and its dependence on climate change decreased, SVP and SVT were driven by hydrology and heat, respectively. These findings will provide scientific basis for vegetation to cope with future extreme events and maintain vegetation stability.

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.

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota

The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in mitigating climate change, there are very few studies that have assessed the overall spatial distribution of cover crops and tillage practices based on the farm's pedoclimatic and topographic characteristics. Hence, the primary objective of this study was to use multiple satellite-derived indices and environmental drivers to infer the level of tillage intensity and identify the presence of cover crops in eastern South Dakota (SD). We used a machine learning classifier trained with in situ field samples and environmental drivers acquired from different remote sensing datasets for 2022 and 2023 to map the conservation agriculture practices. Our classification accuracies (>80%) indicate that the employed satellite spectral indices and environmental variables could successfully detect the presence of cover crops and the tillage intensity in the study region. Our analysis revealed that 4% of the corn (Zea mays) and soybean (Glycine max) fields in eastern SD had a cover crop during either the fall of 2022 or the spring of 2023. We also found that environmental factors, specifically seasonal precipitation, growing degree days, and surface texture, significantly impacted the use of conservation practices. The methods developed through this research may provide a viable means for tracking and documenting farmers' agricultural management techniques. Our study contributes to developing a measurement, reporting, and verification (MRV) solution that could help used to monitor various climate-smart agricultural practices.

Disproportionate contributions of land cover and changes to ecosystem functions in Kazakhstan and Mongolia

Land use and land cover change (LULCC) have profoundly altered land surface properties and ecosystem functions, including carbon and water production. While mapping these changes from local to global scales has become more achievable due to advancements in earth observations and remote sensing, linking land cover changes to ecosystem functions remains challenging, especially at regional scale. Our study attempts to fill this gap by employing a computationally efficient method and two types of widely used high-resolution satellite images. We first investigated the contribution of landscape composition to ecosystem function by examining how land cover and proportion affected gross primary production (GPP) and evapotranspiration (ET) at six macro-landscapes in Mongolia and Kazakhstan. We hypothesized that both ecosystem and landscape GPP and ET are disproportionate to their composition and, therefore, changes in land cover will have asymmetrical influences on landscape functions. We leveraged a computational-friendly linear downscaling approach to align the coarse spatial resolution of MODIS (500 m) with a fine-grain and localized land cover map developed from Landsat (30 m) for six provinces in countries where intensive LULCC occurred in recent decades. By establishing two metrics-function to composition ratio (F/C) and function to changes in composition change (ΔF/ΔC)-we tested our hypothesis and evaluated the impact of land cover change on ecosystem functions within and among the landscapes. Our results show three major themes. (1) The five land cover types have signature downscaled ET and GPP that appears to vary between the two countries as well as within each country. (2) F/C of ET and GPP of forests is statistically greater than 1 (i.e., over-contributing), whereas F/C of grasslands and croplands is close to or slightly less than 1 (i.e., under-contribution). F/C of barrens is clearly lower than 1 but greater than zero. Specifically, a unit of forest generates 1.085 unit of ET and 1.123 unit of GPP, a unit of grassland generates 0.993 unit of ET and GPP, and a unit of cropland produces 0.987 unit of ET and 0.983 unit of GPP. The divergent F/C values among the land cover classes support the hypothesis that landscape function is disproportionate to its composition. (3) ΔET/ΔC and ΔGPP/ΔC of forests and croplands showed negative values, while grasslands and barrens showed positive values, indicating that converting a unit of forest to other land cover leads to a decrease in ET and GPP, while converting units of grassland or barren to other land cover classes will result in increased ET and GPP. This linear downscaling approach for calculating F/C and ΔF/ΔC is labor-saving and cost-effective for rapid assessment on the impact of land use land cover change on ecosystem functions.

Mapping livestock density distribution in the Selenge River Basin of Mongolia using random forest

Mapping dynamically distributed livestock in the vast steppe area based on statistical data collected by administrative units is very difficult as it is limited by the quality of statistical data and local geographical environment factors. While, spatial mapping of livestock gridded data is critical and necessary for animal husbandry management, which can be easily integrated and analyzed with other natural environment data. Facing this challenge, this study introduces a spatialization method using random forest (RF) in the Selenge River Basin, which is the main animal husbandry region in Mongolia. A spatialized model was constructed based on the RF to obtain high-resolution gridded distribution data of total livestock, sheep & goats, cattle, and horses. The contribution of factors influencing the spatial distribution of livestock was quantitatively analyzed. The predicted results showed that (1) it has high livestock densities in the southwestern regions and low in the northern regions of the Selenge River Basin; (2) the sheep & goats density was mainly concentrated in 0-125 sheep/km2, and the high-density area was mainly distributed in Khuvsgul, Arkhangai, Bulgan and part soums of Orkhon; (3) horses and cattle density were concentrated in 0-25 head/km2, mainly distributed in the southwest and central parts of the basin, with few high-density areas. This indicates that the RF simulation results effectively depict the characteristics of Selenge River Basin. Further study supported by Geodetector showed human activity was the main driver of livestock distribution in the basin. This study is expected to provide fundamental support for the precise regulation of animal husbandry in the Mongolian Plateau or other large steppe regions worldwide.

Exploring the relationships between human consumption and environmental pressure: A case study of the Yangtze river economic zone in China

It is crucial to decouple and coordinate human consumption and its environmental pressure for achieving sustainable development. As an important aspect of United Nations Sustainable Development Goal (SDG12), sustainability on material consuming is still in its early stages of research. To address the research gap in sustainable consumption of vegetation net primary productivity (NPP), this study analyzed the spatio-temporal dynamics of human consumption and environmental pressure in the Yangtze River Economic Zone (YREZ) using consumption-based HANPP (cHANPP) and Human Appropriation of Net Primary Production (HANPP) as indicators. Later, we measured their decoupling relationship using Tapio decoupling approach. We found that distribution of HANPP and cHANPP were regionally separated, with the former mainly concentrated in the middle and upper reaches provinces, while the latter concentrated in the lower reach provinces. From 2004 to 2019, the relationship between HANPP and cHANPP changed from strong negative decoupling to weak decoupling in the YREZ. Furthermore, the relationship was differed among different regions. As a whole, developing regions showed a weak decoupling state, experiencing an increase in environmental pressure (i.e., HANPP) alongside increased human consumption (i.e., cHANPP). In contrast, developed regions showed a strong decoupling state, experiencing a decrease in environmental pressure (i.e., HANPP) alongside increased human consumption (i.e., cHANPP). Our study highlights that different countermeasures should be formulated by regions according to their own situation to realize sustainable regional development.

Effects of coal mining and climate-environment factors on the evolution of a typical Eurasian grassland

Coal mining can significantly impact vegetation evolution, yet the limited information on its patterns and driving factors hampers efforts to mitigate these effects and reclaim abandoned mines. This study aimed to 1) examine vegetation evolution in a semiarid steppe watershed in northeast China; and 2) characterize the driving factors behind this evolution. We analyzed the impact of twelve selected driving factors on fractional vegetation coverage (FVC) from 2000 to 2021 using a dimidiate pixel model, Sen's slope analysis, Mann-Kendall trend test, coefficient of variation analysis, and Geodetector model. At a significance level of α = 0.05, our findings revealed a south-to-north decline pattern in FVC, a significant decrease trend in proximity to coal mines, and a notable increase trend adjacent to river channels. Approximately 37% of the watershed exhibited low FVC, while the overall temporal trend across the watershed was deemed insignificant. Areas surrounding the mines experienced a substantial reduction in FVC due to coal mining activities, while FVC variations across the watershed were linked to precipitation, temperature, and soil type. FVC predictions improved notably when interactions between multiple two-way factors were considered. Each driving factors displayed an optimal range (e.g., precipitation = 63-71 mm) for maximizing FVC. Given the study watershed's status as a national energy base, understanding vegetation responses to coal mining and climate-environment changes is crucial for sustaining fragile terrestrial ecosystems and socioeconomic development. Achieving a long-time balance between coal extraction and ecological protection is essential. The study outcomes hold significant promise for advancing ecological conservation, vegetation restoration, and mitigation of environmental degradation in semiarid regions affected by extensive coal mining and climate fluctuations. These findings contribute to the strategic management of such areas, promoting sustainable practices amidst evolving environmental challenges.

Assessing long-term trends in vegetation cover change in the Xilin River Basin: Potential for monitoring grassland degradation and restoration

Under the influence of climate change and human activities, the problem of grassland degradation is becoming increasingly severe. Detection of changes in vegetation cover is crucial for a better understanding of the interaction between humans and ecosystems. This study maps changes in vegetation cover using the Google Earth Engine (GEE). We used 36 years of Landsat satellite imagery (1985-2020) in the Xilin River Basin, China, to classify grassland conditions and validated the results with field observation data. The overall classification of the model accuracy assessment was 83.3%. The Dynamic Reference Vegetation Cover Method (DRCM) was adopted to remove the effect of interannual variation of rainfall, allowing to focus on the impact of human activities on vegetation cover changes. The results identify five categories of vegetation cover changes: significantly increased, potentially increased, stable, potentially decreased, and significantly decreased. The reference level is derived from the most persistent land surface coverage across different grassland types and all years. Overall, 9.3% of the study area had a significant increase in vegetation cover, 14.2% a potential increase, 48.6% of the area showed a stable vegetation condition, 9.8% showed a potential decrease, and 18.1% a significant decrease in vegetation cover. The largest proportion of combined potential and significant reduction was 35.2% for desert grassland, where the vegetation faced the most severe reduction. This study will provide a basis for identifying grassland degradation and developing scientific management policies.

Gridded livestock density database and spatial trends for Kazakhstan

Livestock rearing is a major source of livelihood for food and income in dryland Asia. Increasing livestock density (LSKD) affects ecosystem structure and function, amplifies the effects of climate change, and facilitates disease transmission. Significant knowledge and data gaps regarding their density, spatial distribution, and changes over time exist but have not been explored beyond the county level. This is especially true regarding the unavailability of high-resolution gridded livestock data. Hence, we developed a gridded LSKD database of horses and small ruminants (i.e., sheep & goats) at high-resolution (1 km) for Kazakhstan (KZ) from 2000-2019 using vegetation proxies, climatic, socioeconomic, topographic, and proximity forcing variables through a random forest (RF) regression modeling. We found high-density livestock hotspots in the south-central and southeastern regions, whereas medium-density clusters in the northern and northwestern regions of KZ. Interestingly, population density, proximity to settlements, nighttime lights, and temperature contributed to the efficient downscaling of district-level censuses to gridded estimates. This database will benefit stakeholders, the research community, land managers, and policymakers at regional and national levels.

Change characteristics and influencing factors of grassland degradation in adjacent areas of the Qinghai-Tibet Plateau and suggestions for grassland restoration

Natural grasslands are being progressively degraded around the world due to climate change and socioeconomic factors. Most of the drivers, processes, and consequences of grassland degradation are studied separately, and it is not yet clear whether the change characteristics and influence factors of adjacent areas of grassland are identical. We analyzed changes in grassland area and quality, and the influences of climate changes and socioeconomic factors from 1980-2018 in Maqu County, Xiahe County and Luqu County on the eastern Qinghai-Tibet Plateau (QTP). We found that areas with high and medium coverage grassland in Maqu County and Luqu County decreased continuously with time, while low coverage grassland areas increased in three counties. In Xiahe County, the medium coverage grassland area reduced with time (except for 2010), while the high and low coverage grassland areas increased. The actual net primary productivity of the three counties showed a downward trend. In Maqu County, the total grassland area had an extremely significant positive correlation with number of livestock going to market, commodity rate, gross domestic product (GDP), primary industry, tertiary industry, household density, and levels of junior middle school education and university education in the area. In Luqu County, the total grassland area high coverage grassland area were significantly negatively correlated with total number of livestock, secondary industry, levels of primary school education, and temperature. Ecological education was positively correlated with high coverage grassland, and negatively correlated with low coverage grassland in all three areas. The results of this study suggest that the best ways to restore the area and quality of grasslands in these areas would be to reduce the local cultivated land area and slow down the development of the primary and tertiary industries in Maqu County, and to control industry development and the total number of livestock in Luqu County. This study also suggests that improving education level and strengthening the level of ecological education are conducive to the restoration of grasslands.

Seasonal differences in the spatial patterns of wildfire drivers and susceptibility in the southwest mountains of China

Wildfires directly affect global ecosystem stability and severely threaten human life. The mountainous areas of Southwest China experience frequent wildfires. Mapping the susceptibility patterns and analyzing the drivers of wildfires are crucial for effective wildfire management, especially considering that the inclusion of seasonal dimensions will produce more dynamic results. Using Yunnan Province of China as a case study area, a method was attempted to distinguish dependable wildfires by season, while possible wildfire drivers were obtained and refined within seasons. The patterns of wildfire susceptibility in different seasons were modeled based on the Maxent and random forest models. Then, the spatial relationships between wildfire and potential drivers were analyzed integrating with GeoDetector to evaluate the variable importance of drivers and the marginal effect of drivers. The results showed that the two models effectively depicted each season's wildfire susceptibility. The susceptible wildfire areas in spring and winter are located throughout Yunnan Province, with anthropogenic factors being the most significant drivers. During the summer and autumn, wildfire risk areas are relatively concentrated, showing a trend of dominant drought-driven and humid conditions. The differences in wildfire drivers across seasons reflect the lagged effect of climate factors on wildfires, leading to significant discrepancies in the marginal effects of how seasonal drivers affect wildfires. The findings improve our understanding of the effects of the interseasonal variability of environmental variables on wildfires and promote the development of specific seasonal wildfire management strategies.

Analysis of driving factors of spatial distribution of heavy metals in soil of non-ferrous metal smelting sites: Screening the geodetector calculation results combined with correlation analysis

Heavy metals (HMs) discharged from smelting production may pose a major threat to human health and soil ecosystems. In this study, the spatial distribution characteristics of HMs in the soil of a non-ferrous metal smelting site were assessed. This study employed the geodetector (GD) by optimizing the classification condition and supplementing the correlation analysis (CA). The contribution of driving factors, such as production workshop distributions, hydrogeological conditions, and soil physicochemical properties, to the distribution of HMs in soil in the horizontal and vertical dimensions was assessed. The results showed that the main factors underlying the spatial distribution of As, Cd, Hg, Pb, Sb, and Zn in the horizontal direction were the distance from the sintering workshop (the maximum q value of that factor, q=0.28), raw material yard (q=0.14), and electrolyzer (q=0.29), while those in the vertical direction were the soil moisture content (q=0.17), formation lithology (q=0.12), and soil pH (q=0.06). The findings revealed that the CA is a simple and effective method to supplement the GD analysis underlying the spatial distribution characteristics of HMs at site scale. This study provides useful suggestions for environmental management to prevent HMs pollution and control HMs in the soil of non-ferrous metal smelting sites.