How Large-Scale Anthropogenic Activities Influence Vegetation Cover Change in China? A Review

Assessment of climatic and anthropogenic influences on vegetation dynamics in China: a consideration of climate time-lag and cumulative effects

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration.

Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan-Yanshan region

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

In response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.

Understanding the indirect impacts of urbanization on vegetation growth using the Continuum of Urbanity framework

Numerous studies investigated the direct impacts of urbanization on the loss and fragmentation of vegetated lands associated with urban expansion. Fewer studies, however, have examined the indirect impacts of urbanization on vegetation related to changes in livelihoods, lifestyles, and connectivity in non-urbanized areas, especially in the context of large-scale urban-rural migration. Here, we employ the Continuum of Urbanity framework to examine how changes in livelihoods, lifestyles, and connectivity in non-urbanized areas associated with urbanization affect vegetation, and thereby to understanding the indirect impacts of urbanization. We found there was a significant trend in human-induced EVI (HEVI) increase in non-urban areas, and such trend was coupled with decreased population density (PD) in forest land and grassland, but increased population density in cropland. The negative correlation between PD and HEVI became increasingly stronger from 2000 to 2011, but weakened since 2011. Livelihood income, lifestyles represented by consumption, and information connectivity to the outside world indirectly impacted HEVI by driving PD changes in non-urban areas. This indirect effect has shifted from positive to negative over the 20 years. These findings suggest that the indirect impacts of urbanization on vegetation growth are complicated and multifaceted, and understanding such impacts would be critically important to help turn urbanization into an opportunity for regional sustainable development.

Significant land cover change in China during 2001-2019: Implications for direct and indirect effects on surface ozone concentration

China has become one of the most prominent areas of global land cover change during the past few decades. These changes can directly influence meteorological parameters thus further regulating tropospheric ozone (O3) formation. Moreover, changes in biogenic emissions due to land cover variation can also have an indirect effect on O3 concentration. This study applied the Community Multiscale Air Quality model to comprehensively evaluate the impacts of significant land cover change on O3 levels in China during summertime between 2001 and 2019. The results showed that the daily maximum 8-h average O3 concentration (MDA8 O3) increased by 3.6-8.9 μg/m3, 2.8-8.0 μg/m3, 3.8-9.6 μg/m3, -1.5-6.2 μg/m3, and -0.6-2.5 μg/m3 in Beijing-Tianjin-Hebei region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, and Fenwei Plain, respectively, in response to land cover variation. The research identified that the direct effect was the primary factor in raising O3 levels which mainly altered O3 concentration by changing vertical import and dry deposition velocity. Moreover, land cover variation tended to decrease biogenic nitric oxide emission and increase biogenic volatile organic compounds emission on the whole, and cause an obvious increase of MDA8 O3 by 1.8-4.9 μg/m3 in Pearl River Delta due to the indirect effect. This study offered valuable insights into the impacts of land cover change on O3 levels, highlighting the need for policymakers to consider land cover variation on air pollutants concentration for devising comprehensive multi-pollutant control strategies.

Spatial and temporal variation of vegetation cover in the main mining area of Qibaoshan Town, China: Potential impacts from mining damage, solid waste discharge and land reclamation

The increasing frequency of mining activities in the world has led to many environmental pollution problems, such as mine wastewater discharge, mine solid waste dumps, and mine dust dispersion. These problems have negative implications for the environment and the public health of people living nearby the mining areas. Despite this, there are few methods to determine the state of mine pollution on a regional scale. Therefore, we applied remote sensing technologies to assess the mine pollution situation, especially the mine solid waste pollution, of a mining area, taking Qibaoshan Town, Liuyang City, Hunan Province, China, as an example. In our research, we have calculated the vegetation cover change of the Qibaoshan Town over the years (2000-2020), charted the vegetation coverage grade maps, and analysed the tendency of vegetation cover changes, to infer the mine pollution situation, the progress of pollution treatment and the efforts made by the local government and the mines on mine pollution disposal and the land reclamation. Additionally, mining damage can bring about geological hazards such as surface subsidence leading to vegetation destruction, while mining solid waste pollution and discharge can occupy a large amount of land and thus lead to vegetation reduction. As a result, this method of calculating FVC changes in a mining area is particularly suitable for assessing the extent of mining damage, the status of solid waste pollution and discharge, and the progress of land reclamation. In the abstract, we claim that this short communication article serves as a guide to start a conversation, and encourages experts and scholars to engage in this area of research.

Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.