Detection of long-term vegetation dynamics is important for identifying vegetation improvement and degradation, especially for rapidly urbanizing regions with intensive land cover conversions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration has experienced rapid urbanization during the past decades with profound impacts on vegetation, so there is an urgent need to evaluate vegetation dynamics across land use/cover change (LUCC). Based on the normalized difference vegetation index (NDVI) during 2001–2020, we used coefficient of variation, Theil–Sen median trend analysis, and Hurst exponent to analyze the spatiotemporal change and future consistency of vegetation growth among the main LUCC in the GBA. Results demonstrated that low NDVI values with high fluctuations were mainly distributed in the central urban areas, whereas high NDVI values with low fluctuations were primarily located in the peripheral hilly mountains. The area-averaged NDVI showed an overall increasing trend at a rate of 0.0030 year⁻¹, and areas with vegetation improvement (82.99%) were more than four times those with vegetation degradation (17.01%). The persistent forest and grassland and the regions converted from built-up to vegetation displayed the most obvious greening; NDVI in over 90% of these areas showed an increasing trend. In contrast, vegetation browning occurred in more than 60% of the regions converted from vegetation to built-up. Future vegetation change in most areas (91.37%) will continue the existing trends, and 80.06% of the GBA was predicted to develop in a benign direction, compared to 19.94% in a malignant direction. Our results contribute to in-depth understanding of vegetation dynamics during rapid urbanization in the GBA, which is crucial for vegetation conservation and land-use optimization.

Soil quality evaluation is an effective pathway to understanding the status of soil function and ecosystem productivity. Numerous studies have been made in managed ecosystems and land cover to quantify its effects on soil quality. However, little is coincident regarding soil quality assessment methods and its compatibility in highly heterogeneous soil. This paper used the soil survey database of Taihang Mountains as a case study to: (i) Examine the feasibility of soil quality evaluation with two different indicator methods: Total data set (TDS) and minimum data set (MDS); and (ii) analyze the controlling factors of regional soil quality. Principal component analysis (PCA) and the entropy method were used to calculate soil quality index (SQI). SQI values assessed from the TDS and MDS methods were both significantly correlated with normalized difference vegetation index (p < 0.001), suggesting that both indices were effective to describe soil quality and reflect vegetation growth status. However, the TDS method represented a slightly more accurate assessment than MDS in terms of variance explanation. Boosted regression trees (BRT) models and path analysis showed that soil type and land cover were the most important controlling factors of soil quality, within which soil type had the greatest direct effect and land cover had the most indirect effect. Compared to MDS, TDS is a more sensitive method for assessing regional soil quality, especially in heterogeneous mountains. Soil type is the fundamental factor to determining soil quality. Vegetation and land cover indirectly modulate soil properties and soil quality.

Understanding how soil water source is used spatiotemporally by tree species and if native species can successfully coexist with introduced species is crucial for selecting species for afforestation. In the rocky mountainous areas of the Taihang Mountains, alien Robinia pseudoacacia L. has been widely afforested into the native shrublands dominated by Ziziphus jujuba Mill var. spinosa and Vitex negundo L. var. heterophylla to improve forest coverage and soil nutrients. However, little is known about the water relation among species, especially seasonal water use sources in different microsites. We selected the soil and plant xylem samples of two opposite microtopographic sites (ridge and valley) monthly in the growth season to analyze isotope composition. The proportions of water sources were quantified by the MixSIAR model and compared pairwise between species, microsites and seasons. We found that deep subsoil water at a depth of 40–50 cm contributed up to 50% of the total water uptake for R. pseudoacacia and Z. jujuba in the growing season, indicating that they stably used deeper soil water and had intense water competition. However, V. negundo had a more flexible water use strategy, which derived more than 50% of the total water uptake from the soil layer of 0–10 cm in the rainy season, but majorly captured soil water at a depth of 30–50 cm in the dry season. Therefore, high niche overlaps were shown in V. negundo with the other two species in the dry season, but niche segregation was seen in the rainy season. The microtopographic sites did not shift the seasonal dynamic of the water source use patterns of the three studied species, but the water use niche overlap was higher in the valley than in the ridge. Taken together, the introduced species R. pseudoacacia intensified water competition with the native semi-arbor species Z. jujuba, but it could commonly coexist with the native shrub species V. negundo. Therefore, our study on seasonal water use sources in different microsites provides insight into species interaction and site selection for R. pseudoacacia afforestation in the native shrub community in rocky mountainous areas. It is better to plant R. pseudoacacia in the shrubland in the valley so as to avoid intense water competition and control soil erosion.

How to distinguish the relative role of climate change and human activities in vegetation dynamics has attracted increasing attention. However, most of the current studies concentrate on arid and semiarid regions, while the relative contributions of climate change and human activities to vegetation changes remain unclear in warm-humid regions. Based on the normalized difference vegetation index (NDVI) and climatic variables (temperature, precipitation, radiation) during 2001–2020, this study used the Theil–Sen median trend analysis, partial correlation analysis, and residual trend analysis to analyze the spatiotemporal pattern of vegetation trends, the response of vegetation to climate variations, and the climatic and anthropogenic contributions to vegetation dynamics in the warm and humid Guangdong Province of south China. Results showed that the NDVI in most areas exhibited an increasing trend. Changes in climatic variables displayed different spatial variations which, however, were not significant in most areas. Vegetation responded diversely to climate change with temperature as the most important climatic factor for vegetation improvement in most areas, while precipitation was the dominant climatic factor in the southern edge region and radiation was the dominant climatic factor in the central and western regions. Vegetation in most areas was influenced by both climate change and human activities, but the contribution rate of human activities was commonly much higher than climate change. The findings of this study are expected to enhance our understanding of the relative climatic and anthropogenic contributions to vegetation changes in warm-humid regions and provide a scientific basis for future ecological policies and ecosystem management in highly urbanized regions.

In the granite regions of southern China, benggang poses a substantial threat to the ecological environment due to significant soil erosion. This phenomenon also imposes constraints on economic development, necessitating substantial investments in restoration efforts in recent decades. Despite these efforts, there remains a notable gap in comprehensive risk assessment that integrates both the erosion risk and disaster risk associated with benggang. This study focuses on a representative benggang area in Wuhua County, Guangdong province, employing transformer methods and high-resolution imagery to map the spatial pattern of the benggang. The integrated risk of benggang was assessed by combining soil-erosion risk and disaster risk, and cultivated land, residential land, and water bodies were identified as key disaster-affected entities. The machine-learning Segformer model demonstrated high precision, achieving an Intersection over Union (IoU) of 93.17% and an accuracy (Acc) of 96.73%. While the number of large benggang is relatively small, it constitutes the largest area proportion (65.10%); the number of small benggang is more significant (62.40%) despite a smaller area proportion. Prioritization for benggang management is categorized into high, medium, and low priority, accounting for 17.98%, 48.34%, and 33.69%, respectively. These priorities cover areas of 30.27%, 42.40%, and 27.33%, respectively. The findings of this study, which offer benggang management priorities, align with the nature-based solutions approach. Emphasizing the importance of considering costs and benefits comprehensively when formulating treatment plans, this approach contributes to sustainable solutions for addressing the challenges posed by benggang.