Search Results (14)

Operational landslide susceptibility mapping (LSM) remains challenging in regions with pronounced geo-environmental heterogeneity, where single global models often overlook spatially variable landslide-environment relationships. Northern Guangdong, China, is a typical humid mountainous region where steep terrain, diverse lithology, and highly variable rainfall produce non-stationary landslide controls. To address this challenge, we develop a cluster-informed LSM framework that integrates unsupervised consensus K-means sub-zoning with localized Random Forest (RF) models and SHapley Additive exPlanations (SHAP). We use a harmonized inventory of 1510 landslides (2011–2022), together with twelve 30 m conditioning factors, for model training and validation. Compared with logistic regression, Support Vector Machines (SVM), and Light Gradient Boosting Machine (LightGBM), RF consistently achieves higher accuracy across clusters, and the cluster-wise RF ensemble attains pooled ACC = 0.8212, F1 = 0.8176, and AUC = 0.8956. SHAP highlights both regionally consistent predictors (e.g., NDVI, distance to road) and distinct cluster-specific controls linked to geomorphic and hydrologic settings. The proposed framework enhances predictive accuracy, produces finer susceptibility gradients, and yields better-calibrated probability estimates than a single global model. These results demonstrate that explicitly accounting for geo-environmental heterogeneity can generate interpretable, spatially adaptive susceptibility outputs. By identifying high-risk zones for priority monitoring, land-use regulation, infrastructure protection, and mitigation planning, the proposed framework provides a practical decision-support tool for sustainable mountain development and disaster risk reduction in heterogeneous mountainous regions. Full article

Soil is the principal physical space for water conservation (WC), so analyzing the driving forces of soil on WC is significant for studying WC services and integrated environmental management. Guangdong Province, a major economic province in China, was taken as a research case to deeply analyze the spatiotemporal pattern of WC function from 2000 to 2020 with InVEST, and to reveal its soil driving forces using a classical mathematical statistics method. We found that, from 2000 to 2020, the WC functions in Guangdong Province exhibited significant spatiotemporal differences. High-value regions were mainly concentrated in the northern and western mountainous regions, while low-value areas were primarily in the Pearl River Delta. The total WC in Guangdong showed a fluctuating upward trend, with 10.71% of its area experiencing extremely significant improvement in the Pearl River Delta, followed by Northern Guangdong. Moreover, WC is influenced by the types and distribution areas of different soils. Red soil has the highest WC depth and volume, followed by paddy soil, while lateritic red soil has the lowest WC depth. Furthermore, soil components exhibited complex stratified relationships with precipitation-normalized WC (PNWC). Components characterized by cation exchange capacity (CEC), pH, and total exchangeable bases (TEB) were positively associated with PNWC, whereas aluminum saturation (ALSA) showed a negative association within the corresponding soil components. The findings provide an important scientific basis for the ecological governance of ecosystem WC functions and water resource management. Full article

Ecological vulnerability assessment serves as a prerequisite for ecological governance, yet evaluating large-scale ecological vulnerability remains challenging. To address this challenge, this study integrates geological elements into ecological vulnerability assessment, taking Ruyuan Area in the Northern Guangdong Mountains, China, as a case study. The area faces ecological hazards such as land desertification and soil erosion, indicating severe governance challenges. This study selected 14 ecological vulnerability factors and constructed assessment models based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). A total of 800 ecological vulnerability sampling points were obtained by combining field survey data with remote sensing imagery. The models were trained using binary vulnerability labels. The resulting continuous probability outputs were then classified into five vulnerability levels using the natural breaks method to generate the final ecological vulnerability map. It should be noted that the multi-level vulnerability map represents graded probability-based differentiation rather than supervised multi-class prediction. Model performance was validated using three metrics: Area Under Receiver Operating Characteristic Curve (AUC–ROC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The CNN (AUC = 0.916) model outperformed the DNN model (AUC = 0.895). According to the CNN-based classification results, non-vulnerable, slightly vulnerable, mildly vulnerable, moderately vulnerable, and highly vulnerable areas accounted for 36.19%, 22.85%, 14.24%, 12.31%, and 14.41% of the total area, respectively. High ecological vulnerability zones were concentrated in Daqiao, Luoyang, Dabu, and parts of Rucheng towns, with soil parent material and vegetation coverage identified as the main contributing factors, among which parent material was the most important. This finding underscores the notable impact of geological factors on local ecological vulnerability. Based on these results, nine ecological–geological subareas were delineated, and targeted ecological protection and restoration recommendations were proposed. This study, employing machine learning techniques, constructed an ecological vulnerability assessment model incorporating geological elements, thereby providing scientific support for targeted ecological governance in the study area. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

Symplocos is an ecologically important genus that plays vital roles in subtropical evergreen broad-leaved mountain forests, including contributing to nutrient cycling, providing shelter and habitats for various organisms, and supporting overall plant diversity across East and Southeast Asia. Many species exhibit high levels of endemism and sensitivity to environmental change. China, with its wide range of ecosystems and climatic zones, is home to 18 endemic Symplocos species. Studies revealed that global warming is driving shifts in species diversity, particularly in mountains. Our study explores the current and projected richness patterns of endemic Symplocos species in China under climate change scenarios, emphasizing the implications for conservation planning. We applied stacked species distribution models (SSDMs), using key bioclimatic and environmental variables to predict current and future habitat suitability for endemic Symplocos species, evaluated model performance through multiple accuracy metrics, and generated ensemble projections to assess richness patterns under climate change scenarios. To assess the spatial configuration and fragmentation patterns of the endemic species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The produced models demonstrated high accuracy with AUC > 0.9 and TSS > 0.75, highlighting the critical role of bioclimatic variables, particularly precipitation and temperature, in shaping endemic Symplocos distribution. Our analysis identifies the current hotspots of Symplocos endemism along southeastern China, particularly in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi. These areas are at high risk, with up to 35% of endemic Symplocos species richness predicted to be lost over the next 60 years due to climate change. The study predicts a high decrease in endemic Symplocos species richness, especially in South China (e.g., Fujian, Guangdong, Guizhou, Yunnan, southern Shaanxi), and mid-level decreases in East China (e.g., Heilongjiang, Jilin, eastern Inner Mongolia, Liaoning). Conversely, potential increases in endemic Symplocos species richness are projected in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui, suggesting these regions may serve as future refugia for endemic Symplocos species. The analysis of the landscape structure and configuration revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios. However, under the SSP585 scenario by 2080, the medium richness class showed a more pronounced decrease in aggregation index and increase in number of patches relative to other richness classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. These structural changes suggest a potential reduction in habitat quality and connectivity, posing significant risks to the persistence of endemic Symplocos populations, which underscores the urgent need for targeted smart-climate conservation strategies that prioritize both current hotspots and potential future refugia to enhance the resilience of endemic Symplocos forests and their ecosystems in the face of climate change. Full article

This research focuses on the cultural landscape of traditional village toponyms in the northern Guangdong region, aiming to reveal the spatial distribution, site selection characteristics, temporal evolution patterns, and influencing factors of toponyms. The study employs quantitative statistics and ArcGIS spatial analysis methods, combining place name classification and kernel density analysis to explore the mechanisms through which natural and human factors influence place name distribution. The main findings are as follows: (1) Traditional village toponyms exhibit a characteristic of “large dispersion and small aggregation” with high-density areas mainly concentrated in Meizhou and Qingyuan. (2) Natural toponyms dominate, showing a strong correlation with river valley and plain environments, while village location demonstrates hydrophilicity and terrain suitability. Human toponyms enrich the landscape’s connotation through cultural identity and social memory, reflecting the profound influences of Confucian agricultural education traditions and immigrant cultures. (3) Economic activities and population migrations during historical periods have significantly shaped the cultural landscape of toponyms, not only promoting the evolution of village site selection and distribution patterns but also profoundly affecting naming conventions for toponyms. This research emphasizes the importance of protecting the cultural landscape of toponyms while achieving a symbiotic relationship between cultural value and economic benefits through regional cultural tourism development, laying a theoretical foundation for the long-term preservation and sustainable development of regional cultural heritage. Full article

Based on Doppler radar observation and reanalysis data, the statistical characteristics of mesovortices (MVs) during the first rainy season (April–June) in Guangdong, South China, from 2017 to 2019 are studied, including their spatiotemporal distributions, structural features and favorable environmental conditions. The results show that the MVs usually exhibit short lifetimes; about 70% last for less than 30 min. The intensity and horizontal scale of the MVs are proportional to their lifetime. Long-lived MVs have larger horizontal scales and stronger intensities than short-lived ones. The MVs are mainly observed over the Pearl River Delta region, followed by western Guangdong Province, but relatively fewer in both eastern and northern Guangdong Province. The uneven spatial distribution of the MVs is closely related to the differences in the topographical features and the environment conditions over South China. MVs are prone to form over flat regions. The Pearl River Delta and eastern Guangdong regions are relatively flat compared to the more mountainous western and northern Guangdong regions. Moreover, the monsoonal south-westerlies, water vapor flux, atmospheric instability and vertical wind shear over southwest Guangdong are significantly larger than those in other regions and are favorable for the formation of MVs. The occurrence frequencies of MVs in central and southern parts of Guangdong display similar diurnal variations, reaching the peak during the late afternoon and early evening while dropping to the minimum overnight. However, the situation is opposite in northern Guangdong, with the peak overnight and the minimum during the late afternoon and early evening. The regional differences in diurnal variation are likely related to the moving direction of mesoscale convective systems (MCSs) in Guangdong. Full article

Referencing the land use classification system of the “production-living-ecological” space and using 1 km × 1 km grids, this study examines the spatial pattern changes of “production-living-ecological” space in Guangdong Province, China, from 1990 to 2017. In the study, a multiple linear regression analysis model was constructed to explore the influencing factors and attribution mechanism of the changes. The results showed that between 1990 and 2017, the production spaces were mainly distributed in the Pearl River Delta and other coastal areas, showing a slight expansion trend (1). The expansion of production spaces mainly gathered in the Pearl River Delta, while the reduction was characterized by point-type dispersed. Living spaces were mainly distributed in the Pearl River Delta, the Shantou–Shanwei–Chaozhou–Jieyang urban agglomeration, the Zhanjiang-Maoming–Yangjiang urban agglomeration, and other rapidly growing urbanized areas. They showed a spatial pattern of “large scale agglomeration and small scale dispersion” with a trend towards expansion. Living spaces in urban agglomerations such as the Pearl River Delta showed a large-scale expansion from the core to the peripheral area, while expansion in other areas was small-scale and point-type. The reduction of living spaces was point-type dispersed. The ecological spaces were mainly distributed in mountainous and hilly areas in eastern, western, and northern Guangdong and showed a “regional agglomeration and partially fragmented” spatial pattern. Ecological spaces in urban agglomerations showed large-scale and regional reductions, while reductions in other areas were small-scale and point-type. Ecological space expansions were point-type dispersed. Human, natural, and especially land-use type factors drove the changes of Guangdong’s production-living-ecological spaces (2). The changes of the production-living-ecological space pattern resulted from the interaction between human society, nature, and politics (3). Full article

Guangdong Province is one of China’s largest and most developed regions. It is home to more than 113 million people and features unique geographical and climatic characteristics. Typhoons that pass through often result in heavy rainfall, which causes flooding. The region’s risk of typhoon and flood disasters, and the resulting indirect economic impacts, have not been fully assessed. The purpose of this paper is to introduce a method for assessing the spatial and temporal cumulative risk of typhoon-induced flood disasters, and the resulting indirect economic impacts, in order to deal with the uncertainty of disasters. We combined an analytic hierarchy process (AHP) and spatial analysis using a geographic information system (GIS) to produce a comprehensive weighted-risk assessment from three different aspects of disaster, vulnerability, and resilience, with 11 indicators. A new method for computing risk based on spatial and temporal cumulative patterns of typhoon-induced flood disasters was introduced. We incorporated those direct impacts into a computable general equilibrium (CGE) model to simulate indirect economic losses in alternative scenarios according to different risk levels. We found that the risk in the coastal area is significantly higher than that in the northern mountainous area. The coastal areas of western Guangdong, Pearl River Delta, and Chaoshan Plain face the greatest risk. Our results indicate that typhoon and flood disasters have negative effects on the real GDP, residents’ income, consumption, and several other macroeconomic indicators. We found differing disaster impacts across industrial sectors, including changes in the output, prices, and flow of labor among industries. Our estimates provide scientific support for environmental planning, spatial planning, and disaster-risk management in this important region. They are also of reference value for the development of disaster management strategies in similar climatic regions around the world. Full article

The characteristics of the synoptic situation and the evolution of the organized warm-sector rainfalls (OWSRs) in the coastal region of South China in the pre-summer rainy season were investigated, using a period (2011–2016) of high-resolution observational data and European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-Interim) data. The results show that a strong southwesterly low-level jet (LLJ) ahead of a trough over southwestern China with a marked boundary-layer jet (BLJ) over the northern South China Sea (synoptic situation SWLLJ) or a prominent, low-level anticyclone over the Yangtze River Basin (synoptic situation ACR) is present when the OWSRs occur in the coastal region of South China. The OWSRs are prone to initiate on the windward side of the coastal mountains, owing to the convergence enhanced by the colliding of the BLJ with the mountains and the coupling of double LLJs near the coast (for SWLLJ), or due to the convergence between northerly and southeasterly winds near the coastal mountains (for ACR). The OWSRs present a long extension when the LLJ axis is nearby. The translation of the LLJ itself also promotes the long extension of the OWSRs. In contrast, the OWSRs show a short extension when the LLJ axis is farther away or ACR occurs. Meanwhile, the OWSRs are directed northeastward in Guangxi Province and more eastward in Guangdong Province, probably owing to the orientation difference of the LLJ in these two provinces. The rainfall systems in the ACR situation tend to move eastward, whereas those in the SWLLJ situation are prone to move eastward when equivalently strong or much-stronger upper-level winds overlay the LLJ, but move northeastward when much weaker upper-level winds couple with the LLJ. Full article

China’s coastal areas suffer from typhoon attacks every year. Rainstorms induced by typhoons characteristically are high intensity with a large amount of rain and usually induce floods and waterlogging in the affected area. Guangdong province has the highest frequency of typhoon hits in China. It has a special geographical position as well as unique climatic features, but the typhoon flood disaster risk has not been fully assessed in this area. This article attempts to fill this gap by providing a comprehensive risk assessment for the area. By combining the Analytical Hierarchy Process (AHP) and multi-factor analysis through geographic information system (GIS) and the comprehensive weighted evaluation, the typhoon flood disaster risk is evaluated from four different aspects with seventeen indicators. A comprehensive study of the typhoon flood disaster risk is carried out, and the risk maps with a resolution of 1 km² have been made. There is a good coherence between the typhoon flood risk map and historical records of typhoon floods in Guangdong province. The results indicate that the comprehensive typhoon flood disaster risk in the coastal regions of Guangdong province is obviously higher than in the Northern mountainous areas. Chaoshan plain and Zhanjiang city have the highest risk of typhoon flood disaster. Shaoguan and Qingyuan cities, which are in the Northern mountainous areas, have the lowest risk. The spatial distribution of typhoon flood disaster risks shows that it has certain regulations along the coast and rivers, but it may be affected by economic and human activities. This article is significant for environmental planning and disaster management strategies of the study area as well as in similar climatic regions in other parts of the world. Full article

In the context of climate change, the input of acid substances into rivers, caused by human activities in the process of industrial and agricultural development, has significantly disrupted river systems and has had a profound impact on the carbon cycle. The hydrochemical composition and which main sources of the Lianjiang River (LR), a subtropical karst river in northern Guangdong Province, South China, were analyzed in January 2018. The objective was to explicate the influence on the deficit proportion of CO₂ consumption, resulting from carbonate chemical weathering (CCW), driven by nitric acid (HNO₃) and sulfuric acid (H₂SO₄), which is affected by exogenous acids from the industrial regions in north of the Nanling Mountains and the Pearl River Delta. The response of the riverine carbonate system to exogenous acid-related weathering was also discussed. HCO₃⁻ and Ca²⁺, respectively, accounted for 84.97% of the total anions and 78.71% of the total cations in the surface runoff of the LR, which was characterized as typical karst water. CCW was the most important material source of river dissolved loads in the LR, followed by human activities and silicate chemical weathering (SCW). Dissolved inorganic carbon (DIC), derived from CCW induced by carbonic acid (H₂CO₃), had the largest contribution to the total amount of DIC in the LR (76.79%), and those from CCW induced by anthropogenic acids (HNO₃ and H₂SO₄) and SCW contributed 13.56% and 9.64% to the total DIC, respectively. The deficit proportion of CO₂ consumption associated with CCW resulting from sulfuric acid and nitric acid (13.56%), was slightly lower than that of the Guizhou Plateau in rainy and pre-rainy seasons (15.67% and 14.17%, respectively). The deficit percentage of CO₂ uptake associated with CCW induced by sulfuric acid and nitric acid, accounted for 38.44% of the total CO₂ consumption related to natural CCW and 18.84% of the anthropogenic acids from external areas. DIC derived from CCW induced by human activities, had a significant positive correlation with the total alkalinity, SIc and pCO₂ in river water, indicating that the carbonate system of the LR was also driven by exogenous acids, with the exception of carbonic acid. More attention should be paid to the effects of human activities on the chemical weathering and riverine carbonate system in the karst drainage basin. Full article

Spatially explicit knowledge of aboveground biomass (AGB) in large areas is important for accurate carbon accounting and quantifying the effect of forest disturbance on the terrestrial carbon cycle. We estimated AGB from 1990 to 2011 in northern Guangdong, China, based on a spatially explicit dataset derived from six years of national forest inventory (NFI) plots, Landsat time series imagery (1986–2011) and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radars (PALSAR) 25 m mosaic data (2007–2010). Four types of variables were derived for modeling and assessment. The random forest approach was used to seek the optimal variables for mapping and validation. The root mean square error (RMSE) of plot-level validation was between 6.44 and 39.49 (t/ha), the normalized root-mean-square error (NRMSE) was between 7.49% and 19.01% and mean absolute error (MAE) was between 5.06 and 23.84 t/ha. The highest coefficient of determination R² of 0.8 and the lowest NRMSE of 7.49% were reported in 2006. A clear increasing trend of mean AGB from the lowest value of 13.58 t/ha to the highest value of 66.25 t/ha was witnessed between 1988 and 2000, while after 2000 there was a fluctuating ascending change, with a peak mean AGB of 67.13 t/ha in 2004. By integrating AGB change with forest disturbance, the trend in disturbance area closely corresponded with the trend in AGB decrease. To determine the driving forces of these changes, the correlation analysis was adopted and exploratory factor analysis (EFA) method was used to find a factor rotation that maximizes this variance and represents the dominant factors of nine climate elements and nine human activities elements affecting the AGB dynamics. Overall, human activities contributed more to short-term AGB dynamics than climate data. Harvesting and human-induced fire in combination with rock desertification and global warming made a strong contribution to AGB changes. This study provides valuable information for the relationships between forest AGB and climate as well as forest disturbance in subtropical zones. Full article

Hakka culture reveals how the ancient Chinese lived. Hakka architecture yields much evidence that modern Hakka culture of the south flows from the ancient stream of the north. The genius of the Hakka is best seen in the unique roundhouses of the mountainous borderland of three provinces—Guangdong, Fujian and Jiangxi. However, in completing the fourth of five migrations, the Hakka returned to the traditional building styles of the northern plains of China and built Wufenglou on the plains of southern Guangdong province. The structures not only facilitate environmental sustainability, but endow the inhabitants with material, social and spiritual sustainability. Full article