Search Results (5)

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach. Full article

Phyllostachys praecox is a valuable tree species in karst ecosystems, but improper mulching practices can worsen soil degradation. Understanding soil nutrient limitations is crucial for successful restoration and sustainable development. However, it remains unclear whether and how mulching management of Phyllostachys praecox affects soil enzyme stoichiometry and nutrient limitation in karst areas. Here, we conducted a field experiment in Chongqing karst bamboo forest ecosystems with four mulching treatments: 1-year (T1), 2-years (T2), 1-year and recovery and 1-year (T3), and no mulching (CK). We investigated the activities of the C-acquiring enzyme β-1,4-glucosidase (BG), N-acquiring enzymes L-leucine aminopeptidase (LAP) and β-1,4-N-acetylglucosaminidase (BNA), as well as P-acquiring enzyme phosphatase activity (AP), to assess the limitations of C, N or P and identify the main factors influencing soil microbial nutrient limitation. Compared with the CK treatment, both the T2 and T3 management treatments significantly increased the SOC, TN, MBC, and MBN. Furthermore, the soil enzyme stoichiometric ratio in the karst bamboo forests deviated from the global ecosystem ratio of 1:1:1. T1 > T3 > CK > T2 presented higher values of C/(C + N) and C/(C + P), with T1 having values that were 1.10 and 1.12 greater than those of T2, respectively. Additionally, there was a significant negative correlation between microbial C and N limitations and total nutrients, but a positive correlation with microbial biomass ratios. In conclusion, changes in mulching management of Phyllostachys praecox affect soil enzyme stoichiometry activities and their ratios by influencing total nutrients and microbial biomass ratios. This study suggests an alternate year cover pattern (mulching in one year and resting in the next) as a scientific management approach for bamboo forests, contributing to a better understanding of nutrient limitation mechanisms in karst bamboo forest ecosystems. Full article

Intercropping affects soil microbial community structure significantly; however, the effects on understory medicinal plants in karst areas remain unclear. We investigated the effects of four intercropping systems (Moso bamboo, Chinese fir, bamboo-fir mixed forest, and forest gap) on the rhizosphere microbial communities of giant lily (Cardiocrinum giganteum), an economically important medicinal plant in China. We assessed the intercropping impact on rhizosphere microbial diversity, composition, and co-occurrence networks and identified key soil properties driving the changes. Bacterial and fungal diversity were assessed by 16S rRNA and ITS gene sequencing, respectively; soil physicochemical properties and enzyme activities were measured. Moso bamboo system had the highest fungal diversity, with relatively high bacterial diversity. It promoted a distinct microbial community structure with significant Actinobacteria and saprotrophic fungi enrichment. Soil organic carbon, total nitrogen, and available potassium were the most influential drivers of microbial community structure. Co-occurrence network analysis revealed that the microbial network in the Moso bamboo system was the most complex and highly interconnected, with a higher proportion of positive interactions and a greater number of keystone taxa. Thus, integrating Moso bamboo into intercropping systems can enhance soil fertility, microbial diversity, and ecological interactions in the giant lily rhizosphere in karst forests. Full article

Bamboo invasion into broadleaf forests is a common phenomenon in karst areas; however, the effect of bamboo invasion on soil organic carbon (SOC) in karst areas and the mechanism of the effect are not clear. We selected the study site with broad-leaved forests (BF), mixed forests (MF), and pure bamboo (Indosasa shibataeoides McClure) forests (IF). Furthermore, we sampled the soil from 0 cm to 20 cm and 20 cm to 40 cm layers in the region and investigated the soil properties, organic carbon fractions, and microbial communities. At the same time, we sampled the litterfall layer of different stands and determined the biomass. The results showed that bamboo invasion increased the litterfall biomass per unit area of karst forest, increased the bulk weight of the 0–20 cm soil layer, and lowered the soil pH in the 0–20 cm and 20–40 cm soil layers, bamboo invasion consistently increased the content of soil AN and AK, whereas the content of AP was significantly reduced after bamboo invasion. Both active organic carbon groups (MBC, DOC, and EOC) and passive organic carbon groups (Fe/Al-SOC and Ca-SOC) increased significantly after bamboo invasion. The bamboo invasion increased the diversity of soil microorganisms and bacterial communities; the relative abundance of Actinobacteriota increased in MF and IF, while the relative abundance of Firmicutes decreased in IF. The structure of fungal communities was altered during the bamboo invasion, with an increase in the relative abundance of Mortierellomycota and a decrease in the relative abundance of Basidiomycota at the level of fungal phyla. Partial least squares path modeling analysis identified bamboo invasion enhanced SOC sequestration mainly by increasing litterfall biomass and altering the structure of the fungal community, and the effect of bacteria on SOC was not significant. Our study suggests that bamboo invasion of broadleaf forests is more favorable to soil organic carbon sequestration in karst areas. Full article

Combining machine learning algorithms with multi-temporal remote sensing data for fine classification of wetland vegetation has received wide attention from researchers. However, wetland vegetation has different physiological characteristics and phenological information in different growth periods, so it is worth exploring how to use different growth period characteristics to achieve fine classification of vegetation communities. To resolve these issues, we developed an ensemble learning model by stacking Random Forest (RF), CatBoost, and XGBoost algorithms for karst wetland vegetation community mapping and evaluated its classification performance using three growth periods of UAV images. We constructed six classification scenarios to quantitatively evaluate the effects of combining multi-growth periods UAV images on identifying vegetation communities in the Huixian Karst Wetland of International Importance. Finally, we clarified the influence and contribution of different feature bands on vegetation communities’ classification from local and global perspectives based on the SHAP (Shapley Additive explanations) method. The results indicated that (1) the overall accuracies of the four algorithms ranged from 82.03% to 93.37%, and the classification performance was Stacking > CatBoost > RF > XGBoost in order. (2) The Stacking algorithm significantly improved the classification results of vegetation communities, especially Huakolasa, Reed-Imperate, Linden-Camphora, and Cephalanthus tetrandrus-Paliurus ramosissimus. Stacking had better classification performance and generalization ability than the other three machine learning algorithms. (3) Our study confirmed that the combination of spring, summer, and autumn growth periods of UAV images produced the highest classification accuracy (OA, 93.37%). In three growth periods, summer-based UAVs achieved the highest classification accuracy (OA, 85.94%), followed by spring (OA, 85.32%) and autumn (OA, 84.47%) growth period images. (4) The interpretation of black-box stacking model outputs found that vegetation indexes and texture features provided more significant contributions to classifying karst wetland vegetation communities than the original spectral bands, geometry features, and position features. The vegetation indexes (COM and NGBDI) and texture features (Homogeneity and Standard Deviation) were very sensitive when distinguishing Bermudagrass, Bamboo, and Linden-Camphora. These research findings provide a scientific basis for the protection, restoration, and sustainable development of karst wetlands. Full article