Search Results (142)

This paper utilizes a localized Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to calculate the spatio-temporal distribution of water conservation in the river source area of northwestern Sichuan over the past 15 years. It explores the influence of climatic and topographic factors on water conservation under different vegetation cover systems (forest, alpine grassland, and plateau marsh) through trend analysis and correlation analysis. The study reveals an upward trend in total water conservation over the 15-year period, following a low–middle–high–low spatial pattern from east to west. Analyzing the correlation between precipitation, evapotranspiration (ET), elevation, and slope with water conservation under three vegetation cover systems, the study found ET negatively correlated with water conservation depth, with correlation coefficients (Rs) of −0.69, −0.71, and −0.70, respectively. Precipitation and elevation are positively correlated with water conservation depth, with R values of 0.21, 0.24, and 0.14 and 0.23, 0.05, and 0.21, respectively. Slope is negatively correlated in forests (R = −0.19), but positively correlated in alpine grassland and swamp systems (R = 0.02 and 0.29, respectively). These findings highlight the significant influence of climate, topography, and subsurface factors on water conservation, offering valuable insights for precise water resource management and ecological protection in the study area. Full article

Mutualisms characterized by reciprocal benefits between species are a fundamental relationship of tropical ecosystems. Ant Gardens (AGs) represent an interesting ant-plant mutualism, involving specialized interactions between vascular epiphytes and ants. While this relationship has been extensively studied in various tropical regions, the available information on Peruvian ecosystems is limited. The objective of this study was to identify the ant and epiphyte species that constitute AGs. From February 2023 to January 2024, a study was conducted on two 50 × 10 m transects within the Mauritia flexuosa peat swamp forest, located within the Water Association Aguajal Renacal del Alto Mayo (ADECARAM) Tingana in San Martín, Peru. A total of 69 ant gardens were documented, comprising 18 phorophyte species, 19 epiphyte species, and three ant species. The results demonstrated that neither the height nor the diameter at breast height (DBH) of phorophytes exhibited a statistically significant correlation with the number of AGs per host. However, a positive correlation was observed between the length and width of the AGs and the number of ants per AG. The findings of this study contribute to the understanding of AG mutualism in Peruvian ecosystems. Full article

The rapid spread of invasive plants presents significant challenges for the management of grasslands. Uncrewed aerial vehicles (UAVs) offer a promising solution for fast and efficient monitoring, although the optimal methodologies require further refinement. The objective of this research was to establish a rapid, repeatable, and cost-effective computer-assisted method for extracting Pedicularis kansuensis (P. kansuensis), an invasive plant species. To achieve this goal, an investigation was conducted into how different backgrounds (swamp meadow, alpine steppe, land cover) impact the detection of plant invaders in the Bayanbuluk grassland in Xinjiang using Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) with three feature combinations: spectral band, vegetation index (VI), and spectral band + VI. The results indicate that all three feature combinations achieved an overall accuracy ranging from 0.77 to 0.95. Among the three models, XGBoost demonstrates the highest accuracy, followed by Random Forest (RF), while Support Vector Machine (SVM) exhibits the lowest accuracy. The most significant feature bands for the three field plots, as well as the invasive species and land cover, were concentrated at 750 nm, 550 nm, and 660 nm. It was found that the green band proved to be the most influential for improving invasive plant extraction while the red edge 750 nm band ranked highest for overall classification accuracy among these feature combinations. The results demonstrate that P. kansuensis is highly distinguishable from co-occurring native grass species, with accuracies ranging from 0.9 to 1, except for SVM with six spectral bands, indicating high spectral variability between its flowers and those of co-occurring native background species. Full article

This study aims to investigate the depositional environment, sediment sources, and elemental occurrence of Upper Paleozoic coal in the Renjiazhuang Mining District, Western Ordos Basin. Furthermore, SEM-EDX, optical microscope (OM), ICP-AES, ICP-MS, and AAS were used. Compared with hard coal of the world, M3 coals were enriched in Ga, Li, Zr, Be, Ta, Hf, Nb, Pb, and Th, M5 coals were enriched in Li (CC = 10.21), Ta (CC = 6.96), Nb (CC = 6.95), Be, Sc, Ga, Hf, Th, Pb, Zr, In, and REY, while M9 coals were enriched in Li (CC = 14.79), Ta (CC = 5.41), Ga, W, Hf, Nb, Zr, Pb, and Th. In addition, minerals were mainly composed of kaolinite, dolomite, pyrite, feldspar, calcite, and quartz, locally visible minor amounts of monazite, zircon, clausthalite, chalcopyrite, iron dolomite, albite, fluorite, siderite, galena, barite, boehmite, and rutile. In addition, maceral compositions of M3 coals and M9 coals were dominated by vitrinite (up to 78.50%), while M5 coals were the main inertite (up to 76.26%), and minor amounts of liptinite. REY distribution patterns of all samples exhibited light REY enrichment and negative Eu anomalies. The geochemistry of samples (TiO₂ and Al₂O₃, Nb/Y and Zr × 0.0001/TiO₂ ratios, and REY enrichment types) indicates that the sediment sources of samples originated from felsic igneous rocks. Indicator parameters (TPI, GI, VI, GWI, V/I, Sr/Ba, Th/U, and Ce_N/Ce_N*) suggest that these coals were formed in different paleopeat swamp environments: M3 coal was formed in a lower delta plain and terrestrial (lacustrine) facies with weak oxidation and reduction, and M5 coal was formed in a terrestrial and dry forest swamp environment with weak oxidation–oxidation, while M9 coal was formed in a seawater environment of humid forest swamps and the transition from the lower delta plain to continental sedimentation with weak oxidation and reduction. Statistical methods were used to study the elemental occurrence. Moreover, Li, Ta, Hf, Nb, Zr, Pb, and Th elements were associated with aluminosilicates, and Ga occurred as silicate. Full article

The Kampar Peninsula, spanning approximately 735,091 hectares, is critical for its carbon reserves and biodiversity, including the endangered Sumatran tiger. However, nearly half of the 4 million hectares of peat swamp in the region is deforested, drained, decomposing, or burning, largely due to settlements and development projects. This research employs a mixed-method approach, using quantitative spatial analysis of Landsat imagery from 1990 to 2020 based on the Spectral Mixture Analysis (SMA) model to detect forest disturbances and classify land cover changes, utilizing the Normalized Difference Fraction Index (NDFI). Ground truthing validates the image interpretation with field conditions. Additionally, qualitative analysis through interviews and regulatory review examines spatial change trends, context, and driving factors. The result showed, over 30 years, that natural forest in the Kampar Peninsula decreased significantly from 723,895.30 hectares in 1990 to 433,395.20 hectares in 2020. The primary factors driving land use changes include the construction of access roads by oil companies in 1975, leading to extensive deforestation, and government policies during the New Order period that issued forest exploitation concessions and promoted transmigration programs, resulting in widespread establishment of oil palm and acacia plantations. Full article

Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study’s methodology can provide valuable reference information for wetland data extraction research and can be applied to a wide range of future research studies. Full article

Subtropical moss peatlands have important ecological functions, and their protection and restoration are urgent. The lack of understanding of the biogeochemical changes in subtropical moss peatlands after human disturbance, particularly regarding their underground ecological changes, limits the efforts towards their protection and restoration. In this study, typical subtropical moss peatlands and the Cryptomeria swamp forest (CSF) formed by long-term (more than 20 years) drainage and afforestation in the Yunnan–Guizhou Plateau of China were selected as the research sites. Moreover, 16S rRNA high-throughput sequencing technology was used to study the differences in soil bacterial community diversity and composition among a natural Sphagnum fen (SF), Polytrichum bog (PB), and CSF to explore the effects of drainage and afforestation on different types of moss peatlands and its mechanism combined with soil physicochemical properties. Results showed that (1) drainage and afforestation significantly reduced the α diversity of soil bacterial communities in SF while significantly increasing the α diversity of soil bacterial communities in PB. Soil bacterial communities of SF had the highest α diversity and had many unique species or groups at different taxonomic levels. (2) The impact of drainage and afforestation on the soil bacterial community composition in SF was significantly higher than that in PB. Drainage and afforestation caused significant changes in the composition and relative abundance of dominant groups of soil bacteria in SF at different taxonomic levels, such as significantly reducing the relative abundance of Proteobacteria, significantly increasing the relative abundance of Acidobacteria, and significantly reducing the ratio of Proteobacteria to Acidobacteria, but did not have a significant impact on the corresponding indicators of PB. The changes in the ratio of Proteobacteria to Acidobacteria may reflect changes in the trophic conditions of peatlands. (3) Soil moisture content, available phosphorus content, and pH were key driving factors for changes in soil bacterial community composition and diversity, which should be paid attention to in the restoration of moss peatlands. This study provides insights into the protection and restoration of subtropical moss peatlands. Full article

The standardized methods used in carbon markets require measurement of the biomass and carbon stored in trees, which can be quantified through allometric equations. The objective of this study was to analyze aboveground biomass estimates with allometric models in three mangrove species and compare them with those used by the Climate Action Reserve (CAR) standard. The mangrove forest in Tabasco, Mexico, was certified with the Forest Protocol for Mexico Version 2.0 (FPM) of the CAR standard. Allometric equations for mangrove species were reviewed to determine the most suitable equation for the calculation of biomass. The predictions of the allometric equations of the FPM were analyzed with data from Tabasco from the National Forest and Soil Inventory 2015–2020, and the percentages of trees within the ranges of diameters of the FPM equations were determined. The FPM equations generated higher biomass values for Rhizophora mangle and lower values for Avicennia germinans than the seven equations with which they were compared. In the mangrove swamp of Ejido Úrsulo Galván, Tabasco, 81.8% of the biomass of A. germinans, 34.4% of Laguncularia racemosa and 24.0% of R. mangle were within the diameter range of the FPM equations, and in Tabasco, 28.5% of A. germinans, 16.7% of L. racemosa and 5.7% of R. mangle were within the diameter range. For A. germinans and R. mangle, we recommend using the equation that considers greater maximum diameters. The allometric equations of the FPM do not adequately predict a large percentage of the biomass. Full article

Climate change poses significant challenges to ecosystems globally, demanding innovative methods for environmental conservation and restoration. Restoration initiatives require significant amounts of appropriate vegetation that is both adaptive and tolerant to the specific environmental factors. This study introduces an adaptive-vegetation model designed to support ecosystem resilience in the face of climate change. Traditional restoration methods often neglect dynamic environmental conditions and ecosystem interactions, but the model employs real-time data and predictive analytics to adapt strategies to evolving climate variables. The model takes a comprehensive approach, incorporating climate projections, soil health metrics, species adaptability, and hydrological patterns to inform restoration practices. By using a mix of adaptable native species, the model promotes biodiversity. In conclusion, according to the findings of our review, paludiculture and agroforestry could be implemented as models for improving climate resilience, particularly in tropical degraded peat swamp forests. These two models could improve the environment, the economy, and social functions. Finally, improving all three of these factors improves ecological stability. This adaptive-vegetation model represents a significant shift from static, uniform restoration approaches to dynamic, data-driven strategies tailored to specific environments. The future research directions underscore the need for ongoing innovation in conservation practices to safeguard ecosystems amid unprecedented environmental changes. Future efforts will focus on enhancing the model with advanced machine learning techniques and expanding its application to additional ecological contexts. Full article

Tidal freshwater forested wetlands (TFFWs) typically occur at the interface between upriver non-tidal forests and downstream tidal marshes. Due to their location, these forests are susceptible to estuarine and riverine influences, notably periodic saltwater intrusion events. The Mobile-Tensaw (MT) River Delta, one of the largest river deltas in the United States, features TFFWs that are understudied but threatened by sea level rise and human impacts. We surveyed 47 TFFW stands across a tidal gradient previously determined using nine stations to collect continuous water level and salinity data. Forest data were collected from 400 m² circular plots of canopy and midstory species composition, canopy tree diameter and basal area, stem density, and tree condition. Multivariate hierarchical clustering identified five distinct canopy communities (p = 0.001): Mixed Forest, Swamp Tupelo, Water Tupelo, Bald Cypress, and Bald Cypress and Mixed Tupelo. Environmental factors, such as river distance (p = 0.001) and plot elevation (p = 0.06), were related to community composition. Similar to other TFFWs along the northern Gulf of Mexico, forests closest to Mobile Bay exhibited lower basal areas, species density, diversity, and a higher proportion of visually stressed individual canopy trees compared to those in the upper tidal reach. Results indicate a strong tidal influence on forest composition, structure, and community-level responses. Full article

Effective planning and management strategies for restoring and conserving tropical peat swamp ecosystems require accurate and timely estimates of aboveground biomass (AGB), especially when monitoring the impacts of restoration interventions. The aim of this research is to assess changes in AGB and evaluate the effectiveness of restoration efforts in the North Selangor Peat Swamp Forest (NSPSF), one of the largest remaining peat swamp forests in Peninsular Malaysia, using advanced remote sensing techniques. A Random Forest machine learning method was employed to upscale AGB estimates, derived from a ‘LiDAR AGB model’, to larger landscape-scale areas with Sentinel-2 spectral and textural variables. The time period under investigation (2015–2018) marked a concentrated phase of restoration and regeneration efforts in NSPSF. The results demonstrate an overall increase in tropical peat swamp AGB during these years, where the total amount of estimated AGB stored in NSPSF increased from 19.3 Tg in 2015 to an estimated 19.8 Tg in 2018. The research found that a tailored variable selection approach improved predictions of AGB, with optimised input variables (n = 62) and parameter adjustments producing a good plausible result (R² = 0.80; RMSE = 55.2 Mg/ha). This paper concludes by emphasizing the importance of long-term studies (>5 years) for analyzing the success of tropical peat swamp restoration methods, with a potential for integrating remote sensing technology. Full article

Understanding and managing land use/cover changes (LUCC) is crucial for ensuring the sustainability of the region. With the support of remote sensing technology, intensity analysis, the geodetic detector model, and the Mixed-Cell Cellular Automata (MCCA) model, this paper constructs an integrated framework linking historical evolutionary pattern-driving mechanisms for future simulation for LUCC in the Lower Liaohe Plain. From 1980 to 2018, the increasing trends were in built-up land and water bodies, and the decreasing trends were in grassland, cropland, forest land, unused land, and swamps. Overall, the changes in cropland, forest land, and built-up land are more active, while the changes in water bodies are more stable; the sources and directions of land use conversion are more fixed. Land use changes in the Lower Liaohe Plain are mainly influenced by socio-economic factors, of which population density, primary industry output value, and Gross Domestic Product (GDP) have a higher explanatory power. The interactive influence of each factor is greater than any single factor. The results of the MCCA model showed high accuracy, with an overall accuracy of 0.8242, relative entropy (RE) of 0.1846, and mixed-cell figure of merit (mcFoM) of 0.1204. By 2035, the built-up land and water bodies will increase, while the rest of the land use categories will decrease. The decrease is more pronounced in the central part of the plains. The findings of the study provide a scientific basis for strategically allocating regional land resources, which has significant implications for land use research in similar regions. Full article

Wetland mapping is a critical component of environmental monitoring, requiring advanced techniques to accurately represent the complex land cover patterns and subtle class differences innate in these ecosystems. This study aims to address these challenges by proposing CVTNet, a novel deep learning (DL) model that integrates convolutional neural networks (CNNs) and vision transformer (ViT) architectures. CVTNet uses channel attention (CA) and spatial attention (SA) mechanisms to enhance feature extraction from Sentinel-1 (S1) and Sentinel-2 (S2) satellite data. The primary goal of this model is to achieve a balanced trade-off between Precision and Recall, which is essential for accurate wetland mapping. The class-specific analysis demonstrated CVTNet’s proficiency across diverse classes, including pasture, shrubland, urban, bog, fen, and water. Comparative analysis showed that CVTNet outperforms contemporary algorithms such as Random Forest (RF), ViT, multi-layer perceptron mixer (MLP-mixer), and hybrid spectral net (HybridSN) classifiers. Additionally, the attention mechanism (AM) analysis and sensitivity analysis highlighted the crucial role of CA, SA, and ViT in focusing the model’s attention on critical regions, thereby improving the mapping of wetland regions. Despite challenges at class boundaries, particularly between bog and fen, and misclassifications of swamp pixels, CVTNet presents a solution for wetland mapping. Full article

There is a lack of published literature on coal deposits in Madagascar. The Imaloto Basin is a sub-basin of the Morondava Basin, Southwestern Madagascar, and hosts the Sakoa Coal Measures. The aim of this study was to increase our understanding of the petrography, geochemistry, and mineralogy of coal deposits hosted in the Imaloto Basin. Three coal seams (from the bottom: Main Seam, Upper Seam, and Top Seam) were intersected during a drilling program conducted by the Lemur Holdings in 2019. Coal samples were characterized using organic petrography (type and rank determination); the ash chemistry was assessed (XRF), and the mineralogy was considered using X-ray diffraction. The depositional environment at the time of peat accumulation was considered. The Main Seam samples are of better quality compared to the Upper Seam and Top Seam samples in terms of calorific value (CV) and ash yield. The coals are borderline Sub-bituminous Low Rank A to Bituminous Medium Rank D. An abundance of inertinite macerals was determined in the Main Seam, while the Upper and Top Seams are more vitrinite-rich. An unusual mineral, possibly albite or analcime, was determined in samples with a high Na content. The Imaloto coal samples show varied depositional settings (dry forest swamp, wet forest swamp, and piedmont plain), which influences coal quality. Full article

Peatlands play an important role in the global environment and the well-being of humans by providing valuable ecosystem services. Yet, anthropogenic activities pose significant hazards for peatland management, including low levels of community participation due to lack of awareness and financial incentives. Understanding the social–cultural and economic value of these ecosystems will raise awareness to protect these important ecosystems. Here, we estimated a total economic value (TEV) of peatland ecosystem services and examined relationships between the TEV and landscape characteristics in Riau province, Indonesia. A questionnaire was used to investigate household socioeconomics, perception of peatland importance, peatland product collection, and willingness to pay for habitat and biodiversity protection from May to June 2023. A total of 200 household individuals (92% confidence) in five villages across distinct landscapes in the Sungai Kiyap-Sungai Kampar Kiri Peatland Hydrological Unit participated in the survey. The respondents obtained numerous advantages from the peatlands with an estimated TEV of USD 3174 per household per year (about 1.3 times their annual income). Approximately 81% showed a use value, especially food provisioning from fish and soil fertility. To a lesser extent, non-use values included a habitat for endemic and endangered species, biodiversity conservation for future generations, and community bonds with sacred forests. The landscape characteristics, illustrating habitat types, biophysical conditions, and property rights regimes, interplay with the relative benefits derived from the peatlands. Proximity to secondary peat swamp forests and riparian zones, especially within protected areas, enhanced economic value. Protected area co-management is essential to balance peatland conservation with sustainable livelihoods. Primary forests need restrictive protection. Meanwhile, buffer zone designation and agroforestry practices, especially in the peatland–farm interface, reduce land use tensions and promote local stewardship. This study can be used as a reference by planners and policymakers to recognize factors that promote effective peatland management, especially those that balance ecosystem protection and livelihood maintenance. Full article