Search Results (50)

Redwood (Sequoia sempervirens (Lamb. ex D. Don) Endl.) is a fast-growing, long-lived conifer native to a narrow coastal zone along the western seaboard of the United States. Redwood can accumulate very high amounts of carbon in plantation settings and continuous cover forestry (CCF) represents a highly profitable option, particularly for small-scale forest growers in the North Island of New Zealand. We evaluated the profitability of conceptual CCF regimes using two case study forests: Blue Mountain (109 ha, Taranaki Region, New Zealand) and Spring Creek (467 ha, Manawatu-Whanganui Region, New Zealand). We ran a strategic harvest scheduling model for both properties and used its results to guide a tactical-spatially explicit model harvesting small 0.7 ha units over a period that spanned 35 to 95 years after planting. The internal rates of return (IRRs) were 9.16 and 10.40% for Blue Mountain and Spring Creek, respectively, exceeding those considered robust for other forest species in New Zealand. The study showed that small owners could benefit from carbon revenue during the first 35 years after planting and then switch to a steady annual income from timber, maintaining a relatively constant carbon stock under a continuous cover forestry regime. Implementing adjacency constraints with a minimum green-up period of five years proved feasible. Although small coupes posed operational problems, which were linked to roading and harvesting, these issues were not insurmountable and could be managed with appropriate operational planning. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

Mangrove forests are vital blue carbon (BC) ecosystems that significantly contribute to climate change mitigation through carbon sequestration. Accurate, scalable, and cost-effective methods for estimating carbon stocks in these environments are essential for conservation planning. In this study, we assessed the potential of drones, also known as unmanned aerial vehicles (UAVs), for estimating above-ground biomass (AGB) and BC in Avicennia marina stands by integrating drone-based canopy measurements with field-measured tree heights. Using structure-from-motion (SfM) photogrammetry and a consumer-grade drone, we generated a canopy height model and extracted structural parameters from individual trees in the Melgonze mangrove patch, southern Iran. Field-measured tree heights served to validate drone-derived estimates and calibrate an allometric model tailored for A. marina. While drone-based heights differed significantly from field measurements (p < 0.001), the resulting AGB and BC estimates showed no significant difference (p > 0.05), demonstrating that crown area (CA) and model formulation effectively compensate for height inaccuracies. This study confirms that drones can provide reliable estimates of BC through non-invasive means—eliminating the need to harvest, cut, or physically disturb individual trees—supporting their application in mangrove monitoring and ecosystem service assessments, even under challenging field conditions. Full article

The focus of this study was on young populations of Kandelia obovata within a constructed coastal wetland in Haicang Bay, Xiamen, Southeast China. The objective was to systematically examine their allometric growth characteristics and carbon sequestration potential over an 8-year monitoring period (2016–2024). Allometric equations were developed to estimate biomass, and the spatiotemporal variation in both plant and soil carbon stocks was estimated. There was a significant increase in total biomass per tree, from 120 ± 17 g at initial planting to 4.37 ± 0.59 kg after 8 years (p < 0.001), with aboveground biomass accounting for the largest part (72.2% ± 7.3%). The power law equation with D²H as an independent variable yielded the highest predictive accuracy for total biomass (R² = 0.957). Vegetation carbon storage exhibited an annual growth rate of 4.2 ± 0.8 Mg C·ha⁻¹·yr⁻¹. In contrast, sediment carbon stocks did not show a significant increase throughout the experimental period, although long-term accumulation was observed. The restoration of mangroves in urban coastal constructed wetlands is an effective measure to sequester carbon, achieving a carbon accumulation rate of 21.8 Mg CO₂eq·ha⁻¹·yr⁻¹. This rate surpasses that of traditional restoration methods, underscoring the pivotal role of interventions in augmenting blue carbon sinks. This study provides essential parameters for allometric modeling and carbon accounting in urban mangrove afforestation strategies, facilitating optimized restoration management and low-carbon strategies. Full article

Coastal wetlands are vital ecosystems at the land–sea interface. They intercept land-based pollutants, regulate microclimates, and mediate carbon cycles. They play a significant role in enhancing carbon sequestration capacity and maintaining ecological structure and functioning. This study proposes an improved method for estimating blue carbon storage in coastal salt marsh wetlands, considering soil thickness, by utilizing an enhanced Soil Land Inference Model (SoLIM) to estimate soil thickness in coastal wetlands with a restricted number of sample points. The wetland soil thickness index is integrated into the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) blue carbon storage estimation model, ultimately enabling the estimation and visualization of blue carbon storage in the Liaohe Estuary coastal wetland. Results indicate the following: (1) The studied area’s soil thickness shows a spatial distribution pattern that becomes progressively thinner from north to south. Soil thickness is more significant in the salt marsh vegetation areas and more minor in the coastal tidal flat areas, with 52% of the region having soil thickness between 40 and 60 cm. (2) In 2023, the blue carbon stock in the study area is estimated at 389.85 × 10⁶ t, with high-value areas concentrated in the northern natural landscapes, and low-value areas in the southern coastal zone, characterized by flat terrain and human influence. The coupled soil thickness–blue carbon storage estimation model provides methodological support for refining the estimation of blue carbon storage in coastal wetlands. It also offers technical support for formulating policies on the ecological restoration, compensation, protection, and management of coastal wetlands. Full article

Wetlands are essential carbon sinks in the global ecosystem, absorbing CO₂ in their biomass and soils and mitigating global warming. Accurate aboveground biomass (AGB) and organic carbon (Corg) estimation are crucial for wetland carbon sink research. Remote sensing (RS) data effectively estimate and map AGB and Corg in wetlands using various techniques, but there is still room to improve the efficiency of machine learning (ML)-based approaches. This study examined how different sample data treatments and plot sizes impact a random forest model’s performance based on RS for AGB and Corg prediction. The model was trained with samples of emergent vegetation collected in a palustrine wetland in southern Brazil and spectral variables (single bands and vegetation indices—VIs) from medium- and high-resolution optical images from Sentinel-2 and PlanetScope, respectively. The treatments involved AGB and Corg values dimensioned for three different plot sizes (G1) and the same subjected to normalized natural logarithmic transformation—NL (G2). Therefore, six AGB and Corg models were created for each sensor. Models and sensor performance and spectral variable importance were compared. In our results, NL sample data RF models proved more accurate. Larger plots produced smaller prediction errors with S2 models, indicating the influence of plot size on the reliability of the estimate. S2 surpassed PS in AGB/Corg prediction, respectively—S2 (R² 0.87; 0.89, RMSE OOB: between 19.7% and 22.7%); PS (R² 0.86; 0.86, RMSE OOB: between 21% and 35.9%)—but PS was superior in mapping spatial variability. The VI CO₂Flux and S2’s SWIR, blue, green, and RE bands 6 and 7 were more important for AGB/Corg prediction. The contribution of this study is the finding that in addition to optimizing RF model parameters, optimizing the AGB and Corg dataset collected in the field, i.e., evaluating normalization and plot sizes, is crucial to obtain more accurate estimates with RS- and ML-based models. This approach enhances AGB/Corg stock estimation in wetlands, and the highlighted predictors can act as spectral indicators of these ecological functions. These results have the potential to guide standardization in the collection and processing of input data for predictive models of AGB/Corg in wetlands, with the aim of ensuring consistent predictions in inventories and monitoring. Full article

Individual tree parameters are essential for forestry decision-making, supporting economic valuation, harvesting, and silvicultural operations. While extensive research exists on uniform and simply structured forests, studies addressing complex, dense, and mixed forests with highly overlapping, clustered, and multiple tree crowns remain limited. This study bridges this gap by combining structural, textural, and spectral metrics derived from unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) and multispectral (MS) imagery to estimate individual tree parameters using a random forest regression model in a complex mixed conifer–broadleaf forest. Data from 255 individual trees (115 conifers, 67 Japanese oak, and 73 other broadleaf species (OBL)) were analyzed. High-resolution UAV orthomosaic enabled effective tree crown delineation and canopy height models. Combining structural, textural, and spectral metrics improved the accuracy of tree height, diameter at breast height, stem volume, basal area, and carbon stock estimates. Conifers showed high accuracy (R² = 0.70–0.89) for all individual parameters, with a high estimate of tree height (R² = 0.89, RMSE = 0.85 m). The accuracy of oak (R² = 0.11–0.49) and OBL (R² = 0.38–0.57) was improved, with OBL species achieving relatively high accuracy for basal area (R² = 0.57, RMSE = 0.08 m² tree⁻¹) and volume (R² = 0.51, RMSE = 0.27 m³ tree⁻¹). These findings highlight the potential of UAV metrics in accurately estimating individual tree parameters in a complex mixed conifer–broadleaf forest. Full article

Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests to carbon storage and emission goals. In this study, the effectiveness of plot-level AGB estimation using height and crown diameter derived from UAV-LiDAR, calibration of GEDI-L4A AGB and GEDI-L2A rh98 heights, and spectral variables derived from UAV-multispectral and RGB data were assessed. These calibrated AGB and height values and UAV-derived spectral variables were used to fit AGB estimations using a random forest (RF) regression model in Fuling District, China. Using Pearson correlation analysis, we identified 10 of the most important predictor variables in the AGB prediction model, including calibrated GEDI AGB and height, Visible Atmospherically Resistant Index green (VARIg), Red Blue Ratio Index (RBRI), Difference Vegetation Index (DVI), canopy cover (CC), Atmospherically Resistant Vegetation Index (ARVI), Red-Edge Normalized Difference Vegetation Index (NDVIre), Color Index of Vegetation (CIVI), elevation, and slope. The results showed that, in general, the second model based on calibrated AGB and height, Sentinel-2 indices, slope and elevation, and spectral variables from UAV-multispectral and RGB datasets with evaluation metric (for training: R² = 0.941 Mg/ha, RMSE = 13.514 Mg/ha, MAE = 8.136 Mg/ha) performed better than the first model with AGB prediction. The result was between 23.45 Mg/ha and 301.81 Mg/ha, and the standard error was between 0.14 Mg/ha and 10.18 Mg/ha. This hybrid approach significantly improves AGB prediction accuracy and addresses uncertainties in AGB prediction modeling. The findings provide a robust framework for enhancing forest carbon stock assessment and contribute to global-scale AGB monitoring, advancing methodologies for sustainable forest management and ecological research. Full article

Global warming is a critical factor driving climate change, impacting every aspect of life on Earth. The escalating concentration of greenhouse gasses in the atmosphere, the primary contributor to global warming, necessitates immediate action through effective climate mitigation strategies. This study aimed to quantify the biomass and blue carbon stocks in the eastern coastal mangrove forests of North Sumatra and Aceh Provinces in Indonesia, focusing on key sites in Langkat, Deli Serdang, Batu Bara, Tanjung Balai, and Aceh Tamiang Regencies. We measured carbon stock in three carbon pools: biomass (above and below ground), necromass, and soil. By analyzing tree stands using parameters such as tree height and diameter at breast height within circular plots (7 m in radius, 125 m apart), we gathered fundamental data on forest structure, species composition, and above- and below-ground biomass. Additionally, we collected soil samples at various points and depths, measuring the amount of wood, stems, or branches (necromass) that fell to or died on the forest floor. Data were collected in plots along a line transect, comprising three transects and six circular plots each. Sixteen diverse mangrove species were found, demonstrating rich mangrove biodiversity. The mangrove forests in the five regencies exhibited significant carbon storage potential, with estimated average above-ground carbon ranging from 96 to 356 MgC/ha and average below-ground carbon from 28 to 153 MgC/ha. The estimated average deadwood carbon varied between 50 and 91 MgC/ha, while soil carbon ranged from 1200 to 2500 MgC/ha. These findings underscore the significant carbon storage potential of these mangrove forests, highlighting their importance to global carbon cycling and climate change mitigation. This research contributes to a broader understanding of mangroves as vital blue carbon ecosystems, emphasizing the necessity of conservation efforts such as forest restoration and rehabilitation to enhance their role in stabilizing coastal areas and improving global climate resilience. Full article

Coralligenous bioconstructions are a key Mediterranean ecosystem for their associated biodiversity and role in the blue carbon cycle. They are also sensitive to environmental alterations (e.g., climate change) and other anthropic impacts related to coastal anthropization (e.g., fishing activities). Marine-coastal zone protection, conservation programs and management strategies are essential to guarantee a good ecological status of the coralligenous habitat. In this context, environmental and ecosystem accounting are useful tools to measure natural capital stocks and ecosystem service flows associated with marine ecosystems, conveying their importance in scientific and policy contexts. Indeed, the importance of marine ecosystems is often overlooked due to the difficulty of expressing their value in common units, making it challenging for decision-makers to explore trade-offs between conservation and exploitation of marine ecosystems. In this study, a biophysical and trophodynamic environmental accounting model was used to assess the biophysical value of natural capital stocks of the coralligenous habitat in three Marine Protected Areas (MPAs) of the Campania Region (Southern Italy): Punta Campanella, Santa Maria di Castellabate, and Costa degli Infreschi e della Masseta. The natural capital value per unit area associated with the coralligenous habitat ranged from 2.44 × 10¹² to 4.72 × 10¹² sej m⁻² for Santa Maria di Castellabate and Punta Campanella, respectively. Despite the different intensive values of natural capital calculated for the MPAs, there were no significant differences both in the biomass values of the taxonomic groups and in the biomass-based Shannon diversity index. Additionally, the biophysical values were also converted into monetary units, with the aim of facilitating the understanding of the importance of natural stocks in socio-economic and political contexts. The economic equivalent of natural capital value refers to the total extent of the coralligenous habitat and ranged from about EUR 1 to 15 million for Costa degli Infreschi e della Masseta and Santa Maria di Castellabate, respectively. The results of this study could be useful for local managers and policy makers and may make them more likely to achieve biodiversity conservation and sustainable development goals in MPAs. This is the first study devoted to the assessment of natural capital value of coralligenous habitats. Future studies could complement the results of this study with biophysical and economic assessments of ecosystem service flows generated by coralligenous habitats, focusing on the role they play in human well-being. Full article

Concern for the future requires local steward-led cooperation between natural and social scientists and decision-makers to develop informed and policy-relevant nature-based mitigation solutions, including blue carbon (BC), which can help secure the future. Salt marshes, kelp forests, and seagrass meadows (and to a lesser extent mangroves) are significant BC ecosystems in temperate areas. We discuss the concept of blue carbon stocks and the scientific approaches to building BC stocks considering the variability in local conditions and the co-benefits of blue carbon ecosystems to improve climate change mitigation and adaptation mechanisms. The study examines (1) methods to assess the potential of BC ecosystems and the impact of disturbances, while (2) building relevant policy based on socio-economic assessments of impacted communities. We highlight economic and social approaches to rebuilding BC using financial tools such as blue bonds, development plans, cost-benefit analyses, cross-ecosystem restoration projects, AI and blockchain, and economic accounts of coastal ecosystems, while emphasizing that cutting carbon emissions is more important than (re)building BC stocks. Full article

For decades in China, carbon neutrality policies have spurred the establishment of northern margin mangroves as artificial blue carbon ecosystems. However, there has been limited research on the impact of plantation and invasion on the stocks and sources of soil carbon and nitrogen in rehabilitated coastal wetlands. Non-native Kandelia obovata afforestation began on Ximen Island, Zhejiang, China, where Spartina alterniflora invasion had also occurred decades ago. Soil cores were collected from both mangrove and salt marsh habitats with depths from 0 to 50 cm and were analyzed for total carbon (TC), soil organic carbon (SOC), total nitrogen (TN), and the isotope of carbon and nitrogen in sediments. The results indicated that there were no significant differences in the TC, SOC, and C/N ratio between the K. obovata and the S. alterniflora, but there were significant differences in TN, isotope δ¹³C, and δ¹⁵N. The SOC content of both ecosystems in the 0–20 cm layer was significantly higher than that in the 30–50 cm layer. Our study has shown that the main sources of carbon and nitrogen for mangroves and salt marshes are different, especially under the impact of external factors, such as tidal waves and aquaculture. These findings provide insight into the ecological functioning of subtropical coastal wetlands and an understanding of the biogeochemical cycles of northern margin mangrove ecosystems. Full article

Mangrove forests are significant blue carbon pools on the Earth with strong carbon sequestration capacity and play an important role in combating climate change. To improve the capacity of regional carbon sinks, China has implemented a Special Action Plan for Mangrove Protection and Restoration (2020–2025). In this context, based on the MaxEnt model, this study analyzed the important environmental factors affecting the distribution of mangrove forests, combined with the planning objectives and carbon density parameters of different regions; assessed the habitat suitability areas of China’s mangrove forests; and predicted their future carbon stock potential. The results showed the following: (1) Elevation was the most important factor affecting the overall distribution of mangrove forests in China, and the optimal elevation of mangrove distribution was 0.52 m. (2) The most suitable areas of mangrove forests in China were mainly distributed in Hainan, Guangxi, and Guangdong, which had great potential for carbon stock. Danzhou Bay and Hongpai Harbor in Hainan, Lianzhou Bay in Guangxi, and the Huangmao Sea in Guangdong are potential areas for habitat suitability but are not yet under high levels of protection. (3) Achieving the goals of this action plan was expected to increase carbon stocks by 4.13 Tg C. Other suitable areas not included in this plan could still increase carbon stocks by 7.99 Tg C in the long term. The study could provide a scientific basis for siting mangrove restoration areas and developing efficient management policies. Full article

“Blue carbon”, apart from marine humus, includes the carbon (C) stock of coastal ecosystems such as mangroves, saltmarshes, and seagrass meadows, which have been overlooked until recently. Information about the role of coastal wetlands in C sequestration and providing other ecosystem services is still insufficient. In the present study, we assessed the C reserves of soils and vegetation biomass in two complex coastal landscapes (tombolos) located on the coasts of the White and Baltic seas. The soil and plant C stocks were slightly higher at the plot on the Baltic Sea (93.4 ± 46.7 Mg C·ha⁻¹ and 5.22 ± 2.51 Mg C·ha⁻¹, respectively) than at the plot on the White Sea (71.4 ± 38.2 Mg C·ha⁻¹ and 3.95 ± 2.42 Mg C·ha⁻¹, respectively). We attributed the higher values of the C reserved to a warmer climate and less saline water at the plot on the Baltic Sea. Both soil and plant C showed high heterogeneity due to geomorphological complexity and differences in vegetative communities. The Phragmites australis community showed the highest plant biomass and, in some places, high soil C reserves. Allochthonous C contributed to the soil C stock at the site on the White Sea. Though P. australis sequestered more C than other communities, its effect on ecosystem services was mostly negative because the invasion of reeds reduced the biological diversity of the marshes. Full article

This research evaluates the capability of Sentinel-2 satellite imagery for mapping Cymodocea nodosa meadows in El Médano (Tenerife, Canary Islands, Spain). A Level-1C image from 27 October 2022 was used. Atmospheric correction was addressed using the Sen2Cor tool, while Lyzenga’s method was employed to account for the water column effect. Three supervised classifications were performed using Random Forest, K-Nearest Neighbors (KNN) and KDTree-KNN algorithms. These classifications were complemented by an unsupervised classification and in situ data. Additionally, the amount of blue carbon sequestered by the C. nodosa in the study area was also estimated. Among the classifiers, the Random Forest algorithm produced the highest F1 scores, ranging from 0.96 to 0.99. The results revealed an average area of 237 ± 5 ha occupied by C. nodosa in the study region, translating to an average sequestration of 111,000 ± 2000 Mg CO₂. Notably, the seagrass meadows in this study area have the potential to offset the CO₂ emissions produced by the industrial combustion plant sector throughout the Canary Islands. This research represents a significant step forward in the protection and understanding of these invaluable ecosystems. It effectively underlines the potential of Sentinel-2 satellite data to map seagrass meadows and highlights their crucial role in achieving net zero carbon emissions on our planet. Full article