Search Results (722)

Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R² = 0.648 vs. 0.630; RMSE = 0.224 vs. 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, suggesting a structural source of error in carbon stock estimation in tropical dry forests that warrants empirical validation. Full article

Accurate mapping of aboveground biomass (AGB) in mountainous forests is essential for carbon stock assessment and ecological management, yet remains challenging due to the difficulty of linking local high-precision observations with regionally continuous coverage. To address this issue, we developed a hierarchical framework integrating local reference construction, UAV–GEDI bridging, footprint-level modeling, and regional continuous mapping, applied to the mountainous forests of Simao District, Pu’er City, Yunnan Province, China. Field plot measurements and UAV-borne LiDAR data were first used to construct a local AGB reference product, which was then transferred to the GEDI footprint scale through geolocation correction and footprint-scale quality control, yielding 252 valid bridging samples across three UAV flight zones, with approximately 65% originating from the TYH zone. Among five candidate models evaluated for GEDI footprint-level AGB estimation, the Stacking ensemble model performed best, with a pooled out-of-fold R² of 0.736 and RMSE of 24.15 Mg ha⁻¹, and was subsequently applied to 89,579 GEDI footprints across the study area. For regional continuous mapping, the empirical Bayesian kriging regression prediction (EBKRP) scheme combining Landsat TCW, Sentinel-2 IRECI, and the Sentinel-1 polarization ratio achieved the best external validation performance, with R² of 0.622 and RMSE of 26.05 Mg ha⁻¹ based on 61 independent field plots. These results indicate that the proposed hierarchical framework effectively bridges local high-precision observations and regional continuous AGB mapping in complex mountainous forest environments, offering a systematic methodological reference for GEDI-based forest carbon monitoring. Full article

National forest parks play an important role in maintaining the integrity of ecosystems, the sustainability of biodiversity, and the improvement of ecological service functions. Aboveground carbon storage (AGC) is an important indicator for evaluating forest ecosystem functions. Qianjiangyuan–Baishanzu National Park, located in the southern part of Lishui City, serves as a typical representative of the mid-subtropical evergreen broad-leaved forest ecosystem. It is characterized by high biodiversity and serves as a concentration area for rare and endangered species. Therefore, assessing the spatiotemporal dynamics of forest AGC in the typical subtropical forests of Qianjiangyuan–Baishanzu National Park is of great scientific significance. Taking Qianjiangyuan–Baishanzu National Park as a case study, this research utilized three periods of Landsat satellite remote sensing data (2009, 2014, and 2019) alongside contemporaneous ground-based AGC survey samples. Feature variables were extracted and subsequently screened using the Boruta algorithm. There were three algorithms, including least squares (LS), support vector regression (SVR), and random forest (RF), constructed to estimate forest AGC. The optimal AGC estimation model was selected, and the spatiotemporal variation characteristics of forest AGC within the national park were systematically analyzed. Research has shown that (1) texture features are important parameters for constructing forest AGC estimation models, accounting for up to 71%, with the 7 × 7 window having the greatest impact. (2) All three models can achieve high accuracy in estimating the forest AGC and its spatial distribution in Qianjiangyuan Baishanzu National Park. Among them, the RF model has the highest overall accuracy in estimating forest AGC, with a training set R² of 0.938, RMSE of 5.550 Mg/ha, rRMSE of 12.517%, a test set R² of 0.954, RMSE of 4.653 Mg/ha, and rRMSE of 10.087%. (3) The distribution of forest AGC in Qianjiangyuan Baishanzu National Park shows significant spatial heterogeneity, with higher carbon storage in the central, southern, and southeastern regions, while the northern region has relatively lower carbon storage. From 2009 to 2019, the forest AGC in the Qianjiangyuan–Baishanzu National Park exhibited a steady annual increase, with AGC density rising from 37.64 Mg/ha to 66 Mg/ha and total AGC stock increasing from 2.16 Tg C to 4.36 Tg C. These findings provide precise data support and a scientific basis for decision-making regarding differentiated ecological carbon enhancement and functional zone management within the national park. Full article

This study proposed an RF-guided heuristic feature-selection framework that integrates multi-source remote-sensing data for estimating Pinus densata aboveground carbon stock (AGCS) in Shangri-La, Yunnan Province, China. Compared with four baseline feature-selection methods, the Random Forest–Alpha Evolution (RFA) and Random Forest–Markov Chain Monte Carlo (RFM) algorithms generated more informative feature subsets and improved model performance, with the Optuna-optimized AdaBoost model based on RFM features achieving the highest accuracy (R² = 0.71, RMSE = 10.53 t/ha). These results suggest that RF-guided heuristic feature selection can effectively improve AGCS estimation in complex mountainous environments. Vegetation indices and texture features were consistently prioritized across different feature-selection methods. Shapley Additive Explanations (SHAP)-based interpretation revealed that the most influential predictors were the Sentinel-2A green normalized difference vegetation index (S2_GNDVI) and precipitation of the wettest month (bio13) in the RFA Method, and the Sentinel-2A red-edge normalized difference vegetation index (S2_NDVI45) and bio13 in the RFM Method. These findings underscore the critical importance of canopy greenness, moisture availability, and structural complexity in regulating carbon accumulation in montane conifer forests. The final AGCS maps yielded total estimates of 9.83 Mt (RFA) and 10.46 Mt (RFM), and revealed a consistent spatial pattern, with moderate AGCS values dominating the landscape and a general tendency for higher values in the northwest and lower values in the southeast. In summary, the combination of RF-guided heuristic feature selection, Optuna-optimized machine learning and SHAP provides an effective and interpretable framework for AGCS estimation in mountain forests. Full article

Temperate seagrass carbon-stock data remain limited in northern China, especially for island meadow systems with mapped distribution and repeated field verification. This study quantified standing seagrass ecosystem carbon stocks in the Changshan Archipelago, Dalian, using a standardized field survey covering eight meadow zones, 39 sampling stations, and 323.37 ha of confirmed seagrass area. Plant biomass carbon and sediment organic carbon were assessed, and the 0–100 cm sediment profile was sampled at all stations. The mapped meadows stored 29,305.75 Mg C in total ecosystem carbon. Sediment organic carbon accounted for 28,965.71 Mg C, representing 98.84% of the total stock. Plant biomass carbon contributed 340.04 Mg C, or 1.16%. The area-weighted ecosystem carbon stock per unit area was 90.63 Mg C ha⁻¹. This per-area stock ranged from 52.11 Mg C ha⁻¹ in Xiaochangshan to 209.50 Mg C ha⁻¹ in Haiyang Island. Guanglu Island contained the largest total carbon stock, with 9247.73 Mg C, because of its large meadow area and relatively high per-area carbon stock. The results show how mapped meadow area, sediment carbon dominance, and local sediment setting jointly shape regional carbon-storage patterns. This standardized baseline provides field-based evidence for comparing northern Chinese seagrass meadows with other temperate Zostera systems. The estimates describe standing ecosystem carbon stocks. Annual carbon sequestration rates were outside the scope of the assessment. Full article

Integrated crop–livestock systems (ICLS) that couple crop production, cover crops, and grazing present a promising strategy for soil organic carbon (SOC) sequestration. Long-term assessments of SOC change under ICLS management are limited. This study quantified SOC stocks from management systems typical of the warm, humid southern Midwest, USA, including conventional continuous cereal crop production, permanent pasture, hardwood forest, and decadal-scale ICLS management. The ICLS consisted of no-till production of corn silage with a winter ryegrass cover crop grazed by cattle. We hypothesized greater SOC stocks in the ICLS relative to conventional management, with the greatest increase in surface horizons. Soil cores were collected to a depth of 120 cm, subset into 0–30 cm, 30–60 cm, and 60–120 cm sections, and analyzed for SOC, particulate, and mineral-associated organic matter. Results demonstrated that after 15 years, ICLS SOC stocks were significantly greater than conventionally managed fields and comparable to those of permanent pasture and hardwood forest. The SOC differences were predominantly in the upper 30 cm. Using a space-for-time approach, we calculated an average annual SOC accrual rate of 1.3 Mg C ha⁻¹ yr⁻¹, similar to estimated sequestration rates from biogeochemical model simulations. The majority of additional SOC was allocated to particulate organic matter. Significantly greater mineral-associated organic carbon was also observed. Stable carbon isotope data indicated the ryegrass cover crop was likely the primary source of additional SOC in the ICLS. These findings demonstrate the potential of ICLS to increase SOC and enhance soil health over decadal timescales. Full article

Long-term decarbonization of urban residential buildings in southern China depends on the joint evolution of building stock, end-use efficiency, and electricity carbon intensity. This study develops a dynamic stock-energy-carbon framework for urban residential buildings in China’s hot-summer warm-winter region from 2010 to 2060, using Guangdong, Guangxi, Fujian, and Hainan as case provinces. The model links demographic and housing-space change with stock survival, retrofit of the base-year stock, cohort-specific performance levels for post-2022 new construction, and time-varying provincial grid emission factors. EnergyPlus simulations of seven high-rise residential archetypes show that nearly zero-energy performance reduces province-level EUI by 19.2–26.5% relative to the baseline, with cooling-load reductions forming the dominant part of the improvement in the warmer provinces. Across coupled demand-side scenarios, stricter new-build performance standards reduce 2026–2060 cumulative operational energy by 5.3–10.1% relative to the conservative demand-side setting, while increasing retrofit intensity provides a smaller but consistent additional reduction. Carbon outcomes are more sensitive to electricity-sector assumptions: under the main demand-side setting, moving from the conservative to the accelerated grid pathway advances the operational-carbon peak by 8–15 years across the four provinces and lowers 2060 residual emissions by about 71%. A comparison with available observed provincial household-electricity statistics is added as a plausibility check; it confirms the relevant order of magnitude but also indicates that absolute demand estimates should be interpreted cautiously because of boundary and EUI-representation differences. These results suggest that demand-side efficiency policies must be coordinated with rapid provincial power-sector decarbonization if the residential sector in Hot-Summer Warm-Winter Region is to reach earlier carbon peaks and lower residual operational emissions. Full article

Industrial areas, which represent a specific type of urbanised area with an extremely high concentration of material reserves, can be considered key anthropogenic raw material reservoirs in the context of urban mining. Industrial areas, characterised by a high material density and a specific composition of structural systems, show extraordinary potential for providing secondary raw materials with high material and energy value. This increases the need for their systematic evaluation. The aim of the present study was to define the role of the selected industrial area as a strategic node for secondary raw material extraction, to identify the structure and quality of “urban deposits” in the selected location of the Ostrava–Karviná region (CZ), and to provide an analytical framework for its integration into circular planning processes. The methodological approach is based on a combination of pre-demolition audit, material flow mapping, spatial analysis, and structural element characterisation. It is becoming apparent that industrial areas have a high material density and contain significant amounts of recyclable metals, reinforced concrete elements, etc. These stocks are often concentrated in structural systems with predictable geometries, such as serial assembly prefabricated and steel frames, allowing for more accurate estimates of recoverable volumes. The results show that the incorporation of industrial areas into the process of urban mining can significantly reduce the consumption of primary raw materials, mitigate the environmental impacts associated with the extraction of raw materials, and, at the same time, promote the regeneration of industrial areas (or brownfields) through the planned decomposition of structures. The inclusion of urban mining in urban development strategies and the regeneration of industrial sites leads to the prediction that urban mining is one of the key elements for achieving a material-efficient and low-carbon urban environment. Full article

Peatlands cover approximately 3% of the global land area but store about 44% of the world’s soil carbon, making them a major carbon sink. Indonesia alone accounts for about 37% of global tropical peat carbon stocks. However, large-scale carbon emissions caused by fires and drainage during past economic development have transformed peatlands from carbon sinks into carbon sources. In response, restoration efforts have been implemented at both international and national levels. Tropical peatland restoration typically includes rewetting, revegetation, and community-based approaches, highlighting the need for quantitative assessments of carbon storage under different restoration strategies. This study focuses on the Perigi peatland in South Sumatra, Indonesia. We conducted field surveys of vegetation and soils to estimate carbon stocks per unit area and developed time-series land cover maps using satellite imagery. Based on these data, we assessed potential carbon storage under different restoration intensity scenarios. The results show that carbon stocks in the Perigi peatland are lower than the Indonesian average. However, under a full restoration scenario, up to 950,259 tC of additional carbon storage is possible, indicating high restoration potential. In contrast, without restoration, further carbon emissions are likely, underscoring the necessity of restoration efforts. Effective restoration requires a phased strategy from vegetation recovery to peat layer recovery, combined with socioeconomic approaches that consider local livelihoods, enabling degraded tropical peatlands to function as effective carbon mitigation systems. Full article

As a key indicator of low-carbon economic transformation, the influencing factors of carbon productivity (CP) have attracted considerable academic attention. However, the study of the role of artificial intelligence (AI) technology innovation is comparatively confined. Using China’s prefecture-level-and-above cities as the sample, this study measures regional AI technology innovation based on AI patent stocks and empirically examines its impact on carbon productivity. The principal findings of this paper are as follows: (1) AI technology innovation boosts urban carbon productivity through three channels: enhancing green innovation, reducing transaction costs, and increasing AI attention. (2) The regional heterogeneity analysis shows that this positive impact of AI technology innovation on carbon productivity exerts a stronger facilitating effect on eastern regions, resource-dependent cities, and central cities. The heterogeneity analysis at the technological level further provides evidence of the effect of AI technology innovation on carbon productivity varying along different tiers of technological development, innovation mode, and innovation role. (3) The analysis identifies the energy structure as a pivotal threshold variable governing the efficacy of AI innovation in bolstering carbon productivity. Notably, crossing the threshold of clean energy penetration triggers an escalating positive feedback loop between AI innovation and carbon productivity. (4) Estimation of temporal effect dynamics via non-parametric panel model shows that the impact of AI technology innovation on CP exhibits phased characteristics. The coefficient became significantly positive in 2010 and peaked in 2015, after which its effect gradually weakened. This study provides comprehensive empirical evidence for understanding the relationship between AI technology innovation and CP and provides policy references for the use of AI technology to promote the coordinated achievement of economic growth and carbon reduction. Full article

Urban green spaces play a key role in supporting biodiversity, climate regulation, and carbon storage in rapidly expanding cities. Urban and peri-urban parks can differ markedly in tree-community structure, floristic diversity, and carbon-storage capacity. The aim of the study was to compare these attributes between an urban and a peri-urban park. The study compared these attributes between an urban park and a peri-urban park in western Mexico using data collected in 500 m² circular plots. Tree structure was assessed through diameter at breast height, height, crown diameter, basal area, and crown projection area, while floristic composition and diversity were evaluated using richness, Shannon, Simpson, Pielou, and Menhinick indices. Aboveground biomass, belowground biomass, and carbon stocks were estimated using generalized allometric equations. A total of 1675 trees belonging to 19 families, 33 genera, and 49 species were recorded. The peri-urban park showed greater structural development, with significantly higher DBH, height, crown diameter, basal area, biomass, and carbon stocks, whereas the urban park supported greater species richness and higher Shannon diversity. Species composition also differed strongly between parks, and carbon storage was concentrated in a reduced number of dominant taxa in each site. DBH was the structural variable most strongly associated with total carbon per tree. These findings show that floristic diversity and carbon-storage capacity do not necessarily increase in parallel and that urban and peri-urban parks can provide contrasting but complementary ecological functions. Full article

This study quantifies how different land-use types influence surface soil characteristics (0–5 cm) and the dynamics of soil organic carbon (SOC) and nitrogen in the mountainous ecosystems of the Rila Mountains. Across 54 forest and agricultural plots, pH, bulk density, coarse fraction, C:N ratio, SOC, total nitrogen (TN), and their respective stocks were assessed using standard analytical methods and statistical tests (Shapiro–Wilk, ANOVA, Kruskal–Wallis, correlation and regression analysis). Land use significantly affected all soil parameters except pH. Forest soil showed lower bulk density and lower SOC stocks compared with grasslands. Unmown meadows exhibited the highest SOC and TN concentrations and stocks, while potato fields recorded the highest bulk density and elevated TN stocks, reflecting intensive management impacts on surface soil properties. Forest soils displayed species-specific patterns, with Scots pine and Silver fir showing comparatively lower SOC and TN stocks attributable to historical degradation and site limitations. As the study focused on the uppermost soil layer (0–5 cm), the results should be interpreted more as indicators of surface soil dynamics rather than as estimates of total topsoil carbon and nutrient storage. Correlation analysis revealed strong positive relationships among SOC, TN, and the C:N ratio, and strong negative relationships between SOC and both bulk density and coarse fraction in managed agricultural lands. The findings demonstrate that minimizing soil disturbance and maintaining permanent vegetation cover—particularly through conservation of unmanaged grasslands—offer great capacity for enhancing the soil organic matter accumulation in mountainous ecosystems. Full article

Soil Organic Matter (SOM) and Soil Organic Carbon (SOC) are essential for regulating ecosystem functions, soil fertility, and influencing climate change processes, especially in semi-arid regions. The recent improvements in remote sensing instruments and the development of artificial intelligence methodologies, such as machine learning, enable an improved understanding of carbon dynamics, facilitate the estimation of SOC content, and support predictive modeling. This study presents an integrated framework to analyze past and future carbon dynamics in the Sfax Governorate (Tunisia). Land-use and land-cover (LULC) maps for the years 2019, 2020, 2022, and 2024 were generated using a Random Forest algorithm applied to multispectral satellite data in the Google Earth Engine platform, achieving high classification accuracy (overall accuracy up to 0.90). Carbon stocks and their temporal variations were estimated using the InVEST Carbon Storage and Sequestration model, while carbon emissions and the Net Ecosystem Carbon Balance (NECB) were derived by integrating land-use-specific emission factors. Future LULC scenarios for 2030 were simulated through a Cellular Automata model under three alternative development pathways: conservation-oriented (CONS), business-as-usual (BAU), and urban expansion (URB+). The study demonstrates how the integration of machine learning, remote sensing, and ecosystem modeling supports spatially explicit assessment of SOC-related carbon dynamics and provides useful insights for land management and climate mitigation strategies. Full article

The construction sector is a major consumer of raw materials and a significant source of greenhouse gas emissions, necessitating data-driven approaches to support circular economy implementation and sustainable project management. This study develops an integrated framework combining machine learning-based material stock prediction, carbon footprint assessment, and Environmental, Social, and Governance (ESG) performance evaluation for construction projects. A dataset of 128 residential buildings was compiled from official use-permit documentation. After dimensionality reduction using variance filtering and Spearman correlation analysis, 25 regression algorithms were evaluated to estimate quantities of concrete, reinforcement, and brick products. The K-Nearest Neighbor (KNN) Regressor achieved the best predictive performance, with mean absolute percentage errors of 10.64% for concrete, 10.23% for reinforcement, and 16.05% for brick products. Predicted material quantities were used to calculate CO₂ emissions across materialization, demolition, and disposal phases under linear and circular scenarios. The results indicate that circular economy implementation significantly reduces total emissions, particularly for concrete, with reductions of up to 97% under idealized full-substitution conditions, representing an upper-bound estimate. ESG assessment using the Delphi method identified environmental indicators as the most significant sustainability dimension. The proposed framework enables early-stage emission estimation and supports informed decision-making toward low-carbon and resource-efficient construction practices. Full article

As a fast-growing and multifunctional crop, bamboo plays a pivotal role in food security and climate change mitigation by leveraging its high carbon sequestration potential. Monitoring aboveground carbon (AGC) stock in bamboo forests is crucial for guiding field management, growth observation, and yield prediction. Unmanned aerial vehicle (UAV)-based point cloud sensors offer a rapid and scalable solution for measuring bamboo AGC. This study evaluates the potential of UAV-LiDAR and machine learning (ML) for organ-level AGC estimation in bamboo forests. From LiDAR point clouds, we extracted structural features—including height, density, canopy, and intensity metrics—aggregated by mean plot-level metric (Mean-PM) and maximum plot-level metric (Max-PM) values at a 1 m² grid scale. Key predictors were selected using ML-based recursive feature elimination (ML-RFE) to develop organ-specific AGC inversion models. Results showed that organ-specific carbon content and allometric equations effectively eliminated biases associated with a uniform coefficient. Max-PM features outperformed Mean-PM features in stem and leaf AGCs, with the XGBoost and Random Forest models achieving the highest accuracy (R² = 0.82 for stems, 0.73 for leaves). Height percentiles and canopy structural metrics emerged as dominant predictors. This UAV-LiDAR-ML framework provides a cost-effective solution for precise bamboo carbon estimation, offering critical insights for carbon neutrality management and informed decision-making in bamboo forest ecosystems. Full article