Search Results (229)

Tropical forests are major contributors to the global carbon budget but are affected by disturbances such as hurricanes, which cause extensive yet spatially variable tree damage and mortality. High-resolution maps of forest aboveground biomass (AGB) and its temporal change aid in quantifying disturbance impacts, assessing resilience, and supporting forest management. This study presents wall-to-wall, high-resolution mapping of pre- and post-hurricane AGB and AGB change across Puerto Rico. The maps represent forest AGB measured 0–2 years before and after two major hurricanes (Irma and Maria), as well as longer-term conditions up to four years post-disturbance. AGB was modeled using Random Forest (RF) algorithms that integrated Forest Inventory and Analysis (FIA) plot data with canopy height and cover derived from discrete-return LiDAR, multi-temporal satellite imagery, and additional geospatial predictors. Model performance was evaluated using a 10% holdout dataset. Predicted versus observed regressions yielded, at 10 m and 90 m spatial resolutions, respectively, r = 0.75 and 0.79 with model residual mean standard deviation (RMSD) = 87.7 and 39.2 Mg ha⁻¹ for pre-hurricane AGB, and r = 0.77 and 0.74 with RMSD = 69.7 and 58.1 Mg ha⁻¹ for post-hurricane AGB. AGB change models at 10 m and 90 m resolutions yielded r = 0.58 and 0.73 with RMSD = 17.0 and 18.7 Mg ha⁻¹, respectively. Ten-fold cross-validation produced stronger correlations and reduced RMSD values. Frequency distributions of mapped pixels of forest AGB and AGB change, in comparison with previously published maps and island-wide field-based estimates, indicate that, although hurricane-driven biomass reductions of up to 20% were recorded in field data, patterns consistent with longer-term recovery from historical deforestation are evident within four years after the hurricanes. The 10 m maps capture fine-scale heterogeneity in canopy damage and regrowth, whereas the 90 m maps emphasize broader regional patterns. This integrated framework provides a transferable approach for monitoring forest structure and biomass dynamics in disturbance-prone tropical ecosystems. Full article

Global warming, which is mainly linked to CO₂ increase, has led to a growing interest in assessing carbon conservation in forest biomass. Despite evidence that treelines have advanced by hundreds of meters, knowledge of associated stand biomass changes is insufficient for comprehensive estimation of their role in carbon sequestration. Traditionally, the biomass assessment is based on data collected by field measurements. While this approach provides accurate data for local sites, it cannot be extrapolated properly to larger areas. A more appropriate approach would be to combine field measurements with remote sensing methods. We used data obtained by tree morphometry and annual ring measurements, model-based biomass estimation, processing of laser scanning results, and satellite imagery to model and calculate changes in stand above-ground biomass (AGB) since 1900 at treeline ecotone in Altai and Western Sayan. We developed simulations to predict AGB changes over the coming four decades in these regions. Our findings revealed that the upslope shift of the treeline ecotone by 58–86 m of altitude over the past century was accompanied by an exponential increase in AGB of stands within the 200–400 m forest–tundra transition zone. This resulted in an AGB increment of 120–139 tons per 100 m of treeline. We expect that stand AGB at the treeline ecotone will become 2.3–3.3 times bigger by 2060. All exposures must be considered when estimating stand AGB within treeline ecotones because there are significant differences in treeline elevation, tree-dominant proportions, and stand structure on different slopes. Full article

Forests constitute a fundamental component of terrestrial carbon stocks and play a pivotal role in mitigating climate change through carbon sequestration. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon budgets and informing ecosystem models. This study takes Wolong Nature Reserve in Sichuan Province, China, a mountainous area with high vegetation coverage and diverse forest types dominated by coniferous and mixed forests, as the study area, and constructs and evaluates AGB estimation models by integrating canopy height, leaf area index (LAI), vegetation indices (VIs), and topographic variables. Initially, univariate parametric models (linear, exponential, logarithmic, power, and polynomial) were established to relate canopy height to field-measured AGB. Subsequently, multivariate regression models incorporating VIs, LAI, and topographic metrics were developed. Finally, a decision tree-based machine learning framework was implemented to exploit the combined predictor set. Comparative analysis revealed that both canopy height-based and conventional multivariate regression models tended to overestimate AGB, limiting their applicability for large-scale assessments. In contrast, the optimized decision tree model, following parameter tuning and cross-validation, achieved superior predictive accuracy. Full article

Allometric equations are essential tools for estimating sustainable biomass and carbon dynamics in riparian tree species. This study derived and validated log–log transformation regression equations that relate diameter at breast height (DBH) to the dry weight, stem volume, and total biomass of Salix chaenomeloides Kimura across five river systems in Korea (Byeongcheon, Andong, Boseong, Topyeong, and Yeongdong). DBH was significantly correlated with biomass components and whole-tree biomass, with explanatory power ranging from 0.47 (Byeongcheon-root) to 0.99 (Topyeong-stem) (R²). Model evaluation metrics (RMSE, MAE, MPE) indicated high predictive accuracy across sites. Using the derived allometric equations, net primary productivity (NPP) of individual was 9.40 kg·tree⁻¹·yr⁻¹ and 2.45 ton C·ha⁻¹·yr⁻¹ at the stand level, with site-specific variability reflecting environmental differences. Biomass conversion coefficients, expansion factors, and root-to-aboveground biomass ratios were also obtained, with mean values of 0.29 (branches/stem), 0.10 (leaves/stem), and 0.25 (roots/AGB), a wood density of 0.63 g·cm⁻³, and a biomass expansion factor of 1.37. Independently derived NPP estimates based on stem analysis were comparable (9.02 kg tree⁻¹ yr⁻¹ and 2.43 t C ha⁻¹ yr⁻¹ at individual and stand levels, respectively), supporting the robustness of the approach. These findings provide robust, site-calibrated allometric models for S. chaenomeloides, supporting accurate biomass estimation, carbon accounting, and the evaluation of riparian ecosystems in climate change mitigation and restoration contexts. From a sustainability perspective, these results highlight the development of tools for evaluating the carbon budget of riparian vegetation, which are not yet incorporated into the Korean national IPCC report. They also demonstrate progress in carbon budget assessment by integrating both allometry and stem analysis. Full article

Dry forests are increasingly threatened by degradation, which determines their structural integrity, functional capacity, and the ability to provide essential ecosystem services. Degradation is the consequence of processes that reduce the different attributes of forests. This study aimed to (i) identify remote sensing proxies for above-ground biomass (AGB) and tree regeneration in three ecoregions of dry forest localized in west Argentina; (ii) analyze the temporal dynamics between 1993 and 2023; (iii) assess the role of precipitation in their temporal variability, and (iv) map their spatial distribution. The median Tasseled Cap Transformation Wetness (TCTW) was the best-performing spectral proxy for AGB, while median Enhanced Vegetation Index (EVI) best captured tree regeneration. In the time series of TCTW, no significant breakpoint was detected; however, a pronounced decline in the median EVI occurred in 1998 in the Monte of Plains and Plateaus and Monte of Hills and Basins ecoregions, particularly near watercourses. In the Dry Chaco, tree regeneration recovered after 2013; however, a decline after a breakpoint coincided with decreased precipitation. Overall, AGB and tree regeneration exhibited contrasting temporal and spatial patterns, underscoring the heterogeneity of dry forests. A weakening relationship between precipitation, a key driver of forests, and forest attributes suggests the influence of other factors, including topography and land use change. Full article

As an important ecological obstacle prone to climatic changes, the Tibetan Plateau has been transformed by retreating glaciers, degrading permafrost, and deteriorating grasslands. Recent ecological remote sensing evaluations typically use medium-resolution and single-source optical imagery, highlight natural factors while ignoring human impacts, and encounter difficulties with time-focused interpretability and continuity within complex terrains. This research proposes a theory combining large language models with satellite embedding to holistically examine the ecology of the Tibetan Plateau between 2000 and 2024. We created an ecological satellite embedding (ESE) model applying self-supervised learning to integrate 12 ecological variables into combined space and time representations as of 2024, according to the Prithvi-Earth Observation (Prithvi-EO) foundational model involving low-rank adaptation (LoRA). GeoChat reasoning was applied to turn the embedded variables into a comprehensive representation feature (CRF). Field research demonstrated strong accuracy for the fraction of absorbed photosynthetically active radiation (FAPAR, R² = 0.9923) and aboveground biomass (AGB, R² = 0.8690). Space and temporal analyses demonstrated a general ecology-dependent enhancement accompanied by significant space-based clustering (Moran’s I = 0.50–0.80), hotspots in humid southeastern areas, major upward trends in vegetation indices and productivity metrics (p < 0.05), and higher shifts in transition regions. Despite the marginal degradation risk, the grassland carrying capacity has expanded extensively in the main farming regions. The comprehensible CRF schema identified three management areas: potential risk, enhancement potential, and stable conservation management. This transferable modular approach connects expert reasoning with data-driven modeling, presenting adaptable methods for assessing ecosystems in high-altitude, data-sparse environments, and practical ways to promote ecological management. Full article

Accurate monitoring of aboveground biomass (AGB) is essential for forest carbon accounting and climate change mitigation, yet signal saturation and the treatment of forest landscapes as biophysically homogeneous entities remain significant barriers to high-fidelity mapping. This study implements an ecologically integrated model that leverages forest-type specific (coniferous vs. broadleaf) to enhance regional AGB retrieval. By refining established data fusion techniques with structural and compositional parameters, this approach seeks to mitigate systematic biases often found in generic regional assessments. Compared with 360 geo-referenced subplots, our stratified Support Vector Regression (SVR) model significantly outperformed non-classified counterparts, achieving an R² of 0.76 and a reduced RMSE of 18.48 Mg/ha. This refined precision enabled a nuanced time-series analysis (2013–2020), revealing that while regional AGB increased from 157.13 to 192.23 Mg/ha, this trajectory was punctuated by a distinct sub-regional growth plateau between 2016 and 2018. By correlating these trends with disturbance data, we identified a 11.27% biomass decline in southwestern sectors linked to a tripling of burned area, pinpointing intensified fire regimes as the primary driver overriding recovery-driven carbon gains. These findings demonstrate that harmonizing multi-sensor signals with functional forest differentiation provides the necessary sensitivity to track carbon resilience, offering a scalable and robust tool for operational forest management and global carbon cycle research. Full article

Grassland aboveground biomass (AGB) is a crucial component of the global carbon budget in climate change studies. Precise estimation of the AGB of grassland ecosystems is essential to better understand the carbon cycle and to improve grassland conservation as well as to achieve optimal growth. Traditional vegetation indices (VIs) derived from remote sensing often saturate at medium-high biomass levels, limiting estimation accuracy. In this study, we introduced a novel AGB estimation framework by explicitly integrating near-infrared radiance (L_nir) with UAV-based hyperspectral vegetation indices (VIs×Lnir), which effectively alleviated saturation effects commonly observed in conventional VI-based models. Field measurements and hyperspectral imagery were collected in a temperate meadow steppe, and model performance was evaluated using leave-one-out cross-validation (LOOCV). The proposed VIs×Lnir model achieved the highest accuracy (R² = 0.72, RMSE = 7.52 g/m²), outperforming conventional VIs-based (R² < 0.39, RMSE > 11.13 g/m²) estimations. The study further investigated the results of fAPAR_green-related VIs×Lnir model, which yielded higher AGB estimation accuracy than that using NDVI×Lnir. Furthermore, we examined the influence of plant diversity using Menhinick’s index (DMn) and found that AGB estimation uncertainty was lowest when DMn ranged from 0.2 to 0.4, likely due to reduced spectral mixing and optimal canopy structural homogeneity. Under both lower (DMn < 0.2) and higher diversity conditions (DMn > 0.4), AGB could still be estimated, but with increased uncertainty likely caused by insufficient spectral variability at low diversity and stronger spectral mixing at high diversity. This study demonstrates the potential of incorporating L_nir into UAV hyperspectral analysis to enhance grassland AGB estimation and provides insights into the role of biodiversity in remote sensing-based biomass monitoring. Full article

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Forests play a vital role in the global carbon cycle but face growing anthropogenic pressures, with climate change and forest fragmentation among the most critical. In West Africa, particularly in Ghana, the interaction between increasing aridity and forest fragmentation remains underexplored, despite its significance for forest biomass dynamics and carbon storage processes. This study examined how spatial variation in climatic aridity (Aridity Index, AI) affects above-ground biomass (AGB) in Ghana’s ecological zones, both directly and indirectly through forest fragmentation and biodiversity, using structural equation modeling (SEM) and generalized additive models (GAMs). Results from this study show that AGB declines along the aridity gradient, with humid zones supporting the highest biomass and semi-arid zones the lowest. The SEM analysis revealed that areas with a lower aridity index (drier conditions) had significantly lower AGB, indicating that arid conditions are associated with lower forest biomass. Fragmentation patterns align with this relationship, while biodiversity (as measured by species richness) showed weak associations, likely reflecting both ecological and data limitations. GAMs highlighted nonlinear fragmentation effects: mean patch area (AREA_MN) was the strongest predictor, showing a unimodal relationship with biomass, whereas number of patches (NP), edge density (ED), and landscape shape index (LSI) reduced AGB. Overall, these findings demonstrate that aridity and spatial configuration jointly control biomass, with fragmentation acting as a key mediator of this relationship. Dry and transitional forests emerge as particularly vulnerable, emphasizing the need for management strategies that maintain large, connected forest patches and integrate restoration into climate adaptation policies. Full article

Accurate estimation of individual tree aboveground biomass (AGB) is essential for understanding forest carbon dynamics, optimizing resource management, and addressing climate change. Conventional methods rely on destructive sampling, whereas unmanned aerial vehicle (UAV) remote sensing provides a non-destructive alternative. In this study, spectral indices, textural features, and canopy height attributes were extracted from high-resolution UAV optical imagery and Light Detection And Ranging (LiDAR) point clouds. We developed an improved YOLOv8 model (NB-YOLOv8), incorporating Neural Architecture Manipulation (NAM) attention and a Bidirectional Feature Pyramid Network (BiFPN), for individual tree detection. Combined with a random forest algorithm, this hybrid framework enabled accurate biomass estimation of Chinese fir, Chinese pine, and larch plantations. NB-YOLOv8 achieved superior detection performance, with 92.3% precision and 90.6% recall, outperforming the original YOLOv8 by 4.8% and 4.2%, and the watershed algorithm by 12.4% and 11.7%, respectively. The integrated model produced reliable tree-level AGB predictions (R² = 0.65–0.76). SHapley Additive exPlanation (SHAP) analysis further revealed that local feature contributions often diverged from global rankings, underscoring the importance of interpretable modeling. These results demonstrate the effectiveness of combining deep learning and machine learning for tree-level AGB estimation, and highlight the potential of multi-source UAV remote sensing to support large-scale, fine-resolution forest carbon monitoring and management. Full article

Aboveground biomass (AGB) is a key indicator reflecting the metabolic mechanisms of wetland plants. This study simulated the AGB of multi-community in Poyang Lake (PYL) wetland based on long-term high-resolution (30 m, 8 d) NDVI fused from MODIS and Landsat images and analyzed the spatial distribution of AGB of different wetland plants and their relationships with wetland surface elevation. Comparative analysis showed that the cubic polynomial regression model performed the best in describing the quantitative relationship between AGB and NDVI, with the R² of 0.83 for fitting data, the Root Mean Square Error (RMSE) of 51.8 g/m², and prediction accuracy (G) of 71.7% for validation data. The results showed that the maximum AGB of Carex cinerascens (Cc) and Phragmites australis-Triarrhena lutarioriparia (P-T) communities during the spring growth period reached 1352 g/m² and 1529 g/m², respectively. The total AGB value of the Polygonum hydropiper-Phalaris arundinacea (P-P) community was the lowest from June to August, due to the flooding of PYL. Trend analysis found that the AGB of the Cc and P-P communities presented increasing trends during 2001–2020. In spatial terms, the Southern and Western areas had the largest AGB, with an average of 1340 g/m² and 1283 g/m², respectively, while the AGB in the Northern lake area was the lowest. Additionally, more than 78% of the total vegetation AGB was distributed in areas with elevations of 11.0–15.0 m (total AGB values of up to 332.7–376.3 × 10⁷ kg). The changes in water level and the timing of soil exposure in PYL dominated the spatiotemporal patterns of wetland vegetation AGB. Full article

Flood events, intensified by climate change, pose significant threats to both human settlements and ecological systems. This study presents an integrated approach to evaluate flood impacts on ecosystem carbon dynamics using remote sensing and machine learning techniques. The case of the Emilia-Romagna region in Italy is presented, which experienced intense flooding in 2023. To understand flood-induced changes in the short term, we quantified the differences in net primary productivity (NPP) and above-ground biomass (AGB) before and after flood events. Short-term analysis of NPP and AGB revealed substantial localized losses within flood-affected areas. NPP showed a net deficit of 7.0 × 10³ g C yr⁻¹, and AGB a net deficit of 0.5 × 10³ Mg C. While the wider region gained NPP (6.7 × 10⁵ g C yr⁻¹), it suffered a major AGB loss (3.3 × 10⁵ Mg C), indicating widespread biomass decline beyond the flood zone. Long-term ecological assessment using the Remote Sensing Ecological Index (RSEI) showed accelerating degradation, with the “Fair” ecological class shrinking from 90% in 2014 to just over 50% in 2024, and the “Poor” class expanding. “Good” and “Very Good” classes nearly disappeared after 2019. High-hazard flood zones were found to contain 9.0 × 10⁶ Mg C in AGB and 1.1 × 10⁷ Mg C in soil organic carbon, highlighting the vulnerability of carbon stocks. This study underscores the importance of integrating flood modeling with ecosystem monitoring to inform climate-adaptive land management and carbon conservation strategies. It represents a clear, quantifiable carbon loss that should be factored into regional carbon budgets and post-flood ecosystem assessments. Full article

Grassland degradation stems from disordered energy flow and material cycling caused by heavy grazing pressure. Fertilization is an effective measure to restore degraded grasslands. However, the mechanisms through which organic fertilizers influence soil nematode communities remain poorly understood. The objective of this study was to explore the correlation between soil nematode community structure and key environmental variables, and to identify the optimal local fertilization rates. This study was conducted in subalpine meadows located in the southeastern Qinghai-Tibetan Plateau, where organic fertilizer was applied for two consecutive years. The type of organic fertilizer is fully decomposed sheep manure. A total of seven treatments were established, including a no-fertilizer control group (CK) and six organic-fertilizer-application gradient groups (O1 to O6). The application rates of organic fertilizer for the gradient groups were as follows: 2250 kg·ha⁻¹, 3750 kg·ha⁻¹, 5250 kg·ha⁻¹, 6650 kg·ha⁻¹, 8250 kg·ha⁻¹, and 9750 kg·ha⁻¹, respectively. The results demonstrated that organic fertilizer significantly improved soil fertility and increased the relative abundance of phytophagous nematodes. In the soil nematode community, Aporcelaimellus, Criconemoides and Acrobeles were the dominant genera. Key environmental factors, including alkaline nitrogen (AN), soil bulk density (BD), soil pH (pH), and aboveground biomass (AGB), were identified as the primary drivers of changes in nematode community structure across different trophic types. The results of the principal component analysis (PCA) showed that O4 (6750 kg·ha⁻¹, corresponding to 135 kg·ha⁻¹ nitrogen and 67.5 kg·ha⁻¹ phosphorus) was the ideal fertilizer rate for the region. This approach aimed to provide a scientific foundation for the enhanced restoration of degraded subalpine meadows. Full article

Frequent channel migrations of the Yellow River, coupled with increasing human disturbances, have driven significant land cover changes in the Yellow River Delta (YRD) over time. Accurate estimation of aboveground biomass (AGB) and clarification of the impact of land cover changes on AGB are crucial for monitoring vegetation dynamics and supporting ecological management. However, field-based biomass samples are often time-consuming and labor-intensive, and the quantity and quality of such samples greatly affect the accuracy of AGB estimation. This study developed a robust AGB estimation framework for the YRD by synthesizing 4717 field-measured samples from the published scientific literature and integrating two critical ecological indicators: leaf area index (LAI) and length of growing season (LGS). A random forest (RF) model was employed to estimate AGB for the YRD from 2001 to 2022, achieving high accuracy (R² = 0.74). The results revealed a continuous spatial expansion of AGB over the past two decades, with higher biomass consistently observed in western cropland and along the Yellow River, whereas lower biomass levels were concentrated in areas south of the Yellow River. AGB followed a fluctuating upward trend, reaching a minimum of 204.07 g/m² in 2007, peaking at 230.79 g/m² in 2016, and stabilizing thereafter. Spatially, western areas showed positive trends, with an average annual increase of approximately 10 g/m², whereas central and coastal zones exhibited localized declines of around 5 g/m². Among the changes in land cover, cropland and wetland changes were the main contributors to AGB increases, accounting for 54.2% and 52.67%, respectively. In contrast, grassland change exhibited limited or even suppressive effects, contributing −6.87% to the AGB change. Wetland showed the greatest volatility in the interaction between area change and biomass density change, which is the most uncertain factor in the dynamic change in AGB. Full article