Search Results (189)

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R²). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R² of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO₂) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO₂ sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

The commercial production of grapevines (Vitis vinifera L.) relies heavily on rootstocks that are hybrids of non-vinifera parentage. The relatively newly released GRN rootstocks (GRN-1, GRN-2, GRN-3, GRN-4, and GRN-5) were bred from especially under-studied genetic backgrounds. This study aimed to evaluate ungrafted GRN-series grape rootstocks under moderate water-stress conditions and to characterize and compare their physiological performances. Each of the GRN rootstocks had specific physiological characteristics that would make them suitable for a wide range of growing conditions and vineyard management goals. GRN-1 had growth habits which were more vigorous and the highest carbohydrate storage levels, while GRN-2 had the highest level of nitrogen and the largest leaf area, but the lowest levels of carbohydrate storage. GRN-3 was less tolerant to high-salinity soils, and had the longest internodes, while GRN-4 had high boron levels, which supports flowering and fruit set, and short internodes. GRN-5 was consistently moderate across all measured areas, except internode thickness, for which it was the highest. These findings show the variations in physiological growth habits among the ungrafted GRN-series rootstocks and suggest that growth habits, carbohydrate storage, leaf canopy, fruit production, and nutrition vary based on rootstock parentage. Further investigation is needed to determine whether these characteristics persist when grafted onto Vitis vinifera L. scions. Full article

Accurately estimating the forest canopy height is essential for quantifying forest biomass and carbon storage. Recently, the ICESat-2 and GEDI spaceborne LiDAR missions have significantly advanced global canopy height mapping. However, due to inherent sensor limitations, their footprint-level estimates often show systematic bias. Tall forests tend to be underestimated, while short forests are often overestimated. To address this issue, we used coincident G-LiHT airborne LiDAR measurements to correct footprint-level canopy heights from both ICESat-2 and GEDI, aiming to improve the canopy height retrieval accuracy across Puerto Rico’s tropical forests. The bias-corrected LiDAR dataset was then combined with multi-source predictors derived from Sentinel-1/2 and the 3DEP DEM. Using these inputs, we trained a canopy height inversion model based on the AutoGluon stacking ensemble method. Accuracy assessments show that, compared to models trained on uncorrected single-source LiDAR data, the new model built on the bias-corrected ICESat-2–GEDI fusion outperformed in both overall accuracy and consistency across canopy height gradients. The final model achieved a correlation coefficient (R) of 0.80, with a root mean square error (RMSE) of 3.72 m and a relative RMSE of 0.22. The proposed approach offers a robust and transferable approach for high-resolution canopy structure mapping and provides valuable support for carbon accounting and tropical forest management. Full article

Urban forests are remnants of forest habitats within urban areas. Their structural alterations create stressful microclimatic conditions that can influence the morphology of sensitive plants, such as ferns. This study analyzed variations in the morphological traits of ferns in four urban forest sites in central Veracruz, Mexico, considering the microclimatic differences arising from vegetation structure. Temperature, humidity, canopy openness, and radiation were measured, along with eight foliar traits, while assessing the impact of site and habit (terrestrial or epiphytic) on the response. Sites with greater alterations in vegetation structure exhibited increased canopy openness, solar radiation, temperature, and a higher number of days with lower relative humidity. In these sites, leaves showed an increase in dry matter content and vein density, indicating a greater investment in resource storage and structural resistance. In the less-disturbed sites, terrestrial ferns demonstrated larger leaf area and specific leaf area, suggesting greater growth potential. Conversely, epiphytes generally had smaller leaves, which could represent an adaptive advantage for these species. The results also suggest a process of biotic homogenization within this plant group, reflecting a similar morphological response, except for indicator species restricted to less disturbed sites. Thus, this study reveals that microclimatic variations induced by urbanization significantly affect plant morphology and, ultimately, species diversity. Full article

Forest growing stock volume (GSV) is a fundamental indicator for assessing the status of forest resources. It reflects forest carbon storage levels and serves as a key metric for evaluating the carbon sequestration capacity of forest ecosystems, thereby playing a crucial role in supporting national “dual-carbon” objectives. Traditional allometric models typically estimate GSV using tree species, diameter at breast height (DBH), and canopy height. However, at larger spatial scales, these models often neglect stand density, resulting in substantial estimation errors in regions characterized by significant density variability. To enhance the accuracy of large-scale GSV estimation, this study incorporates high-resolution, spatially continuous forest structural parameters—including dominant tree species, stand density, canopy height, and DBH—extracted through the synergistic utilization of active (e.g., Sentinel-1 SAR, ICESat-2 photon data) and passive (e.g., Landsat-8 OLI, Sentinel-2 MSI) multi-source remote sensing data. Within an allometric modeling framework, stand density is introduced as an additional explanatory variable. Subsequently, GSV is modeled in a stratified manner according to tree species across distinct ecological zones within Kunming City. The results indicate that: (1) the total estimated GSV of Kunming City in 2020, based on remote sensing imagery and second-class forest inventory data collected in the same year, was 1.01 × 10⁸ m³, which closely aligns with contemporaneous statistical records. The model yielded an R² of 0.727, an RMSE of 537.566 m³, and a MAE of 239.767 m³, indicating a high level of overall accuracy when validated against official ground-based inventory plots organized by provincial and municipal forestry authorities; (2) the incorporation of the dynamic stand density parameter significantly improved model performance, which elevated R² from 0.565 to 0.727 and significantly reduced RMSE. This result confirms that stand density is a critical explanatory factor; and (3) GSV exhibited pronounced spatial heterogeneity across both tree species and administrative regions, underscoring the spatial structural variability of forests within the study area. Full article

Mediterranean wood pastures are the result of traditional silvo-pastoral uses that shaped these ecosystems into a mosaic of trees and open grassland. This ecosystem structure is generally associated with increased soil fertility under tree canopies. However, the response of herbaceous plant functional types (PFTs)—grasses, legumes, and non-legume forbs—to these heterogeneous microenvironments (under the canopy vs. open grassland) remains largely unknown, particularly regarding carbon (C) and nitrogen (N) acquisition and use. Even less is known about how different tree species and environmental conditions influence these responses. In this study, we aim to assess how tree canopies influence carbon and nitrogen cycling by comparing the effects of traditional oak stands and pine plantations on herbaceous PFTs and soil dynamics. For that we use C and N content and natural isotopic abundances (δ¹³C and δ¹⁵N) as proxies for biogeochemical cycling. Our results show that ecosystem C and N patterns depend not only on herbaceous PFTs and the presence or absence of tree canopies but also on tree species identity and environmental conditions, including climate. In particular, pine-dominated plantations exhibited lower nitrogen availability compared to those dominated by oak, suggesting that oak stands may contribute more effectively to enhance soil fertility in Mediterranean wood pastures. Furthermore, the canopy effect was more pronounced under harsher environmental conditions, highlighting the role of trees in buffering environmental stress, particularly in arid regions. This suggests that changes in tree cover and tree species may drive complex changes in ecosystem C and N storage and cycling. Full article

Forest ecosystems critically regulate land surface temperature (LST) from local to regional scales. Over the last three decades (1986–2016), increasingly frequent and severe disturbances have substantially altered the European forest canopy structure and carbon storage. However, the biophysical interactions between forest disturbance severity (FDS) and LST, particularly their spatiotemporal dynamics, remain insufficiently quantified at regional-to-continental scales. This study integrated multi-source, high-resolution remote sensing data spanning 1986–2016 to systematically investigate European FDS and its biophysical control over LST. We find significant spatiotemporal heterogeneity in FDS, which decreased markedly from 5.92 ± 4.6 in 1986 to 0.35 ± 2.36 in 2016, stabilizing after a sharp decline pre-2000. Concurrently, the mean regional LST exhibited significant warming trends, increasing from −27.04 ± 10.15 K to 16.47 ± 10.67 K, and declining FDS indirectly contributed up to 65% of this temperature rise. Mechanistically, the reduced FDS enhanced the secondary forest leaf area index (LAI), decreasing surface albedo and increasing net radiation absorption, thereby inducing positive radiative feedback that drives surface warming. Our findings demonstrate that the carbon sequestration benefits accrued during forest recovery can be partially offset by associated biophysical warming effects. This evidence is crucial for optimizing European forest management strategies to balance carbon sink enhancement and climate regulation functions. Full article

The Atlantic Forest has undergone deforestation and prolonged droughts, affecting ecosystem services. This study assesses the water balance using hydrological observations from representative tree species within a Montane Semideciduous Seasonal Forest (MF) remnant. Gross precipitation (GP), canopy interception (CI), and effective precipitation (EP = Throughfall + Stemflow) were recorded daily, and soil moisture was measured down to 1.80 m every two days during the dry period of the 2023/2024 hydrological year. Additionally, aboveground biomass (AGB), fresh root biomass (BR), and soil hydrological properties in the soil profile were obtained to support the water balance results. The highest EP values were recorded in Miconia willdenowii, while the lowest were in Xylopia brasiliensis. Root zone water storage exhibited a declining trend, with the highest values in Miconia willdenowii. ET remained low, mainly in April, July, and September, with Miconia willdenowii and Copaifera langsdorffii showing the highest values, and AGB correlated with CI and ET. The dynamic of this ecosystem is apparent in the temporal variations (CVt) of soil moisture, influenced by EP and ET. The greatest variability was recorded in the surface layer (0–20 cm), stabilizing with depth, especially below 120 cm. The Temporal Stability Index (TSI) of soil water storage indicated greater stability in Blepharocalyx salicifolius. This study highlights the significance of soil water storage and ET in a tropical forest ecosystem, particularly under drought conditions, suggesting potential species that may be more effective in recovering degraded areas. Full article

Accurate detection of tea shoots in field conditions is a challenging task for production management and harvesting in tea plantations. Deep learning is well-suited for performing complex tasks due to its robust feature extraction capabilities. However, low-complexity models often suffer from poor detection performance, while high-complexity models are hindered by large size and high computational cost, making them unsuitable for deployment on resource-limited mobile devices. To address this issue, a lightweight and high-performance model was developed based on YOLOv5 for detecting tea shoots in field conditions. Initially, a dataset was constructed based on 1862 images of the tea canopy shoots acquired in field conditions, and the “one bud and one leaf” region in the images was labeled. Then, YOLOv5 was modified with a parallel-branch fusion downsampling block and a lightweight feature extraction block. The modified model was then further compressed using model pruning and knowledge distillation, which led to additional improvements in detection performance. Ultimately, the proposed lightweight and high-performance model for tea shoot detection achieved precision, recall, and average precision of 81.5%, 81.3%, and 87.8%, respectively, which were 0.4%, 0.6%, and 2.0% higher than the original YOLOv5. Additionally, the model size, number of parameters, and FLOPs were reduced to 8.9 MB, 4.2 M, and 15.8 G, representing decreases of 90.6%, 90.9%, and 85.3% compared to YOLOv5. Compared to other state-of-the-art detection models, the proposed model outperforms YOLOv3-SPP, YOLOv7, YOLOv8-X, and YOLOv9-E in detection performance while maintaining minimal dependency on computational and storage resources. The proposed model demonstrates the best performance in detecting tea shoots under field conditions, offering a key technology for intelligent tea production management. Full article

Urban green spaces are increasingly recognized for their potential to mitigate climate change by reducing atmospheric concentrations of greenhouse gases, especially carbon dioxide (CO₂). However, enhancing carbon sequestration efficiency in limited urban green areas remains a significant challenge for sustainable urban planning. Trees are among the most cost-effective and efficient natural carbon sinks, surpassing other types of land cover in terms CO₂ absorption and storage. The present study aimed to evaluate the carbon sequestration potential of four native tree species, Pongamia pinnata, Azadirachta indica, Melia azedarach, and Dalbergia sissoo, in urban parks across Multan City, Pakistan. A total of 456 trees of selected species within six parks of Multan City were inventoried to estimate the biomass and carbon stock using species-specific allometric equations. Soil organic carbon at two soil depths beneath the canopy of each tree was also estimated using Walkley–Black method. The findings revealed that the highest mean tree biomass (2.16 Mg ha⁻¹), carbon stock (1.04 Mg ha⁻¹) and carbon sequestration (3.80 Mg ha⁻¹) were estimated for Dalbergia sissoo, while Melia azedarach exhibited the lowest (0.12 Mg ha⁻¹, 0.06 Mg ha⁻¹ & 0.23 Mg ha⁻¹, respectively) across all six parks. The soil carbon stocks ranged from 48.86 Mg ha⁻¹ to 61.68 Mg ha⁻¹ across all study sites. These findings emphasize the importance of species selection in urban green planning for carbon sequestration. Strategic planting of effective native trees like Dalbergia sissoo can mitigate climate change and provide urban forest ecosystem services. Full article

Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. Forest aboveground biomass (AGB), a critical indicator of carbon storage and sequestration capacity, has garnered significant attention in ecological research. Recently, uncrewed aerial vehicle-borne laser scanning (ULS) technology has emerged as a promising tool for rapidly acquiring three-dimensional spatial information on AGB and vegetation carbon storage. This study evaluates the applicability and accuracy of UAV-LiDAR technology in estimating the spatiotemporal dynamics of AGB and vegetation carbon storage in Robinia pseudoacacia (R. pseudoacacia) plantations in the gully regions of the Loess Plateau, China. At the sample plot scale, optimal parameters for individual tree segmentation (ITS) based on the canopy height model (CHM) were determined, and segmentation accuracy was validated. The results showed root mean square error (RMSE) values of 13.17 trees (25.16%) for tree count, 0.40 m (3.57%) for average tree height (AH), and 320.88 kg (16.94%) for AGB. The regression model, which links sample plot AGB with AH and tree count, generated AGB estimates that closely matched the observed AGB values. At the watershed scale, ULS data were used to estimate the AGB and vegetation carbon storage of R. pseudoacacia plantations in the Caijiachuan watershed. The analysis revealed a total of 68,992 trees, with a total carbon storage of 2890.34 Mg and a carbon density of 62.46 Mg ha⁻¹. Low-density forest areas (<1500 trees ha⁻¹) dominated the landscape, accounting for 94.38% of the tree count, 82.62% of the area, and 92.46% of the carbon storage. Analysis of tree-ring data revealed significant variation in the onset of growth decline across different density classes of plantations aged 0–30 years, with higher-density stands exhibiting delayed growth decline compared to lower-density stands. Compared to traditional methods based on diameter at breast height (DBH), carbon storage assessments demonstrated superior accuracy and scientific validity. This study underscores the feasibility and potential of ULS technology for AGB and carbon storage estimation in regions with complex terrain, such as the Loess Plateau. It highlights the importance of accounting for topographic factors to enhance estimation accuracy. The findings provide valuable data support for density management and high-quality development of R. pseudoacacia plantations in the Caijiachuan watershed and present an efficient approach for precise forest carbon sink accounting. Full article

Forests are invaluable natural resources that provide essential ecosystem services, and their carbon storage capacity is critical for climate mitigation efforts. Quantifying this capacity would require accurate estimation of forest structural attributes for deriving their aboveground biomass (AGB). Traditional field measurements, while precise, are labor-intensive and often spatially limited. Handheld Mobile Laser Scanning (HMLS) offers a rapid alternative for building forest inventories; however, its effectiveness and accuracy in diverse subtropical forests with complex canopy structure remain under-investigated. In this study, we employed both HMLS and traditional surveys within structurally complex subtropical forest plots, including old-growth forests (Fung Shui Woods) and secondary forests. These forests are characterized by dense understories with abundant shrubs and lianas, as well as high stem density, which pose challenges in Light Detection and Ranging (LiDAR) point cloud data processing. We assessed tree detection rates and extracted tree attributes, including diameter at breast height (DBH) and canopy height. Additionally, we compared tree-level and plot-level AGB estimates using allometric equations. Our findings indicate that HMLS successfully detected over 90% of trees in both forest types and precisely measured DBH (R² > 0.96), although tree height detection exhibited relatively higher uncertainty (R² > 0.35). The AGB estimates derived from HMLS were comparable to those obtained from traditional field measurements. By producing highly accurate estimates of tree attributes, HMLS demonstrates its potential as an effective and non-destructive method for rapid forest inventory and AGB estimation in subtropical forests, making it a competitive option for aiding carbon storage estimations in complex forest environments. Full article

(1) Background: Mangroves are critical ecosystems that provide essential services, including coastal protection, biodiversity support, and carbon storage. However, urbanization and infrastructure development increasingly threaten their sustainability. This study investigates the spatio-temporal trends of mangrove canopy cover in Benoa Bay, Bali, Indonesia, which is an urban area and a center of tourism activities with various supporting facilities. The analysis was conducted from 2013 to 2023, using Landsat 8 satellite imagery and Normalized Difference Vegetation Index (NDVI) analysis. In addition, the analysis was also linked to mangrove area management policies. (2) Methods: The annual NDVI time series based on Landsat 8 imagery, obtained through the Google Earth Engine (GEE), was used to characterize the vegetation canopy cover in the study area. Statistical analysis of the annual linear trend of the NDVI was conducted to examine the spatio-temporal variation in canopy cover. Additionally, policies related to regional spatial planning and area protection were analyzed to assess their role in preserving mangrove forests in urban areas. (3) Results: There was a net decrease in mangrove area in Benoa Bay of 3.97 hectares, mainly due to infrastructure development and tourism facilities. The NDVI trend shows an overall increase in canopy cover due to reforestation and natural regeneration efforts, although there was a local decrease in some areas. Conservation policies, such as the establishment of the Ngurah Rai Forest Park, have supported mangrove protection. (4) Conclusions: The analysis demonstrated that mangroves surrounded by urban areas and tourism activity centers can still be maintained quite well with the right policies. Full article