Search Results (527)

Smartphone GNSS technology has advanced significantly, but its performance varies considerably among Android devices due to differences in hardware and software. This study compares the GNSS capabilities of the Google Pixel 5 and Pixel 9 Pro XL (Google LLC, Mountain View, CA, USA) using five-hour static measurements under three environmental conditions: open area, canopy, and indoor. Complete raw GNSS data and the tools used for positioning are freely available. The analysis focuses on signal quality and positioning accuracy, derived using raw GNSS measurements. Results show that the Pixel 9 Pro XL provides better signal completeness, a higher carrier-to-noise density (C/N0), and improved L5 frequency reception. However, this enhanced signal quality does not always translate to superior positioning accuracy. In single-point positioning (SPP), the Pixel 5 outperformed the Pixel 9 Pro XL in open conditions when considering mean positional errors, while the Pixel 9 Pro XL performed better under canopy conditions. The precise point positioning results are modest compared to the current state of the art, only achieving accuracies of a few meters. The static method achieved sub-decimeter accuracy for both devices in optimal conditions, with Pixel 9 Pro XL demonstrating a higher fix rate. Findings highlight ongoing challenges in smartphone GNSS, particularly related to the limited quality of signals received by smartphone GNSS receivers. While newer devices show improved signal reception, precise positioning remains limited. Future research should explore software enhancements and the use of various external correction sources to optimize GNSS accuracy for mobile users. Generally, a shift from research to user-ready applications is needed. Full article

Tropical forests face increasing threats and are often replaced by secondary forests that regenerate after disturbances. In the Atlantic Forest, this creates fragments of different successional stages. The aim of this study is to understand how soil nutrients and light availability gradients influence the species composition and structure of trees and regenerating strata in remnants of lowland rainforest. We sampled 15 plots for the tree stratum (DBH ≥ 5 cm) and 45 units for the regenerating stratum (height ≥ 50 cm, DBH < 5 cm), obtaining phytosociological, entropy and equitability data for both strata. Canopy openness was assessed with hemispherical photos and soil samples were homogenized. To analyze the interactions between the vegetation of the tree layer and the environmental variables, we carried out three principal component analyses and two redundancy analyses and applied a linear model. The young fragments showed good recovery, significant species diversity, and positive successional changes, while the older ones had higher species richness and were in an advanced stage of succession. In addition, younger forests are associated with sandy, nutrient-poor soils and greater exposure to light, while mature forests have more fertile soils, display a greater diversity of dispersal strategies, are rich in soil clay, and have less light availability. Mature forests support biodiversity and regeneration better than secondary forests, highlighting the importance of preserving mature fragments and monitoring secondary ones to sustain tropical biodiversity. Full article

Dragon fruit or pitaya (Hylocereus sp.) is an exotic tropical plant gaining popularity in the United States as it is a nutrient-rich fruit with mildly sweet flavor and a good source of fiber. Although high tunnels are being used to produce specialized crops, little is known about how pitaya growth, physiology and nutrient uptake change throughout the production period. This study aims to evaluate the impact of high tunnels and varying rates of vermicompost on three varieties of pitaya, White Pitaya (WP), Yellow Pitaya (YP), and Red Pitaya (RP), to assess the soil and plant nutrient dynamics, spectral reflectance changes and plant growth. Plants were assessed at 120 and 365 DAP (Days After Plantation). YP thrived in a high tunnel compared to an open environment in terms of survival before 120 DAP, with no diseased incidence and higher nutrient retention. The nutrient accumulation in the RP, WP, and YP shoot samples 120 DAP were ranked in the following order, K > N > Ca > Mg > P > Fe > Zn > B > Mn, while 365 DAP, they were ranked as K > Ca > N > Mg > P > S > Fe > Zn > B > Mn. The nutrient accumulation in the RP, WP, and YP, soil samples 120 and 365 DAP were ranked in the following order: N > Ca > Mg > P > K > Na > Zn. Soil nutrients showed a higher concentration of Na and K grown inside the high tunnels in all three pitaya species due to the increased concentration of soluble salts. Spectral reflectance analysis showed that RP and WP had higher reflectance in the visible and NIR region compared to YP due to their higher plant biomass and canopy cover. This study emphasizes the importance of environmental conditions, nutrition strategies, and plant physiology in the different pitaya plant species. The results suggest that high tunnels with appropriate vermicompost can enhance pitaya growth and development. Full article

Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a novel approach that combines radiative transfer models (RTMs) with open-access soil spectral libraries to address this challenge. Focusing on conditions of low soil moisture content (SMC), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV), the coupled Marmit–Leaf–Canopy (MLC) model is used to simulate early crop growth stages. The MLC model, which integrates MARMIT and PRO4SAIL2, enables the generation of mixed soil–vegetation scenarios. A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. The results demonstrated relatively high SOC prediction accuracy compared to previous approaches that rely only on RTMs and/or machine learning approaches. Incorporating soil moisture content significantly improved performance over bare soil alone, yielding an R² of 0.86 and RMSE of 4.05 g/kg, compared to R² = 0.71 and RMSE = 6.01 g/kg for bare soil. Adding PV slightly reduced accuracy (R² = 0.71, RMSE = 6.31 g/kg), while the inclusion of NPV alongside moisture led to modest improvement (R² = 0.74, RMSE = 5.84 g/kg). The most comprehensive model, incorporating bare soil, SMC, PV, and NPV, achieved a balanced performance (R² = 0.76, RMSE = 5.49 g/kg), highlighting the importance of accounting for all surface components in SOC estimation. While further validation with additional scenarios and SOC prediction methods is needed, these findings demonstrate, for the first time, using radiative-transfer simulations of mixed vegetation-soil-water environments, that an EO-DSSL approach enhances machine learning-based SOC modeling from EO data, improving SOC mapping accuracy. This innovative framework could significantly improve global-scale SOC predictions, supporting the design of next-generation EO products for more accurate carbon monitoring. Full article

Dehesas are mosaics of open grassland and standalone trees that are diversity reservoirs. However, they have recently faced abandonment and intensification, being replaced by plantations of fast-growing trees or subject to encroachment. Following a change in dehesa communities and structure, a change in soil microbial diversity and functionality in dehesas is expected, but dehesas’ microbial diversity is still a big unknown. In this work, we bring to light the soil prokaryotic taxonomic diversity in dehesa ecosystems and present a first approach to assessing their metabolic diversity through metabarcoding data. For this, we compared three dehesas dominated by different tree species: (i) one dehesa dominated by Quercus ilex; (ii) one dominated by Pinus pinea; and (iii) one dominated by a mixture of Q. ilex and Q. suber. At each dehesa, samples were taken under the canopy and in the open grassland, as well as through two seasons of peak vegetation productivity (autumn and spring). Our results show the following findings: (1) seasonality plays an important role in prokaryotic richness, showing higher values in autumn, and higher evenness in spring; (2) the effect of seasonality on the soil’s prokaryotic diversity is often modulated by the effect of tree species and canopy; (3) taxonomic diversity is driven mainly by the site effects, i.e., the opposite of the metabolic diversity that seemed to be driven by complex interactions among seasons, tree species, and canopies. Full article

There is a need to evaluate Sentinel-2 (S2) fire severity spectral indices (SFSIs) for predicting vegetation and soil burn severity for a variety of ecosystems. We evaluated the performance of 26 SFSIs across three fires in seasonally dry oak–pine forests in central-western Mexico. The SFSIs were derived from composites of S2 multispectral images obtained with Google Earth Engine (GEE), processed using different techniques, for periods of 30, 60 and 90 days. Field verification was conducted through stratified random sampling by severity class on 100 circular plots of 707 m², where immediate post-fire effects were evaluated for five strata, including the canopy scorch in overstory (OCS)—divided in canopy (CCS) and subcanopy (SCS)—understory (UCS) and soil burn severity (SBS). Best fits were obtained with relative, phenologically corrected indices of 60–90 days. For canopy scorch percentage prediction, the indices RBR3c and RBR5n, using NIR (bands 8 and 8a) and SWIR (band 12), provided the best accuracy (R² = 0.82). SBS could be best mapped from RBR1c (using 11 and 12 bands) with relatively acceptable precision (R² = 0.62). Our results support the feasibility to separately map OCS and SBS from S2, in relatively open oak–pine seasonally dry forests, potentially supporting post-fire management planning. Full article

Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience. Full article

Palaeoecological investigations connected with extensive pre-bog, stone walls, and field systems at Kilmore, Dingle peninsula, Ireland, are presented. The main pollen profile, KLM I, spans the last 4000 years. When the record opened, pine (Pinus sylvestris) was already a minor tree, oak (probably Quercus petraea) was the main tall-canopy tree, and birch and alder were dominant locally. Substantial farming is recorded between ca. 1530 and 600 BCE (Bronze Age) when the stone walls were likely constructed. From ca. 560 CE onwards, intensive farming was conducted for much of the time. A largely treeless landscape emerged in the late twelfth century CE. Fine-spatial reconstructions of landscape and vegetation dynamics, including the timing of blanket bog initiation, are made. Post-glacial change in the western Dingle peninsula, based on published Holocene lake profiles and drawing on the new information presented here, is discussed. Reported are (a) fossil spores of the filmy ferns Hymenophyllum tunbrigense, H. wilsonii, and Trichomanes speciosum; (b) the first fossil pollen record for Arbutus unedo (strawberry tree) in the Dingle peninsula (540 CE); and (c) the first published records for Fagopyrum fossil pollen in Ireland, indicating that buckwheat was grown at Kilmore in the late eighteenth/early nineteenth centuries. Full article

Wooded grasslands are agroforestry systems of high biological and cultural value, which are increasingly threatened by land-use abandonment in Mediterranean marginal areas. In the central-southern Apennines, little is known about their ecological dynamics under different management regimes. This study assesses how three management intensities (High: mowing plus grazing; Low: grazing only; and Abandoned: no management for ~50 years) affect the wooded grasslands in a protected area of the Central Apennines. Vascular plant composition and cover were recorded along radial transects from isolated Fagus sylvatica L. trunks to the adjacent grassland, with plots grouped in four positions (Trunk, Mid-canopy, Edge, and Grassland). The canopy cover, shrub height, species richness, and ecological roles of species were analysed. The results show that light availability, driven by canopy and shrub cover, shapes a gradient from shade-adapted species near the trunk to heliophilous grassland species in open areas. In the Abandoned site, shrub encroachment reduces light even beyond the canopy, facilitating the spread of shade-tolerant and pre-forest species, accelerating succession towards a closed-canopy forest. High-intensity management preserves floristic gradients and grassland species, while Low-intensity management shows early signs of succession at the canopy edge. These findings highlight the importance of traditional mowing and grazing in maintaining the biodiversity and ecological functions of wooded grasslands and emphasize the need for timely interventions where management declines. Full article

Forest ecosystems are vital for biodiversity, climate regulation, and ecosystem services. Their resilience depends not only on species diversity but also on intraspecific variation—the genetic and phenotypic differences within species—which underpins adaptive capacity to environmental change. However, large-scale, continuous monitoring of intraspecific variation remains challenging. Here, we present a remote sensing approach using Sentinel-2 time series of five vegetation indices as proxies for pigment content, canopy structure, and water content to detect intraspecific variation in seven tree species across a broad environmental gradient in Switzerland. Using pure-species plot data from the Swiss National Forest Inventory, we decomposed variation into spatial, temporal, and spatiotemporal components. We found that spatial variation dominated in evergreen species (48–86%), while temporal variation was more pronounced in deciduous species (56–82%), reflecting their stronger seasonality. These findings demonstrate that species-specific Sentinel-2 time series can effectively track intraspecific variation, providing a scalable method for forest monitoring. This approach opens new pathways for studying forest adaptation, informing management strategies, and guiding species selection for conservation under changing climate conditions. Full article

The accurate definition of tree growth descriptors is a crucial step in enhancing orchard management, allowing cultivar identification within an orchard and in new genotype selection for breeding programs. In apple, almond, and olive orchards, Terrestrial Laser Scanning (TLS) technologies have been already used to identify different architectural groups, but not in hazelnut yet. This study utilized TLS to investigate the canopy structure of hazelnut trees of four different Italian varieties, with and without leaves. TLS proved to be a sensor capable of collecting three-dimensional data from hazelnut field trials and allowed the definition and selection of hazelnut plant descriptors by morphological traits and morphological indexes. Nineteen descriptors, eight morphologic traits and 11 morphological indexes have been identified as reliable suitable descriptors of hazelnut cultivar and in breeding evaluations, according to Biodiversity, FAO and CIHEAM. Many of the selected descriptors are related to the tree habit, vigour and branching density. Two useful indexes have also been defined: Canopy Uprightness (CU) Index and the Index of Canopy Opening (ICO). The descriptors allowed us to distinguish the four studied hazelnut cultivars based on their growth habit; in particular the cultivar Tonda Gentile delle Langhe showed a growth habit that is a lot different from that of the other ones. 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

While the methods for monitoring deforestation are relatively well established, there is still no compromise on those for forest degradation. We propose here a systematic review on studies about forest degradation in the Congo Basin. Our analysis focused on seven key anthropogenic causes of forest degradation. Shifting agriculture emerged as the most significant driver, accounting for 61% ± 28.58% (mean ± SD) of canopy opening, 73.16% ± 16.88% aboveground carbon loss, and 30.37% ± 30.67% of tree species diversity loss over a 5–60-year period. Our analysis reveals a significant disconnect. Only 29% of the reviewed studies address this driver, while over 64% focus primarily on the consequences of industrial timber harvesting. Despite its comparatively minor contribution to degradation, with effects range from only 8.98% ± 13.63% of canopy opening, 14.79% ± 22.21 aboveground carbon loss, and 4.27 ± 21.07 tree species diversity loss over 1–20 years. Indeed, most of the methods focus on detecting changes in canopy structure associated with forest logging over a short period (0–5 years). These illustrate the need for a shift in focus in scientific research towards innovative methods, which can be developed over time, to monitor the various impacts of all causes of forest degradation. Full article

With the rapid advancement of smart forestry, 3D reconstruction and the extraction of structural parameters have emerged as indispensable tools in modern forest monitoring. Although traditional methods involving LiDAR and manual surveys remain effective, they often entail considerable operational complexity and fluctuating costs. To provide a cost-effective and scalable alternative, this study introduces FS-MVSNet—a multi-view image-based 3D reconstruction framework incorporating feature pyramid structures and attention mechanisms. Field experiments were performed in three representative forest parks in Beijing, characterized by open canopies and minimal understory, creating the optimal conditions for photogrammetric reconstruction. The proposed workflow encompasses near-ground image acquisition, image preprocessing, 3D reconstruction, and parameter estimation. FS-MVSNet resulted in an average increase in point cloud density of 149.8% and 22.6% over baseline methods, and facilitated robust diameter at breast height (DBH) estimation through an iterative circle-fitting strategy. Across four sample plots, the DBH estimation accuracy surpassed 91%, with mean improvements of 3.14% in AE, 1.005 cm in RMSE, and 3.64% in rRMSE. Further evaluations on the DTU dataset validated the reconstruction quality, yielding scores of 0.317 mm for accuracy, 0.392 mm for completeness, and 0.372 mm for overall performance. The proposed method demonstrates strong potential for low-cost and scalable forest surveying applications. Future research will investigate its applicability in more structurally complex and heterogeneous forest environments, and benchmark its performance against state-of-the-art LiDAR-based workflows. Full article

In the last decade, German forests have been decimated because of extreme events such as drought and windthrow, and bark beetle infestations that occur in the aftermath, primarily in monoculture Norway spruce stands. It is essential for decision makers in forest management to have an educated estimation of potential future loss. We have developed a model to predict future canopy cover loss in German spruce forests. Since, past canopy cover loss is a key predictor, we adapt the spatio-temporal matrix (STM) method used for predicting urban growth, to work with a canopy-cover-loss time-series product based on earth observation data. We configure a hybrid neural network model using the STM, its percentiles along with climatic and topographic data to produce the probability information of canopy cover loss in German spruce forests in the next year. The prediction results from the model show a good capacity of prediction, as validation results present an AUC of the ROC space as high as 82.3%. Our results show that future canopy cover loss can be predicted with reasonable accuracy using open-access earth-observation time-series data supplemented by environmental data without the need for site specific in situ data collection. Full article