Search Results (87)

Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification errors, which modern computational approaches aim to minimize. Convolutional neural networks (CNNs) used to model tabular data have recently gained popularity as a highly efficient classification tool. In the present study, a variation of this method is used to classify satellite multispectral data from the Sentinel-2 mission to distinguish between 18 common Polish tree species. The novel model is trained and tested on data from species-homogeneous forest stands. The data form a multi-seasonal time series and cover five years of observations. The model achieved an overall accuracy of 80% and Cohen Kappa of 0.80 of the raw output and increased to 93% with post-processing procedures. Considering the large number of species classified, this is a promising and encouraging result. The presented results indicate the importance of early vegetation season reflectance data in model training. The spectral bands representing the infrared, red-edge and green wavelengths had the greatest impact on the model. Full article

The pre-training and fine-tuning paradigm has significantly advanced satellite remote sensing applications. However, its potential remains largely underexplored for airborne laser scanning (ALS), a key technology in domains such as forest management and urban planning. In this study, we address this gap by constructing a large-scale ALS point cloud dataset and evaluating its effectiveness in downstream applications. We first propose a simple, generalizable framework for dataset construction, designed to maximize land cover and terrain diversity while allowing flexible control over dataset size. We instantiate this framework using ALS, land cover, and terrain data collected across the contiguous United States, resulting in a dataset geographically covering 17,000 + ${\mathrm{km}}^2$ (184 billion points) with diverse land cover and terrain types included. As a baseline self-supervised learning model, we adopt BEV-MAE, a state-of-the-art masked autoencoder for 3D outdoor point clouds, and pre-train it on the constructed dataset. The resulting models are fine-tuned for several downstream tasks, including tree species classification, terrain scene recognition, and point cloud semantic segmentation. Our results show that pre-trained models consistently outperform their counterparts trained from scratch across all downstream tasks, demonstrating the strong transferability of the learned representations. Additionally, we find that scaling the dataset using the proposed framework leads to consistent performance improvements, whereas datasets constructed via random sampling fail to achieve comparable gains. Full article

Traditional quantitative genetic models in forestry often overlook the influence of an individual’s genes on neighboring trees. However, genetic competition models help bridge this gap. Competition varies among populations, over time, and across environments, yet forest breeders rarely monitor these dynamics or their effects on selected genotypes. We investigated the effects of competition on genetic variances, breeding value accuracy, and selection response in 14 Pinus taeda L. progeny tests using spatial (Spa) and spatial-competition (Spa-Comp) individual-tree mixed models. Our analysis covered traits such as diameter at breast height (DBH), total height (TH), and stem straightness (STR) across ages (3–21 years) and sites (altitude, soil texture, drainage). DBH was more affected by genetic competition than TH and STR, with effects varying across ages and sites. Direct-competition genetic correlations were negative for DBH from age 5 onward but positive for TH, reducing total heritable variance for DBH (<43.1%) while increasing for TH (<95.7%). Genetic competition accounted for less than 26% of direct additive variance. For DBH, the Spa-Comp model slightly improved breeding value accuracy (<~4%), while Spa inflated selection response (<3.83 percentage points), yet rank changes were minimal (common selected trees > 89%). These findings indicate that while competition inflates genetic gains, its impact on selection efficiency is minimal. Full article

With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs’ distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to extend UAV coverage and improve delivery completion rates. For densely distributed depots in wide-area regions, we develop algorithms for task allocation and path planning in a truck-independent UAV system. Specifically, a minimum-cost, maximum-flow model is constructed to obtain sub-paths covering all delivery tasks, and resource tree-based algorithms are used to construct global paths for UAVs and trucks. Simulation results show that our algorithms reduce total energy consumption by 11.53% and 9.15% under different task points, which suggests that our proposed method can significantly enhance delivery efficiency, offering a promising solution for future logistics operations. Full article

Structural changes in savanna trees vary spatially and temporally because of both biotic and abiotic drivers, as well as the complex interactions between them. Given this complexity, it is essential to monitor and quantify woody structural changes in savannas efficiently. We implemented a non-destructive approach based on Terrestrial Laser Scanning (TLS) and Quantitative Structure Models (QSMs) that offers the unique advantage of investigating changes in complex tree parameters, such as volume and branch length parameters that have not been previously reported for savanna trees. Leaf-off multi-scan TLS point clouds were acquired during the dry season, using a Riegl VZ1000 TLS, in September 2015 and October 2019 at the Skukuza flux tower in Kruger National Park, South Africa. These three-dimensional (3D) data covered an area of 15.2 ha with an average point density of 4270 points/m² (0.015°) and 1600 points/m² (0.025°) for the 2015 and 2019 clouds, respectively. Individual tree segmentation was applied on the two clouds using the comparative shortest-path algorithm in LiDAR 360(v5.4) software. We reconstructed optimized QSMs and assessed tree structural parameters such as Diameter at Breast Height (DBH), tree height, crown area, volume, and branch length at individual tree level. The DBH, tree height, crown area, and trunk volume showed significant positive correlations (R² > 0.80) between scanning periods regardless of the difference in the number of points of the matched trees. The opposite was observed for total and branch volume, total number of branches, and 1st-order branch length. As the difference in the point densities increased, the difference in the computed parameters also increased (R² < 0.63) for a high relative difference. A total of 45% of the trees present in 2015 were identified in 2019 as damaged/felled (75 trees), and the volume lost was estimated to be 83.4 m³. The results of our study showed that volume reconstruction algorithms such as TreeQSMs and high-resolution TLS datasets can be used successfully to quantify changes in the structure of savanna trees. The results of this study are key in understanding savanna ecology given its complex and dynamic nature and accurately quantifying the gains and losses that could arise from fire, drought, herbivory, and other abiotic and biotic disturbances. Full article

Argania spinosa (L.) Skeels is a unique endemic species in Morocco, renowned for its ecological characteristics and socio-economic importance. In Morocco, recent years have seen an exacerbation of the harmful effects of climate change, leading to an alarming decline in the natural regeneration of this species in its original habitats. It seems that the only viable solution lies in the domestication of this genetic heritage. This study marks the first in-depth investigation of the impact of various climatic and edaphic factors on the morphological and physiological traits of Argania spinosa young plants, assessed in six separate orchards and observed over four seasons (March 2022 (Winter), June 2022 (Summer), November 2022 (Autumn), and March 2023 (Winter)). A climatic assessment was carried out at each site, including measurements of rainfall, maximum and minimum temperatures, mean temperature, air temperature, and wind speed. The soil was analyzed for the pH, electrical conductivity (EC), water content, limestone (CaCO₃), Kjeldahl nitrogen (N), available phosphorus (P₂O₅), organic matter (OM), and carbon/nitrogen ratio (C/N). To gain a better understanding of the morphophysiological characteristics of young argan seedlings, we carried out various observations, such as measuring the height and diameter of aerial parts, and the water content of leaves (WCL) and branches (WCB), quantifying chlorophyll (mg/m²) and leaf area. The results revealed a significant impact of edaphic and climatic factors on the morphophysiological parameters of young argan trees. Results revealed significant correlations of young argan plants between edaphic and climatic factors and morphophysiological parameters. The Tamjloujt site, characterized by protective vegetation cover, showed optimal growth conditions with the highest leaf and branch water content (46.89 ± 4.06% and 37.76 ± 3.51%, respectively), maximum height growth (91.33 ± 28.68 mm), trunk diameter (24.85 ± 3.78 mm), and leaf surface area (69.33 ± 19.28 mm²) during Summer 2022. The Saharan zone of Laqsabi exhibited peak chlorophyll concentrations (506.9 ± 92.25 mg/m²) during Autumn 2022, due to high temperatures. The mountainous environment of Imoulass negatively impacted plant growth (mean height: 52.61 ± 12.37 mm; diameter: 6.46 ± 1.57 mm) due to harsh climatic and edaphic conditions. This research provides vital knowledge regarding the environmental factors influencing the establishment of young argan plants within the Argan Biosphere Reserve. This contributes to the development of more effective domestication strategies and the restoration of agroecosystems. The aim is to use this knowledge to promote the rehabilitation and sustainability of argan agroecosystems. Full article

Pedunculate oak (Quercus robur L.) is widely distributed across Europe and serves critical ecological, economic, and recreational functions. Investigating its responses to stressors such as drought, extreme temperatures, pests, and pathogens provides valuable insights into its capacity to adapt to climate change. Genetic and dendrochronological studies offer complementary perspectives on this adaptability. Tree-ring analysis (dendrochronology) reveals how Q. robur has historically responded to environmental stressors, linking growth patterns to specific conditions such as drought or temperature extremes. By examining tree-ring width, density, and dynamics, researchers can identify periods of growth suppression or enhancement and predict forest responses to future climatic events. Genetic studies further complement this by uncovering adaptive genetic diversity and inheritance patterns. Identifying genetic markers associated with stress tolerance enables forest managers to prioritize the conservation of populations with higher adaptive potential. These insights can guide reforestation efforts and support the development of climate-resilient oak populations. By integrating genetic and dendrochronological data, researchers gain a holistic understanding of Q. robur’s mechanisms of resilience. This knowledge is vital for adaptive forest management and sustainable planning in the face of environmental challenges, ultimately helping to ensure the long-term viability of oak populations and their ecosystems. The topics covered in this review are very broad. We tried to include the most relevant, important, and significant studies, but focused mainly on the relatively recent Eastern European studies because they include the most of the species’ area. However, although more than 270 published works have been cited in this review, we have, of course, missed some published studies. We apologize in advance to authors of those relevant works that have not been cited. Full article

Rapid population growth accelerates changes in land use and land cover (LULC), straining natural resource availability. Monitoring LULC changes is essential for managing resources and assessing climate change impacts. This study focused on extracting LULC data from 1993 to 2024 using the classification and regression tree (CART) method on the Google Earth Engine (GEE) platform in Qena Governorate, Egypt. Moreover, the cellular automata (CA) Markov model was used to anticipate the future changes in LULC for the research area in 2040 and 2050. Three multispectral satellite images—Landsat thematic mapper (TM), enhanced thematic mapper (ETM+), and operational land imager (OLI)—were analyzed and verified using the GEE code editor. The CART classifier, integrated into GEE, identified four major LULC categories: urban areas, water bodies, cultivated soils, and bare areas. From 1993 to 2008, urban areas expanded by 57 km², while bare and cultivated soils decreased by 12.4 km² and 42.7 km², respectively. Between 2008 and 2024, water bodies increased by 24.4 km², urban areas gained 24.2 km², and cultivated and bare soils declined by 22.2 km² and 26.4 km², respectively. The CA-Markov model’s thematic maps highlighted the spatial distribution of forecasted LULC changes for 2040 and 2050. The results indicated that the urban areas, agricultural land, and water bodies will all increase. However, as anticipated, the areas of bare lands shrank during the years under study. These findings provide valuable insights for decision makers, aiding in improved land-use management, strategic planning for land reclamation, and sustainable agricultural production programs. Full article

In recent years, the “3-30-300” concept by Professor Cecil Konijnendijk has been gaining popularity, outlining what makes a city “green” and what we should strive for. This concept suggests that from every apartment, at least three trees should be visible, 30% of the city’s surface should be covered with greenery, and the nearest park or forest should be no more than 300 m away. However, the lack of detailed guidelines makes the implementation of this concept a significant challenge. The goal of the research presented here was to adapt this concept for Warsaw (Poland) and assess whether it can be considered a green city. We defined parameters such as the maximum distance for visible trees as 20 m and determined what counts toward the 30% green areas. The results showed that 57.82% of apartments in Warsaw have a view of at least three trees, while only 5.44% do not meet this criterion, and the rest meet it only partially. Parks and forests cover 19.95% of the city, while all green areas combined cover 42.01%. Additionally, 45% of buildings are located within 300 m of a park or forest. Ultimately, full compliance with the “3-30-300” concept applies to 22.19% of buildings, while only 12.66% meet the criteria when considering only parks and forests. This indicates that, while Warsaw is relatively green, not all of its areas fulfill these criteria. Full article

The detection of unknown waters has been studied and applied in various fields, such as national defense, military operations, engineering surveying and mapping, and scene reconstruction. To improve exploration efficiency in unknown waters, this paper proposes a framework for autonomous exploration using unmanned surface vehicles (USVs). This framework, comprising a multi-stage exploration strategy and a hierarchical navigation strategy, is designed to mitigate the inherent restrictions between the exploration target point and exploration direction in USV operations. These two strategies are optimized for the exploration target point and feasible navigation route to address the problem of the USV’s limited mobility during exploration. Rapidly exploring random tree (RRT) and boundary detection methods are used in the local layer to find the boundary in front of and behind the USV, and the gain of the target point is optimized. The hierarchical navigation method is implemented in the global layer to plan appropriate navigation paths. The proposed method is tested in simulations in several virtual environments and contrasted with the conventional methods currently in use. The findings indicate that our strategy covers more ground more effectively than other methods (our method achieved an exploration efficiency ranging from 4.9 to 5.3 $m^2$/s, whereas traditional methods ranged from 2.3 to 3.9 $m^2$/s, which demonstrates that our approach can improve exploration efficiency by up to 200% compared to traditional methods), spending less time exploring while significantly reducing collision probability. Full article

Remote sensing datasets offer robust approaches for gaining reliable insights into forest ecosystems. Despite numerous studies reviewing forest aboveground biomass estimation using remote sensing approaches, a comprehensive synthesis of synergetic integration methods to map and estimate forest AGB is still needed. This article reviews the integrated remote sensing approaches and discusses significant advances in estimating the AGB from space- and airborne sensors. This review covers the research articles published during 2015–2023 to ascertain recent developments. A total of 98 peer-reviewed journal articles were selected under the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Among the scrutinized studies, 54 were relevant to spaceborne, 22 to airborne, and 22 to space- and airborne datasets. Among the empirical models used, random forest regression model accounted for the most articles (32). The highest number of articles utilizing integrated dataset approaches originated from China (24), followed by the USA (15). Among the space- and airborne datasets, Sentinel-1 and 2, Landsat, GEDI, and Airborne LiDAR datasets were widely employed with parameters that encompassed tree height, canopy cover, and vegetation indices. The results of co-citation analysis were also determined to be relevant to the objectives of this review. This review focuses on dataset integration with empirical models and provides insights into the accuracy and reliability of studies on AGB estimation modeling. Full article

Trees’ growth and areal responses to changing climate are primarily expected within the edges of the species range. Here, we compared the responses of Siberian pine (Pinus sibirica Du Tour), a moisture-sensitive species, and drought-resistant larch (Larix sibirica Ledeb.) at the southern part of their ranges in the Siberian Mountains (the Tannu-Ola Ridge). We study the species’ growth and proportion in the forests from forest-steppe to treeline ecotone along the elevation gradient. These studies are based on radial growth index (GI) analysis and GI dependence on the climate variables. We used satellite time series to detect the land cover changes (areas of larch and Siberian pine, as well as shrubs and birch). We compared trees’ GI before and after warming “restart” in the late 1990s. Generally, GI dependence on the air temperature was negative at elevations below c. 1600 m a.s.l., whereas GI dependence on the moisture variables (precipitation, vapor pressure deficit, and soil moisture) was positive for both species. Above 1600 m, increasing air temperatures stimulated species growth, whereas the influence of moisture variables was negative (for larch) or neutral (for Siberian pine). After the warming restart, the GI of both conifers increased in moisture-sufficient high elevations and treeline ecotone, whereas within low elevations (<1300 m), the GI was stagnant or suppressed. Both species’, especially Siberian pine, negative growth dependence on air temperature and positive dependence on the moisture variables strongly increased since the warming restart. We found a risen growth dependence of both species on the soil-stored water during the previous year (September–October), which smoothed moisture stress at the beginning of the growing season. Yet both species’ growth also suffered as a result of early spring warms. We found that larch is migrating in both uphill and downhill directions, while Siberian pine is migrating uphill only. Forests loss occurred at low elevations (<1300 m), whereas forest and shrub gain occurred at high (>2000 m) ones. The upper boundary of the forests and shrubs is migrating uphill at rates of about 0.8 and 0.3 m/y, respectively. We observed a decrease in Siberian pine proportion in the forests, whereas areas of larch and birch strongly increased (by 150% and 100%, respectively), which indicates the retreat of Siberian pine from its southern habitat. We suggested afforestation of the areas of Siberian pine mortality by the drought-tolerant larch species. Full article

The intensive management of cropland refers to a reduction in habitat complexity (i.e., shade tree cover, tree species richness, crop species richness) to gain more profits. This usually entails a decrease in biodiversity, but agroforestry systems have been shown to provide a solution to the need for profits while maintaining biodiversity and ecosystem services. Invertebrates are important bioindicators since they are not just affected by a decrease in habitat complexity; they are also key for the maintenance of ecosystems given their ecological roles. We aimed to understand how agricultural intensification impacted invertebrate abundance and richness in an agroforestry system in Bali, Indonesia. We set up 53 × 25 m² plots and collected data via pitfall and pan traps. We linked those data to vegetation data (canopy cover, tree species richness, crop species richness), habitat type (rustic vs. polyculture), and productivity. Overall, we found that the abundance and richness of invertebrate taxa were positively influenced by increasing canopy cover and crop and tree species richness. This supports the habitat heterogeneity hypothesis, which indicates that increased habitat complexity promotes higher invertebrate species richness and abundance. The abundance and richness of certain invertebrate taxa, including agents of biocontrol, were shown to increase in plots with higher yields, thus solidifying the important role of invertebrate communities in the provision of ecosystem services. Harvesting crops from complex agroforestry systems ensures a sustainable income for local communities as well as habitats for invertebrates. Full article

Olive (Olea europaea L.) is a crop of enormous economic and cultural importance. Over the years, the worldwide production of olive oil has been decreasing due to various biotic and abiotic factors. The current drop in olive oil production resulting from climate change raises concerns regarding the fulfillment of our daily demand for olive oil and has led to a significant increase in market prices. In the future, there will be a higher chance that we will face a severe shortage of olive oil, which could harm both the economic sector and the food supply. As olive groves cover more than 5 million hectares in the European Union alone, the need to preserve the crop in the context of extreme climatic events is imperative. As drought is considered one of the most limiting factors in agriculture, drought-resistant varieties and sustainable irrigation strategies are being developed to mitigate the impact of drought on crop productivity and secure the future supply of olive oil. This review focuses on recently gained insights into drought stress in olive trees through omics and phenomics approaches to unravelling mechanisms that may lead to developing new varieties that are tolerant against drought elicited by changes in growing systems. Full article

The distributed denial of service attack poses a significant threat to network security. Despite the availability of various methods for detecting DDoS attacks, the challenge remains in creating real-time detectors with minimal computational overhead. Additionally, the effectiveness of new detection methods depends heavily on well-constructed datasets. This paper addresses the critical DDoS dataset creation and evaluation domain, focusing on the cloud network. After conducting an in-depth analysis of 16 publicly available datasets, this research identifies 15 shortcomings across various dimensions, emphasizing the need for a new approach to dataset creation. Building upon this understanding, this paper introduces a new public DDoS dataset named BCCC-cPacket-Cloud-DDoS-2024. This dataset is meticulously crafted, addressing challenges identified in previous datasets through a cloud infrastructure featuring over eight benign user activities and 17 DDoS attack scenarios. Also, a Benign User Profiler (BUP) tool has been designed and developed to generate benign user network traffic based on a normal user behavior profile. We manually label the dataset and extract over 300 features from the network and transport layers of the traffic flows using NTLFlowLyzer. The experimental phase involves identifying an optimal feature set using three distinct algorithms: ANOVA, information gain, and extra tree. Finally, this paper proposes a multi-layered DDoS detection model and evaluates its performance using the generated dataset to cover the main issues of the traditional approaches. Full article