Hasil untuk "Forestry"

P. G. Curtis, C. M. Slay, N. Harris et al.

Mapping global deforestation patterns Forest loss is being driven by various factors, including commodity production, forestry, agriculture, wildfire, and urbanization. Curtis et al. used high-resolution Google Earth imagery to map and classify global forest loss since 2001. Just over a quarter of global forest loss is due to deforestation through permanent land use change for the production of commodities, including beef, soy, palm oil, and wood fiber. Despite regional differences and efforts by governments, conservationists, and corporations to stem the losses, the overall rate of commodity-driven deforestation has not declined since 2001. Science, this issue p. 1108 A high-resolution global map enables a classification of the main drivers of forest loss. Global maps of forest loss depict the scale and magnitude of forest disturbance, yet companies, governments, and nongovernmental organizations need to distinguish permanent conversion (i.e., deforestation) from temporary loss from forestry or wildfire. Using satellite imagery, we developed a forest loss classification model to determine a spatial attribution of forest disturbance to the dominant drivers of land cover and land use change over the period 2001 to 2015. Our results indicate that 27% of global forest loss can be attributed to deforestation through permanent land use change for commodity production. The remaining areas maintained the same land use over 15 years; in those areas, loss was attributed to forestry (26%), shifting agriculture (24%), and wildfire (23%). Despite corporate commitments, the rate of commodity-driven deforestation has not declined. To end deforestation, companies must eliminate 5 million hectares of conversion from supply chains each year.

M. Taherzadeh, K. Karimi

Lignocelluloses are often a major or sometimes the sole components of different waste streams from various industries, forestry, agriculture and municipalities. Hydrolysis of these materials is the first step for either digestion to biogas (methane) or fermentation to ethanol. However, enzymatic hydrolysis of lignocelluloses with no pretreatment is usually not so effective because of high stability of the materials to enzymatic or bacterial attacks. The present work is dedicated to reviewing the methods that have been studied for pretreatment of lignocellulosic wastes for conversion to ethanol or biogas. Effective parameters in pretreatment of lignocelluloses, such as crystallinity, accessible surface area, and protection by lignin and hemicellulose are described first. Then, several pretreatment methods are discussed and their effects on improvement in ethanol and/or biogas production are described. They include milling, irradiation, microwave, steam explosion, ammonia fiber explosion (AFEX), supercritical CO2 and its explosion, alkaline hydrolysis, liquid hot-water pretreatment, organosolv processes, wet oxidation, ozonolysis, dilute-and concentrated-acid hydrolyses, and biological pretreatments.

H. Haberl, K. Erb, F. Krausmann et al.

D. Pimentel, L. Lach, Rodolfo Zúñiga et al.

P. Bansal

S. Côté, T. Rooney, J. Tremblay et al.

Shuoyuan Xu, Zhipeng Zhong, Tiago Barros et al.

Agricultural robotics is gaining increasing relevance in both research and real-world deployment. As these systems are expected to operate autonomously in more complex tasks, the availability of representative real-world datasets becomes essential. While domains such as urban and forestry robotics benefit from large and established benchmarks, horticultural environments remain comparatively under-explored despite the economic significance of this sector. To address this gap, we present HortiMulti, a multimodal, cross-season dataset collected in commercial strawberry and raspberry polytunnels across an entire growing season, capturing substantial appearance variation, dynamic foliage, specular reflections from plastic covers, severe perceptual aliasing, and GNSS-unreliable conditions, all of which directly degrade existing localisation and perception algorithms. The sensor suite includes two 3D LiDARs, four RGB cameras, an IMU, GNSS, and wheel odometry. Ground truth trajectories are derived from a combination of Total Station surveying, AprilTag fiducial markers, and LiDAR-inertial odometry, spanning dense, sparse, and marker-free coverage to support evaluation under both controlled and realistic conditions. We release time-synchronised raw measurements, calibration files, reference trajectories, and baseline benchmarks for visual, LiDAR, and multi-sensor SLAM, with results confirming that current state-of-the-art methods remain inadequate for reliable polytunnel deployment, establishing HortiMulti as a one-stop resource for developing and testing robotic perception systems in horticulture environments.

Fang Sheng, Qiang Jin, Cuiyun Wu et al.

Walnut is valued for being rich in nutrients and polyphenols, which are key bioactive metabolites; however, a comprehensive and dynamic assessment of metabolites in the husk and pellicle is still lacking. In this study, multi-omics approaches combining untargeted metabolomics and transcriptome analysis were conducted to systematically characterize the differential metabolite profile and regulatory networks in walnut husk and pellicle. Metabolomic profiling revealed a clear divergence in polyphenol compositions between the husk and the pellicle; the husk was predominantly enriched in nine phenolic acid compounds, whereas the pellicle accumulated eleven flavonoid compounds. Through co-expression network analysis, a transcription factor, JrMYB8, was identified and shown to act as a specific inhibitor and regulator of polyphenol biosynthesis. Functional characterization demonstrated that <i>JrMYB8</i> overexpression significantly reduced the accumulation of the total phenol content (TPC) and the total flavonoid content (TFC) by directly repressing the expression of <i>JrC4H</i>. These findings not only provide a molecular target for manipulating polyphenol content in walnut tissues but also offer a target for improving flavor in walnut breeding.

Yulin Tong, Fengzong Zhang, Haiqin Cheng

Hyperspectral remote sensing technology has significant application value in fields such as forestry ecology and precision agriculture, while also putting forward higher requirements for fine ground object classification. However, although hyperspectral images are rich in spectral information and can improve recognition accuracy, they tend to cause prominent feature redundancy due to their numerous bands, high dimensionality, and spectral mixing characteristics. To address this, this study used hyperspectral images from the ZY1F satellite as a data source and selected Yugan County, Shangrao City, Jiangxi Province as the research area to perform ground object classification research. A classification framework named CWSSNet was proposed, which integrates 3D spectral-spatial features and wavelet convolution. This framework integrates multimodal information us-ing a multiscale convolutional attention module and breaks through the classification performance bottleneck of traditional methods by introducing multi-band decomposition and convolution operations in the wavelet domain. The experiments showed that CWSSNet achieved 74.50\%, 82.73\%, and 84.94\% in mean Intersection over Union (mIoU), mean Accuracy (mAcc), and mean F1-score (mF1) respectively in Yugan County. It also obtained the highest Intersection over Union (IoU) in the classifica-tion of water bodies, vegetation, and bare land, demonstrating good robustness. Additionally, when the training set proportion was 70\%, the increase in training time was limited, and the classification effect was close to the optimal level, indicating that the model maintains reliable performance under small-sample training conditions.

Philipp Erler, Lukas Herzberger, Michael Wimmer et al.

Large-scale terrain scans are the basis for many important tasks, such as topographic mapping, forestry, agriculture, and infrastructure planning. The resulting point cloud data sets are so massive in size that even basic tasks like viewing take hours to days of pre-processing in order to create level-of-detail structures that allow inspecting the data set in their entirety in real time. In this paper, we propose a method that is capable of instantly visualizing massive country-sized scans with hundreds of billions of points. Upon opening the data set, we first load a sparse subsample of points and initialize an overview of the entire point cloud, immediately followed by a surface reconstruction process to generate higher-quality, hole-free heightmaps. As users start navigating towards a region of interest, we continue to prioritize the heightmap construction process to the user's viewpoint. Once a user zooms in closely, we load the full-resolution point cloud data for that region and update the corresponding height map textures with the full-resolution data. As users navigate elsewhere, full-resolution point data that is no longer needed is unloaded, but the updated heightmap textures are retained as a form of medium level of detail. Overall, our method constitutes a form of direct out-of-core rendering for massive point cloud data sets (terabytes, compressed) that requires no preprocessing and no additional disk space. Source code, executable, pre-trained model, and dataset are available at: https://github.com/cg-tuwien/lidarscout

Håkon Næss Sandum, Hans Ole Ørka, Oliver Tomic et al.

Forest stands are the fundamental units in forest management inventories, silviculture, and financial analysis within operational forestry. Over the past two decades, a common method for mapping stand borders has involved delineation through manual interpretation of stereographic aerial images. This is a time-consuming and subjective process, limiting operational efficiency and introducing inconsistencies. Substantial effort has been devoted to automating the process, using various algorithms together with aerial images and canopy height models constructed from airborne laser scanning (ALS) data, but manual interpretation remains the preferred method. Deep learning (DL) methods have demonstrated great potential in computer vision, yet their application to forest stand delineation remains unexplored in published research. This study presents a novel approach, framing stand delineation as a multiclass segmentation problem and applying a U-Net based DL framework. The model was trained and evaluated using multispectral images, ALS data, and an existing stand map created by an expert interpreter. Performance was assessed on independent data using overall accuracy, a standard metric for classification tasks that measures the proportions of correctly classified pixels. The model achieved an overall accuracy of 0.73. These results demonstrate strong potential for DL in automated stand delineation. However, a few key challenges were noted, especially for complex forest environments.

Branislava Lalic, Ana Firanj Sremac

Season and their transitions play a critical role in sharpening ecosystems and human activities, yet traditional classifications, meteorological and astronomical, fail to capture the complexities of biosphere-atmosphere interactions. Conventional definitions often overlook the interplay between climate variables, biosphere processes, and seasonal anticipation, particularly as global climate change disrupts traditional patterns. This study addresses the limitations of current seasonal classification by proposing a framework based on phenological markers such as NDVI, EVI, LAI, fPAR, and the Bowen ratio, using plants as a nature-based sensor of seasonal transitions. Indicators derived from satellite data and ground observations provide robust foundations for defining seasonal boundaries. The normalized daily temperature range (DTRT), validated in crop and orchard regions, is hypothesized as a reliable seasonality index to capture transitions. We demonstrated the alignment of this index with phenological markers across boreal, temperate, and deciduous forests. Analyzing trends, extreme values and inflection points in the seasonality index time series, we established a methodology to identify seasonal onset, duration, and transitions. This universal, scalable classification aligns with current knowledge and perception of seasonal shifts and captures site-specific timing. Findings reveal shifts in the Euro-Mediterranean region, with winters shortening, summers extending, and transitions becoming more pronounced. Effects include the Gulf Stream s influence on milder transitions, urban heat islands accelerating seasonal shifts, and large inland lakes moderating durations. This underscores the importance of understanding seasonal transitions to enable climate change adaptive strategies in agriculture, forestry, urban planning, medicine, trade, marketing, and tourism.

Shashank Bhardwaj

California's wildfire season keeps getting worse over the years, overwhelming the emergency response teams. These fires cause massive destruction to both property and human life. Because of these reasons, there's a growing need for accurate and practical predictions that can help assist with resources allocation for the Wildfire managers or the response teams. In this research, we built machine learning models to predict the number of days it will require to fully contain a wildfire in California. Here, we addressed an important gap in the current literature. Most prior research has concentrated on wildfire risk or how fires spread, and the few that examine the duration typically predict it in broader categories rather than a continuous measure. This research treats the wildfire duration prediction as a regression task, which allows for more detailed and precise forecasts rather than just the broader categorical predictions used in prior work. We built the models by combining three publicly available datasets from California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP). This study compared the performance of baseline ensemble regressor, Random Forest and XGBoost, with a Long Short-Term Memory (LSTM) neural network. The results show that the XGBoost model slightly outperforms the Random Forest model, likely due to its superior handling of static features in the dataset. The LSTM model, on the other hand, performed worse than the ensemble models because the dataset lacked temporal features. Overall, this study shows that, depending on the feature availability, Wildfire managers or Fire management authorities can select the most appropriate model to accurately predict wildfire containment duration and allocate resources effectively.

Takayuki Ishikawa, Carmelo Bonannella, Bas J. W. Lerink et al.

National Forest Inventory serves as the primary source of forest information, however, maintaining these inventories requires labor-intensive on-site campaigns by forestry experts to identify and document tree species. Embeddings from deep pre-trained remote sensing models offer new opportunities to update NFIs more frequently and at larger scales. While training new deep learning models on few data points remains challenging, we show that using pre-computed embeddings can proven effective for distinguishing tree species through seasonal canopy reflectance patternsin combination with Random Forest. This work systematically investigates how deep embeddings improve tree species classification accuracy in the Netherlands with few annotated data. We evaluate this question on three embedding models: Presto, Alpha Earth, and Tessera, using three tree species datasets of varying difficulty. Data-wise, we compare the available embeddings from Alpha Earth and Tessera with dynamically calculated embeddings from a pre-trained Presto model. Our results demonstrate that fine-tuning a publicly available remote sensing time series pre-trained model outperforms the current state-of-the-art in NFI classification in the Netherlands, yielding performance gains of approximately 2-9 percentage points across datasets and evaluation metrics. This indicates that classic hand-defined features are too simple for this task and highlights the potential of using deep embeddings for data-limited applications such as NFI classification. By leveraging openly available satellite data and deep embeddings from pre-trained models, this approach significantly improves classification accuracy compared to traditional methods and can effectively complement existing forest inventory processes.

Christopher Ardohain, Songlin Fei

Deep learning (DL) is ubiquitous in remote sensing analysis with continued evolution in model architectures and advancement of model types. However, DL is still constrained by the need for extensive training datasets, which are costly and time-consuming to produce. One potential solution is adapting training data annotations from different spatial resolutions, though the feasibility of such an application has yet to be tested. In this study, we explore the effects of using forest boundary training data derived from the 3D Elevation Program (3DEP) at 1.5m resolution and the National Land Cover Database (NLCD) at 30m to compare the effects on DL model performance. Our research covers diverse landscapes across 11 counties in Indiana (∼11,636 km2), developing 36 DL models to assess the impact of spatial resolution, model architectures, land cover, and training chip sizes. Our results show that higher-resolution training data yield more accurate models, regardless of imagery resolution, though the performance gap (F1 score) was limited to ∼2.7% even at its most extreme. We also found significant variation in performance based on land cover, with average F1 scores of 0.923 in homogeneous forested areas compared to 0.684 in complex urban settings. Despite similar training times between data sources, chipping 3DEP data took roughly five times longer. We expect that the findings from this study will assist future research in optimizing the development of DL training datasets, selection of source imagery at the proper resolution given training data availability, and application of appropriate model tuning depending on landscape complexity.

Arsić Janko, Stojanović Marko, Horáček Petr et al.

Droughts, amplified by climate change, pose a significant threat to the success of both artificially and naturally regenerated forests. Understanding how these changes affect the initial stages of saplings development is crucial for forest establishment, particularly for ecologically and economically important species like Norway spruce and sessile oak in Central Europe. This study investigated the impact of crown reduction (CR) by 50% of crown length on saplings of each species. Automatic dendrometers were installed on 24 saplings per species to precisely monitor growth and water-related stem changes. The main objective was to investigate the potential ameliorative effect of CR on water-stressed saplings during their initial development. Our study hypothesized that CR, by decreasing leaf area and consequently water use, would improve water availability and facilitate sapling growth. The results indicate that CR may enhance soil water availability thereby supporting the growth of water-stressed Norway spruce saplings but not those of sessile oak. The tree water deficit – an indicator of tree water status – significantly improves in Norway spruce saplings subjected to CR (p < 0.05). Conversely, this treatment resulted in the depletion of stem water status in sessile oak saplings. The species-specific growth phenology revealed that CR led to an increase in the number of growing days for Norway spruce compared to sessile oak saplings. In summary, CR may be considered a beneficial method for alleviating stress in Norway spruce saplings, especially during drought. In addition, further testing in field conditions is necessary to confirm these results.

Pengwei Duan, Xiaojian Ma, Haiqiang Shi et al.

Coloring is an important external quality of apples. Commercial apple varieties are bagged before the fruits ripen to ensure they grow in a pesticide-free environment, including preventing mechanical damage, reducing sunburn, etc. When the apple reaches the green fruit stage, the bag can be uncovered until it ripens and the peel is colored. There are two color patterns of “Fuji” apple peels (stripe and blush). In this study, the pattern of metabolite accumulation and gene expression was profiled in the striped and blushed apple peels at three ripening periods to elucidate the color formation mechanism within two weeks after removing the bag. The phenotypes presented in the peel coloring process are different between SF (stripe red) and HM (blush red), which have different accumulated levels of metabolites and gene expression during the coloring process. At the green fruit stage, a total of 83 differentially accumulated metabolites and 674 differentially expressed genes were identified between SF and HM. At the color turning stage, 48 DAMs and 880 DEGs were identified, including 20 flavonoids and 17 related genes. At the complete coloring stage, 95 DAMs and 2258 DEGs were identified, including 34 flavonoids and 23 related genes. In this study, a total of 10 kinds of key anthocyanins were found in the apple peel color-turning process. Keracyanin and cyanin were accumulated significantly at the apple color-turning period, while cyanidin-3-O-(6’’-O-malonyl) glucoside was accumulated at the complete coloring period. These key metabolites and related regulatory genes are responsible for the stripe and blush color formation in apple peel after uncovering the fruit bag.

Dereje Gasheye, Melkamu Abere, Mulat Ayal et al.

Abstract X. americana L. is an important and multipurpose woody plant tree species. Due to the multipurpose uses of the species, it is over-exploited by the nearby communities for different purposes. Additionally, the species also have poor seed viability, and seed germination as well as insufficient pollination caused by long distances between male and female trees. This research was hypothesized whether there was a good distribution and regeneration status of X. americana or not based on the diameter class distribution. This study aimed to examine the population structure, phenological attributes, and fruit yield potential of X. americana in Quara district, Northwestern Ethiopia. The study was carried out by establishing 30 sample plots (20 m × 20 m) systematically. Further, 10 × 10 m subplots were laid out under the main plot quadrants for sapling and seedling count. Thirty reproductively matured trees with easily visible crowns were selected to record phenological characteristics and fruit yield. Quantitative data were determined by computing density, frequency, dominance, importance value index, and Pearson correlation. The findings revealed that X. americana appeared in the study area at about 488.33 population densities per hectare. An inverted J-shaped diameter class distribution was observed. X. americana flower initiation starts in March and sheds its fruit at the end of June. On average, 12.26 kg of fruits per tree were recorded with a maximum of fruits in mid-diameter size class trees. To ensure the species sustainably in the area, anthropogenic factors like deliberate fire, deforestation, and overgrazing should be properly managed.

Robert W. Miller

Ruixin Song, Gabriel Spadon, Ronald Pelot et al.

Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries. Due to the fast growth of global trade and transportation networks, NIS has been introduced and spread unintentionally in new environments. This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide. The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% binary accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of NIS risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing international vessel traffic flow in a changing global landscape.