Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantationsinfo:eu-repo/semantics/publishedVersio

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Evaluation of Forest Features Determining GNSS Positioning Accuracy of a Novel Low-Cost, Mobile RTK System Using LiDAR and TreeNet

Accurate positioning is one of the main components and challenges for precision forestry. This study was established to test the feasibility of a low-cost GNSS receiver, u-blox ZED-F9P, in movable RTK mode with features that determine its positioning accuracy following logging trails in the forest environment. The accuracy of the low-cost receiver was controlled via a geodetic-grade receiver and high-density LiDAR data. The features of nearby logging trails were extracted from the LiDAR data in three main categories: tree characteristics; ground-surface conditions; and crown-surface conditions. An object-based TreeNet approach was used to explore the influential features of the receiver’s positioning accuracy. The results of the TreeNet model indicated that tree height, ground elevation, aspect, canopy-surface elevation, and tree density were the top influencing features. The partial dependence plots showed that tree height above 14 m, ground elevation above 134 m, western direction, canopy-surface elevation above 138 m, and tree density above 30% significantly increased positioning errors by the low-cost receiver over southern Finland. Overall, the low-cost receiver showed high performance in acquiring reliable and consistent positions, when integrated with LiDAR data. The system has a strong potential for navigating machinery in the pathway of precision harvesting in commercial forests

Evaluation of Forest Features Determining GNSS Positioning Accuracy of a Novel Low-Cost, Mobile RTK System Using LiDAR and TreeNet

Accurate positioning is one of the main components and challenges for precision forestry. This study was established to test the feasibility of a low-cost GNSS receiver, u-blox ZED-F9P, in movable RTK mode with features that determine its positioning accuracy following logging trails in the forest environment. The accuracy of the low-cost receiver was controlled via a geodetic-grade receiver and high-density LiDAR data. The features of nearby logging trails were extracted from the LiDAR data in three main categories: tree characteristics; ground-surface conditions; and crown-surface conditions. An object-based TreeNet approach was used to explore the influential features of the receiver’s positioning accuracy. The results of the TreeNet model indicated that tree height, ground elevation, aspect, canopy-surface elevation, and tree density were the top influencing features. The partial dependence plots showed that tree height above 14 m, ground elevation above 134 m, western direction, canopy-surface elevation above 138 m, and tree density above 30% significantly increased positioning errors by the low-cost receiver over southern Finland. Overall, the low-cost receiver showed high performance in acquiring reliable and consistent positions, when integrated with LiDAR data. The system has a strong potential for navigating machinery in the pathway of precision harvesting in commercial forests

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

The Effect of Mounting Height on GNSS Receiver Positioning Accuracy in Forest Conditions

In spite of the high prices of GNSS receivers, many users decide to invest in this equipment because of the high accuracy of X, Y and Z data capture. Measurements in forested environments are affected by the increased positional error because of the signal multipath effect caused by trees. The main idea of this paper is to raise the antenna of a GNSS receiver during measurements, in order to reduce the multipath effect in the highest part of forests. A 15 meter pole was used in order to capture the GNSS signal at a height of 5, 10 and 15 m above ground level, in various forest conditions. The main factor, which determines the precision and accuracy, is the operational mode of the receiver. When in the FIXED mode, the results obtained are more reliable than those obtained when in the FLOAT mode. Due to difficult conditions in the forest stand, FIXED mode occurrence is not always possible, but much more likely at higher elevations. The FLOAT mode, however, is more likely to occur in the forest conditions and the obtained accuracy of the X and Y coordinates was ±0.81 m and 1.11 m for the elevation (Z coordinate). The best results were achieved for X and Y coordinates at an altitude of 10 m in a leafless state with an average error of ±0.54 m for the FLOAT mode. We cannot assume, therefore, that raising the GNSS antenna will improve the precision and accuracy in every case

The Effect of Mounting Height on GNSS Receiver Positioning Accuracy in Forest Conditions

In spite of the high prices of GNSS receivers, many users decide to invest in this equipment because of the high accuracy of X, Y and Z data capture. Measurements in forested environments are affected by the increased positional error because of the signal multipath effect caused by trees. The main idea of this paper is to raise the antenna of a GNSS receiver during measurements, in order to reduce the multipath effect in the highest part of forests. A 15 meter pole was used in order to capture the GNSS signal at a height of 5, 10 and 15 m above ground level, in various forest conditions. The main factor, which determines the precision and accuracy, is the operational mode of the receiver. When in the FIXED mode, the results obtained are more reliable than those obtained when in the FLOAT mode. Due to difficult conditions in the forest stand, FIXED mode occurrence is not always possible, but much more likely at higher elevations. The FLOAT mode, however, is more likely to occur in the forest conditions and the obtained accuracy of the X and Y coordinates was ±0.81 m and 1.11 m for the elevation (Z coordinate). The best results were achieved for X and Y coordinates at an altitude of 10 m in a leafless state with an average error of ±0.54 m for the FLOAT mode. We cannot assume, therefore, that raising the GNSS antenna will improve the precision and accuracy in every case

Biogeographical Patterns of Soil Microbial Communities: Ecological, Structural, and Functional Diversity and their Application to Soil Provenance

The current ecological hypothesis states that the soil type (e.g., chemical and physical properties) determines which microbes occupy a particular soil and provides the foundation for soil provenance studies. As human profiles are used to determine a match between evidence from a crime scene and a suspect, a soil microbial profile can be used to determine a match between soil found on the suspect’s shoes or clothing to the soil at a crime scene. However, for a robust tool to be applied in forensic application, an understanding of the uncertainty associated with any comparisons and the parameters that can significantly influence variability in profiles needs to be determined. This study attempted to address some of the most obvious uncertainties of soil provenance applications such as spatial variability, temporal variability, and marker selection (i.e., taxa discrimination). Pattern analysis was used to validate the ecological theories driving the soil microbial biogeography. Elucidating soil microbial communities’ spatial and temporal variability is critical to improve our understanding of the factors regulating their structure and function. Microbial profiling and bioinformatics analyses of the soil community provided a rapid method for soil provenance that can be informative, easier to perform, and more cost effective than approaches using traditional physico-chemical data. This study also showed that stable profiles may allow comparison between evidence and a possible crime scene despite the time lapse (4 years) between sample collections, however, this is dependent on the analysis method, site, vegetation, and level of disturbance. Marker selection was also an important consideration for profiling. Even though Fungi look promising for single taxon soil discrimination, the additional markers can help discriminate between a wide variety of soil types. As in human identification, the more DNA markers queried the greater the discrimination power. Lastly, this study illustrated a novel method to query the iron relating genes and ability to design a novel marker that can easily be used to profile the functional diversity of a soil community to enhance soil classification. Overall this research demonstrated the potential and effectiveness of using microbial DNA from soil, not just for comparison, but also for intelligence gathering to pinpoint the geographic origin of the soil