•New forest inventory method based on high point density UAS LiDAR data is proposed.•Segmented tree crowns are augmented by segmented trunks in volume predictions.•Plot level RMSE decreased from ...22.2% to 18.8% compared to conventional approach.•The decrease in RMSE can be attributed to the decrease of underestimation of volume.
High density point clouds from unmanned airborne laser scanning (UALS) systems have great potential for small area forest inventories. We propose an UALS-based tree level inventory method that takes advantage of both the segmented crowns and segmented trunks: hybrid tree detection (HTD). The method is tested at twenty 30 m × 30 m validation plots of varying maturity, tree species distribution and stocking density. With the traditional individual tree crown delineation (ITC) approach, tree attributes are only predicted for crown segmented trees. Here, we assume that a segmented crown can contain more than one tree. Our idea is to identify segmented crowns that contain more than one segmented trunk. One of the trunks is linked to a segmented crown (upper most tree) and the remainders are treated as understory trunks. Heights of the crown segmented trees and the diameters of the understory trunks are used as predictor variables in nonlinear mixed-effects models of tree volume. The %RMSE and %MD values of volume predictions at the 30 m × 30 m validation plot level were 22.2%, −13.3% and 18.8%, −8.3% for ITC and HTD, respectively. We conclude that the proposed HTD approach improves the accuracy of ITC in managed boreal forests when using UALS data.
Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying ...terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art’ and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline.
•We present the lidR package for ALS processing.•We document the design and aims of the package with an emphasis on its flexibility.•lidR assembles state-of-the-art algorithms from the literature.•lidR was conceived for users to implement transparent and reproducible workflows.•We provide evidence that liDR is increasingly being used for ALS-focused research.
•ALS data allowed the bark beetle outbreak mapping at the level of single trees.•The current outbreak of bark beetle is the largest in this area since 1945.•The important predictors are: number of ...dead spruces, crown closure and stand age.•The dead spruce in BF has more than 90 years and was grown in spruce dominated stand.
The European spruce bark beetle (Ips typographus L.) is one of the most critical insect disturbance agents in Europe. In recent years, bark beetles have caused tremendous economic losses, and affected ecosystems over large spatial scales. In this study, we evaluated the influence of selected factors on the bark beetle outbreak in 2015, the year with the most severe drought to have been recorded in the last few decades. The main aim of this study was to develop a new approach of identifying and mapping individual trees infested by bark beetles, for more efficient bark beetle management. The study was conducted on an area occupying 62,000 ha, using airborne laser scanning, multispectral imagery data, and digital forest maps.
First, an individual dead tree detection method, based on remote sensing data, was developed, which allowed the quantification of the bark beetle outbreak at an individual tree level, with accurate information on the number and location of killed spruces. Then, topography, habitat, and single tree-based variables were used to examine their influence on tree mortality. Finally, a spatial hot-spot analysis of the outbreak, throughout the 62,000 ha of the Polish part of the Białowieża Forest, was performed.
For the first time, we mapped the status of the spruce bark beetle outbreak across the Białowieża Forest, using the method developed in this study. 283,166 dead Norway spruce trees were detected in the study area, supporting the fact that the current bark beetle outbreak is the largest in this area since 1945. The number of dead spruces surrounding any given dead tree was the single most important predictor in all models, with a relative contribution of 35–79% depending on the neighbourhood analysed (radius from 0 to 2000 m). Crown closure and stand age, as well as the share of dead spruce and dominant tree species were found to be the most important predictors of the development of bark beetle infestation. The method developed in this study was evaluated and proved to be suitable for the monitoring and management of the ongoing insect outbreak.
Quantitative comparisons of tree height observations from different sources are scarce due to the difficulties in effective sampling. In this study, the reliability and robustness of tree height ...observations obtained via a conventional field inventory, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) were investigated. A carefully designed non-destructive experiment was conducted that included 1174 individual trees in 18 sample plots (32 m × 32 m) in a Scandinavian boreal forest. The point density of the ALS data was approximately 450 points/m2. The TLS data were acquired with multi-scans from the center and the four quadrant directions of the sample plots. Both the ALS and TLS data represented the cutting edge point cloud products. Tree heights were manually measured from the ALS and TLS point clouds with the aid of existing tree maps. Therefore, the evaluation results revealed the capacities of the applied laser scanning (LS) data while excluding the influence of data processing approach such as the individual tree detection. The reliability and robustness of different tree height sources were evaluated through a cross-comparison of the ALS-, TLS-, and field- based tree heights. Compared to ALS and TLS, field measurements were more sensitive to stand complexity, crown classes, and species. Overall, field measurements tend to overestimate height of tall trees, especially tall trees in codominant crown class. In dense stands, high uncertainties also exist in the field measured heights for small trees in intermediate and suppressed crown class. The ALS-based tree height estimates were robust across all stand conditions. The taller the tree, the more reliable was the ALS-based tree height. The highest uncertainty in ALS-based tree heights came from trees in intermediate crown class, due to the difficulty of identifying treetops. When using TLS, reliable tree heights can be expected for trees lower than 15–20 m in height, depending on the complexity of forest stands. The advantage of LS systems was the robustness of the geometric accuracy of the data. The greatest challenges of the LS techniques in measuring individual tree heights lie in the occlusion effects, which lead to omissions of trees in intermediate and suppressed crown classes in ALS data and incomplete crowns of tall trees in TLS data.
•There is a need for detailed, accurate and complete cultural heritage mapping.•Simple Faster R-CNN was used for automated detection in airborne lidar data.•Object types were grave mounds, pitfall ...traps and charcoal kilns.•90 % of confirmed charcoal kilns were detected, with 38 % false positives.•38 % of confirmed grave mounds were detected, with 89 % false positives.
The existing cultural heritage mapping in Norway is incomplete. Some selected areas are mapped well, while the majority of areas only contain chance discoveries, often with bad positional accuracy. The goal of this research was to develop automated tools for improving the cultural heritage mapping in Norway, thus enabling detailed mapping of large areas within realistic budgets and time frames. The focus was on three types of cultural heritage that occur frequently in many types of Norwegian landscape: grave mounds, pitfall traps in deer hunting systems and charcoal kilns.
A recent development in deep neural networks for object detection in natural images is the region-proposing convolutional neural network (R-CNN), which may also be used for cultural heritage detection in local relief model (LRM) visualizations of airborne laser scanning (ALS) data. Python code for ‘Simple Faster R-CNN’ was downloaded from Github.
On 737 test images (16.6 km2) not seen during training, 87 % of the true cultural heritage objects were correctly identified, while 24 % of the predicted cultural heritage locations were false. However, all test images were small (150 m × 150 m) and contained at least one cultural heritage object, meaning that the false positive rate may be higher for an entire landscape. In Larvik municipality, Vestfold and Telemark County, on a 67 km2 area not seen during training, the R-CNN correctly identified 38 % of the true grave mounds, with 89 % false positives. On a 937 km2 area covering Øvre Eiker municipality, Viken County, the R-CNN correctly identified 90 % of the charcoal kilns, with 38 % false positives.
In conclusion, we have demonstrated that Faster R-CNN is well suited for semi-automatic detection of cultural heritage objects such as charcoal kilns, grave mounds and pitfall traps in high resolution airborne lidar data. However, it is desirable to reduce the false positive rate in order to limit the amount of visual inspection needed when the method is applied to large areas for detailed archaeological mapping.
Topography is a key driver of tropical forest structure and composition, as it constrains local nutrient and hydraulic conditions within which trees grow. Yet, we do not fully understand how changes ...in forest physiognomy driven by topography impact other emergent properties of forests, such as their aboveground carbon density (ACD). Working in Borneo – at a site where 70‐m‐tall forests in alluvial valleys rapidly transition to stunted heath forests on nutrient‐depleted dip slopes – we combined field data with airborne laser scanning and hyperspectral imaging to characterise how topography shapes the vertical structure, wood density, diversity and ACD of nearly 15 km2 of old‐growth forest. We found that subtle differences in elevation – which control soil chemistry and hydrology – profoundly influenced the structure, composition and diversity of the canopy. Capturing these processes was critical to explaining landscape‐scale heterogeneity in ACD, highlighting how emerging remote sensing technologies can provide new insights into long‐standing ecological questions.
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•Silvicultural experiments were assessed using UAV-lidar and area-based approach.•A new group of lidar metrics (PAD-based metrics) was proposed.•UAV-lidar was effective in assessing ...silvicultural experiments.
Collecting field data in silvicultural experiments can be challenging and time-consuming. Alternatively, unmanned aerial vehicles using laser scanners (UAV-lidar) can be used for cost-effective data collection in forest stands. This work aims to assess the capability of UAV-lidar to estimate biophysical forest attributes in silvicultural experiments. The showcase experiment refers to the IMPAC II (Intensive Management Practices Assessment Center II), a long-term project of 24 plots aiming to assess the effects of fertilization and weed control on forest growth and nutrient cycling in past and ongoing silvicultural treatments in a second rotation of loblolly pine (Pinus taeda L.) plantation at age 12 years. Treatment performances were assessed based on four biometric attributes related to forest productivity: Growing stock biomass (Mg ha−1), stem volume (m3 ha−1), dominant height (m), and leaf area index (LAI, m2 m−2). We used the area-based approach (ABA) and multiple linear models to characterize these forest attributes in the different silvicultural treatments and use their predictions to run the experiment analysis. Two groups of ALS-derived metrics were tested in the modeling, traditional metrics and a novel group of metrics based on plant area density (PAD) distribution. Models using PAD-based metrics increased the correlation between observed and predicted values (R2) from 0.27–0.40 to 0.50–0.85 when compared to the same models using traditional metrics, while the relative root mean square errors (RMSE%) of the predictions were reduced from 6–18% to 4–12%. Experiment analysis using UAV-lidar data and PAD-based model predictors led to the same results as those using field observations: i) fertilization was the most effective treatment for enhancing stand attributes, especially in terms of biomass, stem volume, and LAI; ii) weed control alone provided marginal improvements in the stands; iii) actively retreating stands in both first and second rotation led to increased growth when compared to the carryover effects. UAV-lidar using PAD-based metrics was effective in characterizing enhanced silvicultural treatments and might benefit studies involving understory assessment.
•High spatial resolution and field data were used to analyse gap regeneration.•Gap geometry was not the most important variable for regeneration.•Percentage of tree species covering the land around ...the gap was essential for regeneration.•Hornbeam regeneration was highest in the gaps measured.•Browsing pressure was important factor for birch and hornbeam regeneration.
Forest dynamics is driven by the formation of gaps, especially in temperate forests. A potentially useful tool for identifying canopy gaps at different spatial scales is remote sensing. Our study used two types of data: (1) high spatial resolution Canopy Height Model (CHM) to detect canopy gaps in the forest and (2) field data to quantify natural and artificial regeneration within the gaps. The study aimed to: (i) measure the characteristics of canopy gaps; (ii) identify the regeneration patterns within gaps; (iii) identify the factors influencing tree species regeneration within gaps. We analysed 313 tree canopy gaps in the Polish part of Białowieża Forest (ca. 620 km2), east Poland. Variability in the mean gap area was very high, ranging from 20.2 to 8693.8 m2. The number of gaps with an area>1000 m2 was the largest. The total area of gaps was highest in coniferous stands, where the biggest median area was also found. Gap size did not affect regeneration density in the Białowieża Forest, except for birch and hornbeam. The density of less light-demanding tree species in the gaps was greater than light-demanding ones. Birch regenerated in all gap sizes, and its density increased with gap area. Oak seedlings dominated in small gaps but conditions in the 'matrix of small gaps' was sufficient for short oak saplings. Hornbeam regeneration was highest in the gaps surveyed, especially the density of short saplings. Seedlings of hornbeam were present in 32% of gaps, but saplings were present in as many as 56% of gaps. Spruce regeneration was less numerous than hornbeam regeneration. Birch regeneration was the most numerous in short sapling stage. We analyzed 31 variables from different categories to identify the factors influencing tree species regeneration within gaps. The most essential variables for the birch and hornbeam regeneration were those containing supporting information, especially the percentage of the specific tree species covering the land around the gap. The variables based on gap geometry (size, shape, direction, elongation) were less important than variables from other categories. We found the positive influence of browsing on the density of hornbeam saplings. However, the high density of ungulates and their browsing pressure hinder birch regeneration. Intensive browsing by ungulates seems to be the most important factor in changing species composition and forest regeneration structure in gaps.
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Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest ...monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m × 32 m test sites that were characterized as sparse (n = 42 trees) and obstructed (n = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories.
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