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Bottom-up delineation of individual trees from full-waveform airborne laser scans in a structurally complex eucalypt forest
... Specifically, we can see that all methods obtain satisfactory results for road-trees with consistent tree shapes in simple situations. With regard to the complex position distribution, such as multiple trees distributed in a queue with serious spatial overlap, two traditional methods [80,81] are fast and efficient but easy to result in omission or commission errors. By contrast, two deep learning approaches [64,67] and our method can achieve better tree segmentation results. ...
... To further prove the superiority of our individual tree segmentation method, we designed a number of experiments and compared it with selected popular methods, including two traditional methods (watershed-based method [80] and mean shift-based method [81]) and two deep learning approaches (SGE_Net [64] and DAE_Net [67]). To qualitatively present the effectiveness of the proposed method for individual tree segmentation in complex urban MLS point cloud scenes, a selected examples of visual results are shown in Figure 10. ...
... Specifically, we can see that all methods obtain satisfactory results for roadtrees with consistent tree shapes in simple situations. With regard to the complex position distribution, such as multiple trees distributed in a queue with serious spatial overlap, two traditional methods [80,81] are fast and efficient but easy to result in omission or commission errors. By contrast, two deep learning approaches [64,67] and our method can achieve better tree segmentation results. ...
As one of the most important components of urban space, an outdated inventory of road-side trees may misguide managers in the assessment and upgrade of urban environments, potentially affecting urban road quality. Therefore, automatic and accurate instance segmentation of road-side trees from urban point clouds is an important task in urban ecology research. However, previous works show under- or over-segmentation effects for road-side trees due to overlapping, irregular shapes and incompleteness. In this paper, a deep learning framework that combines semantic and instance segmentation is proposed to extract single road-side trees from vehicle-mounted mobile laser scanning (MLS) point clouds. In the semantic segmentation stage, the ground points are filtered to reduce the processing time. Subsequently, a graph-based semantic segmentation network is developed to segment road-side tree points from the raw MLS point clouds. For the individual tree segmentation stage, a novel joint instance and semantic segmentation network is adopted to detect instance-level roadside trees. Two complex Chinese urban point cloud scenes are used to evaluate the individual urban tree segmentation performance of the proposed method. The proposed method accurately extract approximately 90% of the road-side trees and achieve better segmentation results than existing published methods in both two urban MLS point clouds. Living Vegetation Volume (LVV) calculation can benefit from individual tree segmentation. The proposed method provides a promising solution for ecological construction based on the LVV calculation of urban roads.
... ITD algorithms from terrestrial laser scanning (TLS) data are also available [45], but their adaptability and performance with respect to aerial LiDAR data is still unclear. The success of ITD depends on multiple factors, such as the density and the stand configuration [27,[46][47][48], the point density of the LiDAR acquisition [49,50], the foliage condition [51][52][53], and the type of ITD algorithm that is applied (e.g., [30,42]). Research into ITD development is still actively needed to overcome the previously mentioned challenges and limitations. ...
... There is still little scientific information regarding the potential of ULS systems on heterogeneous mixedwood or hardwood forests [59,78,79]. The structural complexity of these forest types, which pose additional challenges to the ITD algorithms, also increases the difficulty of validating results at the tree-level [49]. ...
... 1. Predicting DBH using height and CD allometry (Equation (1)) from raster-based ITD trees [28]; 2. Predicting DBH using height and CD allometry (Equation (1)) from point cloud-based ITD trees [49]; 3. Estimating DBH using a cylinder-fitting algorithm onto tree stems [39,45,90]. ...
UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R 2 = 0.29 to R 2 = 0.61), and prediction of tree DBH (from R 2 = 0.36 to R 2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R 2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.
... They tried to explore the probability of dead trees detection without tree demidruleation from Voxel-based full-waveform (FW) LiDAR. Shendryk et al. [30] suggested a bottom-up algorithm to detect Eucalyptus tree trunks and the demidruleation of individual trees with complex shapes. Agnieszka Kamińska et al. [31] used remote sensing techniques, including airborne laser scanner and colour infrared imagery, to classify between living or dead trees and concluded that only airborne laser scanner detects dead tree at the single tree level. ...
... W. Yao et al. [35] and Shendryk et al. [38] published their prior work on the identification of dead trees is performed by individual tree crown segmentation prior to the health assessment. Meng R. et al. [39], Shendryk et al. [30], López-López M et al. [40], Barnes et al. [41], Fassnacht et al. [42], mentioned that most of the current tree health studies centred either on evaluating the defoliation of the tree crown or the overall health status of the tree, although there was minimal exposure to the discolouration of the tree crown. Dengkai et al. [43] used a group of fields assessed tree health indicators to define tree health that was classified with a Random Forest classifier using airborne laser scanning (ALS) data and hyperspectral imagery (HSI). ...
... Numerous approaches are studied in the current literature with regards to trees and their health in urban areas. Shendryk et al. [30] worked on the trunks of Eucalyptus trees, as well as their complex shapes. They used Euclidean distance clustering for individual tree trunk detection. ...
Urban greenery is an essential characteristic of the urban ecosystem, which offers various advantages, such as improved air quality, human health facilities, storm-water run-off control, carbon reduction, and an increase in property values. Therefore, identification and continuous monitoring of the vegetation (trees) is of vital importance for our urban lifestyle. This paper proposes a deep learning-based network, Siamese convolutional neural network (SCNN), combined with a modified brute-force-based line-of-bearing (LOB) algorithm that evaluates the health of Eucalyptus trees as healthy or unhealthy and identifies their geolocation in real time from Google Street View (GSV) and ground truth images. Our dataset represents Eucalyptus trees’ various details from multiple viewpoints, scales and different shapes to texture. The experiments were carried out in the Wyndham city council area in the state of Victoria, Australia. Our approach obtained an average accuracy of 93.2% in identifying healthy and unhealthy trees after training on around 4500 images and testing on 500 images. This study helps in identifying the Eucalyptus tree with health issues or dead trees in an automated way that can facilitate urban green management and assist the local council to make decisions about plantation and improvements in looking after trees. Overall, this study shows that even in a complex background, most healthy and unhealthy Eucalyptus trees can be detected by our deep learning algorithm in real time.
... To evaluate the effectiveness of the instance segmentation of tree, we designed a group of experiments and compared it with other three methods, including Li's method [73], ForestMetrics [74], and treeseg [75] in terms of segmentation accuracy, omission error, and commission error for recognizing roadside trees, as listed in Table 8. We apply the same data to evaluate the proposed method and other methods in this paper. ...
... However, the performance of the algorithm is not ideal when applied to urban roadside trees. ForestMetrics [74] mainly detects trunks and delineates individual trees from ALS to be well suited for trees with crowns of structurally complex shapes by a new bottom-up algorithm. Although ForestMetrics achieved a good tree segmentation performance with the AC, OM, and COM values of 85.9%, 14.1%, and 11.8%, respectively. ...
... The proposed method also develops a novel and effective supervoxel-based normalized cut segmentation method, improving segmentation performance for incomplete and small trees. Thus, we have better accuracy of tree segmentation than those of Li's method [73], ForestMetrics [74], and treeseg [75]. ...
Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree.
... tree position, height, apex, and crown size). To extract more tree-level information, a two-step, bottom-up tree segmentation approach was proposed by Shendryk et al. [31], in which tree stems are detected using the conditional Euclidean distance clustering method, individual tree canopies are then isolated utilizing a method based on random walks. The results showed that the determination coefficients (R 2 ) of the measured and lidar-estimated tree heights and crown widths were 0.92 and 0.41, respectively [31]. ...
... To extract more tree-level information, a two-step, bottom-up tree segmentation approach was proposed by Shendryk et al. [31], in which tree stems are detected using the conditional Euclidean distance clustering method, individual tree canopies are then isolated utilizing a method based on random walks. The results showed that the determination coefficients (R 2 ) of the measured and lidar-estimated tree heights and crown widths were 0.92 and 0.41, respectively [31]. It is worth noting that the R 2 of crown width shows a weak correlation. ...
... In general, the disadvantages of the bottom-up methods could be attributed to the use of tree trunk information as seed points for clustering tree segmentation, because selecting different points as seeds might lead to significant differences in the crown description. In addition, the density of the lidar point is an important factor affecting the accuracy of the identification and depiction of single trees, which should be considered before the remote sensing-based survey [5], [31]. ...
To minimize omission and commission errors due to the lack of adequate utilization of forest structure information, this paper presents a tree delineation approach by combining trunk detection with canopy segmentation. First, all potential tree trunk points are detected and detached from leaf-off data by analyzing the points’ vertical histogram, and the obtained points are then clustered using the method based on DBSCAN (Density-Based Spatial Clustering of Application with Noise). Meanwhile, the canopy-based segmentation is implemented using leaf-on data within the same plot. The detected trunks and delineated crown segments are then combined using the matching rules. Finally, single trees are isolated from point clouds, and tree-level structure information is estimated. The novelty of this approach lies in that the trunk detection results and the canopy segmentation results serve as mutual references for final individual tree delineation. Experimental results in a canopy-closed deciduous natural forest show that the presented method can identify 84.0% of trees, 90.7% of the identified trees are correct, and the total segmentation accuracy is 87.2%. The determination coefficient R2 of tree height is 0.96, and the mean difference of tree position is 76 cm. The results imply that the presented approach has good potential for isolating single trees from airborne LiDAR point clouds and estimating tree-level structural parameters in deciduous forests.
... A bottom-up method was proposed in [27] to extract and segment tree trunks based on the intensity and topological relationships of the points and then allocate other points to exact tree trunks according to 2D and 3D distances. Shendryk et al. developed a two-step, bottom-up methodology for individual tree delineation: firstly, identify individual tree trunks based on conditional Euclidean distance clustering, and then, delineate tree crowns using an algorithm based on random walks in the graph paradigm [28]. Essentially, the key idea of a bottom-up strategy is to identify the trunk points of individual trees and use the thus detected trunks as seeds for further crown delineation. ...
... The major limitation or the biggest uncertainty of the proposed approach is that not all tree trunks can be correctly detected. The performance of trunk extraction depends on the LiDAR point density and the forest type [11,28]. The trunk detection works successfully, if there are enough laser hits at the trunk and if the trunk area can be reliably separated from the crown points. ...
... Further accuracy refinements for individual tree detection and tree parameter estimation could be achieved by increasing the average LiDAR point density, which refers to the nominal value influenced by the laser pulse repetition frequency, flight pattern, flying height, flying speed and strip overlap [2,11]. The common means to increase the LiDAR point density include reducing the flying speed, increasing the overlapping of laser strips, accelerating the emission of laser pulses, etc. Alternately, the overall point density could be increased by optimizing the flight pattern; for instance, flight lines can be surveyed twice or even more [28]. Apparently, a laser scanner with a higher point density could provide sufficient data points reflected from the tree trunk and thereby optimize the trunk detection accuracy. ...
Airborne LiDAR (Light Detection And Ranging) remote sensing for individual tree-level forest inventory necessitates proper extraction of individual trees and accurate measurement of tree structural parameters. Due to the inadequate tree finding capability offered by LiDAR technology and the complex patterns of forest canopies, significant omission and commission errors occur frequently in the segmentation results. Aimed at error reduction and accuracy refinement, this paper presents a novel adaptive mean shift-based clustering scheme aided by a tree trunk detection technique to segment individual trees and estimate tree structural parameters based solely on the airborne LiDAR data. Tree trunks are detected by analyzing points’ vertical histogram to detach all potential crown points and then clustering the separated trunk points according to their horizontal mutual distances. The detected trunk information is used to adaptively calibrate the kernel bandwidth of the mean shift procedure in the fine segmentation stage by applying an original 2D (two-dimensional) estimation of individual crown diameters. Trunk detection results and LiDAR point clusters generated by the adaptive mean shift procedures serve as mutual references for final detection of individual trees. Experimental results show that a combination of adaptive mean shift clustering and detected tree trunk can provide a significant performance improvement in individual tree-level forest measurement. Compared with conventional clustering techniques, the trunk detection-aided mean shift clustering approach can detect 91.1% of the trees (“recall”) with a higher tree positioning accuracy (the mean positioning error is reduced by 33%) in a multi-layered coniferous and broad-leaved mixed forest in South China, and 93.5% of the identified trees are correct (“precision”). The tree detection brings the estimation of structural parameters for individual trees up to an accuracy level: −2.2% mean relative error and 5.8% relative RMSE (Root Mean Square Error) for tree height and 0.6% mean relative error and 21.9% relative RMSE for crown diameter, respectively.
... Therefore, bottom-up methods were developed for the UAV-LiDAR data to detect tree trunks under the assumption that the LiDAR points of tree trunks should be distributed along the vertical direction. Shendryk et al. [22] detected trunks using conditional Euclidean distance clustering and delineated tree crowns through random walk segmentation. Jaskierniak et al. [23] applied a novel watershed clustering within horizontally sliced point clouds and merged the slice-specific clusters into branches, canopy clumps, and trunks through a principal component analysis. ...
... Although various methods have been developed to identify individual trees from raster data or point clouds, separating overlapping crowns and detecting understory trees remain great challenges [11]. In particular, in broadleaf forests, the irregular crown shapes and interlacing branches of neighboring trees bring about more difficulties [22,32,33]. Furthermore, the crowns of broadleaf trees usually have multiple peaks and, hence, a tree may be divided into more than one segment, resulting in over-segmentation [34]. ...
Individual tree detection and segmentation in broadleaf forests have always been great challenges due to the overlapping crowns, irregular crown shapes, and multiple peaks in large crowns. Unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) is a powerful tool for acquiring high-density point clouds that can be used for both trunk detection and crown segmentation. A hybrid method that combines trunk detection and crown segmentation is proposed to detect individual trees in broadleaf forests based on UAV-LiDAR data. A trunk point distribution indicator-based approach is first applied to detect potential trunk positions. The treetops extracted from a canopy height model (CHM) and the crown segments obtained by applying a marker-controlled watershed segmentation to the CHM are used to identify potentially false trunk positions. Finally, the three-dimensional structures of trunks and branches are analyzed at each potentially false trunk position to distinguish between true and false trunk positions. The method was evaluated on three plots in subtropical urban broadleaf forests with varying proportions of evergreen trees. The F-score in three plots ranged from 0.723 to 0.829, which are higher values than the F-scores derived by a treetop detection method (0.518–0.588) and a point cloud-based individual tree segmentation method (0.479–0.514). The influences of the CHM resolution (0.25 and 0.1 m) and the data acquisition season (leaf-off and leaf-on) on the final individual tree detection result were also evaluated. The results indicated that using the CHM with a 0.25 m resolution resulted in under-segmentation of crowns and higher F-scores. The data acquisition season had a small influence on the individual tree detection result when using the hybrid method. The proposed hybrid method needs to specify parameters based on prior knowledge of the forest. In addition, the hybrid method was evaluated in small-scale urban broadleaf forests. Further research should evaluate the hybrid method in natural forests over large areas, which differ in forest structures compared to urban forests.
... In comparison to the watershed segmentation algorithm, a higher accuracy was observed in identifying deciduous forests rather than coniferous forests [44]. Shendryk et al. proposed a single tree segmentation algorithm based on conditional Euclidean distance clustering, and the research results showed that the algorithm could detect 67% of trees with larger diameters, and the detection rate and correct recognition rate of high cloud density were 11% and 13% higher than that of the low cloud density, respectively [47]. The performance of these algorithms varied case-by-case as the segmentation accuracy was no doubt affected by factors including the settings of algorithm parameters [27], study area (e.g., species, topography) [37,48,49] and sensor (e.g., point density) [50]. ...
... Since most individual tree segmentation studies only examine the performance of a specific segmentation approach within a specific region [40,47], there are few studies that explore the applicability of multiple algorithms and the sensitivity of parameter settings, as all approaches have pros and cons [28,41]. Especially in the fast-growing Eucalyptus plantations with irregular shapes, it is necessary to explore robust and repeatable frameworks for mapping and modeling at the individual tree level, which is essential for forest management. ...
As an emerging powerful tool for forest resource surveys, the unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) sensors provide an efficient way to detect individual trees. Therefore, it is necessary to explore the most suitable individual tree segmentation algorithm and analyze the sensitivity of the parameter setting to determine the optimal parameters, especially for the Eucalyptus spp. forest, which is one of the most important hardwood plantations in the world. In the study, four methods were employed to segment individual Eucalyptus spp. plantations from normalized point cloud data and canopy height model generated from the original UAV-LiDAR data. And the parameter sensitivity of each segmentation method was analyzed to obtain the optimal parameter setting according to the extraction accuracy. The performance of the segmentation result was assessed by three indices including detection rate, precision, and overall correctness. The results indicated that the watershed algorithm performed better than other methods as the highest overall correctness (F = 0.761) was generated from this method. And the segmentation methods based on the canopy height model performed better than those based on normalized point cloud data. The detection rate and overall correctness of low-density plots were better than high-density plots, while the precision was reversed. Forest structures and individual wood characteristics are important factors influencing the parameter sensitivity. The performance of segmentation was improved by optimizing the key parameters of the different algorithms. With optimal parameters, different segmentation methods can be used for different types of Eucalyptus plots to achieve a satisfying performance. This study can be applied to accurate measurement and monitoring of Eucalyptus plantation.
... Compared to aerial imagery, LiDAR is less affected by weather and lighting conditions during data collection, and provides significant advantages in precisely characterizing the three-dimensional structure of tree canopies. Since the start of the 21st century, the number of studies on single-tree crown extraction has increased annually, and individual tree segmentation and subsequent individual-tree level forest parameter extraction have gradually become the focus of LiDAR in forest applications [19], [20], [21]. ...
... We did not consider the impact of different point cloud densities on the algorithms in this study because the high-density point cloud brought by current UAV-LiDAR includes the phenomenon of saturation for the requirement of ITCD. Some studies show that an increase in point cloud density greater than 10 pt/m 2 will not significantly improve the accuracy of ITCD [21], [79]. ...
Individual tree crown delineation (ITCD) employing unmanned aerial vehicle light detection and ranging (UAV-LiDAR) data can directly obtain high-precision tree-level structural information within a block, with this information being the foundation for monitoring and management of the forest, thus reducing time-consuming labour. Despite the fact that numerous ITCD algorithms have been proposed, there has not yet been a robust and comprehensive comparison of these algorithms in plantations. In this paper, we evaluated the performance of seven classic ITCD methods under various stand densities and crown classes and analysed the parameter sensitivity as well as the correlation of segmentation accuracy with optimal parameters and stand metrics. The results demonstrate that the segmentation and crown description accuracy, stability and adaptability of the algorithm should be comprehensively considered when choosing an algorithm. The forest characteristics impact the accuracy of the algorithms, and the complexity of the forest canopy structure and omission error of suppressed trees are the key factors impacting ITCD accuracy. Furthermore, this study shows that it is feasible to control the parameters of the algorithm through stand measurement. These results will be helpful in guiding the selection of ITCD methods and will provide support for improving the ITCD algorithm in the future.
... Airborne LiDAR can measure distance of the sensor from both the ground and leaf canopy using lasers, producing accurate and fine spatial scale remote sensing estimates of vegetation biomass (Zolkos et al., 2013), but at considerable cost (Lu, 2006) and seldom accounting for small branches and leaf canopy biomass (Verschuyl et al., 2018;Zolkos et al., 2013). Terrestrial Laser Scanning (ground-based LiDAR) can be used to estimate biomass for individual trees (Kankare et al., 2013;Shendryk et al., 2016) but is time-consuming for stationary equipment, particularly in remote areas and steep terrain. Mobile equipment generates complex data, limiting application in temporal vegetation surveys, particularly of individual plants. ...
... As consumer-grade drone cameras increase in resolution above 12 MP, finer plant parts will be resolved in images, reducing error in the dry weight biomass to volume relationship. Further, automatic shape recognition and separation of the plant in the 3D model from the ground surface will increase efficiency as it has for 3D airborne laser scanning data (Shendryk et al., 2016), increasing the potential for automated data processing and analysis for species identification and size variation. ...
Measurement of variation in plant biomass is essential for answering many ecological and evolutionary questions. Quantitative estimates require plant destruction for laboratory analyses, while field studies use allometric approaches based on simple measurement of plant dimensions.
We estimated the biomass of individual shrub‐sized plants, using a low‐cost unmanned aerial system (drone), enabling rapid data collection and non‐destructive sampling. We compared volume measurement (a surrogate for biomass) and sampling time, from the simple dimension measurements and drone, to accurate laboratory‐derived biomass weights. We focused on three Australian plant species which are ecologically important to their terrestrial and floodplain ecosystems: porcupine grass Triodia scariosa, Queensland bluebush Chenopodium auricomum, and lignum Duma florulenta.
Estimated volume from the drone was more accurate than simple dimension measurements for porcupine grass and Queensland bluebush, compared to estimates from laboratory analyses but, not for lignum. The latter had a sparse canopy, with thin branches, few vestigial leaves and a similar color to the ground. Data collection and analysis consistently required more time for the drone method than the simple dimension measurements, but this would improve with automation.
The drone method promises considerable potential for some plant species, allowing data to be collected over large spatial scales and, in time series, increasing opportunities to answer complex ecological and evolutionary questions and monitor the state of ecosystems and plant populations.
... Other efforts have focused not only on determining the geometric characteristics but also on enhancing tree crown detection by combining the radiation characteristics of the fullwaveform data [27], [46], [47] using the intensity information of leaf-off point clouds [48] or the spectral characteristics of the multispectral LiDAR data [49], [50]. Although additional optical information has been indicated to benefit individual tree detection, these methods are limited by data acquisition. ...
... Although additional optical information has been indicated to benefit individual tree detection, these methods are limited by data acquisition. The reflectance and calibration of the object and sensor system are not easily applicable to different cases [46], [48]. In contrast, the use of 3-D geometric characteristics of forest point clouds is potentially more robust for segmenting individual trees. ...
Over an extended period, remote-sensing-based individual tree analysis has played a critical role in modern forest inventory and management research. The segmentation of individual trees from aerial point clouds usually depends on the characteristics of peak-like uplift on the crown surface; however, the performance inevitably decreases with increasing visibility of such features in point clouds, especially for high-density forests. Herein, we developed a novel hierarchical region-merging algorithm that first over-segmented the entire forest scene based on local density and then merged the over-segmented partitions into pairs through a stepwise optimal process to produce the final segmentation. In the region-merging method, a global merging cost was introduced to shift from local detection of crown features to utilize the overall compactness of forest point clouds. The experiments were conducted using unmanned aerial vehicle light detection and ranging (UAV-LiDAR) point clouds from three coniferous stands with different densities and a high-density coniferous and broad-leaved mixed stand. A total of 5510 field-measured trees in 36 plots were used to assess the accuracy of the proposed method. Our method achieved F-scores of 0.91, 0.88, 0.84 and 0.80 for low- (~700 stems/ha), medium- (~1000 stems/ha), and high-density (~2000 stems/ha) conifer stands and coniferous and broad-leaved mixed forests (~1800 stems/ha), respectively. Compared to the classical individual tree segmentation methods (marker-controlled watershed segmentation and point cloud region-growing algorithm), our method obtained comparable performance in low-density conifer stands and superior performance in the other stands. Furthermore, the region-merging algorithm could detect 10% more suppressed trees on average, which led to an apparent improvement in detection accuracy. The proposed algorithm provides a flexible segmentation framework that could be further improved by a different design that merges costs or applies multiscale segmentation with different stopping criteria.
... In recent years several methods for extracting tree stems using ALS have been developed [1,[18][19][20][21][22][23]. Due to differing objectives, differing ALS acquisition designs and differing investigated forest types and structures, the accuracy of the trunk detection methods is hard to compare. ...
... The bottom-up approach of Shendryk et al. [20] uses full-waveform ALS data to delineate individual trees using tree trunks as seeding points. In extension to Lu et al. [18], next to an intensity filter, the ALS pulse width is used to filter points associated with trunks in range of 1 and 10 m above ground. ...
Although gravitropism forces trees to grow vertically, stems have shown to prefer specific orientations. Apart from wind deforming the tree shape, lateral light can result in prevailing inclination directions. In recent years a species dependent interaction between gravitropism and phototropism, resulting in trunks leaning down-slope, has been confirmed, but a terrestrial investigation of such factors is limited to small scale surveys. ALS offers the opportunity to investigate trees remotely. This study shall clarify whether ALS detected tree trunks can be used to identify prevailing trunk inclinations. In particular, the effect of topography, wind, soil properties and scan direction are investigated empirically using linear regression models. 299.000 significantly inclined stems were investigated. Species-specific prevailing trunk orientations could be observed. About 58% of the inclination and 19% of the orientation could be explained by the linear models, while the tree species, tree height, aspect and slope could be identified as significant factors. The models indicate that deciduous trees tend to lean down-slope, while conifers tend to lean leeward. This study has shown that ALS is suitable to investigate the trunk orientation on larger scales. It provides empirical evidence for the effect of phototropism and wind on the trunk orientation.
... Globally, airborne LiDAR sensing has proven to be efficient and accurate for the fine-scale estimation of above-ground tree biomass by indirect allometry (primarily tree height) (Maltamo et al., 2014). In Australia, airborne LiDAR surveying of ecosystems has increased over the last decade (Kandel et al., 2011;Lee and Lucas, 2007;Miura and Jones, 2010;Rombouts et al., 2010;Shendryk et al., 2016). However, the applications of LiDAR have not been fully tested for tropical savannas, and biomass uncertainty needs further attention. ...
... The obtained overall tree detection rate (63%) match those observed in recent Shendryk et al. (2016) study for Eucalyptus spp. forest in south Australia. ...
Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R² = 0.754, RMSE = 90 kg), and that 63% of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4 ha plots: R² = 0.86, RMSE = 3.4 Mg ha− 1; and 1 ha plots: R² = 0.83, RMSE = 4.0 Mg ha− 1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.
... Many studies have demonstrated that bottom-up approaches offer advantages over top-down methods in the understory and in broadleaf forests (Ferraz et al., 2012;Jaskierniak et al., 2021). However, existing methods often rely on conditions such as the leaf-off period (Lu et al., 2014), specific trunk features (Chen et al., 2018), and point cloud intensity (Shendryk et al., 2016) or density , limiting their practicality in the diverse forest environment. Therefore, we propose an improved point cloud clustering method that solely utilizes 3D coordinates, focusing on detecting trees from multi-layered mixed structures of forests. ...
... Figure 2c shows the effect after the ground features were separated. The random walker segmentation algorithm proposed by Shendryk et al. was used for mono-wood segmentation in this study [45]. This was achieved in the Linux environment in combination with the PCL library and C++. ...
The 3D reconstruction of point cloud trees and the acquisition of stand factors are key to supporting forestry regulation and urban planning. However, the two are usually independent modules in existing studies. In this work, we extended the AdTree method for 3D modeling of trees by adding a quantitative analysis capability to acquire stand factors. We used unmanned aircraft LiDAR (ALS) data as the raw data for this study. After denoising the data and segmenting the single trees, we obtained the single-tree samples needed for this study and produced our own single-tree sample dataset. The scanned tree point cloud was reconstructed in three dimensions in terms of geometry and topology, and important stand parameters in forestry were extracted. This improvement in the quantification of model parameters significantly improves the utility of the original point cloud tree reconstruction algorithm and increases its ability for quantitative analysis. The tree parameters obtained by this improved model were validated on 82 camphor pine trees sampled from the Northeast Forestry University forest. In a controlled experiment with the same field-measured parameters, the root mean square errors (RMSEs) and coefficients of determination (R²s) for diameters at breast height (DBHs) and crown widths (CWs) were 4.1 cm and 0.63, and 0.61 m and 0.74, and the RMSEs and coefficients of determination (R²s) for heights at tree height (THs) and crown base heights (CBHs) were 0.55 m and 0.85, and 1.02 m and 0.88, respectively. The overall effect of the canopy volume extracted based on the alpha shape is closest to the original point cloud and best estimated when alpha = 0.3.
... Airborne LiDAR can use lasers to measure the sensor's distance from the ground and the leaf canopy, producing accurate and fine spatial scale remote sensing estimates of vegetation biomass [27] but at a high cost [26] and rarely accounting for small branches and leaf canopy biomass [27,28]. Terrestrial laser scanning (ground-based LiDAR) can be used to estimate biomass for individual trees [29,30] but is time-consuming for stationary equipment, especially in remote areas and steep terrain. ...
Galician forests in northwestern Spain are subject to frequent wildfires with high environmental and economic costs. In addition, due to the consequences of climate change, these fires are becoming more virulent, occurring throughout the year, and taking place in populated areas, in some cases involving the loss of human life. Therefore, forest fire prevention is even more relevant than mitigating its consequences. Given the costs involved in forestry work, alternative measures to reduce fuel load and create vegetation gaps are needed. One involves grazing by an endemic species of feral horses ( Equus ferus atlanticus ) that feed on thicket-forming gorse ( Ulex europaeus ). In a 100-ha forest fenced study area stocked with 11 horses, four 50 m ² enclosed plots prevented the access of these wild animals to the vegetation, with the aim of manipulating their impact on the reduction of forest biomass. The measurement of biomass volumes is an important method that can describe the assessment of wildfire risks, unfortunately, high-resolution data collection at the regional scale is very time-consuming. The best result can be using drones (unmanned aerial vehicles - UAVs) as a method of collecting remotely sensed data at low cost. From September 2018 to November 2020, we collected information about aboveground biomass from these four enclosed plots and their surrounding areas available for horses to forage, via UAV. These data, together with environmental variables from the study site, were used as input for a fire model to assess the differences in the surface rate of spread (SROS) among grazed and ungrazed areas. Our results indicated a consistent but small reduction in the SROS between 0.55 and 3.10 m/min in the ungrazed enclosured plots in comparison to their grazed surrounding areas (which have an SROS between 15 and 25 m/min). The research showed that radar remote sensing (UAV) can be used to map forest aboveground biomass, and emphasized the importance and role of feral horses in Galicia as a prevention tool against wildfires in gorse-dominated landscapes.
... The individual tree segmentation algorithms based on ALS data can be mainly categorized into two groups, i.e., tree segmentation based on the canopy height model (CHM) and tree segmentation based on the point cloud [17]. The point cloud-based methods either segment tree crowns using the gaps between crowns [18] or detect trunks first and then segment the tree crowns [19]. However, ALS acquires data using a topdown scanning mode; hence, the laser points concentrate on the canopy layer. ...
Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R² of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35–0.48 m, while the R² of the DBH fit was increased to a range of 0.97–0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001–0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.
... Moso bamboo exhibits rapid growth, achieving maturity within a short span of 50 to 60 days from the emergence of shoots [7]. Its distinctive mechanism of biomass accumulation highlights its substantial potential for carbon sequestration, making a significant contribution to the global carbon sink [8,9]. ...
The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and enhanced the accuracy of diameter fitting by optimizing denoising parameters. Specifically, we fine-tuned two denoising parameters, neighborhood point number and standard deviation multiplier, across five gradient levels for denoising. Subsequently, DBH fitting was conducted on data processed with varying denoising parameters, followed by a precision evaluation to investigate the key factors influencing the accuracy of Moso bamboo DBH fitting. The research results indicate that a handheld laser was used to scan six plots, from which 132 single Moso bamboo trees were selected. Out of these, 122 single trees were successfully segmented and identified, achieving an accuracy rate of 92.4% in identifying single Moso bamboo trees, with an average accuracy of 95.64% in extracting DBH for individual plants; the mean error was ±1.8 cm. Notably, setting the minimum neighborhood point to 10 resulted in the highest fitting accuracy for DBH. Moreover, the optimal standard deviation multiplier threshold was found to be 1 in high-density forest plots and 2 in low-density forest plots. Forest condition and slope were identified as the primary factors impacting the accuracy of Moso bamboo DBH fitting.
... Laser scanner technology enables a full 3D representation of trees structure, providing an alternative to allometric equations for accurately estimating AGB in stand trees. Since AGB estimation needs to be performed at the tree level, in literature, several algorithms that perform Individual Tree Detection (ITD) are proposed (Luo et al., 2021;Shendryk et al., 2016;Xu et al., 2023), differing according to the support (e.g., terrestrial, aerial), the type of data (e.g., photogrammetric, LiDAR point cloud) and the approach (e.g., voxel-based, raster-based, etc.). Furthermore, a correct calculation of the AGB requires that the points of the cloud are classified into woody and foliage classes to proceed with estimating the woody biomass only (Arseniou et al., 2023). ...
Forests play a fundamental role in carbon stocking since about a third of the carbon dioxide produced by activities of human origin is absorbed by forests. Forest biomass is an essential indicator of carbon dioxide absorption, enabling an understanding the interaction between forest dynamics and climate change effects. However, biomass and wood material changes are challenging to quantify in forest stands. Nowadays, recent 3D remote sensing technologies, such as laser scanning systems, have allowed accurate measures of single trees. This study evaluates three approaches to classify wood and non-wood materials and quantify biomass based on LiDAR data, aiming at biomass change detection. Specifically, we propose an automated methodology for estimating the single tree-level biomass of a portion of forest monitored through a LiDAR oblique acquisition. The classification of wood and foliage points was performed with machine learning algorithms, while the tree modelling was conducted rigorously through a Quantitative Structure Model (QSM). The purpose of this study is to evaluate (1) two different unsupervised and one semi-supervised classification approaches for wood and foliage separation and (2) the accuracy of the biomass assessment performed on a QSM-based approach on innovative LiDAR acquisitions. The results are promising; the wood-leaf classification performs effectively in all 20 silver birches considered; as regards the biomass, when the noise is limited, it is estimated in a manner consistent with the reference values calculated using an appropriate allometric equation. Higher values are found mainly in dense undergrowth, which negatively affects the modelling of the tree. The research is ongoing, and further tests will be performed to generalize the methodology on different tree species, deepen the multitemporal variability, and improve the accuracy of the assessment.
... In contrast, the point-based methods operate directly on the 3D point cloud then, the information loss is mitigated. The point-based methods involve different implementations in- * Corresponding author cluding converting point clouds to voxels (Wang et al., 2008), clustering such as mean shift (Hui et al., 2021) and densitybased spatial clustering of applications with noise (DBSCAN) (Fu et al., 2022), conditional euclidean distance (Shendryk et al., 2016) and graph-based segmentation (Yao et al., 2012). There are numerous methods depending on point-based tree segmentation approaches. ...
In the last two decades, Light detection and ranging (LiDAR) has been widely employed in forestry applications. Individual tree segmentation is essential to forest management because it is a prerequisite to tree reconstruction and biomass estimation. This paper introduces a general framework to extract individual trees from the LiDAR point cloud based on a graph link prediction problem. First, an undirected graph is generated from the point cloud based on K-nearest neighbors (KNN). Then, this graph is used to train a convolutional autoencoder that extracts the node embeddings to reconstruct the graph. Finally, the individual trees are defined by the separate sets of connected nodes of the reconstructed graph. A key advantage of the proposed method is that no further knowledge about tree or forest structure is required. Seven sample plots from a plantation forest with poplar and dawn redwood species have been employed in the experiments. Though the precision of the experimental results is up to 95 % for poplar species and 92 % for dawn redwood trees, the method still requires more investigations on natural forest types with mixed tree species.
... Jucker et al. confirmed that the height and crown diameter of trees are sufficient to estimate the trunk diameter by a single equation. Crown diameter and height are easily derived from airborne laser scanning (ALS) data [19], [20], [23]- [25]. However, the crown diameter estimation is a source of significant model error. ...
We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameters estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual tree level employing a Gaussian process regressor. In order to validate the proposed model, a set of 8,342 samples on tree height, trunk diameter, and biomass has been assembled. It covers seven biomes globally present. We reference our model to four other models based on both, the Jucker data and our own dataset. Although our approach deviates from standard biomass–height–diameter models, we demonstrate the Gaussian process regression model as a viable alternative. In addition, we decompose the uncertainty of tree biomass estimates into the model- and fitting-based contributions. We verify the Gaussian process regressor has the capacity to reduce the fitting uncertainty down to below 5%. Exploiting airborne LiDAR measurements and a field inventory survey on the ground, a stand-level (or plot-level) study confirms a low relative error of below 1% for our model. The data used in this study are available at https://github.com/zhu-xlab/BiomassUQ.
... Jucker et al. confirmed that the height and crown diameter of trees are sufficient to estimate the trunk diameter by a single equation. Crown diameter and height are easily derived from airborne laser scanning (ALS) data [19], [20], [23]- [25]. However, the crown diameter estimation is a source of significant model error. ...
We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameters estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual tree level employing a Gaussian process regressor. In order to validate the proposed model, a set of 8,342 samples on tree height, trunk diameter, and biomass has been assembled. It covers seven biomes globally present. We reference our model to four other models based on both, the Jucker data and our own dataset. Although our approach deviates from standard biomass-height-diameter models, we demonstrate the Gaussian process regression model as a viable alternative. In addition, we decompose the uncertainty of tree biomass estimates into the model- and fitting-based contributions. We verify the Gaussian process regressor has the capacity to reduce the fitting uncertainty down to below 5%. Exploiting airborne LiDAR measurements and a field inventory survey on the ground, a stand-level (or plot-level) study confirms a low relative error of below 1% for our model. The data used in this study are available at https://github.com/zhu-xlab/BiomassUQ .
... The segmentation algorithm used in this paper was proposed by Shendryk et al. [27], which is a lightweight, bottom-up, individual tree segmentation method. The final results are shown in Figure 6. ...
Tree species surveys are crucial in forest resource management and can provide references for forest protection policymakers. Traditional tree species surveys in the field are labor-intensive and time-consuming. In contrast, airborne LiDAR technology is highly capable of penetrating forest vegetation; it can be used to quickly obtain three-dimensional information regarding vegetation over large areas with a high level of precision, and it is widely used in the field of forestry. At this stage, most studies related to individual tree species classification focus on traditional machine learning, which often requires the combination of external information such as hyperspectral cameras and has difficulty in selecting features manually. In our research, we directly processed the point cloud from a UAV LiDAR system without the need to voxelize or grid the point cloud. Considering that relationships between disorder points can be effectively extracted using Transformer, we explored the potential of a 3D deep learning algorithm based on Transformer in the field of individual tree species classification. We used the UAV LiDAR data obtained in the experimental forest farm of Northeast Forestry University as the research object, and first, the data were preprocessed by being denoised and ground filtered. We used an improved random walk algorithm for individual tree segmentation and made our own data sets. Six different 3D deep learning neural networks and random forest algorithms were trained and tested to classify the point clouds of three tree species. The results show that the overall classification accuracy of PCT based on Transformer reached up to 88.3%, the kappa coefficient reached up to 0.82, and the optimal point density was 4096, which was slightly higher than that of the other deep learning algorithms we analyzed. In contrast, the overall accuracy of the random forest algorithm was only 63.3%. These results show that compared with the commonly used machine learning algorithms and a few algorithms based on multi-layer perceptron, Transformer-based networks provide higher accuracy, which means they can provide a theoretical basis and technical support for future research in the field of forest resource supervision based on UAV remote sensing.
... One of the most common methods includes the voxel-based normalized cutting algorithm (Jaskierniak et al., 2021). Although several studies have proposed different methods for optimal ITS, variability in forest structure and the parameter settings can significantly influence the result of segmentation accuracy (Lurii et al., 2016;Balsi et al., 2018), and consequently, it is difficult to compare their accuracies. ...
Light detection and ranging (LiDAR) technology has become one of the most dominant acquisition methods for modeling forest attributes, such as very accurate tree structure information, which is necessary for qualitative forest management and research activities. In this study, we evaluated the efficacy of standalone unmanned aerial vehicle-laser scanning (UAV-LS) and terrestrial laser scanning (TLS) data to accurately estimate forest tree metrics under differing management types. Furthermore, we combined the UAV-LS and TLS data to test whether fusion can improve the mapping of the three-dimensional (3D) structure of individual trees to favor accurate estimates of tree metrics. We initially calculated the percentage of point density per square meter aboveground in each height class at intervals of 1 m for the UAV-LS, TLS, and fusion datasets. This helped illustrate the vertical point density distribution that reflects the structural complexity between broadleaf and conifer trees. We then used tree-level point clouds to assess several tree metrics, such as diameter at breast height (DBH), total tree height (H T), crown projection area (PA C), crown width (W C), crown length (L C), 3D crown surface (S C), and 3D crown volume (V C). Our results indicated that LiDAR fusion can increase the estimation accuracy of DBH and H T , especially in broadleaves (97.8% accuracy). In addition, fusion significantly reshaped the modeled crown structures in both plots, which led to improved estimates for all crown metrics. The results show empirical evidence that LiDAR fusion can also have a determining role in supporting ecosystem services. For example, detailed 3D mapping of tree crowns can be used to assess the mitigation of rainfall`s kinetic energy by tree crowns concerning soil erosion and sedimentation near habitable zones.
... However, the increased accuracy of the fitted models for some of the studied species may make it worthwhile measuring at least a subsample of trees in order to obtain a more precise estimate of the amount of biomass present. In addition, recent advances in remote sensing allow crown diameter and height at individual tree level to be estimated using airborne laser scanning and the equations developed could be very useful in these cases [30,[55][56][57][58]. If this option is considered, it must also be taken into account that the cost of the flight can be high, depending on the study area and the degree of precision. ...
Biomass models are key to the correct quantification of aboveground biomass and carbon stocks. Most of the current available sets of biomass models were developed for adult trees although very few have been employed to accurately estimate biomass during the first years of reforestations, despite the increasing number of reforestations over recent years.
In this study, species-specific and generalized models for aboveground biomass of single trees have been developed for the main 14 species used in reforestations in Spain. A comparison with the existing biomass models at individual tree level for larger trees of these species was also conducted to confirm the hypothesis that specific models are required for the first years of the reforestation.
A new set of biomass models was fitted simultaneously per species, based on three different independent variables – root-collar-diameter, total height and crown projection area- and their combinations. The fitted models provide more accurate and unbiased predictions of aboveground biomass in the first years after reforestation when compared with previously existing models for adult trees. The generalized models can be applied to reforestations when the main species is unknown or for mixed plantations where species are not individualized.
The set of models provided can be used in a wide variety of surveys and monitoring conditions depending on the available data, from classical field inventories to more novel approaches such as airborne LiDAR data or aerial photographs.
... However, the increased accuracy of the fitted models for some of the studied species may make it worthwhile measuring at least a subsample of trees in order to obtain a more precise estimate of the amount of biomass present. In addition, recent advances in remote sensing allow crown diameter and height at individual tree level to be estimated using airborne laser scanning and the equations developed could be very useful in these cases [30,[55][56][57][58]. If this option is considered, it must also be taken into account that the cost of the flight can be high, depending on the study area and the degree of precision. ...
... These wetlands play a significant role in landscape connectivity (Bishop-Taylor et al., 2018, 2015. The riparian vegetation of the MDB exhibits high variability and change in condition in response to water availability Shendryk et al., 2016) due primarily to intermittent river flows. ...
Spatiotemporal quantification of surface water and flooding is essential given that floods are among the largest natural hazards. Effective disaster response management requires near real-time information on flood extent. Satellite remote sensing is the only way of monitoring these dynamics across vast areas and over time. Previous water and flood mapping efforts have relied on optical time series, despite cloud contamination. This reliance on optical data is due to the availability of systematically acquired and easily accessible optical data globally for over 40 years. Prior research used either MODIS or Landsat data, trading either high temporal density but lower spatial resolution or lower cadence but higher spatial resolution. Both MODIS and Landsat pose limitations as Landsat can miss ephemeral floods, whereas MODIS misses small floods and inaccurately delineates flood edges. Leveraging high temporal frequency of 3–4 days of the existing Landsat-8 (L8) and two Sentinel-2 (S2) satellites combined, in this research, we assessed whether the increased temporal frequency of the three sensors improves our ability to detect surface water and flooding extent compared to a single sensor (L8 alone). Our study area was Australia’s Murray-Darling Basin, one of the world’s largest dryland basins that experiences ephemeral floods. We applied machine learning to NASA’s Harmonized Landsat Sentinel-2 (HLS) Surface Reflectance Product, which combines L8 and S2 observations, to map surface water and flooding dynamics. Our overall accuracy, estimated from a stratified random sample, was 99%. Our user’s and producer’s accuracy for the water class was 80% (±3.6%, standard error) and 76% (±5.8%). We focused on 2019, one of the most recent years when all three HLS sensors operated at full capacity. Our results show that water area (permanent and flooding) identified with the HLS was greater than that identified by L8, and some short-lived flooding events were detected only by the HLS. Comparison with high resolution (3 m) PlanetScope data identified extensive mixed pixels at the 30 m HLS resolution, highlighting the need for improved spatial resolution in future work. The HLS has been able to detect floods in cases when one sensor (L8) alone was not, despite 2019 being one of the driest years in the area, with few flooding events. The dense optical time-series offered by the HLS data is thus critical for capturing temporally dynamic phenomena (i.e., ephemeral floods in drylands), highlighting the importance of harmonized data such as the HLS.
... Many previous studies have shown that factors beyond the advanced nature and performance of segmentation algorithms influence the tree segmentation accuracy; these factors include the type of study area and the algorithm parameter settings [36,37]. Nevertheless, most studies on the segmentation of individual trees discussed only the effect of a certain segmentation method in a specific type of research area, whereas analyses and comparisons of the applicability of algorithms and sensitivity of parameter settings for multiple types of sample plots remain lacking [38,39]. ...
Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.
... For example, stems have been detected and reconstructed from a watershed segmentation combined with a RANSAC-based estimation of the stem points, and ITC segmentation then implemented using a normalized cut segmentation (Reitberger et al., 2009). Shendryk et al. (Shendryk et al., 2016) proposed a 3D random walk algorithm, with the segmentation starting from trunk detection. Using detected trunks as seed points, tree crowns are segmented into different trees according to the weights calculated by conditional Euclidean distance clustering. ...
Obtaining low vegetation data is important in order to quantify the structural characteristics of a forest. Dense three-dimensional (3D) laser scanning data can provide information on the vertical profile of a forest. However, most studies have focused on the dominant and subdominant layers of the forest, while few studies have tried to delineate the low vegetation. To address this issue, we propose a framework for individual tree crown (ITC) segmentation from laser data that focuses on both overstory and understory trees. The framework includes 1) a new algorithm (SSD) for 3D ITC segmentation of dominant trees, by detecting the symmetrical structure of the trees, and 2) removing points of dominant trees and mean shift clustering of the low vegetation. The framework was tested on a boreal forest in Sweden and the performance was compared 1) between plots with different stem density levels, vertical complexities, and tree species composition, and 2) using airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data, and merged ALS and TLS data (ALS + TLS data). The proposed framework achieved detection rates of 0.87 (ALS + TLS), 0.86 (TLS), and 0.76 (ALS) when validated with field-inventory data (of trees with a diameter at breast height ≥ 4 cm). When validating the estimated number of understory trees by visual interpretation, the framework achieved 19%, 21%, and 39% root-mean-square error values with ALS + TLS, TLS, and ALS data, respectively. These results show that the SSD algorithm can successfully separate laser points of overstory and understory trees, ensuring the detection and segmentation of low vegetation in forest. The proposed framework can be used with both ALS and TLS data, and achieve ITC seg-mentation for forests with various structural attributes. The results also illustrate the potential of using ALS data to delineate low vegetation.
... For this study, trees were sampled once during the growing season in late June 2019 at Beaver Creek and once in early September 2019 at Goodwin Island, Phillips Creek, Monie Bay, and Moneystump (Table 1). All the trees sampled in this study were estimated to be at mid-to late-growth stages, according to stem DBH threshold being greater than 13 cm (Blackwood et al., 2010;Shendryk et al., 2016). ...
Methane (CH4) exchange between trees and the atmosphere has recently emerged as an important, but poorly quantified process regulating global climate. The sources (soil and/or tree) and mechanisms driving the increase of CH4 in trees and degassing to the atmosphere are inadequately understood, particularly for coastal forests facing increased exposure to seawater. We investigated the eco‐physiological relationship between tree stem wood density, soil and stem oxygen saturation (an indicator of redox state), soil and stem CH4 concentrations, soil and stem carbon dioxide (CO2) concentrations, and soil salinity in five forests along the United States coastline. We aim to evaluate the mechanisms underlying greenhouse gas increase in trees and the influence of seawater exposure on stem CH4 accumulation. Seawater exposure corresponded with decreased tree survival and increased tree stem methane. Tree stem wood density was significantly correlated with increased stem CH4 in seawater exposed gymnosperms, indicating that dying gymnosperm trees may accumulate higher levels of CH4 in association with seawater flooding. Further, we found that significant differences in seawater exposed and unexposed gymnosperm tree populations are associated with increased soil and stem CH4 and CO2, indicating that seawater exposure significantly impacts soil and stem greenhouse gas abundance. Our results provide new insight into the potential mechanisms driving tree CH4 accumulation within gymnosperm coastal forests.
... Analysis techniques applied to small-footprint airborne scanner data have generally focused on the problem of more robust extraction of ground returns 7 , or have extended into fitting Gaussian distributions to the digitized returns. 8,9,10,11,12 A modified approach is to use moments of the spectra, or moments of the distribution function for returned energy. 13 The above techniques generally make some compensation for range effects, but do not deal with complexities caused by illumination angle. ...
... Bottom-to-top delineation was proposed by Lu et al. [49] for segmenting deciduous trees from data collected during the leaf-off season. Similarly, Shendryk et al. [50] published an interesting bottom-to-top red gum delineation algorithm [22]. Once trees are delineated, they can be classified as either dead or alive. ...
In southern Australia, many native mammals and birds rely on hollows for sheltering, while hollows are more likely to exist on dead trees. Therefore, detection of dead trees could be useful in managing biodiversity. Detecting dead standing (snags) versus dead fallen trees (Coarse Woody Debris—CWD) is a very different task from a classification perspective. This study focuses on improving detection of dead standing eucalypt trees from full-waveform LiDAR. Eucalypt trees have irregular shapes making delineation of them challenging. Additionally, since the study area is a native forest, trees significantly vary in terms of height, density and size. Therefore, we need methods that will be resistant to those challenges. Previous study showed that detection of dead standing trees without tree delineation is possible. This was achieved by using single size 3D-windows to extract structural features from voxelised full-waveform LiDAR and characterise dead (positive samples) and live (negative samples) trees for training a classifier. This paper adds on by proposing the usage of multi-scale 3D-windows for tackling height and size variations of trees. Both the single 3D-windows approach and the new multi-scale 3D-windows approach were implemented for comparison purposes. The accuracy of the results was calculated using the precision and recall parameters and it was proven that the multi-scale 3D-windows approach performs better than the single size 3D-windows approach. This open ups possibilities for applying the proposed approach on other native forest related applications.
... This study confirms the specificity of tropical forests and the need for specific segmentation methods calibrated for this kind of forests [3]. For instance, the method developed in [52] to detect trunks in eucalyptus forests from Australia relies precisely on the ability of the ALS data to detect trunks, and is not widely transferable to other forests in particular closed canopy forests. More flexible methods, like those used in this benchmark, are more generic but also benefit from local allometric relations between crown size and tree height to improve fit. ...
Tropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. Airborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. Therefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in French Guiana. We measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. We also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. The different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. Segmentation methods based on the point cloud (AMS3D and Graph-Cut) globally outperformed methods based on the Canopy Height Models, especially for small crowns; the AMS3D method outperformed the other methods tested for the overlap analysis, and AMS3D and Graph-Cut performed the best for the automatic matching validation. Nevertheless, other methods based on the Canopy Height Model performed better for very large emergent crowns. The dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method.
... Aerial and even satellite imagery increasingly offers an alternative for precisely and accurately measuring crown areas of fully sun-exposed trees, an alternative we took advantage of here. However, these methods do not enable crown area estimates for subcanopy trees (but see e.g., Paris et al., 2016;Shendryk et al., 2016), which differ systematically in their crown allometries. Finally, we evaluated only crown area, even though crown depth and crown shape are also im-portant for the estimation of tree biomass Ploton et al., 2016) and for characterizing tree species life history strategies (Canham et al., 1994;Bohlman and O'Brien, 2006;Poorter et al., 2006). ...
Tree allometric relationships are widely employed for estimating forest biomass and production and are basic building blocks of dynamic vegetation models. In tropical forests, allometric relationships are often modeled by fitting scale-invariant power functions to pooled data from multiple species, an approach that fails to capture changes in scaling during ontogeny and physical limits to maximum tree size and that ignores interspecific differences in allometry. Here, we analyzed allometric relationships of tree height (9884 individuals) and crown area (2425) with trunk diameter for 162 species from the Barro Colorado Nature Monument, Panama. We fit nonlinear, hierarchical models informed by species traits – wood density, mean sapling growth, or sapling mortality – and assessed the performance of three alternative functional forms: the scale-invariant power function and the saturating Weibull and generalized Michaelis–Menten (gMM) functions. The relationship of tree height with trunk diameter was best fit by a saturating gMM model in which variation in allometric parameters was related to interspecific differences in sapling growth rates, a measure of regeneration light demand. Light-demanding species attained taller heights at comparatively smaller diameters as juveniles and had shorter asymptotic heights at larger diameters as adults. The relationship of crown area with trunk diameter was best fit by a power function model incorporating a weak positive relationship between crown area and species-specific wood density. The use of saturating functional forms and the incorporation of functional traits in tree allometric models is a promising approach for improving estimates of forest biomass and productivity. Our results provide an improved basis for parameterizing tropical plant functional types in vegetation models.
... Different from the CHM-based approach, this density based algorithm contains allocation information of stem-related points to assist locating individual tree positions. Lu et al. (2014) and Shendryk et al. (2016) had a similar finding that stem-related points are critical and valuable for tree position detection in deciduous forests. Moreover, the LiDAR data set used in this study were low-density and collected during leaf-off season. ...
Delineation of individual deciduous trees with Light Detection and Ranging (LiDAR) data has long been sought for accurate forest inventory in temperate forests. Previous attempts mainly focused on high-density LiDAR data to obtain reliable delineation results, which may have limited applications due to the high cost and low availability of such data. Here, the feasibility of individual deciduous tree delineation with low-density LiDAR data was examined using a point-density-based algorithm. First a high-resolution point density model (PDM) was developed from low-density LiDAR point cloud to locate individual trees through the horizontal spatial distribution of LiDAR points. Then, individual tree crowns and associated attributes were delineated with a 2D marker-controlled watershed segmentation. Additionally, the PDM-based approach was compared with a conventional canopy height model (CHM) based delineation. The results demonstrated that the PDM-based approach produced an 89% detection accuracy to identify deciduous trees in our study area. The tree attributes derived from the PDM-based algorithm explained 81% and 83% of tree height and crown width variations of forest stands, respectively. The conventional CHM-based tree attributes, on the other hand, could explain only 71% and 66% of tree height and crown width, respectively. Our results suggest that the application of the PDM-based individual tree identification in deciduous forests with low-density LiDAR data is feasible and has relatively high accuracy to predict tree height and crown width, which are highly desired in large-scale forest inventory and analysis.
... However, these methods do not enable crown area estimates for subcanopy trees (but see e.g. Paris et al., 2016;Shendryk et al., 2016), which differ systematically in their crown allometries. ...
Tree allometric relationships are widely employed to estimate forest biomass and production, and are basic building blocks of dynamic vegetation models. In tropical forests, allometric relationships are often modeled by fitting scale-invariant power functions to pooled data from multiple species, an approach that fails to reflect finite size effects at the smallest and largest sizes, and that ignores interspecific differences in allometry. Here, we analyzed allometric relationships of tree height (9884 individuals) and crown area (2425) with trunk diameter using species-specific morphological and life history data of 162 species from Barro Colorado Island, Panamá. We fit nonlinear, hierarchical models informed by species traits and assessed the performance of three alternative functional forms: the scale-invariant power function, and the saturating Weibull and generalized Michaelis-Menten (gMM) functions. The relationship of tree height with trunk diameter was best fit by a saturating gMM model in which variation in allometric parameters was related to interspecific differences in sapling growth rates, a measure of regeneration light demand. Light-demanding species attained taller heights at comparatively smaller diameters as juveniles and had shorter asymptotic heights at larger diameters as adults. The relationship of crown area with trunk diameter was best fit by a power function model incorporating a weak positive relationship between crown area and species-specific wood density. The use of saturating functional forms and the incorporation of functional traits in tree allometric models is a promising approach to improve estimates of forest biomass and productivity. Our results provide an improved basis for parameterizing tropical tree functional types in vegetation models.
... At present to describe the canopy structure of flood valleys satellite multispectral data [15] and airborne (ALS) [23][24][25] and terrestrial laser scanning data (TLS) are used [26]. Laser scanning is an active remote-sensing technique, which provides high-resolution spatial data [27]. ...
The paper presents an original method for the evaluation of bark structure characteristics of tree trunks on the basis of terrestrial laser scanning data. Measurements testing the method proposed were performed in laboratory conditions for trunks of pine (Pinus sylvestris L.) and oak (Quercus robur L.). The laser scanner used was a FARO Focus 3D. The scanning was carried out in two variants for natural trunks (variant I: samples Oak-I, Pine-I) and for trunks wrapped in foil (variant II: samples Oak-II, Pine-II). The point clouds obtained were combined into a three-dimensional (3D) model, filtered, and exported to the *.xyz format in SCENE (v. 5×) software provided by FARO. For calculation of the bark structure characteristics the geoprocessing Tree Trunk Bark Structure Model (TTBSM) operating in the ArcGIS environment was developed. The mean bark height factor (BHF) of the natural pine and oak tree trunks was calculated to be 0.39 cm and 0.37 cm, while the values for the trunks wrapped in foil were 0.27 cm and 0.25 cm, respectively. The BHF of the tree trunks wrapped in foil varied in the range 0.26–0.28 cm and 0.24–0.26 cm for pine and oak, respectively, while for the natural tree trunks the range was 0.38–0.46 cm and 0.35–0.38 cm for pine and oak, respectively. The effect of BHF on the flow resistance was evaluated in a measuring trough and proved to be significant. The coefficient of flow resistance was on average 20% higher for the natural tree trunks than for those foil-wrapped.
... We quantified seasonal vegetation dynamics using the EVI, hence change in vegetation chlorophyll content and leaf area index and by using standard anomalies we accounted for different vegetation densities. However, vegetation response to flooding may also vary as a function of vegetation structure, type, composition, health, and land use such as irrigation Broich et al., 2014;Kaptué et al., 2015;Nightingale and Phinn, 2003;Shendryk et al., 2016aShendryk et al., , 2016bWestbrooke and Florentine, 2005). Time series of soil moisture content for the entire MDB with sufficient accuracy and spatial detail were also unavailable (Heimhuber et al., 2017;Heimhuber et al., 2015) as were time series of groundwater depth. ...
... Li et al. (2012) took advantage of the spacing between treetops at their highest points to identify trees and used a region growing algorithm to segment them. More recently, Shendryk et al. (2016a) used Euclidean distance clustering to delineate trunks in eucalypt forests. These methods proved highly effective in identifying trees in forested areas but are unsuitable for use in urban environments since the assumption of highly dense collections of trees does not apply to isolated individual trees. ...
... Local maxima filtering, used for tree detection, leads to over-segmentation because each tree trunk split forms a local maxima. Shendryk et al. (2016a) published a Eucalyptus delineation algorithm that performs segmentation from bottom to top; the trunks point cloud is separated from the leaves and individual trunks are identified before the segmentation. Nevertheless, the density resolution starts from 12 points/ m 2 and goes up to 36 points/m 2 around forested areas. ...
In Australia, many birds and arboreal animals use hollows for shelters, but studies predict shortage of hollows in near future. Aged dead trees are more likely to contain hollows and therefore automated detection of them plays a substantial role in preserving biodiversity and consequently maintaining a resilient ecosystem. For this purpose full-waveform LiDAR data were acquired from a native Eucalypt forest in Southern Australia. The structure of the forest significantly varies in terms of tree density, age and height. Additionally, Eucalyptus camaldulensis have multiple trunk splits making tree delineation very challenging. For that reason, this paper investigates automated detection of dead standing Eucalyptus camaldulensis without tree delineation. It also presents the new feature of the open source software DASOS, which extracts features for 3D object detection in voxelised FW LiDAR. A random forest classifier, a weighted-distance KNN algorithm and a seed growth algorithm are used to create a 2D probabilistic field and to then predict potential positions of dead trees. It is shown that tree health assessment is possible without tree delineation but since it is a new research directions there are many improvements to be made.
Effective individual tree crown delineation plays a critical role for assessing mangrove quality and health. However, mangrove tree crowns often form large, interconnected clusters in dense coverage areas, making it difficult to separate them individually. Moreover, many mangrove trees have multiple large branches that result in irregular tree shapes and create internal gaps inside their crowns. Unmanned Aerial Vehicle (UAV) lidar data can potentially overcome these challenges, but a comprehensive assessment of UAV-lidar derived features is still missing, as well as how to best use them for dense mangrove forests. In this study, we set forth two objectives: (1) to derive optimal features for mangrove individual tree crown delineation through an exclusive examination of all the UAV-lidar features; (2) to develop effective methods that can best incorporate these optimal UAV-lidar features. To achieve the first objective, we extracted 224 features from UAV-lidar data based on three groups of attributes: height, intensity, and point density. Seven spatial scales ranging from 0.2 to 0.5 m were tested in this process. For the second objective, we applied two state-of-the-art Convolutional Neural Networks: Mask Region-based Convolutional Network method (Mask R–CNN) and Ultralytics You Only Look Once version 8 (YOLOv8). At last, the derived three optimal features are: canopy height model at 0.20 m, coefficient of point cloud height variation at 0.25 m, and ground point percentage at 0.25 m. Comparing our methods with traditional methods that only use canopy height model, we found that integrating Convolutional Neural Networks and the optimal UAV-lidar features improved the accuracy by more than 13%. Mask R–CNN was better for dense mangroves, while YOLOv8 was excellent for sparse and short mangroves. The derived optimal UAV-lidar features further enhanced the detection of short trees, densely clumped trees and trees with irregular shapes. To conclude, we developed three novel features based on UAV-lidar data that are especially suitable for individual tree crown delineation in dense mangrove forests. Using these features, we demonstrated that Convolutional Neural Networks can achieve high performance. We hope that our methods will facilitate various mangrove forest studies that rely on accurate
individual tree crown delineation.
Forests are vital for ecological, economic, and social reasons, and adopting sustainable forest management practices is necessary. While traditional forest monitoring techniques provide detailed data, they are time-consuming; conversely, geomatic techniques can provide more detailed data for forest resource management. This study aims to assess the suitability of Mobile Mapping Systems (MMS) with simultaneous localisation and mapping (SLAM) technology for precision forestry purposes in challenging environments. We compared the performance of MMS data with Terrestrial Laser Scanning (TLS) data and evaluated the Forest Structural Complexity Tool (FSCT), which was developed for TLS datasets, on MMS data. The case study area is a highly sloped coniferous forest in the Italian Alps affected by a severe fire in 2017. Data were processed using a fully automated open-source Python tool that detects each tree's position, Diameter at Breast Height (DBH), and height. The validation procedure was conducted with respect to the TLS point cloud manually segmented. The results show that using MMS with SLAM technology is suitable for precision forestry purposes in challenging environments and that FSCT performs well on MMS data.
Estimates of forest stocking density per hectare (NHa) are important in characterising ecological conditions and assessing changes in forest dynamics after disturbances due to pyrogenic, anthropogenic and biotic factors. We use Unmanned Aircraft Systems (UAS) LiDAR with mean point density of 1485 points m⁻² across 39 flight sites to develop a bottom-up approach for individual tree and crown delineation (ITCD). The ITCD algorithm was evaluated across mixed species eucalypt forests (MSEF) using 2790 field measured stem locations across a broad range of dominant eucalypt species with randomly leaning trunks and highly irregular intertwined canopy structure. Two top performing ITCD algorithms in benchmarking studies resulted in poor performance when optimised to our plot data (mean Fscore: 0.61 and 0.62), which emphasises the challenge posed for ITCD in the structurally complex conditions of MSEF. To address this, our novel bottom-up ITCD algorithm uses kernel densities to stratify the vegetation profile and differentiate understorey from the rest of the vegetation. For vegetation above understorey, the ITCD algorithm adopted a novel watershed clustering procedure on point density measures within horizontal slices. A Principal Component Analysis (PCA) procedure was then applied to merge the slice-specific clusters into trunks, branches, and canopy clumps, before a voxel connectivity procedure clustered these biomass segments into overstorey trees. The segmentation process only requires two parameters to be calibrated to site-specific conditions across 39 MSEF sites using a Shuffled Complex Evolution (SCE) optimiser. Across the 39 field sites, the ITCD algorithm had mean Fscore of 0.91, True Positive (TP) trees represented 85% of measured trees and predicted plot-level stocking (NP) averaged 94% of actual stocking (NOb). As a representation of plot-level basal area (BA), TP trees represented 87% of BA, omitted trees represented slightly smaller trees and made up 8% of BA, and a further 5% of BA had commission error. Spatial maps of NHa using 0.5 m grid-cells showed that omitted trees were more prevalent in high density forest stands, and that 63% of grid-cells had a perfect estimate of NHa, whereas a further 31% of the grid-cells overestimate or underestimate one tree within the search window. The parsimonious modelling framework allows for the two calibrated site-specific parameters to be predicted (R²: 0.87 and 0.66) using structural characteristics of vegetation clusters within sites. Using predictions of these two site-specific parameters across all sites results in mean FScore of 0.86 and mean TP of 0.77, under circumstances where no ground observations were required for calibration. This approach generalises the algorithm across new UAS LiDAR data without undertaking time-consuming ground measurements within tall eucalypt forests with complex vegetation structure.
• Point data obtained from real‐time location systems (RTLSs) can be processed into animal contact networks, describing instances of interaction between tracked individuals. Proximity‐based definitions of interanimal “contact,” however, may be inadequate for describing epidemiologically and sociologically relevant interactions involving body parts or other physical spaces relatively far from tracking devices. This weakness can be overcome by using polygons, rather than points, to represent tracked individuals and defining “contact” as polygon intersections.
• We present novel procedures for deriving polygons from RTLS point data while maintaining distances and orientations associated with individuals' relocation events. We demonstrate the versatility of this methodology for network modeling using two contact network creation examples, wherein we use this procedure to create (a) interanimal physical contact networks and (b) a visual contact network. Additionally, in creating our networks, we establish another procedure to adjust definitions of “contact” to account for RTLS positional accuracy, ensuring all true contacts are likely captured and represented in our networks.
• Using the methods described herein and the associated R package we have developed, called contact, researchers can derive polygons from RTLS points. Furthermore, we show that these polygons are highly versatile for contact network creation and can be used to answer a wide variety of epidemiological, ethological, and sociological research questions.
• By introducing these methodologies and providing the means to easily apply them through the contact R package, we hope to vastly improve network‐model realism and researchers' ability to draw inferences from RTLS data.
Light Detection and Ranging (LiDAR) derived individual tree crown attributes can potentially serve as a tool for ecology and forest dynamics studies and reduce field inventory costs. In this study, four methods of individual tree detection (ITD), Watershed, Silva et al. (2016), Dalponte and Coomes (2016), and Coomes et al. (2017), were evaluated in a tropical forest under sustainable forest management, situated in the State of Rondônia, Brazil. An automated tree matching procedure was developed in order to minimize the error when matching individual tree count from LiDAR and field data. In order to compare the four methods, results were expressed in recall, precision, and F score. Silva et al. (2016) outperformed the other methods, detecting 48% of trees with 46% of precision. Omission of trees was the leading source of error, caused primarily by overlapped trees in lower vegetation. However, errors of over-segmentation were relevant, caused by large and heterogeneous crowns that had multiple detections. Current canopy height model-based methods are ineffective to tropical forests, due to its complexity, which present a challenge for ITD algorithms. We believe that future studies that use complete 3D information from the point cloud, and multi-layer approaches should help to improve the accuracy of individual tree detection.
Detailed information on the number and density of trees is important for conservation and sustainable use of forest resources. In this respect, remote sensing technology is a reliable tool for deriving timely and fine-scale information on forest inventory attributes. However, to better predict and understand the functioning of the forest, fine-scale measures of tree number and density must be extrapolated to the forest plot or stand levels through upscaling. In this study, we compared and combined three sources of remotely sensed data, including low point density airborne laser scans (ALS), synthetic aperture radar (SAR) and very-high resolution WorldView-2 imagery to upscale the total number of trees to the plot level in a structurally complex eucalypt forest using random forest regression. We used information on number of trees previously derived from high point density ALS as training data for a random forest regressor and field inventory data for validation. Overall, our modelled estimates resulted in significant fits (p < 0.05) with goodness-of-fit (R 2) of 0.61, but systematically underestimated tree numbers. The ALS predictor variables (e.g. canopy cover and height) were the best for estimating tree numbers (R 2 = 0.48, nRMSE = 61%), as compared to WorldView-2 and SAR predictor variables (R 2 < 0.35). Overall, the combined use of WorldView-2, ALS and SAR predictors for estimating tree numbers showed substantial improvement in R 2 of up to 0.13 as compared to their individual use. Our findings demonstrate the potential of using low point density ALS, SAR and WorldView-2 imagery to upscale high point density ALS derived tree numbers at the plot level in a structurally complex eucalypt forest.
The paper investigated the possible gains in using low density (average 1 pulse/m²) full-waveform (FWF) airborne laser scanning (ALS) data for individual tree detection and tree species classification and compared the results to the ones obtained using discrete return laser scanning. The aim is to approach a low-cost, fully ALS-based operative forest inventory method that is capable of providing species-specific diameter distributions required for wood procurement. The point data derived from waveform data were used for individual tree detection (ITD). Features extracted from segmented tree objects were used in random forest classification by which both feature selection and classification were performed. Experiments were conducted with 5532 ground measured trees from 292 sample plots and using FWF data collected with Leica ALS60 scanner over a boreal forest, mainly consisting of pine, spruce and birch, in southern Finland. For the comparisons, system produced multi-echo discrete laser data (DSC) were also analyzed with the same procedures. The detection rate of individual trees was slightly higher using FWF point data than DSC point data. Overall detection accuracy, however, was similar because commission error was increased when omission error was decreasing. The best overall classification accuracy was 73.4% which contains an 11 percentage points increase when FWF features were included in the classification compared with DSC features alone. The results suggest that FWF ALS data contains more information about the structure and physical properties of the environment that can be used in tree species classification of pine, spruce and birch when comparing with DSC ALS data.
Full-waveform airborne laser scanning systems provide fundamental observations for each echo, such as the echo width and amplitude. Geometric and physical information about illuminated surfaces are simultaneously provided by a single scanner. However, there are concerns about whether the physical meaning of observations is consistent among different scanning missions. Prior to the application of waveform features for multi-temporal data classification, such features must be normalized. This study investigates the transferability of normalized waveform features to different surveys. The backscatter coefficient is considered to be a normalized physical feature. A normalization process for the echo width, which is a geometric feature, is proposed. The process is based on the coefficient of variation of the echo widths in a defined neighborhood, for which the Fuzzy Small membership function is applied. The normalized features over various land cover types and flight missions are investigated. The effects of different feature combinations on the classification accuracy are analyzed. The overall accuracy of the combination of normalized features and height-based attributes achieves promising results ( > 93% overall accuracy for ground, roof, low vegetation, and tree canopy) when different flight missions and classifiers are used. Nevertheless, the combination of all possible features, including raw features, normalized features, and height-based features, performs less well and yields inconsistent results.
Full waveform lidar has a unique capability to characterise vegetation in more detail than any other practical method. The reflectance, calculated from the energy of lidar returns, is a key parameter for a wide range of applications and so it is vital to extract it accurately. Fifteen separate methods have been proposed to extract return energy (the amount of light backscattered from a target), ranging from simple to mathematically complex, but the relative accuracies have not yet been assessed. This paper uses a simulator to compare all methods over a wide range of targets and lidar system parameters. For hard targets the simplest methods (windowed sum, peak and quadratic) gave the most consistent estimates. They did not have high accuracies, but low standard deviations show that they could be calibrated to give accurate energy. This may be why some commercial lidar developers use them, where the primary interest is in surveying solid objects. However, simulations showed that these methods are not appropriate over vegetation. The widely used Gaussian fitting performed well over hard targets (0.24% root mean square error, RMSE), as did the sum and spline methods (0.30% RMSE). Over vegetation, for large footprint (15 m) systems, Gaussian fitting performed the best (12.2% RMSE) followed closely by the sum and spline (both 12.7% RMSE). For smaller footprints (33 cm and 1 cm) over vegetation, the relative accuracies were reversed (0.56% RMSE for the sum and spline and 1.37% for Gaussian fitting). Gaussian fitting required heavy smoothing (convolution with an 8 m Gaussian) whereas none was needed for the sum and spline. These simpler methods were also more robust to noise and far less computationally expensive than Gaussian fitting. Therefore it was concluded that the sum and spline were the most accurate for extracting return energy from waveform lidar over vegetation, except for large footprint (15 m), where Gaussian fitting was slightly more accurate. These results suggest that small footprint (≪ 15 m) lidar systems that use Gaussian fitting or proprietary algorithms may report inaccurate energies, and thus reflectances, over vegetation. In addition the effect of system pulse length, sampling interval and noise on accuracy for different targets was assessed, which has implications for sensor design.
The paper provides an assessment of the performance of commercial Real Time Kinematic (RTK) systems over longer than recommended inter-station distances. The experiments were set up to test and analyse solutions from the i-MAX, MAX and VRS systems being operated with three triangle shaped network cells, each having an average inter-station distance of 69km, 118km and 166km. The performance characteristics appraised included initialization success rate, initialization time, RTK position accuracy and availability, ambiguity resolution risk and RTK integrity risk in order to provide a wider perspective of the performance of the testing systems. The results showed that the performances of all network RTK solutions assessed were affected by the increase in the inter-station distances to similar degrees. The MAX solution achieved the highest initialization success rate of 96.6% on average, albeit with a longer initialisation time. Two VRS approaches achieved lower initialization success rate of 80% over the large triangle. In terms of RTK positioning accuracy after successful initialisation, the results indicated a good agreement between the actual error growth in both horizontal and vertical components and the accuracy specified in the RMS and part per million (ppm) values by the manufacturers.
Individual tree crowns can be delineated from dense airborne laser scanning (ALS) data and their species can be classified from the spatial distribution and other variables derived from the ALS data within each tree crown. This study reports a new clustering approach to delineate tree crowns in three dimensions (3-D) based on ellipsoidal tree crown models (i.e., ellipsoidal clustering). An important feature of this approach is the aim to derive information also about the understory vegetation. The tree crowns are delineated from echoes derived from full-waveform (fwf) ALS data as well as discrete return ALS data with first and last returns. The ellipsoidal clustering led to an improvement in the identification of tree crowns. Fwf ALS data offer the possibility to derive also the echo width and the amplitude in addition to the 3-D coordinates of each echo. In this study, tree species are classified from variables describing the fwf (i.e., the mean and standard deviation of the echo amplitude, echo width, and total number of echoes per pulse) and the spatial distribution of the clusters for pine, spruce, birch, oak, alder, and other species. Supervised classification is done for 68 field plots with leave-one-out cross-validation for one field plot at a time. The total accuracy was 71% when using both fwf and spatial variables, 60% when using only spatial variables, and 53% when using discrete return data. The improvement was greatest for discriminating pine and spruce as well as pine and birch.
Knowledge of the forest biomass and its change in time is crucial to understanding the carbon cycle and its interactions with climate change. LiDAR (Light Detection and Ranging) technology, in this respect, has proven to be a valuable tool, providing reliable estimates of aboveground biomass (AGB). The overall goal of this study was to develop a method for assessing AGB using a synergy of low point density LiDAR-derived point cloud data and multi-spectral imagery in conifer-dominated forest in the southwest of Sweden. Different treetop detection algorithms were applied for forest inventory parameter extraction from a LiDAR-derived canopy height model. Estimation of AGB was based on the power functions derived from tree parameters measured in the field, while vegetation classification of a multi-spectral image (SPOT-5) was performed in order to account for dependences of AGB estimates on vegetation types. Linear regression confirmed good performance of a newly developed grid-based approach for biomass estimation (R² = 0.80). Results showed AGB to vary from below 1 kg/m² in very young forests to 94 kg/m² in mature spruce forests, with RMSE of 4.7 kg/m². These AGB estimates build a basis for further studies on carbon stocks as well as for monitoring this forest ecosystem in respect of disturbance and change in time. The methodology developed in this study can be easily adopted for assessing biomass of other conifer-dominated forests on the basis of low-density LiDAR and multispectral imagery. This methodology is hence of much wider applicability than biomass derivation based on expensive and currently still scarce high-density LiDAR data.
Low-rank coal contains more inherent moisture, high alkali metals (Na, K, Ca), high oxygen content, and low sulfur than high-rank coal. Low-rank coal gasification usually has lower efficiency than high-rank coal, since more energy has been used to drive out the moisture and volatile matters and vaporize them. Nevertheless, Low-rank coal comprises about half of both the current utilization and the reserves in the United States and is the largest energy resource in the United States, so it is worthwhile and important to investigate the low-rank coal gasification process.
In this study, the two-stage fuel feeding scheme is investigated in a downdraft, entrained-flow, and refractory-lined reactor. Both a high-rank coal (Illinois No.6 bituminous) and a low-rank coal (South Hallsville Texas Lignite) are used for comparison under the following operating conditions: (1) low-rank coal vs. high-rank coal, (2) one-stage injection vs. two-stage injection, (3) low-rank coal with pre-drying vs. without pre-drying, and (4) dry coal feeding without steam injection vs. with steam injection at the second stage.
The results show that (1)With predrying to 12% moisture, syngas produced from lignite has 538 K lower exit temperature and 18% greater HHV than syngas produced from Illinois #6. (2) The two-stage fuel feeding scheme results in a lower wall temperature (around 100 K) in the lower half of the gasifier than the single-stage injection scheme. (3) Without pre-drying, the high inherent moisture content in the lignite causes the syngas HHV to decrease by 27% and the mole fractions of both H2 and CO to decrease by 33%, while the water vapor content increases by 121% (by volume). The low-rank coal, without pre-drying, will take longer to finish the demoisturization and devolatilization processes, resulting in delayed combustion and gasification processes.
Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in the strategy had to be empirically determined. This paper highlights a comprehensive study for the sensitivity analysis of 3D single tree detection from airborne LiDAR data. By varying key parameters, their influences on results are to be quantified. The aim of the study is to enlighten the optimal combination of parameter values towards new applications. For the experiment, a number of sample plots from two temperate forest sites in Europe were selected. LiDAR data with a point density of 25 pts/m² over the first site in the Bavarian forest national park were captured with under both leaf-on and leaf-off conditions. Moreover, a Riegl scanner was used to acquire data over the Austrian Alps forest with four-fold point densities of 5 pts/m², 10 pts/m², 15 pts/m² and 20 pts/m², respectively, under leaf-off conditions. The study results proved the robustness and efficiency of the 3D segmentation approach. Point densities larger than 10 pts/m² did not seem to significantly contribute to the improvement in the performance of 3D tree detection. The performance of the approach can be further examined and improved by optimizing the parameter settings with respect to different data properties and forest structures.
Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can increase the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest.
Airborne laser scanning data and corresponding field data were acquired from boreal forests in Norway and Sweden, coniferous
and broadleaved forests in Germany and tropical pulpwood plantations in Brazil. Treetop positions were extracted using six
different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation
was assessed. Furthermore, the weaknesses and strengths of the methods under different types of forest were analyzed. The
results showed that forest structure strongly affected the performance of all algorithms. Particularly, the success of tree
detection was found to be dependent on tree density and clustering. The differences in performance between methods were more
pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions
for which they were developed, while some could be adapted by different parameterization according to training with local
data. The results of this study may help guiding the choice of method under different forest types and may be of great value
for future refinement of the single-tree detection algorithms.
Terrestrial LiDAR scanners have been shown to hold great potential for estimating and mapping three dimensional (3-D) leaf area distribution in forested environments. This is made possible by the capacity of LiDAR scanners to record the 3-D position of every laser pulse intercepted by plant material. The laser pulses emitted by a LiDAR scanner can be regarded as light probes whose transmission and interception may be used to derive leaf area density at different spatial scales using the Beer–Lambert law or Warren Wilson's contact frequency method among others. Segmenting the canopy into cubic volumes –or voxels- provides a convenient means to compute light transmission statistics and describe the spatial distribution of foliage area in tree crowns. In this paper, we investigate the optimal voxel dimensions for estimating the spatial distribution of within crown leaf area density. We analyzed LiDAR measurements from two field sites, located in Mali and in California, with trees having different leaf sizes during periods with and without leaves.
We found that there is a range of voxel sizes, which satisfy three important conditions. The first condition is related to clumping and requires voxels small enough to exclude large gaps between crowns and branches. The second condition requires a voxel size large enough for the conditions postulated by the Poisson law to be valid, i.e., a turbid medium with randomly positioned leaves. And, the third condition relates to the appropriate voxel size to pinpoint the location of those volumes within the canopy which were insufficiently sampled by the LiDAR instrument to derive reliable statistics (occlusion effects). Here, we show that these requirements are a function of leaf size, branching structure, and the predominance of occlusion effects. The results presented provide guiding principles for using voxel volumes in the retrieval of leaf area distributions from terrestrial LiDAR measurements.
Light Detection and Ranging (LiDAR) remote sensing has demonstrated potential in measuring forest biomass. We assessed the ability of LiDAR to accurately estimate forest total above ground biomass (TAGB) on an individual stem basis in a conifer forest in the US Pacific Northwest region using three different computer software programs and compared results to field measurements. Software programs included FUSION, TreeVaW, and watershed segmentation. To assess the accuracy of LiDAR TAGB estimation, stem counts and heights were analyzed. Differences between actual tree locations and LiDAR-derived tree locations using FUSION, TreeVaW, and watershed segmentation were 2.05 m (SD 1.67), 2.19 m (SD 1.83), and 2.31 m (SD 1.94), respectively, in forested plots. Tree height differences from field measured heights for FUSION, TreeVaW, and watershed segmentation were −0.09 m (SD 2.43), 0.28 m (SD 1.86), and 0.22 m (2.45) in forested plots; and 0.56 m (SD 1.07 m), 0.28 m (SD 1.69 m), and 1.17 m (SD 0.68 m), respectively, in a plot containing young conifers. The TAGB comparisons included feature totals per plot, mean biomass per feature by plot, and total biomass by plot for each extraction method. Overall, LiDAR TAGB estimations resulted in FUSION and TreeVaW underestimating by 25 and 31% respectively, and watershed segmentation overestimating by approximately 10%. LiDAR TAGB underestimation occurred in 66% and overestimation occurred in 34% of the plot comparisons.
Light detection and ranging (lidar) data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California's Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA) of a canopy height model (CHM). The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m 2), discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r 2 : 0.93–0.96). Tree detection rates increased for more dominant trees (8–100 percent). The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure.
Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient γ was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and γ. For single-peak waveforms the scatterplot of γ versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return γ values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the γ versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient γ of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties.
Small‐footprint full‐waveform airborne laser scanning (ALS) is a remote sensing technique capable of mapping vegetation in three dimensions with a spatial sampling of about 0.5–2 m in all directions. This is achieved by scanning the laser beam across the Earth's surface and by emitting nanosecond‐long infrared pulses with a high frequency of typically 50–150 kHz. The echo signals are digitized during data acquisition for subsequent off‐line waveform analysis. In addition to delivering the three‐dimensional (3D) coordinates of scattering objects such as leaves or branches, full‐waveform laser scanners can be calibrated for measuring the scattering properties of vegetation and terrain surfaces in a quantitative way. As a result, a number of physical observables are obtained, such as the width of the echo pulse and the backscatter cross‐section, which is a measure of the electromagnetic energy intercepted and re‐radiated by objects. The main aim of this study was to build up an understanding of the scattering characteristics of vegetation and the underlying terrain. It was found that vegetation typically causes a broadening of the backscattered pulse, while the backscatter cross‐section is usually smaller for canopy echoes than for terrain echoes. These scattering properties allowed classification of the D point cloud into vegetation and non‐vegetation echoes with an overall accuracy of 89.9% for a dense natural forest and 93.7% for a baroque garden area. In addition, by removing the vegetation echoes before the filtering process, the quality of the digital terrain model could be improved.
Small-footprint airborne laser scanning (ALS) is increasingly used in vegetation and forest related applications. This paper explores the potential of full-waveform (FWF) ALS information (i.e. echo width and backscatter cross section) for tree species classification and forest structure parameterization. In order to obtain defined physical quantities, radiometric calibration of the recorded FWF data is performed by using a natural radiometric reference target (asphalt road). Based on a segmentation of the canopy surface, descriptive statistical values of laser echo attributes are derived and attached to the segment polygons, which represent large crown parts or even single trees. We found that average segment-based values of echo width and cross section are well suited to separate larch from deciduous trees (i.e. oak and beech). Additionally, the vertical distribution of the FWF information within a segment is specific for each tree species. On forest stand level a visual agreement of the segment-based FWF values with forest inventory reference data exists. We expect that with further investigation on the laser beam's interaction with vegetation calibrated FWF information can assist tree species classification and forest inventory.
Light Detection and Ranging (lidar) has been widely applied to characterize the 3-dimensional (3D) structure of forests as it can generate 3D point data with high spatial resolution and accuracy. Individual tree segmentations, usually derived from the canopy height model, are used to derive individual tree structural attributes such as tree height, crown diame-ter, canopy-based height, and others. In this study, we develop a new algorithm to segment individual trees from the small footprint discrete return airborne lidar point cloud. We experimentally applied the new algorithm to segment trees in a mixed conifer forest in the Sierra Nevada Moun-tains in California. The results were evaluated in terms of recall, precision, and F-score, and show that the algorithm detected 86 percent of the trees ("recall"), 94 percent of the segmented trees were correct ("precision"), and the overall F-score is 0.9. Our results indicate that the proposed algorithm has good potential in segmenting individual trees in mixed conifer stands of similar structure using small footprint, discrete return lidar data.
As of March 2009, network real-time kinematic (RTK) GPS surveying is available in Great Britain with the aid of two commercial service providers, Leica's "SmartNet and Trimble 's "VRS Now", both of which rely largely on the Ordnance Survey's "OS Net" network of around 120 continuously operating reference stations. With the aim of testing the performance of Network RTK under both ideal and less-ideal conditions (greater distances and elevation differences from the nearest reference stations, proximity to the edges of OS Net, and increased susceptibility to ocean tide loading effects), we hove tested the positional accuracy of both commercial Network RTK systems by comparison with precise coordinates determined using the Bernese scientific GPS processing software, at six representative locations spanning England and Wales. We find that the coordinate quality measures provided by the Network RTK solutions are overall representative of the actual coordinate accuracy, which is typically 10-20 mm in plan and 15-35 mm in height, and can be successfully used to identify outliers. Positional accuracy tends to be poorest outside of the bounds of OS Net and at greater elevation differences from nearby reference stations. Averaging of coordinates over two short windows separated by 20-45 minutes can be used to achieve moderate improvements in coordinate accuracy without the need for single long occupations of sites.
This paper highlights a novel segmentation approach for single trees from LIDAR data and compares the results acquired both from first/last pulse and full waveform data. In a first step, a conventional watershed-based segmentation procedure is set up, which robustly interpolates the canopy height model from the LIDAR data and identifies possible stem positions of the tallest trees in the segments calculated from the local maxima of the canopy height model. Secondly, this segmentation approach is combined with a special stem detection method. Stem positions in the segments of the watershed segmentation are detected by hierarchically clustering points below the crown base height and reconstructing the stems with a robust RANSAC-based estimation of the stem points. Finally, a new three-dimensional (3D) segmentation of single trees is implemented using normalized cut segmentation. This tackles the problem of segmenting small trees below the canopy height model. The key idea is to subdivide the tree area in a voxel space and to set up a bipartite graph which is formed by the voxels and similarity measures between the voxels. Normalized cut segmentation divides the graph hierarchically into segments which have a minimum similarity with each other and whose members (= voxels) have a maximum similarity. The solution is found by solving a corresponding generalized eigenvalue problem and an appropriate binarization of the solution vector. Experiments were conducted in the Bavarian Forest National Park with conventional first/last pulse data and full waveform LIDAR data. The first/last pulse data were collected in a flight with the Falcon II system from TopoSys in a leaf-on situation at a point density of 10 points/m2. Full waveform data were captured with the Riegl LMS-Q560 scanner at a point density of 25 points/m2 (leaf-off and leaf-on) and at a point density of 10 points/m2 (leaf-on). The study results prove that the new 3D segmentation approach is capable of detecting small trees in the lower forest layer. So far, this has been practically impossible if tree segmentation techniques based on the canopy height model were applied to LIDAR data. Compared to a standard watershed segmentation procedure, the combination of the stem detection method and normalized cut segmentation leads to the best segmentation results and is superior in the best case by 12%. Moreover, the experiments show clearly that using full waveform data is superior to using first/last pulse data.
The paper demonstrates the advantage of full waveform LIDAR data for segmentation and classification of single trees. First, a new 3D segmentation technique is highlighted that detects single trees with an improved accuracy. The novel method uses the normalized cut segmentation and is combined with a special stem detection method. A subsequent classification identifies tree species using salient features that utilize the additional information the waveform decomposition extracts from the reflected laser signal. Experiments were conducted in the Bavarian Forest National Park with conventional first/last pulse and full waveform LIDAR data. The first/last pulse data result from a flight with the Falcon II system from TopoSys in leaf-on situation at a point density of 10 points/m 2 . Full waveform data were captured with the Riegl LMS-Q560 system at a point density of 25 points/m 2 (leaf-off and leaf-on) and at a point density of 10 points/m 2 (leaf-on). The study results prove that the new 3D segmentation approach is capable of detecting small trees in the lower forest layer. This was practically impossible so far if tree segmentation techniques based on the canopy height model (CHM) were applied to LIDAR data. Compared to the standard watershed segmentation the combination of the stem detection method and the normalized cut segmentation performs better by 12%. In the lower forest layers the improvement is even more than 16%. Moreover, the experiments show clearly that the usage of full waveform data is superior to first/last pulse data. The unsupervised classification of deciduous and coniferous trees is in the best case 93%. If a supervised classification is applied the accuracy is slightly increased with 95%. Classification with first/last pulse data ends up with only 80% overall accuracy. Interestingly, it turns out that the point density has practical no impact on the segmentation and classification results.
CORSnet-NSW is a rapidly growing network of Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) providing fundamental positioning infrastructure for New South Wales that is accurate, reliable and easy to use. This positioning infrastructure supports a wide range of GNSS applications in areas such as surveying, agriculture, mining and construction. This paper presents the current status of CORSnet-NSW and briefly outlines the difference between the traditional, single-base Real Time Kinematic (RTK) and the Network RTK (NRTK) approaches. Initial results from some of the extensive testing of NRTK performance undertaken by LPI across eastern NSW are then presented. These tests have shown that while NRTK has the same 'look and feel' as single-base RTK, it produces superior coordinate results in regards to both precision (i.e. repeatability) and accuracy (i.e. agreement with the State's survey ground control network). The benefit of averaging observations over a 1-minute window and re-occupying points 20-40 minutes later is illustrated. It is also shown that coordinate quality (CQ) indicators provided by the GNSS rover equipment are often overly optimistic, even under favourable satellite visibility and multipath conditions, and should therefore be used with caution.
The study highlights a novel method to segment single trees in 3D from dense airborne full waveform LIDAR data using the normalized cut segmentation. The key idea is to subdivide the tree area in a voxel space and to setup a bipartite graph which is formed by the voxels and similarity measures between the voxels. The normalized cut segmentation divides the graph hierarchically into segments which have a minimum similarity among each other and whose members (=voxels) have a maximum similarity. The solution is found by solving a corresponding generalized eigenvalue problem and an appropriate binarization of the solution vector. We applied the method to small-footprint full waveform data that have been acquired in the Bavarian Forest National Park with a mean point density of 25 points per m 2 in leaf-off situation. The segmentation procedure is evaluated in different steps. First, a linear discriminant analysis shows that the mean intensity of the voxels derived from the full waveform data contributes significantly to the segmentation of deciduous and coniferous tree segments. Second, a sample-based sensitivity analysis examines the best value of the most important control parameter that stops the division process of the graph. Third, we show examples how the segmentation can cope with even difficult situations. We also discuss examples showing the limits of the current implementation. Finally, we present the detection rate of the new method in controlled tests using reference data. If we compare the new method to a standard watershed-based segmentation approach the overall improvement for all tree layers is 9%. However, the biggest improvement can be achieved in the intermediate layer with 14% and in the lower layer with 16% showing clearly the advantage of the new approach to a 3D segmentation of single trees.
The main objective of this study was to develop reliable processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating tree crown diameter by measuring individual trees identifiable on the three-dimensional lidar surface. In addition, the study explored the importance of the lidar-derived crown diameter for estimating tree volume and biomass. The lidar dataset was acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern United States. For identifying individual trees, lidar processing techniques used data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Linear regression was also used to compare plot level tree volume and biomass estimation with and without lidar-derived crown diameter measures from individual trees. Results for estimating crown diameter were similar for both pines and deciduous trees, with R-2 values of 0.62-0.63 for the dominant trees (root mean square error (RMSE) 1.36 to 1.41 m). Lidar-measured crown diameter improved R 2 values for volume and biomass estimation by up to 0.25 for both pines and deciduous plots (RMSE improved by up to 8 m(3)/ha for volume and 7 Mg/ha for biomass). For the pine plots, average crown diameter alone explained 78% of the variance associated with biomass (RMSE 31.28 Mg/ha) and 83% of the variance for volume (RMSE 47.90 m(3)/ha).
Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications compiles comprehensive review articles to examine the developments in concepts, methods, techniques, and applications as well as focused articles and case studies on the latest on a particular topic.
Divided into four sections, the first deals with various sensors, systems, or sensing operations using different regions of wavelengths. Drawing on the data and lessons learned from the U.S. Landsat remote sensing programs, it reviews key concepts, methods, and practical uses of particular sensors/sensing systems. Section II presents new developments in algorithms and techniques, specifically in image preprocessing, thematic information extraction, and digital change detection. It gives correction algorithms for hyperspectral, thermal, and multispectral sensors, discusses the combined method for performing topographic and atmospheric corrections, and provides examples of correcting non-standard atmospheric conditions, including haze, cirrus, and cloud shadow.
Section III focuses on remote sensing of vegetation and related features of the Earth’s surface. It reviews advancements in the remote sensing of ecosystem structure, process, and function, and notes important trade-offs and compromises in characterizing ecosystems from space related to spatial, spectral, and temporal resolutions of the imaging sensors. It discusses the mismatch between leaf-level and species-level ecological variables and satellite spatial resolutions and the resulting difficulties in validating satellite-derived products.
Finally, Section IV examines developments in the remote sensing of air, water, and other terrestrial features, reviews MODIS algorithms for aerosol retrieval at both global and local scales, and demonstrates the retrieval of aerosol optical thickness (AOT). This section rounds out coverage with a look at remote sensing approaches to measure the urban environment and examines the most important concepts and recent research.
Here we report on a model built to predict stand condition of forests and woodlands across The Living Murray Icon Sites in 2009. This is the first of three years (2009-2011) of modelling required to build a tool that will allow the Murray-Darling Basin Authority to estimate stand condition of forests and woodlands across the Icon Sites from a combination of ground surveys and satellite imagery. Stand condition of forest types dominated by river red gum and black box was assessed by ground surveys of 175 reference sites in 2009. These assessments were successfully predicted (R2 = 0.68) from Landsat imagery using an artificial neural network. The addition of survey data and modelling in coming years (2010 and 2011) will increase the predictive power of the final Stand Condition Tool. The 2009 Stand Condition Model was used to backcast stand condition of the Icon Sites in 2003 and 2008 from historical Landsat imagery. The 2009 Stand Condition Model, using Landsat imagery from 2003, 2008 and 2009 as inputs, predicted that:
• 79% of the area covered by river red gum, black box and box communities in The Living Murray Icon Sites was in a stressed condition (moderate to severely degraded condition) in 2009.
• Stand condition differed among the Icon Sites in 2009, with the River Murray Channel having the smallest extent of stressed stands (72%) and Hattah Lakes having the largest extent of stressed stands (97%).
• The extent of poor to severely degraded condition stands in 2009 was much larger in the Mallee Icon Sites (Hattah Lakes, Chowilla Floodplain and Lindsay-Wallpolla Islands, 50-70%) than in the Riverina Icon Sites (Barmah-Millewa and Gunbower-Koondrook-Perricoota Forests, 5-13%).
• Of the forest types, river red gum forests and woodlands had the largest extent (28%) of good condition stands, with good condition stands less extensive (17%) in mixed river red gum-black box woodlands and limited (8-12%) in pure black box and mixed box woodlands.
• Stand condition at the Icon Sites was similar between 2008 and 2009.
• The extent of stressed stands across the Icon Sites was substantially lower (66%) in 2003 compared with 2009.
• Half of the areas in the Riverina forests that had good condition stands in 2003 were in a stressed condition in 2009.
• The majority of stands in the Mallee Icon Sites were in a stressed condition in 2003 and the extent of stressed stands has expanded (4% increase) in 2009.
• Areas where stand condition changed from stressed to good condition between 2003 and 2009 correspond to areas that received environmental watering over this period.
These predictions of the 2009 Stand Condition Model suggest that 1) current water availability (rainfall and flooding) across The Living Murray Icon Sites remains insufficient to maintain the majority of forests and woodlands in good condition, and 2) environmental watering, although limited in coverage, is an effective way to mitigate and improve the condition of these important forests and woodlands.
Label propagation has become a successful method for transductive learning. In this paper, we propose a unified label propagation model named Component Random Walk. We demonstrate that besides most of the existing label propagation algorithms, a novel Multilevel Component Propagation (MCP) algorithm can be derived from this Component Random Walk model as well. Promising experimental results are provided for MCP algorithm.
High-resolution airborne laser scanner data offer the possibility to detect and measure individual trees. In this study, an algorithm which estimated position, height, and crown diameter of individual trees was validated with field measurements. Because all the trees in this study were measured on the ground with high accuracy, their positions could be linked with laser measurements, making validation on an individual tree basis possible. In total, 71 percent of the trees were correctly detected using laser scanner data. Because a large portion of the undetected trees had a small stem diameter, 91 percent of the total stem volume was detected. Height and crown diameter of detected trees could be estimated with a root-mean-square error (RMSE) of 0.63 m and 0.61 m, respectively. Stem diameter was estimated, using laser measured tree height and crown diameter, with an RMSE of 3.8 cm. Different laser beam diameters (0.26 to 3.68 m) were also tested, the smallest beam size showing a better detection rate in dense forest. However, estimates of tree height and crown diameter were not affected much by different beam size.
Small footprint, full waveform airborne laser scanning provides the opportunity to derive high-resolution geometric and physical information simultaneously from a single scanner system. This study evaluates the influence of various factors on the shape of the returned waveform and investigates the possibility of improving terrain classification by applying waveform-derived information. The factors discussed are surface roughness, slope angle, scan angle, amplitude, and footprint size. It is statistically demonstrated that roughness is the most significant factor affecting pulse width, and that, over relatively smooth surfaces, there is no significant variation in pulse width behavior resulting from different footprint sizes. Pulse width also exhibits a relatively stable behavior when amplitude, range distance, or scan angle vary substantially. The overall accuracy of classification achieved by applying pulse width information over all the different land-cover types examined in this study (including scrub, hillside, single trees, and forest areas) was greater than 85 percent, with 94 percent achieved for open vegetation areas. Physical surface information provided by small footprint waveform data is considered to be at the microscale, therefore it is recommended to combine such information with geometry (e.g., filtering algorithms) for the optimal identification of terrain points.
Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in the strategy had to be empirically determined. This paper highlights a comprehensive study for the sensitivity analysis of 3D single tree detection from airborne LiDAR data. By varying key parameters, their influences on results are to be quantified. The aim of the study is to enlighten the optimal combination of parameter values towards new applications. For the experiment, a number of sample plots from two temperate forest sites in Europe were selected. LiDAR data with a point density of 25 pts/m2 over the first site in the Bavarian forest national park were captured with under both leaf-on and leaf-off conditions. Moreover, a Riegl scanner was used to acquire data over the Austrian Alps forest with four-fold point densities of 5 pts/m2, 10 pts/m2, 15 pts/m2 and 20 pts/m2, respectively, under leaf-off conditions. The study results proved the robustness and efficiency of the 3D segmentation approach. Point densities larger than 10 pts/m2 did not seem to significantly contribute to the improvement in the performance of 3D tree detection. The performance of the approach can be further examined and improved by optimizing the parameter settings with respect to different data properties and forest structures.
This paper introduces PTrees, a multi-scale dynamic point cloud segmentation dedicated to forest tree extraction from lidar point clouds. The method process the point data using the raw elevation values (Z) and compute height (H = Z − ground elevation) during post-processing using an innovative procedure allowing to preserve the geometry of crown points. Multiple segmentations are done at different scales. Segmentation criteria are then applied to dynamically select the best set of apices from the tree segments extracted at the various scales. The selected set of apices is then used to generate a final segmentation. PTrees has been tested in 3 different forest types, allowing to detect 82% of the trees with under 10% of false detection rate. Future development will integrate crown profile estimation during the segmentation process in order to both maximize the detection of suppressed trees and minimize false detections.
Light detection and Ranging (LiDAR) is becoming an increasingly used tool to support decision-making processes within forest operations. Area-based methods that derive information on the condition of a forest based on the distribution of points within the canopy have been proven to produce reliable and consistent results. Individual tree-based methods, however, are not yet used operationally in the industry. This is due to problems in detecting and delineating individual trees under varying forest conditions resulting in an underestimation of the stem count and biases toward larger trees. The aim of this paper is to use high-resolution LiDAR data captured from a small multirotor unmanned aerial vehicle platform to determine the influence of the detection algorithm and point density on the accuracy of tree detection and delineation. The study was conducted in a four-year-old Eucalyptus globulus stand representing an important stage of growth for forest management decision-making process. Five different tree detection routines were implemented, which delineate trees directly from the point cloud, voxel space, and the canopy height model (CHM). The results suggest that both algorithm and point density are important considerations in the accuracy of the detection and delineation of individual trees. The best performing method that utilized both the CHM and the original point cloud was able to correctly detect 98% of the trees in the study area. Increases in point density (from 5 to 50 points/m2) lead to significant improvements (of up to 8%) in the rate of omission for algorithms that made use of the high density of the data.
3D surface representation has long been a source of information describing surface character and facilitating an understanding of system dynamics from micro-scale (e.g. sand transport) to macro-scale (e.g. drainage channel network evolution). Data collection has been achieved through field mapping techniques and the use of remotely sensed data. Advances in this latter field have been considerable in recent years with new rapid-acquisition methods being developed centered around laser based technology. The advent of airborne and field based laser scanning instruments has allowed researchers to collect high density accurate data sets and these are revealing a wealth of new information and generating important new ideas concerning terrain characterisation and landform dynamics. The proposed book collates a series of invited peer revieved papers presented at the a conference on geoinformatics and LIDAR to be held at the National Centre for Geocomputation based in the National University of Ireland, Maynooth. Current constraints in field survey and DEM construction are reviewed together with technical and applied issues around the new technology. The utility of the data in process modelling is also covered. The book will be of great value to researchers in the field of geomorphology, geostatistics, remote sensing and GIS and will prove extremely useful to students and practitioners concerned with terrain analysis. The proposed work will: Highlight major technological breakthrough in 3D data collection. Feature examples of application across a wide range of environmental areas. Critically evaluate the role of laser based techniques in the environment. Detail theory and application of laser techniques in the natural environment.
Deriving individual tree information from discrete return, small footprint LiDAR data may improve forest aboveground biomass estimates, and provide tree-level information that is important in many ecological studies. Several crown delineation algorithms have been developed to extract individual tree information from LiDAR point clouds or rasterized canopy height models (CHM), but many of these algorithms have difficulty discriminating between overlapping crowns, and also may fail to detect understory trees. Our approach uses a watershed-based delineation of a CHM, which is subsequently refined using the LiDAR point cloud. Individual tree detection was validated with stem mapped field data from the Smithsonian Environmental Research Center (SERC), Maryland, and on a plot and stand level through comparisons of stem density and basal area to delineated metrics at both SERC and a study area in the Sierra Nevada, California. For individual tree detection, the algorithm correctly identified 70% of dominant trees, 58% of codominant trees, 35% of intermediate trees and 21% of suppressed trees at SERC. The algorithm had difficulty distinguishing between crowns of small, dense understory trees of approximately the same height. Delineated crown volume alone explained 53% and 84% of the variability in basal area at the SERC and Sierra Nevada sites, respectively. The algorithm produced crown area distributions comparable to diameter at breast height (DBH) size class distributions observed in the field in both study sites. The algorithm detected understory crowns better in the conifer-dominated Sierra Nevada site than in the closed-canopy deciduous site in Maryland. The ability for the algorithm to reproduce both accurate tree size distributions and individual crown geometries in two dissimilar and complex forests suggests great promise for applicability to a wide range of forest systems.
A three-dimensional simulation model was used for modeling the scanning angle effect when measuring tree height and canopy closure in boreal forest with a laser scanner. The height distribution of the laser returns and the proportion of laser returns from the canopy were simulated using ray-tracing applied to a computer modeled forest. The proportion of canopy returns is commonly used as a measure of canopy closure, and height percentiles are commonly used to estimate mean tree height. Laser scanner data and field measurements of tree position, tree height, crown diameter, and crown base height were used for validating the simulation model. The correlation coefficient between simulated and real laser height percentiles was 0.96 and the simulation model systematically overestimated the laser height percentiles by 2.25 m. Simulations show that laser height percentiles and proportion of canopy returns changed more with an increased scanning angle for long crown species like spruce, compared with short crown species like pine. The change of height percentiles due to scanning angle was greater in forests with low stem numbers than with high stem numbers. The proportion of canopy returns was more affected by scanning angle than were the laser height percentiles.
The paper highlights recent results of forest structure analysis at single tree level based on analyzing airborne full waveform LiDAR data. Single trees are automatically detected by a 3D segmentation technique applied directly to laser point clouds, which uses the normalized cut segmentation combined with a stem detection method. A subsequent classification identifies tree species using salient features that are defined on single 3D tree segments and utilize the additional information extracted from the reflected laser signal by the waveform decomposition. The stem volume and diameter at breast height (DBH) are estimated by a multiple linear regression analysis which uses tree shape parameters derived from the 3D model of the trees. Experiments were conducted in the Bavarian Forest National Park with full waveform LiDAR data. The data were captured with the Riegl LMS Q-560 system at a point density of 25 points/m2 under leaf-off and leaf-on conditions. The analysis of waveform data in the tree structure shows that the intensity and pulse width discriminate stem points, crown points and ground points significantly. The unsupervised classification of deciduous and coniferous trees is in the best case 93%. If a supervised classification is applied the accuracy is slightly increased to 95%. Concerning stem volume estimation, in the case of coniferous trees the study shows a low RMSE of about 0.46 m3 to 0.43 m3 both for the watershed segmentation and the new normalized cut segmentation. In the case of deciduous trees RMSE has increased by 14% in leaf off condition and by 4% in leaf on condition for the normalized cut segmentation. A similar trend can be confirmed for DBH estimation as well, even demonstrating a larger benefit from 3D segmentation. The study results proved that the 3D segmentation approach is not only capable of detecting more small trees in the lower forest layer but also can allow to derive more promising features of single trees used for yielding better performance in species classification and estimation of forest structural parameters, especially for deciduous trees.
Understory trees in multilayered stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement, and biodiversity management. Cost-efficient inventory methods are needed and airborne LiDAR is a promising addition to fieldwork. The overstory, however, obstructs wall-to-wall sampling of the understory using LiDAR, because transmission losses affect echo-triggering probabilities and intensity (peak amplitude) observations. We examined the potential of LiDAR in mapping of understory trees in pine (Pinus sylvestris L.) stands (62°N, 24°E), using careful experimentation. We formulated a conceptual model for the transmission losses and illustrated that loss normalization is highly ill-posed, especially for vegetation. The losses skew the population of targets that produce a subsequent echo. Losses up to 10–15% can occur even if an overstory echo is not triggered. In LiDAR sensors, quantized intensity values start from binary zero, but actually should include an offset, the noise level. We estimated these empirically. Constraining to low-loss pulses and ground data, we estimated parameters for compensation models that were based on the radar equation and employed the geometry of the pulse, as well as the overstory intensity observations as predictors. Intensity variation of second-return data was reduced, but, the intensity data were deemed of low value in species discrimination. Our results highlight differences between sensors in near-ground echo-triggering and height data. Area-based LiDAR height metrics from the understory had reasonable correlation with the density and mean height of the understory trees, whereas tree species seemed out of reach even if the transmission losses were compensated for. We conclude that transmission losses are a general impediment for radiometric analysis of multi-echo pulses in discrete-return and waveform LiDAR data.
Extraction of individual tree crowns is meaningful for many applications. In this paper, a new method is proposed to extract individual trees from airborne LiDAR point clouds in human settlements. In the process of extraction, an improved slope-based filter is employed to separate the non-ground measurements from the ground measurements, the surface growing algorithm is utilized to segment the point clouds into segments, multiple echoes information is used to distinguish the tree points from other types of non-ground measurements, and spoke wheel algorithm is employed to get the accurate edges of each tree at last. Two datasets are employed to test the above method. Experiments show that our approach is capable of extracting more than 85% trees from the point clouds with the accuracy higher than 95%, which suggests the promising applications.
Airborne lidar (Light Detection And Ranging) is a proven technology that can be used to accurately assess aboveground forest biomass and bio-energy feedstocks. The overall goal of this study was to develop a method for assessing aboveground biomass and component biomass for individual trees using airborne lidar data in forest settings typical for loblolly pine stands (Pinus taeda L.) in the southeastern United States. More specific objectives included: (1) assessing the accuracy of estimating diameter at breast height (dbh) for individual pine trees using lidar-derived individual tree measurements, such as tree height and crown diameter, and (2) investigating the use of lidar-derived individual tree measurements with linear and nonlinear regression to estimate per tree aboveground biomass. In addition, the study presents a method for estimating the biomass of individual tree components, such as foliage, coarse roots, stem bark, and stem wood, as derived quantities from the aboveground biomass prediction. A lidar software application, TreeVaW, was used to extract forest inventory parameters at individual tree level from a lidar-derived canopy height model. Lidar-measured parameters at individual tree level, such as height and crown diameter, were used with regression models to estimate dbh, aboveground tree biomass, and tree-component biomass. Field measurements were collected for 45 loblolly pine trees over 0.1- and 0.01-acre plots. Linear regression models were able to explain 93% of the variability associated with individual tree biomass, 90% for dbh, and 79–80% for components biomass.
Detailed information on the spatiotemporal dynamic in surface water bodies is important for quantifying the effects of a drying climate, increased water abstraction and rapid urbanization on wetlands. The Swan Coastal Plain (SCP) with over 1500 wetlands is a global biodiversity hotspot located in the southwest of Western Australia, where more than 70% of the wetlands have been lost since European settlement. SCP is located in an area affected by recent climate change that also experiences rapid urban development and ground water abstraction. Landsat TM and ETM+ imagery from 1999 to 2011 has been used to automatically derive a spatially and temporally explicit time-series of surface water body extent on the SCP. A mapping method based on the Landsat data and a decision tree classification algorithm is described. Two generic classifiers were derived for the Landsat 5 and Landsat 7 data. Several landscape metrics were computed to summarize the intra and interannual patterns of surface water dynamic. Top of the atmosphere (TOA) reflectance of band 5 followed by TOA reflectance of bands 4 and 3 were the explanatory variables most important for mapping surface water bodies. Accuracy assessment yielded an overall classification accuracy of 96%, with 89% producer’s accuracy and 93% user’s accuracy of surface water bodies. The number, mean size, and total area of water bodies showed high seasonal variability with highest numbers in winter and lowest numbers in summer. The number of water bodies in winter increased until 2005 after which a decline can be noted. The lowest numbers occurred in 2010 which coincided with one of the years with the lowest rainfall in the area. Understanding the spatiotemporal dynamic of surface water bodies on the SCP constitutes the basis for understanding the effect of rainfall, water abstraction and urban development on water bodies in a spatially explicit way.
Measuring individual trees can provide valuable information about forests, and airborne light detection and ranging (LiDAR) sensors have been used recently to identify individual trees and measure structural tree parameters. Past results, however, have been mixed because of reliance on interpolated (image) versions of the LiDAR measurements and search methods that do not adapt to variations in canopies. In this work, an adaptive clustering method is developed using airborne LiDAR data acquired over two distinctly different managed pine forests in North-Central Florida, USA. A crucial issue in isolating individual trees is determining the appropriate size of the moving window (search radius) when locating seed points. The proposed approach works directly on the three-dimensional (3D) 'cloud' of LiDAR points and adapts to irregular canopies sizes. The region growing step yields collectively exhaustive sets in an initial segmentation of tree canopies. An agglomerative clustering step is then used to merge clusters that represent parts of whole canopies using locally varying height distribution. The overall tree detection accuracy achieved is 95.1% with no significant bias. The tree detection enables subsequent estimation of tree height and vertical crown length to an accuracy better than 0.8 and 1.5 m, respectively.
a b s t r a c t Terrestrial lidar (TLiDAR) has been used increasingly over recent years to assess tree architecture and to extract metrics of forest canopies. Analysis of TLiDAR data remains a difcult task mainly due to the effects of object occlusion and wind on the quality of the retrieved results. We propose to link TLiDAR and tree structure attributes by means of an architectural model. The proposed methodology uses TLiDAR scans combined with allometric relationships to dene the total amount of foliage in the crown and to build the tree branching structure. It uses the range (distance) and intensity information of the TLiDAR scans (i) to extract the stem and main branches of the tree, (ii) to reconstruct the ne branching structure at locations where the presence of foliage is very likely, and (iii) to use the availability of light as a criterion to add foliage in the center of the crown where TLiDAR information is sparse or absent due to occlusion effects. An optimization algorithm guides the model towards a realistic tree structure that ts the information gathered from TLiDAR scans and eld inventory. The robustness and validity of the proposed model is assessed on ve trees belonging to four different conifer species from natural forest environments. This approach addresses the data limitation of TLiDAR scans and aims to extract forest architectural metrics at different structural levels.