Search

In the case of tree species delineation with very high spatial resolution (VHR) images, is desirable that each segment corresponds to one individual tree crown (ITC). However, in order to have a segmentation algorithm that generates segments matching to ITCs, its parameters ought to be properly tuned. Aiming to avoid time-consuming trial-and-error procedures associated with this task, some initiatives for the automatic search of segmentation parameters have been developed, such as metaheuristic methods. The objective of this work was to test the automatic tuning of segmentation parameters of three segmentation algorithms for the delineation of ITCs belonging to a native endangered species in a subtropical forest area, comparing this method with the traditional trial-and-error approach. Two datasets (WorldView-2 and an orthoimage) and three segmentation algorithms (multiresolution, mean-shift and graph-based) were tested. For the automatic approach, a hybrid metaheuristic method was applied to accomplish the automatic search of parameters for the segmentation algorithms, while for the trial-and-error, a visual assessment was conducted for each set of parameters tested. Four supervised metrics were used to assess the quality of the segmentation results for the optimization approach and for the final set of parameters chosen in the trial-and-error approach. Results showed that none of the algorithms, datasets or approaches differ too much. The evaluation metrics values were lower, indicating that the reference ITCs polygons matched with the segmentation results. Despite the similar results, the automatic tuning of segmentation parameters proved to be a feasible alternative to reduce the subjectivity and the human effort in the choice of segmentation parameters as compared to the trial-and error approach. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (LiDAR) data are increasingly used to inform sustainable forest management practices. Information about species composition is needed for a range of applications; however, commonly used area-based summaries of LiDAR data are limited to accurately differentiate tree species. The objective of this study was to map dominant species groups across a large (>580,000 ha) boreal forest by combining area-based and individual tree metrics derived from single photon LiDAR data with multispectral information derived from Sentinel-2 imagery. Classification of the forest into jack pine (Pinus banksiana), black spruce (Picea mariana), mixed conifer, mixedwood, and hardwood species groups resulted in an overall accuracy of 67.8% compared with field data. A more generalized classification into softwood, hardwood, and mixedwood had an overall accuracy of 83.2%. The reflectance in the red edge region of the electromagnetic spectrum (λ = 740 nm), the area and volume of tree crowns, and the cumulative distribution of LiDAR returns in the canopy were particularly important variables to discriminate between species groups. Wall-to-wall predictions of species groups based on remotely sensed data—as derived herein—could provide a spatially-detailed alternative to stand-level species composition information traditionally derived from photo-interpreted aerial imagery. [ABSTRACT FROM AUTHOR]

Crown fire damage is a mixture of three principal fire-related components: charred material, scorched foliage, and unaltered green canopy. This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts (Portugal and Italy) by applying linear spectral mixture analysis (LSMA) on Sentinel-2 imagery. The tree crowns fire damage was subsequently mapped, integrating fractional abundance information in a random forest (RF) algorithm, comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal. Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification (LMSA-derived RMSE < 0.1), the F-scores always were ≥ 90% whether generic endmembers or image endmembers derived information was employed. The environmental heterogeneity of the two study areas affected the fire severity gradients, with a prevalence of the charred (PT) (45–46%) and green class (IT) (44–53%). Post-fire temporal monitoring was initialized by applying the proposed strategies, and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event, with a reduced charcoal predominance and an increasing proportion of green components. [ABSTRACT FROM AUTHOR]

To sustainably manage forest biodiversity and monitor changes in species patterning, mapping the spatial distribution of tree species is indispensable. Remote sensing can provide powerful tools for mapping species, but this task is complex in areas with high plant diversity and multi-layered canopies. This paper addresses the issue of classifying wet eucalypt forest plants by examining tree crown segmentation and species classification using different combinations of remote sensing datasets against mapped tree locations. This study explores optimal segmentation parameters for tree crown delineation compared to manually digitized tree crowns. The best segmentation accuracy of 88.71%, resulted from segmenting a combined Minimum Noise Fraction (MNF) dataset derived from hyperspectral imagery (HSI) and a LiDAR-derived Canopy Height Model (CHM). Object-based classification of tree species was performed using a random forest classifier. The fused dataset of MNF and CHM produced the highest overall accuracy of 78.26% for four vegetation classes, while the fused HSI, indices, and CHM performed best (66.67%) with five vegetation classes. However, both approaches had a high overall performance. The CHM contributed to tree crown segmentation and species classification accuracy, and fused datasets were more robust to spatially discriminate wet eucalypt forest species compared to a single dataset. Eucalyptus obliqua was classified with the highest accuracy of 90.86% for four classes using the fused MNF and CHM dataset, and 86.11% for five classes using the fused HSI, indices, and CHM dataset. An important understorey species – the tree fern (Dicksonia antarctica) – was classified with the highest accuracy of 83.54% for four classes using HSI. Therefore, fusing hyperspectral and LiDAR data could classify both the overstorey and dominant understorey species, and thus play a crucial role in identifying forest biological diversity. This approach will be useful for forest managers and ecologists to plan sustainable management of eucalypt forest biodiversity and produce maps for monitoring species of interest. [ABSTRACT FROM AUTHOR]

This article describes algorithms to extract tree crowns using two-dimensional (2D) and three-dimensional (3D) segmentation. As a first step, a 2D-search detected the tallest trees but was unable to detect trees located below other trees. However, a 3D-search for local maxima of model fits could be used in a second step to detect trees also in lower canopy layers. We compared tree detection results from ALS carried out at 1450 m above ground level (high altitude) and tree detection results from ALS carried out at 150 m above ground level (low altitude). For validation, we used manual measurements of trees in ten large field plots, each with an 80 m diameter, in a hemiboreal forest in Sweden (lat. 58°28' N, long. 13°38' E). In order to measure the effect of using algorithms with different computational costs, we validated the tree detection from the 2D segmentation step and compared the results with the 2D segmentation followed by 3D segmentation of the ALS point cloud. When applying 2D segmentation only, the algorithm detected 87% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 91% using low-altitude ALS data. However, when applying 3D segmentation as well, the algorithm detected 92% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 99% using low-altitude ALS data. For all combinations of algorithms and data resolutions, undetected trees accounted for, on average, 0–5% of the total stem volume in the field plots. The 3D tree crown segmentation, which was using crown density models, made it possible to detect a large percentage of trees in multi-layered forests, compared with using only a 2D segmentation method. [ABSTRACT FROM AUTHOR]

In this study primarily, high-resolution (HR) satellite image is segmented into tree and non-tree regions. Thereafter plots the local minima in the segmented image. Point surrounded by the higher intensity values is called as local minima. The local minimum is the starting point for marking the tree crown boundary. The adjacent darker points along the local minima are plotted in a specific direction for marking the tree crown boundary. Subsequently a seven steps iterative procedure is followed for delineating and counting the tree crowns. The validation of the method is done on WorldView-2 data. [ABSTRACT FROM AUTHOR]

Finite-length vegetation has been previously studied for its ability to provide resistance against the tsunami by using just the trunk section of the tree stand structures. However, investigating the resilience of the vegetation while taking the crown section into account is yet unknown. Using a limited emergent vegetation model (EVM), this research examined the impact of changing the tree crown heights on the flow structure behavior between the vegetation and its surrounding gap region. Experimental variables included such as tree crown height ( h c ′ = h c / h t in which h c is the crown height from the ground surface and h t is the total tree height), vegetation width to length ratio (W/L), and the initial Froude number condition (F r o ) , which ranged between 0.67 and 0.76. The outcomes demonstrated that, when the vegetation width to length ratio was small, the crown height ratio ( h c < 0.3) significantly reduced the velocity and fluid force behind the vegetation patch region as compared to no crown (NC) and highest crown height ratio ( h c > 0.3). On the other hand, the effect of crown cases in the small vegetation patch did not increase the velocity and fluid force in the gap region in comparison with NC. Whereas the increased width of the vegetation patch along with the lower crown height ratio ( h c < 0.4) further decreased the velocity and fluid force behind the vegetation patch but the increased width of the vegetation patch in combination with the lower crown height ratio increased the velocity and fluid force in the surrounding gap region as compared to its NC case. [ABSTRACT FROM AUTHOR]

This letter describes a new algorithm for automatic tree crown delineation based on a model of tree crown density, and its validation. The tree crown density model was first used to create a correlation surface, which was then input to a standard watershed segmentation algorithm for delineation of tree crowns. The use of a model in an early step of the algorithm neatly solves the problem of scale selection. In earlier studies, correlation surfaces have been used for tree crown segmentation, involving modelling tree crowns as solid geometric shapes. The new algorithm applies a density model of tree crowns, which improves the model's suitability for segmentation of Airborne Laser Scanning (ALS) data because laser returns are located inside tree crowns. The algorithm was validated using data acquired for 36 circular (40 m radius) field plots in southern Sweden. The algorithm detected high proportions of field-measured trees (40–97% of live trees in the 36 field plots: 85% on average). The average proportion of detected basal area (cross-sectional area of tree stems, 1.3 m above ground) was 93% (range: 84–99%). The algorithm was used with discrete return ALS point data, but the computation principle also allows delineation of tree crowns in ALS waveform data. [ABSTRACT FROM AUTHOR]

Previous studies demonstrate the effectiveness of the Euclidean distance shape descriptor for characterizing individual tree crowns derived from Light Detection and Ranging (LiDAR) data. This paper evaluates the effectiveness of the longitude–latitude shape descriptor for tree crown shape characterization in comparison with the Euclidean distance descriptor using LiDAR data for 43 individual tree crowns. The three-dimensional (3D) shape signatures obtained from longitude–latitude and Euclidean distance measures of the tree crowns are compared with 3D shape signatures of simulated ellipsoid and hemisphere tree crowns. Kolmogorov–Smirnov test (K–S test) and Mean Square Error (MSE) are used to measure the similarity of longitude–latitude and Euclidean distance signatures. The experimental results from all 43 examined trees suggest that the longitude–latitude shape descriptor can determine tree crown shapes with different leaf traits, while Euclidean distance is only able to characterize the shape of evergreen trees. Furthermore, the longitude–latitude shape descriptor, unlike the Euclidean distance shape descriptor, is not sensitive to the number of LiDAR points from the tree crown. [ABSTRACT FROM AUTHOR]

Individual tree crown segmentation is important step for deriving various information for fine-scale analysis of ecological process. However, only several studies have applied tree crown segmentation in tropical forest ecosystems, especially in mixed peat swamp forests. In this study, hyperspectral data were used to detect changes in the biochemical and biophysical characteristics, which are important factors for tree crown segmentation. Principal Component Analysis method was performed to investigate its influence on crown segmentation. Visually Selected PCs, 160 PCs and 160 Spectral Bands image were used and two segmentation techniques; Watershed Transformation and Region Growing segmentation were applied on those images. The highest accuracy was achieved for the crown segmentation is using Region Growing segmentation, based on 1:1 measurement, D value and RMSE value. The results obtained from 160 PCs image using region growing algorithm shows better accuracy with D value of 0.2 (80% accuracy, 20% error) and RMSE of 9.9 m2. [ABSTRACT FROM AUTHOR]

Detecting individual-tree crowns provides a fundamental analysis unit bridging macro ecological patterns and micro physiological functions. This study adapted an anchor-free deep learning model, CenterNet, to detect individual crown locations and regions from dense 3 D terrestrial laser scans. A total of 1181 crowns from twelve plots were manually delineated as reference, among which eight plots were used for training the CenterNet, and another four independent plots for testing model accuracies characterized as the F1-score of location detection and Intersection over Union (IoU) of bounding box area. The maximum training F1-score and IoU were 0.881 and 0.670 over 40k training iterations, respectively. The result testing F1-score and IoU were 0.754 and 0.583, respectively. Five morphological factors were quantified to investigate the causes of accuracy variation among different plots and species, including crown area, tree height, full-width-at-half-maximum, nearest neighbor crown distance, and overlapping ratio of neighboring crowns. Results show that tree height was most important trait for crown detection. A taller, larger, smoother, less crowded, and less overlapped tree was found easier to detect. Among six species, red pine, Scots pine, and silver birch were successfully detected, and Norway spruce, lodgepole pine, and trembling aspen were more difficult to detect. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems (UAS) have emerged as a platform capable of providing valuable data on vegetation structure, health, and productivity. The platform provides maximum temporal flexibility in data collection and customizability of spatial footprints which is important for mapping and monitoring vegetation conditions especially for high spectrally and structurally heterogeneous landscapes. In this letter, we investigate the potential improvement in structural information when using sensor payloads that include infrared bands (730–810 nm) in addition to the typical visible bands of many UAS sensor payloads. Specifically, we use derived point clouds and imagery collected from UAS-mounted sensors at a test site in Louisville, Kentucky, USA to assess whether multispectral sensors improve estimates of vegetation structure for plant greenness (leaf-on) and senescence (leaf-off) periods. Results indicate that including detailed multispectral reflectance information beyond the visible portion of the electromagnetic spectrum aids in estimating structural characteristics of woody vegetation, regardless of season. While both leaf-on and leaf-off periods result in reasonable crown height estimates, leaf-on conditions also improve radial estimates, likely due to continuous canopy surfaces. These findings advance research for remote sensing analyses assessing structural composition in heterogeneous landscapes where varying levels of vegetation structure have implications on land use and land function. [ABSTRACT FROM AUTHOR]

The demand for tree height derivation is increasing year by year, especially for large plantation and forest area. The conventional method needs a long time to complete tree measurement for large forest area, especially when using a pole, measuring tape, rangefinder, clinometer, and tree climbing. This study aims to evaluate the height of oil palm tree based on crown diameter by using a multi-rotor Unmanned Aerial Vehicle (UAV). Digital Elevation Model (DEM) and orthophoto were generated by using Agisoft software, while oil palm tree crown diameter was delineated by using seed generation with Quantum Geographic Information System (QGIS) and Seeded Region Growing (SRG) segmentation methods in the System for Automated Geoscientific Analysis (SAGA). The study validates the results between the actual tree height and tree height estimated from UAV. The results showed that the orthophoto was successfully generated with a Ground Sampling Distance (GSD) of 2.95 cm and 129 tree crowns were successfully analyzed. The accuracy of the tree height as compared to the actual measurement was 57.7 cm. In conclusion, UAV images are capable of determining the tree height after going through the correct procedure to help foresters in their daily task. [ABSTRACT FROM AUTHOR]

Crop yield forecasting in a region has become an important research area due to global warming and related climate changes. Although this can be performed by available statistical information, obtaining recent and up to date data to extract reliable statistical information is not easy. Very high resolution satellite images can be used for this purpose. However, manually processing these images acquired from large regions is neither feasible nor reliable. Therefore, automated methods are needed for this purpose. In this study, we propose a novel method to help forecasting the crop yield in an orchard. The number of trees in an orchard with the size and type of each tree crown gives an approximate crop that can be harvested. Therefore, we focus on both tree crown detection and delineation for this purpose. The proposed method for tree crown detection is based on probabilistic voting. For tree crown delineation, we propose a watershed segmentation based ellipse fitting method. We tested the proposed method on 17 satellite images containing 13,476 trees. We compared the method with the classical local maxima/minima filtering and a recent method in literature using three more test images. These tests indicate the strengths and weaknesses of the proposed method. [ABSTRACT FROM AUTHOR]

In the present study, we propose an identification method for tree crown areas from imagery captured by a near-infrared (NIR) camera mounted on an unmanned aerial vehicle (UAV) platform over an undulating area planted with eucalyptus in Guangdong Province, China. The method extracts the eucalyptus crown areas by applying mathematical morphology, unsupervised segmentation based onJ-value segmentation, local spatial statistics, and Iterative Self-Organising Data Analysis Technique Algorithm. Two morphology filters and four segmentation scales were compared in densely sunlit, sparsely sunlit, densely shaded, and sparsely shaded plantation plots. Analyses of variance (ANOVAs) revealed that the identification of crowns is more accurate in sparse plantation areas than in dense plantation areas, and more accurate in sunlit plantation areas than in shaded plantation areas. Although there is not an optimal morphological filtering operation for identifying crown areas, the combination of opening operation by the kernel size of 9 × 9 pixels and segmentation by the seed area sized 65 × 65 pixels achieved the best performance with overall accuracies (OAs) of 91%, 93%, 89%, and 91% in densely sunlit, sparsely sunlit, densely shaded, and sparsely shaded plots, respectively. Based on these results, our method could produce an acceptable tree crown area identification technique for eucalyptus trees. [ABSTRACT FROM PUBLISHER]

Light detection and ranging (LiDAR) is a promising technology for extracting forest biophysical parameters, including tree height, total volume, biomass, etc. However, tree crown width estimation at the individual level is challenging due to overlapping crown surfaces in urban forests. The objective of this study, therefore, is to develop an approach for estimating tree crown width at the individual tree level using airborne LiDAR data in an urban area. To reach this objective, we constructed a general geometric model for each tree, centering at the tree position on the ground to fit the LiDAR-derived crown surface and then predicted crown width using tree height and shape-related parameters as independent variables and the ground measurement of all the crown widths identified by LiDAR data as dependent variables. Analyses of results suggest that tree crown widths in a mixed forest can be successfully extracted through model-based linear regression analysis, which can explain 88.5 % of variance associated with crown width for all 135 trees. Moreover, the best model and the algorithm identified in this research can be applied to forests of similar composition and a satisfactory accuracy without any ground measurement can be expected. [ABSTRACT FROM PUBLISHER]

A new algorithm for tree crown segmentation from airborne laser scanning data was validated at a test site in southern Sweden (lat. 58° N, long. 13° E). The tree crown segmentation algorithm used a correlation surface created by fitting a geometric tree crown model and was also controlled using an a priori probability function. If the model fit alone was used, 69% of the field-measured trees were detected but when a priori information was used, the proportion of detected trees increased to 75%. The proportion of detected trees represented 95% of the total stem volume for all field measured living trees. The tree crown segments, with zero, one, or several trees, were used as input to an imputation algorithm for prediction of forest variables, which yielded relative root mean square errors of 8.9% for stem volume, 7.2% for basal area, 3.8% for mean tree height, 6.3% for mean stem diameter, and 15% for stem density, after aggregation to plot level for cross-validation. Thus, automatic tree crown delineation using the segmentation algorithm could be used for imputation of tree stems to obtain high accuracy predictions of several forest variables. [ABSTRACT FROM AUTHOR]

Olgan larch is a traditional construction material used for the renovation of historical timber-frame buildings in China. However, acquiring the necessary large-sized larch trees from old-growth forests has become a challenge in China because of the rare and inaccessible distribution of these trees. In recent years, remote sensing imagery has provided a more effective alternative for delineating tree crowns automatically with high accuracy. In this study, an object-based method for delineating old-growth larch tree crowns using Geoeye-1 imagery is developed. Tree crown delineation results are tested and evaluated by field data. In addition, the correlation between delineated tree-crown and basal areas are quantitatively validated to ensure that the developed method can be applied for estimating the distribution of old-growth larch trees. Results demonstrate that the developed object-based larch tree-crown delineation method is reliable, thus providing a new technique for detecting old-growth larch tree resources in Northeastern China. [ABSTRACT FROM AUTHOR]

We tested an automated multi-scale approach for detecting individual trees and estimating tree crown geometry using high spatial resolution satellite imagery. Individual tree crowns are identified as local extrema points in the Laplacian-of-Gaussian scale-space pyramid that is constructed based on linear scale-space theory. The approach simultaneously detects tree crown centres and estimates tree crown sizes (radiuses). We evaluated our method using two 0.6-m resolution QuickBird images of a forest site that underwent a large shift in tree density between image captures due to drought-associated mortality. The automated multi-scale approach produced tree count estimates with an accuracy of 54% and 73% corresponding to the dense and sparse forests, respectively. Estimated crown diameters were linearly correlated with field-measured crown diameters (r= 0.73–0.86). Tree count accuracies and size estimates were comparable with alternative methods. Future use of the presented approach is merited based on the results of our study, but requires further investigation in a broader range of forest types. [ABSTRACT FROM AUTHOR]

In this article, six individual tree crown (ITC) detection/delineation algorithms are evaluated, using an image data set containing six diverse forest types at different geographical locations in three European countries. The algorithms use fundamentally different techniques, including local maxima detection, valley following (VF), region-growing (RG), template matching (TM), scale-space (SS) theory and techniques based on stochastic frameworks. The structurally complexity of the forests in the aerial images used ranges from a homogeneous plantation and an area with isolated tree crowns to an extremely dense deciduous forest type. None of the algorithms alone could successfully analyse all different cases. The study shows that it is important to partition the imagery into homogeneous forest stands prior to the application of individual tree detection algorithms. It furthermore suggests a need for a common, publicly available suite of test images and common test procedures for evaluation of individual tree detection/delineation algorithms. Finally, it highlights that, for complex forest types, monoscopic images are insufficient for consistent tree crown detection, even by human interpreters. [ABSTRACT FROM AUTHOR]

Efficient forest management demands detailed, timely information. As high spatial resolution remotely sensed imagery becomes more available, there is a great potential for conducting high accuracy forest inventory and analysis automatically and cost-efficiently. Recent research aimed at providing tree-based forest inventory measurements has generated numerous algorithms for automatic individual tree-crown detection and delineation. This article reviews this research with a focus on algorithms applied to passive remote-sensing imagery. The article categorizes and evaluates methods for automatic tree-crown detection and delineation. It considers the types of imagery and the characteristics of the study areas these algorithms are applied to and evaluates the influence of these factors on the methods. The article also reviews and evaluates quantitative accuracy assessment methods for tree-crown delineation and detection. Finally, the article summarizes the commonalities of current algorithms, and the new development that can be expected in the future. [ABSTRACT FROM PUBLISHER]

This article compares the performance of three algorithms representative of published methods for tree crown detection and delineation from high spatial resolution imagery, and demonstrates a standardized accuracy assessment framework. The algorithms - watershed segmentation, region growing and valley-following - were tested on softwood and hardwood sites using Emerge natural colour vertical aerial imagery with 60 cm ground sampled distance and QuickBird panchromatic imagery with an 11˚ look angle. The evaluation considered both plot-level and individual tree crown detection and delineation results. The study shows that while all three methods reasonably delineate crowns in the softwood stand on the Emerge image, region growing provided the highest accuracies, with producer's and user's accuracy for tree detection reaching 70% and root mean square error for crown diameter estimation of 15%. Crown detection accuracies were lower on the QuickBird image. No algorithm proved accurate for the hardwood stand on either image set (both producer's and user's accuracies < 30%). [ABSTRACT FROM AUTHOR]

Leaves are the primary interface where energy, water and carbon exchanges occur between the forest ecosystems and the atmosphere. Leaf area index (LAI) is a measure of the amount of leaf area in a stand, and the tree crown size characterizes how leaves are clumped in the canopy. Both LAI and tree crown size are of essential ecological and management value. There is a lot of interest in extracting both canopy structural parameters from remote sensing. The LAI is generally estimated with spectral information from remotely sensed images at relatively coarse spatial resolution. There has been much less success in estimating tree crown size with remote sensing. The recent availability of abundant high spatial resolution imagery from space offers new potential for extracting LAI and tree crown size, particularly in the spatial domain. This study found that the spatial information in Ikonos imagery is highly valuable in estimating both tree crown size and LAI. When the conifer- and hardwood-dominated stands are pooled, tree crown sizes of conifer stands relate best to the ratio of image variance at 2×2 m spatial resolution to that at 3×3 m spatial resolution, while LAI relates best to image variance at 4×4 m spatial resolution. When the conifer- and hardwood-dominated stands are separated, image spatial information estimates tree crown size much better for conifer-dominated stands than for the hardwood-dominated stands, while the relationship between image spatial information and LAI is strengthened after the two types of stands are combined. Tree crown size is more sensitive to image spatial resolution than LAI. Image variance is more useful in estimating LAI than normalized difference vegetation index (NDVI) and simple ratio vegetation index (SRVI). Combining both spatial and spectral information provides some improvement in estimating LAI compared with using spatial information alone. Therefore, future efforts to estimate canopy structure with high resolution imagery should also use image spatial information. [ABSTRACT FROM AUTHOR]

Tree crown size is a critical biophysical parameter that influences carbon, water and energy exchanges between forest ecosystems and the atmosphere. This study explores the potential of using spatial information of high resolution optical imagery in estimating mean tree crown diameter on a stand basis with an Ikonos image in the Blackwood Division of Duke Forest and its surrounding areas. The theory is based on the disc scene model that the ratio of image variances at two spatial resolutions is determined by the scene structure only. The mean tree crown diameter of a stand on the ground was estimated with a circular sampling plot made in the middle of the stand. The stands were then delineated in the panchromatic band of the Ikonos image. The relationship between mean tree crown diameter with image variance at a single spatial resolution, the ratio of image variances at two spatial resolutions, and the difference of image variances at two spatial resolutions were studied for conifer and hardwood stands, respectively. It was found that the ratio of image variances at 2 m and 3 m spatial resolutions best estimate conifer tree crown diameter (R 2 = 0.7282). Though the image variance at a single resolution and the difference of image variances at two spatial resolutions are also significantly correlated to conifer tree crown diameter, the R 2 is lower. Due to the continuity in canopy structure, the approach does not work well for hardwood stands. [ABSTRACT FROM AUTHOR]

Although open forests represent approximately 30% of the world's forest resources, there is a clear lack of reliable inventory data to allow sustainable management of this valuable resource from semi-arid areas. This paper demonstrates that the low ground cover of open forest offers a unique opportunity for deriving single tree attributes from high-resolution satellite imagery, allowing reliable biomass estimation. More particularly, this study investigates the relationship between field-measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan-sharpened Quickbird imagery with a 0.61 m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south-western Turkey. First tree shadows and crowns were identified and delineated as individual polygons. Both visual delineation and computer-aided automatic classification methods were tested. After delineation, stem volume as a function of these image-measured attributes was modelled using linear regression. The statistical analyses indicated that stem volume was correlated with both shadow area and crown area. The best model for stem volume using shadow area resulted in an adjusted R 2 = 0.67, with a root mean square error (RMSE) of 12.5%. The model for stem volume using crown area resulted in an adjusted R 2 = 0.51, with a RMSE of 15.2%. The results showed that pan-sharpened Quickbird imagery is suitable for estimating stem volume and may be useful in reducing the time required for obtaining inventory data in open Crimean juniper forests and other similar open forests. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a superior machine learning methodology with great results in classification of remotely sensed data sets. Determination of optimal parameters applied in SVM classification technique is still vague to some scientists. This study was aimed to detect tree crowns on UltraCam-D (UCD), very high spatial resolution aerial imagery in Zagros woodlands by SVM optimised by Taguchi method. A 500 × 600 m2plot covered with Persian oak (Quercus brantiivar.persica) coppice trees was selected in Zagros woodlands, Iran. The UCD aerial imagery of the plot (0.06-m spatial resolution) was obtained to extract crowns of the trees in this study. The SVM classification technique parameters were optimised by Taguchi method, and thereafter, the imagery was classified with optimal parameters. The results showed that Taguchi method is a robust approach to optimise the combination of parameters of SVM classification technique. It was also concluded that the technique could detect the tree crowns with a KHAT coefficient of 0.961, which showed a great agreement with the observed samples, and overall accuracy of 97.7% that showed the accuracy of the final map. Finally, the authors suggest applying this method to optimise the parameters of classifiers like SVM classification technique. [ABSTRACT FROM PUBLISHER]

The cultivation of rubber trees in the humid tropical Amazon region is only possible with the use of crown grafts with species ofHeveasp. that are resistant to South American leaf blight – (SALB) [Microcyclus ulei(P. Henn.) v. Arx.]. However, it has been observed that plants with grafted crowns are not efficient in remobilizing magnesium from the crown to the latex. The experimental design was a randomized block, with three replicates. Five crown clones were studied (CPAA C 01, CPAA C 06, CPAA C 18, CPAA C 45, and CBA 2) with two magnesium (Mg) levels [source: magnesium sulfate(MgSO4): low-Mg (150 g plant−1of MgSO4× 7H2O] and high-Mg (450 g plant−1of MgSO4× 7H2O). The panel clone used for tapping was CNS AM 7905 (Hevea brasiliensis). The results showed that the Mg levels influenced the crown clones differently in relation to yield, efficiency index and leaf and soil Mg concentration. Clone CPAA C 01 with a high-Mg produced over 2.0 t ha−1of dry rubber. Clones CPAA C 01 and CPAA C 45 were the most efficient, while clone CPAA C 06 showed moderate efficiency and clones CPAA C 18 and CBA 2 were inefficient in using Mg. The Mg concentration in the latex was not influenced by the two Mg levels, with the only differences being between the crown clones. [ABSTRACT FROM PUBLISHER]

The aim of this study was to evaluate the use of high-resolution airborne laser scanner (ALS) data to detect and measure individual trees. We developed and tested a new mixed pixel- and region-based algorithm (using Definiens Developer 7.0) for locating individual tree positions and estimating their total heights. We computed a canopy height model (CHM) of pixel size 0.25 m from dense first-pulse point data (8 pulses m−2) acquired with a small-footprint discrete-return lidar sensor. We validated the results of individual tree segmentation with accurate field measurements made in 37 plots of Monterey pine (Pinus radiataD. Don) distributed over an area of 36 km2. Fieldwork consisted of labelling all of the trees in each plot and measuring their height and position, for posterior integration of the data from both sources (field and lidar). The proposed algorithm correctly detected and linked 59.8% of the trees in the 37 sample plots. We also manually located the trees by using FUSION software to visualize the raw lidar data cloud. However, because the latter method is extremely time-consuming, we only considered 10 randomly selected plots. Manual location correctly detected and linked 71.9% of the trees (in this subsample the algorithm correctly detected and measured 63.5% of the trees). TheR2values for the linear model relating field- and lidar-measured heights of the linked trees located manually and with the automatic location algorithm were 0.90 and 0.88, respectively. [ABSTRACT FROM PUBLISHER]

Although simple geometrical shapes are commonly used to describe tree crowns, computational geometry enables calculation of the individual crown properties directly from airborne lidar point clouds. Our objective was to calculate crown volumes (CVs) using this technique and validate the results by comparing them with field-measured values and modelled ellipsoidal crowns. The CVs of standing trees were obtained by measuring the crown radii at different heights, integrating the obtained crown profiles as solids of revolution, and finally averaging the volumes obtained from the four separate profiles. With the lidar data, the CVs were extracted using 3D alpha shape and 3D convex hull techniques. Crown base heights (CBHs) were also estimated from the lidar data and used to exclude echoes from the understory, which was also done using field-based CBHs to exclude this error source. The results show that the field-measured CVs had a high correlation with lidar-based estimates (bestR2 = 0.83), but the lidar-based estimates were generally smaller than the field values. The best correspondence (root mean square difference (RMSD) = 45.0%, average difference = –24.7%) was obtained using the convex hull of the point data and field-measured CBH. The CBHs were consistently overestimated (RMSD = 37.3%; average difference = –20.0%), especially in spruces with long crowns. Thus using lidar-based CBH also increased the inaccuracy of the CV estimates. While the underestimation of CV is mainly explained by the inadequate number of echoes from the lower regions of the crowns, the CVs obtained from the lidar were better than those obtained with ellipsoids fitted by using general models for crown dimensions. The utility of the estimated CVs in the prediction of stem diameter is also demonstrated. [ABSTRACT FROM PUBLISHER]

Automated methods for capturing geometric and spectral properties of individual tree crowns are becoming increasingly viable options for use in natural resource planning. Crown isolation techniques are needed that are capable of adapting to the changing availability and resolutions of remotely sensed data. Data integration, or the fusion of two distinct data entities, offers a methodological framework that can compensate for the shortcomings of individual datasets while enhancing their desirable features. This study sought to develop a method of data integration for high-resolution optical images of varying spatial and temporal resolutions to improve the automatic detection and delineation of individual tree crowns. A marker-controlled watershed segmentation (MCWS) algorithm was developed for a 30-cm-per-pixel-side airborne colour infrared (CIR) digital image of a leaf-on apple (Malus spp.) orchard. Three methods of obtaining the markers needed for the MCWS algorithm were tested: (1) manual marker selection, (2) template/correlation selection using the 30-cm CIR image, and (3) template/correlation selection using a 15-cm-per-pixel-side true (TRU) colour leaf-off aerial image. The effectiveness of integrating marker data of different temporal and spatial resolutions with the segmentation process of the CIR image scene was tested. A comparison of crown isolation results using markers derived within the segmented 30-cm CIR digital imagery with results from markers derived from the 15-cm TRU image scene indicated greater accuracies to detect and isolate tree crowns with data integration. [ABSTRACT FROM AUTHOR]

The goal of this study was to develop a decision rule heuristic that would incorporate theories of forest stand dynamics and crown competition into an automatic crown detection and crown size search algorithm. Specifically, we sought to develop new multi-dimensional template matching methods fused with knowledge-based decision rules that model spatial considerations for crown competition and develop multi-scale assessment criteria for appraisal of crown detection and crown size at the stand, local neighbourhood and individual crown levels. The decision rule approach to crown detection and crown size was tested on a mature mixed coniferous and deciduous forest typical of southern New England, USA. Multi-dimensional template matching was applied to a high resolution (30 cm per pixel side) colour infrared image of the study site. The decision rule heuristic developed for this study effectively reduced 2626 potential crown detections to 568 crowns, producing a 91% rate of crown detection when compared with the 516 field-measured crowns. The automatically derived crown size class distribution was shown to be statistically similar to the distribution of field crown size classes using the Kolomogorov-Smirnov statistic. Finally, a fuzzification of classification assignments to crown classes either one above or below actual crown size class resulted in an 80% match between individual field crowns and remotely sensed crowns within a 6 m spatial lag. [ABSTRACT FROM AUTHOR]

Timely and reliable mapping of individual date palm trees is essential for their monitoring, health and risk assessment, pest control, and sustainable management of the date palm industry. This study presents an instance segmentation framework for large-scale detection and mapping of date palm trees using unmanned aerial vehicle (UAV)-based images. First, a data conversion framework is created to convert UAV image tiles and ground-truth vector data into annotation format of Common Objects in Context. Second, this study examines the efficacy of various instance segmentation models, namely, mask region convolutional neural network (Mask R-CNN), Mask Scoring R-CNN, You Only Look At CoefficientTs, Point-based Rendering, Segmenting Objects by Locations (SOLO), and SOLOv2) with varying residual learning networks (ResNets) in detecting and delineating individual date palm trees. Furthermore, the performance of two variants of Swin Transformer networks with a feature pyramid network (FPN) (Swin-small-FPN and Swin-tiny-FPN) as Mask RCNN network backbones was also evaluated. Third, we assess the generalizability of the evaluated instance segmentation models and backbones on different testing datasets with varying spatial resolutions. Results show that Mask R-CNN models based on Swin Transformers backbones outperform those with ResNets in the detection and segmentation of date palm trees with mAP50 of 92% and 91% and F-measures of 94% and 93%. Moreover, the Mask scoring R-CNN-based ResNet-50 and Mask R-CNN with a Swin-small-FPN backbone outperform the evaluated models and demonstrate great generalizability in different datasets with diverse spatial resolutions. The proposed instance segmentation framework provides an efficient tool for date palm tree mapping from multi-scale UAV-based images and is valuable and suitable for individual tree crown delineations and other earth-related applications. [ABSTRACT FROM AUTHOR]

Trees influence daylight availability inside and outside buildings by attenuating, scattering, and transmitting light. They resemble a complex fenestration around buildings that change in form, materiality, and permeability based on species of trees, seasonal variations, and environmental or human interventions. The current practice of modeling trees in daylight simulations ignores this complexity and models tree crowns as cones, spheres, or cylinders with assumed reflectance or transmittance value. In this paper, the authors propose an open-source, low-cost method using photography and image processing to measure the on-site transmittance of a tree crown described as gap percentage. Gap percentage is used to generate a 3D primitive crown model that mimics the distribution of leaves and gaps. When used in daylight simulation platforms such as Radiance, the proposed model predicts vertical light transmittance and creates shadow patterns similar to the measured tree crown. The 3D crown model also predicts luminance and illuminance-based daylight metrics similar to a detailed morphological tree model. An open-source program is created and described to generate trees from measured gap percentage data based on this research. [ABSTRACT FROM AUTHOR]

Individual tree detection and delineation (ITDD) is an important subject in forestry and urban forestry. This study represents the first research to propose the concept of crown resolution to comprehensively evaluate the co-effect of image resolution and crown size on deep learning. Six images with different resolutions were derived from a DJI Unmanned Aerial Vehicle (UAV), and 1344 manually delineated Chinese fir (Cunninghamia lanceolata (Lamb) Hook) tree crowns were used for six training and validation mask region-based convolutional neural network (Mask R-CNN) models, while additional 476 delineated tree crowns were reserved for testing. The overall detection accuracy, the influence of different crown sizes, and crown resolutions were calculated to evaluate model performance accuracy with different image resolutions for ITDD. Results show that the highest accuracy was achieved when the crown resolution was between 800 and 12800 pixels/tree. The accuracy of ITDD was impacted by crown resolution, and it was unable to effectively identify Chinese fir when the crown resolution was less than 25 pixels/tree or higher than 12800 pixels/tree. The study highlights crown resolution as a critical factor affecting ITDD and suggests selecting the appropriate resolution based on the target detected crown size. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS's ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted. Novel LiDAR-based allometric models were tested using new LiDAR acquisitions; LiDAR predictions were based on tree crown size and immediate neighbourhood metrics; Model inputs were derived from drone and crewed aircraft LiDAR acquisitions; Model predictions were most accurate when applied to similar stand structures as the original model; Normalized root means square error for stem volume and diameter in pine stands with no thinning was <15%; Stem size estimates in thinned stands were consistently overestimated. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) is increasingly used to estimate various forest characteristics. Technological improvements in unmanned aerial vehicles (UAVs) and drone laser scanning (DLS) sensors have permitted the acquisition of high pulse density datasets. There is an assumption that higher pulse densities yield greater accuracies in estimating forest characteristics. In this study, we investigated the effect of pulse density (.25,.5, 1, 5, 10, 50, 100 and 300 pulses m−2) on the ability to delineate individual tree crowns (ITCs) and estimate ITC height and crown horizontal diameter, in addition to plot-level leaf area index (LAI). The current study took place in an experimentally varied Pinus taeda L. forest, which included three stem densities: (i) 618; (ii) 1236; and (iii) 1853 trees per hectare (TPH). ITCs were classified directly from the DLS point cloud for each of the pulse densities. The correct delineation of ITCs relative to field tree-coordinates was relatively consistent (±5%) for pulse densities of 5 to 300 pulses m−2. ITC delineation accuracy decreased with lower pulse densities. Planting stem density did impact ITC delineation accuracy. Higher pulse densities, plots with 618 TPH correctly classified ~88% of ITCs, and plots with the 1853 TPH correctly classified ~50% of ITCs. Estimates of tree height were largely unaffected by changes in tree density. Root mean square error (RMSE) for tree height varied from.5 to 2.5 m at pulse densities of 300 to.25 pulses m−2, respectively. Estimates of crown horizontal diameter varied with regard to both pulse and stem density from 1.2 (300 ppm−2 and 1853 TPH) to 4.2 m (.25 ppm−2 and 618 TPH). RMSE varied among stem densities from.6 to 1.2 m as pulse density decreased. There was significant difference in ITC delineation accuracy, particularly when considering stem density, and the estimates of tree height and crown horizontal diameter among the DLS pulse densities used. The accuracy of predicted LAI was largely unaffected by changes in pulse density, when pulse density was above.5 pulses m−2. There was little or no difference in estimates of LAI at these pulse densities. Our results suggest that low-density DLS data may be capable of estimating plot-level forest metrics reliably in some situations, however once the analysis scale is reduced to the individual-tree-level, the influence of pulse density is more substantial. The results here provide guidance to forest managers who must balance metric estimation accuracy and price when planning new ALS or DLS acquisitions. [ABSTRACT FROM AUTHOR]

In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars. [ABSTRACT FROM AUTHOR]

Using the object-based image analysis (OBIA) technique, this study examined structural characteristics, quantified carbon stocks, and identified tree species in the tropical forests of southwestern Nigeria. The Structure from Motion (SfM) technique was used to produce orthomosaics with a ground sampling distance of 10 m from orthophotos recorded during a survey and optimized for forest assessment with the near-infrared band. In addition to tree species variables, GPS coordinates, tree diameter, and tree crown were all employed to estimate carbon stock in the study area. Carbon stock estimates of 450 tons per hectare were estimated , with a tree-specific range of 65 kg per tree to 8,488 kg per tree. There was a correlation between tree canopies and diameters, but no conclusive relationships between tree species distribution and position in their natural formations. OBIA significantly enhanced the current carbon inventory and provided a spatial characterization of species distribution in the research area. [ABSTRACT FROM AUTHOR]

The need for objective methods to determine tree canopy cover (CC) across large numbers of stands has led to the development of techniques that utilise airborne laser scanning (ALS) data, which provides a reproducible and detailed representation of canopy geometry. We developed a method for determining CC area and evaluated the estimation accuracy for stands of different sizes, structure and composition. This method is based on tree crown geometries obtained from ALS data, and verified with field measurements using data for 3245 stands of the Białowieża Forest District in Poland. In relatively large stands (3–5 ha), the theoretical error of prediction decreased from 0.13 to 0.10 with increasing stand area. In stands larger than 10 ha, however, the error in estimating CC was less than ±0.10. Although every estimation method comes with its own assumptions and errors, the presented method eliminates the subjectivity in observer bias prevalent in traditional field-based ocular assessments and provides a more transparent and methodologically uniform approach for estimating CC in forests. [ABSTRACT FROM AUTHOR]

Individual tree crown segmentation from airborne laser scanning (ALS) data often fails to detect all trees depending on the forest structure. This paper presents a new method to produce tree lists consistent with unbiased estimates at area level. First, a tree list with height and diameter at breast height (DBH) was estimated from individual tree crown segmentation. Second, estimates at plot level were used to create a target distribution by using a k-nearest neighbour (k-NN) approach. The number of trees per field plot was rescaled with the estimated stem volume for the field plot. Finally, the initial tree list was calibrated using the estimated target distribution. The calibration improved the estimates of the distributions of tree height (error index (EI) from 109 to 96) and DBH (EI from 99 to 93) in the tree list. Thus, the new method could be used to estimate tree lists that are consistent with unbiased estimates from regression models at field plot level. [ABSTRACT FROM AUTHOR]

We investigated conifer plantation management in Japan using high-resolution airborne data based on an individual tree crown (ITC) approach. This study is the first to apply this technique to Japanese forests. We found that forest resources can be measured at the level of a single tree. We also produced a tree-crown map for a test site with Chamaecyparis obtusa, Pinus densiflora, Larix kaempferi, Cryptomeria japonica, other conifers, and broadleaved trees, with a classification accuracy of 78%. Forest-stand polygons with tree-cover types were generated from this map, a tree-density map, and a crown-occupied-area map. Forest information for the stand polygons was extracted automatically and compared with detailed field-survey data. The error between our ITC estimates and the field-survey data ranged from 0.3 to 30.2%, depending on tree crown size, density, and other factors. Errors were highest for high-density stands with mixed compositions and tree crown diameters ≤5.0 m. However, the error for stands with crown diameters ≥6.2 m was 11.6% or less. Therefore, this technique is best suited to pure Japanese conifer plantations without multiple layers or high-density stands. [ABSTRACT FROM AUTHOR]

Forest data acquisition, which is of crucial importance for modeling global biogeochemical cycles and climate, makes a contribution to building the ecological Digital Earth (DE). Due to the complex calculations and large volumes of data associated with high-resolution images of large areas, accurate and effective extraction of individual tree crowns remains challenging. In this study, two GeoEye-1 panchromatic images of Beihai and Ningbo in China with areas of 5 and 25 km2, respectively, were used as experimental data to establish a novel method for the automatic extraction of individual tree crowns based on a self-adaptive mutual information (SMI) algorithm and tile computing technology (SMI-TCT). To evaluate the performance of the algorithm, four commonly used algorithms were also applied to extract the individual tree crowns. The overall accuracy of the proposed method for the two experimental areas was superior to that of the four other algorithms, with maximum extraction accuracies of 85.7% and 63.8%. Moreover, the results also indicated that the novel method was suitable for individual tree crowns extraction in sizeable areas because of the multithread parallel computing technology. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) measurement technology is well-suited for individual-tree-based urban forest inventory because it can capture a dense three-dimensional (3D) point cloud from vertical structures of roadside trees in a rapid and cost-effective way. Street tree segmentation from MLS data is a preliminary and essential step for further extraction and analysis of tree attributes and is also a challenging problem because of the complex street environment. The accuracy of current methods is limited by information loss introduced by voxelization and artificially designed detection and segmentation rules based on prior knowledge that more or less lack ability to distinguish between street trees and other urban objects. To address the above issues, a two-step strategy for street tree segmentation method from MLS data was proposed, which worked on points directly instead of a set of voxels generated by spatial partition and made use of tree crown and trunk detectors trained by supervised learning algorithm. First, 16 local statistical features, including width, depth, elevation, dimensionality, and density features, were extracted from the sphere domain of each point using the grid index. Then these features were fused and tree crown and trunk detectors were trained from labelled MLS data of street through Discrete AdaBoost algorithm. Second, based on frame projection of point-wise prediction results of tree crown and trunk detectors, individual trees were located and segmented by exploiting non-connectivity between adjacent trees and adjacency within single trees. Experimental results have demonstrated that the proposed method can obtain point-wise street tree segmentation from MLS data with high accuracy tree crown and trunk detection without tedious detection or segmentation rules. The error rates of crown detector on test set were less than 0.3% accompanied by over 99.33% recall rates and over 98.59% precision rates and the error rates of trunk detector on test set were less than 0.06% accompanied by over 92.44% recall rates and over 94.14% precision rates. [ABSTRACT FROM AUTHOR]

Tree crown can be described by various crown dimension variables, including crown width (CW), an important predictor variable in forest growth-and-yield models. Because CW is quantified as the arithmetic mean of north–south and east–west CW measurements (crown components), issues of additivity and inherent correlation between crown components exist. Using two versatile estimation algorithms (non-linear seemingly unrelated regression − NSUR – and non-linear error-in-variable models − NEIVM), we employed three methods of constructing simultaneous model systems (non-linear summation − NSE, one-step proportional weighting system − OPWS, and two-step proportional weighting system − TPWS) to effectively address those issues. The performance of the three methods was evaluated using CW measurements of moso bamboo (Phyllostachys edulis) in China. Results showed that the OPWS outperformed the NSE and the TPWS for each algorithm. NEIVM-OPWS was more accurate than TPWS, and NEIVM outperformed NSUR. Diameter at breast height, total height and mean diameter at breast height (a variable describing stand density and competition) significantly contributed to CW and its component equations. Each CW model system included additivity of the crown component equations and accounted for the inherent correlations between equations. Overall, NEIVM-OPWS provided a closer estimate of CW to the measured CW than other model systems and methods employed. An effective CW model system will help understand light capture by the bamboo canopy and contribute to the effective management of bamboo forests in China and elsewhere. [ABSTRACT FROM AUTHOR]

Much effort has been spent on the automatic detection and delineation of individual trees from high spatial resolution images. However, delineation errors may lead to an inaccurate crown size when compared with ground measurements. Thus, it is problematic to use delineated crowns to derive information on tree variables, e.g. crown diameter, tree height, diameter at breast height (DBH), stand volume, stem volume or stand competition index. In this study, we investigated two indicators – the mean digital number (MDN) within each delineated crown and the difference between MDNs (DMDNs) for 0.6 m buffer zones outside and inside the boundary of each delineated crown – to separate poorly delineated crowns from well-delineated ones. We modelled the relationships between delineated crowns and field-based crown size, between delineated crowns and tree height, and between delineated crowns and DBH observations in a Norway spruce (Picea abies) stand, separately considering models based on all delineated results and crowns identified as being well delineated. Our results showed that the capability of the two indicators in separating poorly and well-delineated crowns varied under different thresholds. The results also indicated that models considering only well-delineated crowns were more robust and effective in estimating and predicting tree crown diameter, DBH and tree height than models that considered all delineated results. [ABSTRACT FROM AUTHOR]

Aboveground forest biomass and carbon estimation at landscape scale is crucial for implementation of REDD+ programmes. This study aims to upscale the forest carbon estimates using GeoEye-1 image and small footprint lidar data from small areas to a landscape level using RapidEye image. Species stratification was carried out based on the spectral separability curve of GeoEye-1 image, and comparison of mean intensity and mean plot height of the trees from lidar data. GeoEye-1 image and lidar data were segmented using region growing approach to delineate individual tree crowns; and the segmented crowns (CPA) of tree were further used to establish a relationship with field measured carbon and total trees' height. Carbon stock measured from field, individual tree crown (ITC) segmentation approach and area-based approach (ABA) was compared at plot level using one-way ANOVA and post hoc Tukey comparison test. ITC-based carbon estimates was used to establish a relationship with spectral reflectance of RapidEye image variables (NDVI, RedEdge NDVI, PC1, single band of RedEdge, and NIR) to upscale the carbon at landscape level. One-way ANOVA resulted in a highly significant difference (p-value < 0.005) between the mean plot height and lidar intensity to stratify Shorea robusta and Other species successfully. ITC carbon stock estimation models of two major tree species explained about 88% and 79% of the variances, respectively, at 95% confidence level. The ABA estimated carbon was highly correlated (R2 = 0.83, RMSE = 20.04) to field measured carbon with higher accuracy than the ITC estimated carbon. A weak relationship was observed between the carbon stock and the RapidEye image variables. However, upscaling of carbon estimates from ABA is likely to improve the relationship of the RapidEye variables rather than upscaling the carbon estimates from ITC approach. [ABSTRACT FROM AUTHOR]

The tree inventory in orchards is of great interest for orchard management and for government insurance plans. However, the conventional inventory is time-consuming and expensive. Here a remote sensing method is introduced for orchard inventory. Airborne LIDAR (light detection and ranging) data were employed to obtain tree topography, and multispectral images were used as a reference. LIDAR vector data were converted to raster data for tree crown delineating purpose and in order to be easily superimposed on multispectral data in the same database. A tree crown delineation model was developed using a tree height image derived from the difference between canopy and ground LIDAR altitudes. The number of trees was computed from the delineation model. Spatially separated trees were precisely counted by fine definition of their crowns. For larger trees, although they have irregular crown form, like multi-tops, holes in the centre or overlapped branches, the model developed in this study provided reliable results for crown delineation. [ABSTRACT FROM AUTHOR]