Individual Tree Crown Methods for 3D Data from Remote Sensing

Lindberg, Eva; Holmgren, Johan

doi:10.1007/s40725-017-0051-6

Cited by 72 publications

(59 citation statements)

References 70 publications

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“…This seemingly stands in contrast to a previous study reporting over-proportional contributions of large emergent trees to stand E t in old-growth tropical lowland forest [53] however, this divergence is likely due to the lack of considerable emergent trees within our study plots in previously-logged lowland tropical forest. In previous studies applying airborne remote sensing approaches, the detection of small-statured trees was also reported to be particularly low and difficult [54,55]. For tree identification, we used tree location maps in local Cartesian coordinates drawn in ground surveys.…”

Section: Discussionmentioning

confidence: 99%

Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

et al. 2020

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Tropical rainforests comprise complex 3D structures and encompass heterogeneous site conditions; their transpiration contributes to climate regulation. The objectives of our study were to test the relationship between tree water use and crown metrics and to predict spatial variability of canopy transpiration across sites. In a lowland rainforest of Sumatra, we measured tree water use with sap flux techniques and simultaneously assessed crown metrics with drone-based photogrammetry. We observed a close linear relationship between individual tree water use and crown surface area (R2 = 0.76, n = 42 trees). Uncertainties in predicting stand-level canopy transpiration were much lower using tree crown metrics than the more conventionally used stem diameter. 3D canopy segmentation analyses in combination with the tree crown–water use relationship predict substantial spatial heterogeneity in canopy transpiration. Among our eight study plots, there was a more than two-fold difference, with lower transpiration at riparian than at upland sites. In conclusion, we regard drone-based canopy segmentation and crown metrics to be very useful tools for the scaling of transpiration from tree- to stand-level. Our results indicate substantial spatial variation in crown packing and thus canopy transpiration of tropical rainforests.

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Section: Discussionmentioning

confidence: 99%

Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

et al. 2020

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“…Emerging point cloud segmentation methods have taken novel approaches (e.g., Multiclass Graph Cut [15]; Mean Shift [59]) that may or may not be better suited to delineate closed-canopy temperate forests with difficult deciduous crown architecture. However, superior performance of point cloud segmentation methods is likely to be largely limited by availability of LiDAR data with high measurement density [60]. An appeal of exploring CHM-based segmentation is the wide and growing availability of high-quality CHM data, which can make these techniques more broadly applicable than point cloud segmentation approaches.…”

Section: Differences Between Segmentation Methodsmentioning

confidence: 99%

Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest

et al. 2020

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The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factors varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands.

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“…The usefulness and importance of accurate ITC detection and delineation has promoted the development of different methods and approaches to better characterize the forest structure and individual tree crowns and attributes [11,12]. Thus, our paper presents a new approach that substantially improves the overall accuracy in the detection of tree crowns by integrating the information on the vertical structure of trees-derived from LiDAR data-with existing ITC delineation strategies.…”

Section: Discussionmentioning

confidence: 99%

“…The utility of ITC delineation has promoted the development of various methods using airborne LiDAR data [6][7][8][9][10][11]. A comprehensive overview of LiDAR applications for ITC detection and delineation is contained in [12]. Most of these methods are based on raster-based approaches, utilising LiDAR-derived canopy height models (CHM) [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data

Jaafar

Woodhouse

Silva

et al. 2018

Forests

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Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying “problematic” segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe.

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Individual Tree Crown Methods for 3D Data from Remote Sensing

Cited by 72 publications

References 70 publications

Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest

Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest

Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data

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