Characterizing individual tree‐level snags using airborne lidar‐derived forest canopy gaps within closed‐canopy conifer forests

Abstract:Natural tree canopy gaps allow sunlight to penetrate to the forest floor, a major environmental component and resource for many tropical rain-forest species. We compare here how butterflies use sunny areas created by the natural gaps in canopies in comparison with adjacent closed-canopy areas. We chose butterflies as our focal organisms as they are taxonomically tractable and mobile, yet habitat sensitive. Previous studies have shown that butterfly diversity in tropical forests responds to varying degrees of canopy openness. Here we assess butterfly behavioural responses to gaps and equivalent sized closed-canopy patches. Butterfly occupancy time and behaviour were simultaneously observed 61 times in gaps and 61 times in equivalent sized closed-canopy patches across four sites in a tropical rain forest in northern Borneo. Out of the 20 most frequently recorded species, 12 were more frequently recorded or spent more time in gaps, four occurred more frequently in closed-canopy areas, and four showed no significant differences. Overall agonistic, basking, patrolling and resting were more common in gaps compared with the closed canopy. Many butterfly species have complex behavioural requirements for both gaps and closed canopies, with some species using these different areas for different behaviours. Each butterfly species had particular habitat requirements, and needed both canopy gaps and closed canopy areas for ecological and behavioural reasons, emphasizing the need for natural light heterogeneity within these systems.

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Interpretation of Forest Resources at the Individual Tree Level in Japanese Conifer Plantations Using Airborne LiDAR Data

Remote Sensing ◽

10.3390/rs8030188 ◽

2016 ◽

Vol 8 (3) ◽

pp. 188 ◽

Cited By ~ 1

Author(s):

Songqiu Deng ◽

Masato Katoh

Keyword(s):

Forest Resources ◽

Airborne Lidar ◽

Tree Level ◽

Lidar Data ◽

Individual Tree ◽

Conifer Plantations ◽

The Individual ◽

Airborne Lidar Data

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How Much Can We See from a UAV-Mounted Regular Camera? Remote Sensing-Based Estimation of Forest Attributes in South American Native Forests

Remote Sensing ◽

10.3390/rs13112151 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2151

Author(s):

Alejandro Miranda ◽

Germán Catalán ◽

Adison Altamirano ◽

Carlos Zamorano-Elgueta ◽

Manuel Cavieres ◽

...

Keyword(s):

Remote Sensing ◽

Mixed Models ◽

Forest Canopy ◽

Complex Structure ◽

Boosted Regression Trees ◽

Tree Level ◽

Old Growth ◽

Individual Tree ◽

Native Forests ◽

Old Growth Forests

Data collection from large areas of native forests poses a challenge. The present study aims at assessing the use of UAV for forest inventory on native forests in Southern Chile, and seeks to retrieve both stand and tree level attributes from forest canopy data. Data were collected from 14 plots (45 × 45 m) established at four locations representing unmanaged Chilean temperate forests: seven plots on secondary forests and seven plots on old-growth forests, including a total of 17 different native species. The imagery was captured using a fixed-wing airframe equipped with a regular RGB camera. We used the structure from motion and digital aerial photogrammetry techniques for data processing and combined machine learning methods based on boosted regression trees and mixed models. In total, 2136 trees were measured on the ground, from which 858 trees were visualized from the UAV imagery of the canopy, ranging from 26% to 88% of the measured trees in the field (mean = 45.7%, SD = 17.3), which represented between 70.6% and 96% of the total basal area of the plots (mean = 80.28%, SD = 7.7). Individual-tree diameter models based on remote sensing data were constructed with R2 = 0.85 and R2 = 0.66 based on BRT and mixed models, respectively. We found a strong relationship between canopy and ground data; however, we suggest that the best alternative was combining the use of both field-based and remotely sensed methods to achieve high accuracy estimations, particularly in complex structure forests (e.g., old-growth forests). Field inventories and UAV surveys provide accurate information at local scales and allow validation of large-scale applications of satellite imagery. Finally, in the future, increasing the accuracy of aerial surveys and monitoring is necessary to advance the development of local and regional allometric crown and DBH equations at the species level.

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A Multi-Threshold Segmentation for Tree-Level Parameter Extraction in a Deciduous Forest Using Small-Footprint Airborne LiDAR Data

Remote Sensing ◽

10.3390/rs11182109 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2109 ◽

Cited By ~ 3

Author(s):

Xiao-Hu Wang ◽

Yi-Zhuo Zhang ◽

Miao-Miao Xu

Keyword(s):

Parameter Extraction ◽

Airborne Lidar ◽

Tree Level ◽

Surface Model ◽

Individual Tree ◽

Threshold Segmentation ◽

Percentage Points ◽

Segmentation Approach ◽

Small Footprint ◽

Understory Trees

The development of new approaches to tree-level parameter extraction for forest resource inventory and management is an important area of ongoing research, which puts forward high requirements for the capabilities of single-tree segmentation and detection methods. Conventional methods implement segmenting routine with same resolution threshold for overstory and understory, ignoring that their lidar point densities are different, which leads to over-segmentation of the understory trees. To improve the segmentation accuracy of understory trees, this paper presents a multi-threshold segmentation approach for tree-level parameter extraction using small-footprint airborne LiDAR (Light Detection And Ranging) data. First, the point clouds are pre-processed and encoded to canopy layers according to the lidar return number, and multi-threshold segmentation using DSM-based (Digital Surface Model) method is implemented for each layer; tree segments are then combined across layers by merging criteria. Finally, individual trees are delineated, and tree parameters are extracted. The novelty of this method lies in its application of multi-resolution threshold segmentation strategy according to the variation of LiDAR point density in different canopy layers. We applied this approach to 271 permanent sample plots of the University of Kentucky’s Robinson Forest, a deciduous canopy-closed forest with complex terrain and vegetation conditions. Experimental results show that a combination of multi-resolution threshold segmentation based on stratification and cross-layer tree segments merging method can provide a significant performance improvement in individual tree-level forest measurement. Compared with DSM-based method, the proposed multi-threshold segmentation approach strongly improved the average detection rate (from 52.3% to 73.4%) and average overall accuracy (from 65.2% to 76.9%) for understory trees. The overall accuracy increased from 75.1% to 82.6% for all trees, with an increase of the coefficient of determination R2 by 20 percentage points. The improvement of tree detection method brings the estimation of structural parameters for single trees up to an accuracy level: For tree height, R2 increased by 5.0 percentage points from 90% to 95%; and for tree location, the mean difference decreased by 23 cm from 105 cm to 82 cm.

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Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry

Forests ◽

10.3390/f10121052 ◽

2019 ◽

Vol 10 (12) ◽

pp. 1052 ◽

Cited By ~ 4

Author(s):

Zhang ◽

Wu ◽

Yang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Point Cloud ◽

Forest Canopy ◽

Forest Growth ◽

Tree Level ◽

Ground Vegetation ◽

Growth Monitoring ◽

Group Level ◽

Individual Tree

Land cover monitoring is a major task for remote sensing. Comparing to traditional methods for forests monitoring which mostly use orthoimages from satellite or aircraft, there are very few researches use forest 3D canopy structure to monitor the forest growth. UAV aerial can be a novel and feasible platform to provide high resolution and more timely images that can be used to generate high resolution forest 3D canopy. In spring, the small forest is supposed to experience rapid growth. In this research, we used a small UAV to monitor campus forest growth in spring at 2days interval. Each time 140 images were acquired and the ground surface dense point cloud was reconstructed at high precision. Color indexes ExG (Excess Green) was used to extract the green canopy point. The segmented point cloud was triangulated using greedy projection triangulation method into a mesh and its area was calculated. Forest canopy growth was analyzed at 3 level: forest level, selected group level and individual tree level. Logistic curve was used to fit the time series canopy growth. Strong correlation was found R2 = 0.8517 at forest level, R2=0.9652 at selected group level and R2 = 0.9606 at individual tree level. Moreover, high correlation was found between canopy by observing these results, we can conclude that the ground 3D model can act as a useful data type as orthography to monitor the forest growth. Moreover the UAV aerial remote sensing has advantages at monitoring forest in periods when the ground vegetation is growing and changing fast.

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Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2016.03.014 ◽

2016 ◽

Vol 50 ◽

pp. 150-158 ◽

Cited By ~ 15

Author(s):

Yi Lin ◽

Geoff West

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

High Density ◽

Airborne Lidar ◽

Tree Level ◽

Individual Tree ◽

Area Index ◽

Area Density ◽

Leaf Area Density ◽

Effective Leaf Area Index

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

Forests ◽

10.3390/f9120759 ◽

2018 ◽

Vol 9 (12) ◽

pp. 759 ◽

Cited By ~ 19

Author(s):

Wan Wan Mohd Jaafar ◽

Iain Woodhouse ◽

Carlos Silva ◽

Hamdan Omar ◽

Khairul Abdul Maulud ◽

...

Keyword(s):

Tropical Forests ◽

Aboveground Biomass ◽

Airborne Lidar ◽

Tree Level ◽

Lidar Data ◽

Tree Crown ◽

Individual Tree ◽

Tree Crowns ◽

Crown Width ◽

3D Lidar

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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Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

Schweizerische Zeitschrift fur Forstwesen ◽

10.3188/szf.2017.0127 ◽

2017 ◽

Vol 168 (3) ◽

pp. 127-133

Author(s):

Matthew Parkan

Keyword(s):

Cross Sections ◽

Forest Edge ◽

Airborne Lidar ◽

Visual Interpretation ◽

Individual Tree ◽

Small Areas ◽

Interactive 3D ◽

The Individual ◽

Airborne Lidar Data ◽

Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

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Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

Remote Sensing ◽

10.3390/rs13010077 ◽

2020 ◽

Vol 13 (1) ◽

pp. 77

Author(s):

Tianyu Hu ◽

Xiliang Sun ◽

Yanjun Su ◽

Hongcan Guan ◽

Qianhui Sun ◽

...

Keyword(s):

Forest Inventory ◽

Low Cost ◽

Canopy Cover ◽

Tree Height ◽

Flying Height ◽

Tree Level ◽

Forest Site ◽

Lidar System ◽

Individual Tree ◽

Gap Fraction

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

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