scholarly journals Lidar-Derived Tree Crown Parameters: Are They New Variables Explaining Local Birch (Betula sp.) Pollen Concentrations?

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1154 ◽  
Author(s):  
Paweł Bogawski ◽  
Łukasz Grewling ◽  
Katarzyna Dziób ◽  
Kacper Sobieraj ◽  
Marta Dalc ◽  
...  

Birch trees are abundant in central and northern Europe and are dominant trees in broadleaved forests. Birches are pioneer trees that produce large quantities of allergenic pollen efficiently dispersed by wind. The pollen load level depends on the sizes and locations of pollen sources, which are important for pollen forecasting models; however, very limited work has been done on this topic in comparison to research on anthropogenic air pollutants. Therefore, we used highly accurate aerial laser scanning (Light Detection and Ranging—LiDAR) data to estimate the size and location of birch pollen sources in 3-dimensional space and to determine their influence on the pollen concentration in Poznań, Poland. LiDAR data were acquired in May 2012. LiDAR point clouds were clipped to birch individuals (mapped in 2012–2014 and in 2019), normalised, filtered, and individual tree crowns higher than 5 m were delineated. Then, the crown surface and volume were calculated and aggregated according to wind direction up to 2 km from the pollen trap. Consistent with LIDAR data, hourly airborne pollen measurements (performed using a Hirst-type, 7-day volumetric trap), wind speed and direction data were obtained in April 2012. We delineated 18,740 birch trees, with an average density of 14.9/0.01 km2, in the study area. The total birch crown surface in the 500–1500 m buffer from the pollen trap was significantly correlated with the pollen concentration aggregated by the wind direction (r = 0.728, p = 0.04). The individual tree crown delineation performed well (r2 ≥ 0.89), but overestimations were observed at high birch densities (> 30 trees/plot). We showed that trees outside forests substantially contribute to the total pollen pool. We suggest that including the vertical dimension and the trees outside the forest in pollen source maps have the potential to improve the quality of pollen forecasting models.

2019 ◽  
Vol 11 (8) ◽  
pp. 908 ◽  
Author(s):  
Xiangqian Wu ◽  
Xin Shen ◽  
Lin Cao ◽  
Guibin Wang ◽  
Fuliang Cao

Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.


Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5421 ◽  
Author(s):  
Faizaan Naveed ◽  
Baoxin Hu ◽  
Jianguo Wang ◽  
G. Brent Hall

In this study, multispectral Light Detection and Ranging (LiDAR) data were utilized to improve delineation of individual tree crowns (ITC) as an important step in individual tree analysis. A framework to integrate spectral and height information for ITC delineation was proposed, and the multi-scale algorithm for treetop detection developed in one of our previous studies was improved. In addition, an advanced region-based segmentation method that used detected treetops as seeds was proposed for segmentation of individual crowns based on their spectral, contextual, and height information. The proposed methods were validated with data acquired using Teledyne Optech’s Titan LiDAR sensor. The sensor was operated at three wavelengths (1550 nm, 1064 nm, and 532 nm) within a study area located in the city of Toronto, ON, Canada. The proposed method achieved 80% accuracy, compared with manual delineation of crowns, considering both matched and partially matched crowns, which was 12% higher than that obtained by the earlier marker-controlled watershed (MCW) segmentation technique. Furthermore, the results showed that the integration of spectral and height information improved ITC delineation using either the proposed framework or MCW segmentation, compared with using either spectral or height information individually.


2014 ◽  
Vol 19 (4) ◽  
pp. 1078-1087 ◽  
Author(s):  
Hien Phu La ◽  
Yang Dam Eo ◽  
Anjin Chang ◽  
Changjae Kim

Author(s):  
Tan Zhou ◽  
Sorin Popescu ◽  
Lonesome Malambo ◽  
Kaiguang Zhao ◽  
Keith Krause

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as lack of available handy processing tools and technical intricacy of waveform processing. This study introduces a new product, named the Hyper Point Cloud (HPC) derived from FW LiDAR data, and explore its potential applications such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which show a promising solution to the constraints of using FW LiDAR data. Results of the HPC present a new direction to handle FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~ 182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has larger maximum intensity (MAXI) and mean intensity (MI) than the vegetation region while having smaller total intensity (TI) and number of intensities (NI) at the given grid cell. Our analysis of intensity distribution contours at individual tree level exhibit similar patterns, indicating that the MAXI and MI are decreasing from the tree crown center to tree boundary while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives to use FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.


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