Extracting Individual Tree Information

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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Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest

Remote Sensing ◽

10.3390/rs11222614 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2614 ◽

Cited By ~ 3

Author(s):

Nina Amiri ◽

Peter Krzystek ◽

Marco Heurich ◽

Andrew Skidmore

Keyword(s):

Tree Species ◽

Feature Selection Method ◽

Silver Fir ◽

Maximum Point ◽

Individual Tree ◽

Dead Trees ◽

Multi Wavelength ◽

Standing Dead ◽

Standing Dead Trees ◽

532 Nm

Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.

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Modelling growing stock volume of forest stands with various ALS area-based approaches

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpab011 ◽

2021 ◽

Author(s):

Karolina Parkitna ◽

Grzegorz Krok ◽

Stanisław Miścicki ◽

Krzysztof Ukalski ◽

Marek Lisańczuk ◽

...

Keyword(s):

Laser Scanning ◽

Mean Squared Error ◽

Prediction Method ◽

Forest Stand ◽

Boosted Regression Trees ◽

Forest Stands ◽

Individual Tree ◽

Growing Stock ◽

Modelling Techniques ◽

Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

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Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation

Remote Sensing ◽

10.3390/rs13020223 ◽

2021 ◽

Vol 13 (2) ◽

pp. 223

Author(s):

Zhenyang Hui ◽

Shuanggen Jin ◽

Dajun Li ◽

Yao Yevenyo Ziggah ◽

Bo Liu

Keyword(s):

Transfer Learning ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Principal Component ◽

Point Clouds ◽

Gaussian Mixture ◽

Individual Tree ◽

Terrestrial Lidar ◽

Initial Segmentation ◽

Tree Extraction

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

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Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽

10.3390/f12020250 ◽

2021 ◽

Vol 12 (2) ◽

pp. 250

Author(s):

Wade T. Tinkham ◽

Neal C. Swayze

Keyword(s):

Structure From Motion ◽

Ponderosa Pine ◽

Point Cloud ◽

Image Resolution ◽

Canopy Height ◽

Computer Hardware ◽

Forest Monitoring ◽

Individual Tree ◽

Ponderosa Pine Forests ◽

Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

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Urbanization pressures alter tree rhizosphere microbiomes

Scientific Reports ◽

10.1038/s41598-021-88839-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Carl L. Rosier ◽

Shawn W. Polson ◽

Vincent D’Amico ◽

Jinjun Kan ◽

Tara L. E. Trammell

Keyword(s):

Organic Matter ◽

Relative Abundance ◽

Tree Species ◽

High Throughput Sequencing ◽

Regional Climate ◽

Keystone Species ◽

Fagus Grandifolia ◽

Yellow Poplar ◽

Individual Tree ◽

Structural Formation

AbstractThe soil microbial community (SMC) provides critical ecosystem services including organic matter decomposition, soil structural formation, and nutrient cycling. Studies suggest plants, specifically trees, act as soil keystone species controlling SMC structure via multiple mechanisms (e.g., litter chemistry, root exudates, and canopy alteration of precipitation). Tree influence on SMC is shaped by local/regional climate effects on forested environments and the connection of forests to surrounding landscapes (e.g., urbanization). Urban soils offer an ideal analog to assess the influence of environmental conditions versus plant species-specific controls on SMC. We used next generation high throughput sequencing to characterize the SMC of specific tree species (Fagus grandifolia [beech] vs Liriodendron tulipifera [yellow poplar]) across an urban–rural gradient. Results indicate SMC dissimilarity within rural forests suggests the SMC is unique to individual tree species. However, greater urbanization pressure increased SMC similarity between tree species. Relative abundance, species richness, and evenness suggest that increases in similarity within urban forests is not the result of biodiversity loss, but rather due to greater overlap of shared taxa. Evaluation of soil chemistry across the rural–urban gradient indicate pH, Ca+, and organic matter are largely responsible for driving relative abundance of specific SMC members.

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Individual tree extraction from urban mobile laser scanning point clouds using deep pointwise direction embedding

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2021.03.002 ◽

2021 ◽

Vol 175 ◽

pp. 326-339

Author(s):

Haifeng Luo ◽

Kourosh Khoshelham ◽

Chongcheng Chen ◽

Hanxian He

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Individual Tree ◽

Tree Extraction

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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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