Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.

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Mapping Seasonal Tree Canopy Cover and Leaf Area Using Worldview-2/3 Satellite Imagery: A Megacity-Scale Case Study in Tokyo Urban Area

Remote Sensing ◽

10.3390/rs12091505 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1505

Author(s):

Yutaka Kokubu ◽

Seiichi Hara ◽

Akira Tani

Keyword(s):

Leaf Area ◽

Urban Area ◽

Satellite Imagery ◽

Canopy Cover ◽

Tree Canopy ◽

Tree Level ◽

Total Leaf Area ◽

Individual Tree ◽

Total Leaf ◽

Tree Canopy Cover

This study presents a methodology for developing a high-resolution (2 m) urban tree canopy leaf area inventory in different tree phenological seasons and a subsequent application of the methodology to a 625 km2 urban area in Tokyo. Satellite remote sensing has the advantage of imaging large areas simultaneously. However, mapping the tree canopy cover and leaf area accurately is still difficult in a highly heterogeneous urban landscape. The WorldView-2/3 satellite imagery at the individual tree level (2 m resolution) was used to map urban trees based on a simple pixel-based classification method. The comparison of our mapping results with the tree canopy cover derived from aerial photography shows that the error margin is within an acceptable range of 5.5% at the 3.0 km2 small district level, 5.0% at the 60.9 km2 municipality level, and 1.2% at the 625 km2 city level. Furthermore, we investigated the relationship between the satellite data (vegetation index) and in situ tree-measurement data (leaf area index) to develop a simple model to directly map the tree leaf area from the WorldView-2/3 imagery. The estimated total leaf area in Tokyo urban area in the leaf-on season (633 km2) was twice that of the leaf-off season (319 km2). Our results also showed that the estimated total leaf area in Tokyo urban area was 1.9–6.2 times higher than the results of the moderate-resolution (30 m) satellite imagery.

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Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV

Journal of Unmanned Vehicle Systems ◽

10.1139/juvs-2020-0014 ◽

2020 ◽

Vol 8 (4) ◽

pp. 310-333

Author(s):

Sowmya Natesan ◽

Costas Armenakis ◽

Udayalakshmi Vepakomma

Keyword(s):

High Resolution ◽

Species Identification ◽

Tree Species ◽

Tree Canopy ◽

Tree Level ◽

Eastern Canada ◽

Convolutional Network ◽

Individual Tree ◽

Rgb Images ◽

The Individual

Tree species identification at the individual tree level is crucial for forest operations and management, yet its automated mapping remains challenging. Emerging technology, such as the high-resolution imagery from unmanned aerial vehicles (UAV) that is now becoming part of every forester’s surveillance kit, can potentially provide a solution to better characterize the tree canopy. To address this need, we have developed an approach based on a deep Convolutional Neural Network (CNN) to classify forest tree species at the individual tree-level that uses high-resolution RGB images acquired from a consumer-grade camera mounted on a UAV platform. This work explores the ability of the Dense Convolutional Network (DenseNet) to classify commonly available economic coniferous tree species in eastern Canada. The network was trained using multitemporal images captured under varying acquisition parameters to include seasonal, temporal, illumination, and angular variability. Validation of this model using distinct images over a mixed-wood forest in Ontario, Canada, showed over 84% classification accuracy in distinguishing five predominant species of coniferous trees. The model remains highly robust even when using images taken during different seasons and times, and with varying illumination and angles.

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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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Diversity of Medicinal Plants among Different Tree Canopies

Sustainability ◽

10.3390/su13052640 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2640

Author(s):

Muhammad Zubair ◽

Akash Jamil ◽

Syed Bilal Hussain ◽

Ahsan Ul Haq ◽

Ahmad Hussain ◽

...

Keyword(s):

Medicinal Plants ◽

Coniferous Forest ◽

Canopy Cover ◽

Local Communities ◽

Tree Canopy ◽

Tree Cover ◽

Open Spaces ◽

Floral Diversity ◽

Tree Canopy Cover ◽

Closed Canopy

The moist temperate forests in Northern Pakistan are home to a variety of flora and fauna that are pivotal in sustaining the livelihoods of the local communities. In these forests, distribution and richness of vegetation, especially that of medicinal plants, is rarely reported. In this study, we carried out a vegetation survey in District Balakot, located in Northeastern Pakistan, to characterize the diversity of medicinal plants under different canopies of coniferous forest. The experimental site was divided into three major categories (viz., closed canopy, open spaces, and partial tree cover). A sampling plot of 100 m2 was established on each site to measure species diversity, dominance, and evenness. To observe richness and abundance, the rarefaction and rank abundance curves were plotted. Results revealed that a total of 45 species representing 34 families were available in the study site. Medicinal plants were the most abundant (45%) followed by edible plants (26%). Tree canopy cover affected the overall growth of medicinal plants on the basis of abundance and richness. The site with partial canopy exhibited the highest diversity, dominance, and abundance compared to open spaces and closed canopy. These findings are instrumental in identifying the wealth of the medicinal floral diversity in the northeastern temperate forest of Balakot and the opportunity to sustain the livelihoods of local communities with the help of public/private partnership.

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Characterizing Tree Species of a Tropical Wetland in Southern China at the Individual Tree Level Based on Convolutional Neural Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2019.2950721 ◽

2019 ◽

Vol 12 (11) ◽

pp. 4415-4425 ◽

Cited By ~ 2

Author(s):

Ying Sun ◽

Qinchuan Xin ◽

Jianfeng Huang ◽

Bo Huang ◽

Hongsheng Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Tree Species ◽

Southern China ◽

Tree Level ◽

Individual Tree ◽

The Individual

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The transferability of airborne laser scanning based tree-level models between different inventory areas

Canadian Journal of Forest Research ◽

10.1139/cjfr-2018-0128 ◽

2019 ◽

Vol 49 (3) ◽

pp. 228-236 ◽

Cited By ~ 4

Author(s):

Tomi Karjalainen ◽

Lauri Korhonen ◽

Petteri Packalen ◽

Matti Maltamo

Keyword(s):

Laser Scanning ◽

Nearest Neighbor ◽

Airborne Laser Scanning ◽

Tree Level ◽

Stem Volume ◽

K Nearest Neighbor ◽

Individual Tree ◽

Tree Detection ◽

Airborne Laser ◽

Crown Base Height

In this paper, we examine the transferability of airborne laser scanning (ALS) based models for individual-tree detection (ITD) from one ALS inventory area (A1) to two other areas (A2 and A3). All areas were located in eastern Finland less than 100 km from each other and were scanned using different ALS devices and parameters. The tree attributes of interest were diameter at breast height (Dbh), height (H), crown base height (Cbh), stem volume (V), and theoretical sawlog volume (Vlog) of Scots pine (Pinus sylvestris L.) with Dbh ≥ 16 cm. All trees were first segmented from the canopy height models, and various ALS metrics were derived for each segment. Then only the segments covering correctly detected pines were chosen for further inspection. The tree attributes were predicted using the k-nearest neighbor (k-NN) imputation. The results showed that the relative root mean square error (RMSE%) values increased for each attribute after the transfers. The RMSE% values were, for A1, A2, and A3, respectively: Dbh, 13.5%, 14.8%, and 18.1%; H, 3.2%, 5.9%, and 6.2%; Cbh, 13.3%, 15.3%, and 18.3%; V, 29.3%, 35.4%, and 39.1%; and Vlog, 38.2%, 54.4% and 51.8%. The observed values indicate that it may be possible to employ ALS-based tree-level k-NN models over different inventory areas without excessive reduction in accuracy, assuming that the tree species are known to be similar.

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Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification

Forests ◽

10.3390/f5051011 ◽

2014 ◽

Vol 5 (5) ◽

pp. 1011-1031 ◽

Cited By ~ 29

Author(s):

Xiaowei Yu ◽

Paula Litkey ◽

Juha Hyyppä ◽

Markus Holopainen ◽

Mikko Vastaranta

Keyword(s):

Tree Species ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Low Density ◽

Species Classification ◽

Individual Tree ◽

Tree Detection ◽

Tree Species Classification ◽

Full Waveform ◽

Airborne Laser

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Tree canopy arthropods have idiosyncratic responses to plant ecophysiological traits in a warm temperate forest complex

Scientific Reports ◽

10.1038/s41598-020-76868-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Rudi C. Swart ◽

Michael J. Samways ◽

Francois Roets

Keyword(s):

Tree Species ◽

Temperate Forest ◽

Large Scale ◽

Single Species ◽

Tree Canopy ◽

Natural Forests ◽

Individual Tree ◽

Arthropod Diversity ◽

Host Trees ◽

Wide Range

AbstractBiodiversity studies on forest canopies often have narrow arthropod taxonomic focus, or refer to a single species of tree. In response, and to better understand the wide range of drivers of arthropod diversity in tree canopies, we conducted a large-scale, multi-taxon study which (a) included effect of immediate surroundings of an individual tree on plant physiological features, and (b), how these features affect compositional and functional arthropod diversity, in a warm, southern Afro-temperate forest. We found that tree species differed significantly in plant physiological features and arthropod diversity patterns. Surprisingly, we found negative correlation between surrounding canopy cover, and both foliar carbon and arthropod diversity in host trees, regardless of tree species. Subtle, tree intraspecific variation in physiological features correlated significantly with arthropod diversity measures, but direction and strength of correlations differed among tree species. These findings illustrate great complexity in how canopy arthropods respond to specific tree species, to immediate surroundings of host trees, and to tree physiological features. We conclude that in natural forests, loss of even one tree species, as well as homogenization of the crown layer and/or human-induced environmental change, could lead to profound and unpredictable canopy arthropod biodiversity responses, threatening forest integrity.

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Impact of Eidolon helvum on roost trees on UENR campus

Journal of Energy and Natural Resource Management ◽

10.26796/jenrm.v2i0.43 ◽

2018 ◽

Vol 2 (2) ◽

pp. 44-50

Author(s):

E. Danquah

Keyword(s):

Tree Species ◽

Diversity Index ◽

Canopy Cover ◽

Basal Area ◽

Tree Height ◽

Tectona Grandis ◽

Tree Canopy ◽

Species Diversity Index ◽

Tree Canopy Cover ◽

Occupied Zone

Four sample plots, each of size 20m by 20m were systematically distributed in two strata (i.e. two plots in bat-occupied zone andthe remaining two plots in bat-unoccupied zone, to serve as control units). Using six (20m × 20m) sample plots each, basal area,canopy, and heights of trees with DBH 1m were measured. Fourteen individual trees were recorded in the bat-unoccupied zone,resulting in only seven tree species. On the other hand, 16 tree species, corresponding to a total of 25 trees were recorded in thebat occupied zone. Albizia zygia, Antiaris toxicaria, Azadiractha indicia, Holarrhena floribunda, Morinda lucinda, and Sterculiatragacantha were common to both zones. The Shannon Wiener species diversity index was found to be higher (H1=1.92) in batoccupied zones and lower (H1=1.45) in bat-unoccupied zone. Estimates of tree basal area and tree height were much higherin bat occupied zones compared to bat-unoccupied zones. (Mann-Whitney U test: U = 573.0, p < 0.05), tree basal area (U= 674.0, p < 0.05), tree height (U = 632.0, p < 0.05) and tree canopy cover (U = 329.0, p < 0.05). Holarrhena floribunda(0.34 m2/h) and Ceiba pentandra (0.22m2/ha) contributed the largest basal area (32.94% of the total basal area) whilst Sennasiamea (0.01m2/ha) and Tectona grandis (0.01m2/ha) yielded the smallest basal area (1.17%). In general, bats seem to greatlypatronize areas with higher densities of tall trees than relatively open areas with shorter trees.

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