UAV-Based LiDAR Scanning for Individual Tree Detection and Height Measurement in Young Forest Permanent Trials

The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m.

Download Full-text

Determining an Accurate and Cost-Effective Individual Height-Diameter Model for Mongolian Pine on Sandy Land

Forests ◽

10.3390/f12091144 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1144

Author(s):

Yangang Han ◽

Zeyong Lei ◽

Albert Ciceu ◽

Yanping Zhou ◽

Fengyan Zhou ◽

...

Keyword(s):

Management Practice ◽

Predictor Variable ◽

Tree Height ◽

Cost Effective ◽

Northern China ◽

Mixed Effects ◽

Pine Plantations ◽

Sampling Effort ◽

Individual Tree ◽

D Model

Height-diameter (H-D) models are important tools for forest management practice. Sandy Mongolian pine plantations (Pinus sylvestris var. mongolica) are a major component of the Three-North Afforestation Shelterbelt in Northern China. However, few H-D models are available for Mongolian pine plantations. In this paper we compared different equations found in the literature for predicting tree height, using diameter at breast height and additional stand-level predictor variables. We tested if the additional stand-level predictor variable is necessary to produce more accurate results. The dominant height was used as a stand-level predictor variable to describe the variation of the H-D relationship among plots. We found that the basic mixed-effects H-D model provided a similar predictive accuracy as the generalized mixed-effects H-D model. Moreover, it had the advantage of reducing the sampling effort. The basic mixed-effects H-D model calibration, in which the heights of the two thickest trees in the plot were included to calibrate the random effects, resulted in accurate and reliable individual tree height estimations. Thus, the basic mixed-effects H-D model with the above-described calibration design can be an accurate and cost-effective solution for estimating the heights of Mongolian pine trees in northern China.

Download Full-text

Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation

Australian Forestry ◽

10.1080/00049158.2016.1153770 ◽

2016 ◽

Vol 79 (2) ◽

pp. 126-136 ◽

Cited By ~ 10

Author(s):

Amrit Kathuria ◽

Russell Turner ◽

Christine Stone ◽

Joaqin Duque-Lazo ◽

Ron West

Keyword(s):

Pinus Radiata ◽

Point Cloud ◽

Lidar Data ◽

Individual Tree ◽

Detection Model ◽

Tree Detection

Download Full-text

Deep Learning in Forestry Using UAV-Acquired RGB Data: A Practical Review

Remote Sensing ◽

10.3390/rs13142837 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2837

Author(s):

Yago Diez ◽

Sarah Kentsch ◽

Motohisa Fukuda ◽

Maximo Larry Lopez Caceres ◽

Koma Moritake ◽

...

Keyword(s):

Deep Learning ◽

Forest Fires ◽

Autonomous Vehicles ◽

Cost Effective ◽

Data Interpretation ◽

Species Classification ◽

Individual Tree ◽

High Resolution Data ◽

Tree Detection ◽

Public Data

Forests are the planet’s main CO2 filtering agent as well as important economical, environmental and social assets. Climate change is exerting an increased stress, resulting in a need for improved research methodologies to study their health, composition or evolution. Traditionally, information about forests has been collected using expensive and work-intensive field inventories, but in recent years unoccupied autonomous vehicles (UAVs) have become very popular as they represent a simple and inexpensive way to gather high resolution data of large forested areas. In addition to this trend, deep learning (DL) has also been gaining much attention in the field of forestry as a way to include the knowledge of forestry experts into automatic software pipelines tackling problems such as tree detection or tree health/species classification. Among the many sensors that UAVs can carry, RGB cameras are fast, cost-effective and allow for straightforward data interpretation. This has resulted in a large increase in the amount of UAV-acquired RGB data available for forest studies. In this review, we focus on studies that use DL and RGB images gathered by UAVs to solve practical forestry research problems. We summarize the existing studies, provide a detailed analysis of their strengths paired with a critical assessment on common methodological problems and include other information, such as available public data and code resources that we believe can be useful for researchers that want to start working in this area. We structure our discussion using three main families of forestry problems: (1) individual Tree Detection, (2) tree Species Classification, and (3) forest Anomaly Detection (forest fires and insect Infestation).

Download Full-text

Tree height and tropical forest biomass estimation

Biogeosciences Discussions ◽

10.5194/bgd-10-10491-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 10491-10529 ◽

Cited By ~ 12

Author(s):

M. O. Hunter ◽

M. Keller ◽

D. Vitoria ◽

D. C. Morton

Keyword(s):

Tropical Forest ◽

Tropical Forests ◽

Reference Values ◽

Tree Height ◽

Carbon Stocks ◽

Estimation Accuracy ◽

Biomass Estimation ◽

Height Measurement ◽

Individual Tree ◽

Allometric Relations

Abstract. Tropical forests account for approximately half of above-ground carbon stored in global vegetation. However, uncertainties in tropical forest carbon stocks remain high because it is costly and laborious to quantify standing carbon stocks. Carbon stocks of tropical forests are determined using allometric relations between tree stem diameter and height and biomass. Previous work has shown that the inclusion of height in biomass allometries, compared to the sole use of diameter, significantly improves biomass estimation accuracy. Here, we evaluate the effect of height measurement error on biomass estimation and we evaluate the accuracy of recently published diameter : height allometries at four sites within the Brazilian Amazon. As no destructive sample of biomass was available at these sites, reference biomass values were based on allometries. We found that the precision of individual tree height measurements ranged from 3 to 20% of total height. This imprecision resulted in a 5–6% uncertainty in biomass when scaled to 1 ha transects. Individual height measurement may be replaced with existing regional and global height allometries. However, we recommend caution when applying these relations. At Tapajós National Forest in the Brazilian state of Pará, using the pantropical and regional allometric relations for height resulted in site biomass 26% to 31% less than reference values. At the other three study sites, the pan-tropical equation resulted in errors of less that 2%, and the regional allometry produced errors of less than 12%. As an alternative to measuring all tree heights or to using regional and pantropical relations, we recommend measuring height for a well distributed sample of about 100 trees per site. Following this methodology, 95% confidence intervals of transect biomass were constrained to within 4.5% on average when compared to reference values.

Download Full-text

Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

Forests ◽

10.3390/f11030268 ◽

2020 ◽

Vol 11 (3) ◽

pp. 268

Author(s):

Jan Novotný ◽

Barbora Navrátilová ◽

Růžena Janoutová ◽

Filip Oulehle ◽

Lucie Homolová

Keyword(s):

Laser Scanning ◽

Tree Height ◽

Airborne Laser Scanning ◽

Allometric Equations ◽

Spruce Forest ◽

Individual Tree ◽

Detection Rates ◽

Site Specific ◽

Tree Detection ◽

Airborne Laser

Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m2) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region.

Download Full-text

Automatic Delineation and Height Measurement of Regenerating Conifer Crowns under Leaf-Off Conditions Using UAV Imagery

Remote Sensing ◽

10.3390/rs12244104 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4104

Author(s):

Andrew J. Chadwick ◽

Tristan R. H. Goodbody ◽

Nicholas C. Coops ◽

Anne Hervieux ◽

Christopher W. Bater ◽

...

Keyword(s):

Spatial Resolution ◽

Picea Glauca ◽

Pinus Contorta ◽

Tree Height ◽

Point Clouds ◽

Strongly Correlated ◽

Tree Crown ◽

Height Measurement ◽

Individual Tree ◽

Fine Spatial Resolution

The increasing use of unmanned aerial vehicles (UAV) and high spatial resolution imagery from associated sensors necessitates the continued advancement of efficient means of image processing to ensure these tools are utilized effectively. This is exemplified in the field of forest management, where the extraction of individual tree crown information stands to benefit operational budgets. We explored training a region-based convolutional neural network (Mask R-CNN) to automatically delineate individual tree crown (ITC) polygons in regenerating forests (14 years after harvest) using true colour red-green-blue (RGB) imagery with an average ground sampling distance (GSD) of 3 cm. We predicted ITC polygons to extract height information using canopy height models generated from digital aerial photogrammetric (DAP) point clouds. Our approach yielded an average precision of 0.98, an average recall of 0.85, and an average F1 score of 0.91 for the delineation of ITC. Remote height measurements were strongly correlated with field height measurements (r2 = 0.93, RMSE = 0.34 m). The mean difference between DAP-derived and field-collected height measurements was −0.37 m and −0.24 m for white spruce (Picea glauca) and lodgepole pine (Pinus contorta), respectively. Our results show that accurate ITC delineation in young, regenerating stands is possible with fine-spatial resolution RGB imagery and that predicted ITC can be used in combination with DAP to estimate tree height.

Download Full-text

The SOHARC Model System for Growth and Yield of Southern Hardwoods

Southern Journal of Applied Forestry ◽

10.1093/sjaf/32.4.173 ◽

2008 ◽

Vol 32 (4) ◽

pp. 173-183 ◽

Cited By ~ 4

Author(s):

John Paul McTague ◽

David O'Loughlin ◽

Joseph P. Roise ◽

Daniel J. Robison ◽

Robert C. Kellison

Keyword(s):

Tree Growth ◽

Growth Models ◽

Basal Area ◽

Tree Height ◽

Growth And Yield ◽

Permanent Plots ◽

Red Maple ◽

Yellow Poplar ◽

Individual Tree ◽

Individual Tree Growth

Abstract A system of stand level and individual tree growth-and-yield models are presented for southern hardwoods. These models were developed from numerous permanent growth-and-yield plots established across 13 states in the US South on 9 site types, in even-aged (age classes from 20 to 60 years), fully stocked, naturally regenerated mixed hardwood and mixed hardwood-pine stands. Nested plots (⅕ and ac) were remeasured at 5-year intervals. The system of permanent plots was established and maintained by private and public members in the North Carolina State University Hardwood Research Cooperative. Stand level models are presented for dominant height, survival, basal area prediction and projection, and the ingrowth component. Individual tree diameter growth and tree height models were constructed for the most common species: sweetgum, tupelo, yellow-poplar, blackgum, and red maple. All other species were grouped according to growth dynamics into four species groups using cluster analysis. A ranking variable was incorporated into the individual tree growth models to account for competition.

Download Full-text

Integration of UAV, Sentinel-1 and Sentinel-2 Data for Mangrove Plantations Aboveground Biomass Monitoring in Senegal

10.20944/preprints201812.0227.v1 ◽

2018 ◽

Author(s):

José Antonio Navarro ◽

Nur Algeet ◽

Alfredo Fernández-Landa ◽

Jéssica Esteban ◽

Pablo Rodríguez-Noriega ◽

...

Keyword(s):

Aboveground Biomass ◽

Auxiliary Information ◽

Tree Height ◽

Cost Effective ◽

Point Clouds ◽

Support Vector ◽

Monitoring Methods ◽

Individual Tree ◽

Entire Study ◽

Sentinel 2

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurement of AGB may be a challenge because of its remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund’ Oceanium project of 10,000 hectare mangrove plantations monitoring. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric Support Vector Regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2 and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in mangrove young plantations.

Download Full-text

Tree height and tropical forest biomass estimation

Biogeosciences ◽

10.5194/bg-10-8385-2013 ◽

2013 ◽

Vol 10 (12) ◽

pp. 8385-8399 ◽

Cited By ~ 103

Author(s):

M. O. Hunter ◽

M. Keller ◽

D. Victoria ◽

D. C. Morton

Keyword(s):

Tropical Forest ◽

Tropical Forests ◽

Reference Values ◽

Tree Height ◽

Carbon Stocks ◽

Estimation Accuracy ◽

Biomass Estimation ◽

Height Measurement ◽

Individual Tree ◽

Allometric Relations

Abstract. Tropical forests account for approximately half of above-ground carbon stored in global vegetation. However, uncertainties in tropical forest carbon stocks remain high because it is costly and laborious to quantify standing carbon stocks. Carbon stocks of tropical forests are determined using allometric relations between tree stem diameter and height and biomass. Previous work has shown that the inclusion of height in biomass allometries, compared to the sole use of diameter, significantly improves biomass estimation accuracy. Here, we evaluate the effect of height measurement error on biomass estimation and we evaluate the accuracy of recently published diameter–height allometries at four areas within the Brazilian Amazon. As no destructive sample of biomass was available at these sites, reference biomass values were based on allometries. We found that the precision of individual tree height measurements ranged from 3 to 20% of total height. This imprecision resulted in a 5–6% uncertainty in biomass when scaled to 1 ha transects. Individual height measurement may be replaced with existing regional and global height allometries. However, we recommend caution when applying these relations. At Tapajos National Forest in the Brazilian state of Pará, using the pantropical and regional allometric relations for height resulted in site biomass 21% and 25% less than reference values. At the other three study sites, the pantropical equation resulted in errors of less that 2%, and the regional allometry produced errors of less than 12%. As an alternative to measuring all tree heights or to using regional and pantropical relations, we recommend measuring height for a well-distributed sample of about 100 trees per site. Following this methodology, 95% confidence intervals of transect biomass were constrained to within 4.5% on average when compared to reference values.

Download Full-text

UAV-Based Automatic Detection and Monitoring of Chestnut Trees

Remote Sensing ◽

10.3390/rs11070855 ◽

2019 ◽

Vol 11 (7) ◽

pp. 855 ◽

Cited By ~ 16

Author(s):

Pedro Marques ◽

Luís Pádua ◽

Telmo Adão ◽

Jonáš Hruška ◽

Emanuel Peres ◽

...

Keyword(s):

Near Infrared ◽

Vegetation Indices ◽

Tree Height ◽

Temporal Analysis ◽

Individual Tree ◽

Tree Detection ◽

Sensing Platform ◽

Agricultural Applications ◽

Multi Temporal ◽

Combination Bands

Unmanned aerial vehicles have become a popular remote sensing platform for agricultural applications, with an emphasis on crop monitoring. Although there are several methods to detect vegetation through aerial imagery, these remain dependent of manual extraction of vegetation parameters. This article presents an automatic method that allows for individual tree detection and multi-temporal analysis, which is crucial in the detection of missing and new trees and monitoring their health conditions over time. The proposed method is based on the computation of vegetation indices (VIs), while using visible (RGB) and near-infrared (NIR) domain combination bands combined with the canopy height model. An overall segmentation accuracy above 95% was reached, even when RGB-based VIs were used. The proposed method is divided in three major steps: (1) segmentation and first clustering; (2) cluster isolation; and (3) feature extraction. This approach was applied to several chestnut plantations and some parameters—such as the number of trees present in a plantation (accuracy above 97%), the canopy coverage (93% to 99% accuracy), the tree height (RMSE of 0.33 m and R2 = 0.86), and the crown diameter (RMSE of 0.44 m and R2 = 0.96)—were automatically extracted. Therefore, by enabling the substitution of time-consuming and costly field campaigns, the proposed method represents a good contribution in managing chestnut plantations in a quicker and more sustainable way.

Download Full-text