scholarly journals LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 550
Author(s):  
Dandan Xu ◽  
Haobin Wang ◽  
Weixin Xu ◽  
Zhaoqing Luan ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Forest Biomass ◽  
Remotely Sensed ◽  
Tree Level ◽  
Biomass Estimation ◽  
Future Perspectives ◽  
Individual Tree ◽  
The Individual ◽  
Individual Trees

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data.

scholarly journals Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance

2021 ◽  
Vol 11 ◽  
Author(s):  
David Pont ◽  
Heidi S. Dungey ◽  
Mari Suontama ◽  
Grahame T. Stovold
Keyword(s):  
Laser Scanning ◽  
Management Practices ◽  
Spatial Models ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Residual Variance ◽  
Spatial Effects ◽  
Individual Tree ◽  
Airborne Laser ◽  
Individual Trees

Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.

ESTIMATING SINGLE TREE STEM AND BRANCH BIOMASS USING TERRESTRIAL LASER SCANNING

2015 ◽  
Vol 77 (26) ◽  
Author(s):  
Nurliyana Izzati Ishak ◽  
Md Afif Abu Bakar ◽  
Muhammad Zulkarnain Abdul Rahman ◽  
Abd Wahid Rasib ◽  
Kasturi Devi Kanniah ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Stem Volume ◽  
Biomass Estimation ◽  
Forest Plot ◽  
Individual Tree ◽  
Matching Process ◽  
Branch Biomass ◽  
Individual Trees ◽  
Tree Stem

This paper presents a novel non-destructive approach for individual tree stem and branch biomass estimation using terrestrial laser scanning data. The study area is located at the Royal Belum Reserved Forest area, Gerik, Perak. Each forest plot was designed with a circular shape and contains several scanning locations to ensure good visibility of each tree. Unique tree signage was located on trees with diameter at breast height (DBH) of 10cm and above.  Extractions of individual trees were done manually and the matching process with the field collected tree properties were relied on the tree signage and tree location as collected by total station. Individual tree stems were reconstructed based on cylinder models from which the total stem volume was calculated. Biomass of individual tree stems was calculated by multiplying stem volume with specific wood density. Biomass of individual was estimated using similar concept of tree stem with the volume estimated from alpha-hull shape. The root mean squared errors (RMSE) of estimated biomass are 50.22kg and 27.20kg for stem and branch respectively. 

Forest Biomass Estimation Based on Remote Sensing Method for North Daxingan Mountains

2011 ◽  
Vol 339 ◽  
pp. 336-341 ◽  
Author(s):  
Yuan Yuan Zhang ◽  
Feng Ri Li ◽  
Fu Xiang Liu
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Forest Biomass ◽  
Multiple Linear Regression Model ◽  
Biomass Estimation ◽  
Individual Tree ◽  
Forest Region

Using the Landsat 5 TM images in 2002 as source data，the paper constructed individual tree biomass models of seven principal species based on the data from field surveying and fixed Plots in Tahe and Amur forest Region in Daxiangan Mountains. The remote sensing biomass model between TM images and data from forest fixed Plots was developed by the methods of multiple linear regression and BP neutral net. The result showed that R in multiple linear regression model was 0.764 and the model passed the F test, D-W test and multi-collinearity test. In the independent sample estimation，The neutral net model with the precision of 91.25% was significantly higher than multiple linear regression model with the precision of 81.02%. Although the“black-box”neutral net model could not give the concrete analytical equation, this kind of model with high precision might be applied to estimate the forest biomass in large level forest biomass.

scholarly journals A simulation-based approach to a near-optimal thinning strategy: allowing harvesting times to be determined for individual trees

2019 ◽  
pp. 320-331
Author(s):  
Peter Fransson ◽  
Oskar Franklin ◽  
Ola Lindroos ◽  
Urban Nilsson ◽  
Åke Brännström
Keyword(s):  
Optimal Strategy ◽  
Management Strategy ◽  
Basal Area ◽  
Forest Growth ◽  
Tree Level ◽  
Individual Tree ◽  
Area Reduction ◽  
Forest Growth Model ◽  
The Individual ◽  
Individual Trees

As various methods for precision inventories, including light detection and ranging (LiDAR), are becoming increasingly common in forestry, planning at the individual-tree level is becoming more viable. In this study, we present a method for finding the optimal thinning times for individual trees from an economic perspective. The method utilizes a forest growth model based on individual trees that has been fitted to Norway spruce (Picea abies (L.) Karst.) stands in northern Sweden. We find that the optimal management strategy is to thin from above (i.e., harvesting trees that are larger than average). We compare our optimal strategy with a conventional management strategy and find that the optimal strategy results in approximately 20% higher land expectation value. Furthermore, we find that for the optimal strategy, increasing the discount rate will reduce the final harvest age and increase the basal area reduction. Decreasing the cost to initiate a thinning (e.g., machinery-related transportation costs) increases the number of thinnings and delays the first thinning.

scholarly journals The Horizontal Distribution of Branch Biomass in European Beech: A Model Based on Measurements and TLS Based Proxies

2021 ◽  
Vol 13 (5) ◽  
pp. 1041
Author(s):  
César Pérez-Cruzado ◽  
Christoph Kleinn ◽  
Paul Magdon ◽  
Juan Gabriel Álvarez-González ◽  
Steen Magnussen ◽  
...  
Keyword(s):  
Empirical Model ◽  
Laser Scanning ◽  
European Beech ◽  
Horizontal Distribution ◽  
Forest Monitoring ◽  
Projection Area ◽  
Biomass Estimation ◽  
Tree Biomass ◽  
Individual Tree ◽  
Individual Trees

Forest biomass is currently among the most important and most researched target variables in forest monitoring. The common approach of observing individual tree biomass in forest inventory is to assign the total tree biomass to the dimensionless point of the tree position. However, the tree biomass, in particular in the crown, is horizontally distributed above the crown projection area. This horizontal distribution of individual tree biomass (HBD) has not attracted much attention—but if quantified, it can improve biomass estimation and help to better represent the spatial distribution of forest fuel. In this study, we derive a first empirical model of the branch HBD for individual trees of European beech (Fagus sylvatica L.). We destructively measured 23 beech trees to derive an empirical model for the branch HBD. We then applied Terrestrial Laser Scanning (TLS) to a subset of 17 trees to test a simple point cloud metric predicting the branch HBD. We observed similarities between a branch HBD and commonly applied taper functions, which inspired our HBD model formulations. The models performed well in representing the HBD both for the measured biomass, and the TLS-based metric. Our models may be used as first approximations to the HBD of individual trees—while our methodological approach may extend to trees of different sizes and species.

Approaches for estimating stand-level volume using terrestrial laser scanning in a single-scan mode

2014 ◽  
Vol 44 (6) ◽  
pp. 666-676 ◽  
Author(s):  
Rasmus Astrup ◽  
Mark J. Ducey ◽  
Aksel Granhus ◽  
Tim Ritter ◽  
Nikolas von Lüpke
Keyword(s):  
Laser Scanning ◽  
Single Point ◽  
Individual Tree ◽  
Transect Sampling ◽  
Laser Systems ◽  
Southern Norway ◽  
The Individual ◽  
Individual Trees ◽  
Mature Stands ◽  
Volume Estimates

The most efficient way to obtain stand inventory data with terrestrial laser systems (TLS) is with the single-scan mode, which involves taking one scan at a single point. With a single-scan setup, there will be a nondetection of trees in a plot and the representation of the individual trees will be incomplete. We explore how stand-level volume estimates, based on the single-scan mode, perform compared with standard inventory estimates. We base our study on 166 plots in 12 mature stands dominated by Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies L. Karst) in southern Norway. First, we compare individual-tree volume estimates from TLS with estimates from volume functions and measurements from harvesters. We show that individual-tree volumes can be estimated with high precision and accuracy with TLS in single-scan mode. Secondly, we test three approaches for correction of nondetection relying on model-based estimates of the detection probability obtained by point transect sampling estimators. We show that all three approaches adjust for nondetection and yield stand-level volume estimates that are similar to those obtained by fixed-area sampling. In conclusion, our results indicate that stand-level volume estimates, based on single-scan mode TLS data, perform well compared with standard inventory estimates.

scholarly journals A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 35
Author(s):  
Jean-François Prieur ◽  
Benoît St-Onge ◽  
Richard A. Fournier ◽  
Murray E. Woods ◽  
Parvez Rana ◽  
...  
Keyword(s):  
Species Identification ◽  
Laser Scanning ◽  
Time Series Data ◽  
Single Photon ◽  
Series Data ◽  
Tree Level ◽  
Potential Contribution ◽  
Individual Tree ◽  
Airborne Laser ◽  
The Individual

Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83–90%; 12 species, 46–54%; 4 species, 68–79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.

scholarly journals Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

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