An End to End Process Development for UAV-SfM Based Forest Monitoring: Individual Tree Detection, Species Classification and Carbon Dynamics Simulation

To promote Bio-Energy with Carbon dioxide Capture and Storage (BECCS), which aims to replace fossil fuels with bio energy and store carbon underground, and Reducing Emissions from Deforestation and forest Degradation (REDD+), which aims to reduce the carbon emissions produced by forest degradation, it is important to build forest management plans based on the scientific prediction of forest dynamics. For Measurement, Reporting and Verification (MRV) at an individual tree level, it is expected that techniques will be developed to support forest management via the effective monitoring of changes to individual trees. In this study, an end-to-end process was developed: (1) detecting individual trees from Unmanned Aerial Vehicle (UAV) derived digital images; (2) estimating the stand structure from crown images; (3) visualizing future carbon dynamics using a forest ecosystem process model. This process could detect 93.4% of individual trees, successfully classified two species using Convolutional Neural Network (CNN) with 83.6% accuracy and evaluated future ecosystem carbon dynamics and the source-sink balance using individual based model FORMIND. Further ideas for improving the sub-process of the end to end process were discussed. This process is expected to contribute to activities concerned with carbon management such as designing smart utilization for biomass resources and projecting scenarios for the sustainable use of ecosystem services.

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

Remote Sensing ◽

10.3390/rs13122297 ◽

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

Frontiers in Plant Science ◽

10.3389/fpls.2020.596315 ◽

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.

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Predicting Future Diameter Distributions Given Current Stand Attributes

Canadian Journal of Forest Research ◽

10.1139/cjfr-2021-0216 ◽

2022 ◽

Author(s):

Quang V. Cao

Keyword(s):

Prediction Method ◽

Diameter Distribution ◽

Tree Level ◽

Individual Tree ◽

Distribution Parameters ◽

Level Model ◽

Novel Method ◽

Quadratic Mean Diameter ◽

Stand Attributes ◽

Individual Trees

This study discussed four methods to project a diameter distribution from age A1 to age A2. Method 1 recovers parameters of the distribution at age A2 from stand attributes at that age. Method 2 uses a stand-level model to grow the quadratic mean diameter, and then recovers the distribution parameters from that prediction. Method 3 grows the diameter distribution by assuming tree-level survival and diameter growth functions. Method 4 first converts the diameter distribution at age A1 into a list of individual trees before growing these trees to age A2. In a numerical example employing the Weibull distribution, methods 3 and 4 produced better results based on two types of error indices and the relative predictive error for each diameter class. Method 4 is a novel method that converts a diameter distribution into a list of individual-trees, and in the process, successfully links together diameter distribution, individual-tree, and whole stand models.

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Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

Remote Sensing ◽

10.3390/rs12030571 ◽

2020 ◽

Vol 12 (3) ◽

pp. 571 ◽

Cited By ~ 2

Author(s):

Chen ◽

Xiang ◽

Moriya

Keyword(s):

Template Matching ◽

Local Maximum ◽

Airborne Lidar ◽

Forest Monitoring ◽

Lidar Data ◽

Individual Tree ◽

Matching Algorithm ◽

Crown Width ◽

Individual Trees ◽

Airborne Lidar Data

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

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Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models

Remote Sensing ◽

10.3390/rs12213599 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3599

Author(s):

Rodrigo Vieira Leite ◽

Carlos Alberto Silva ◽

Midhun Mohan ◽

Adrián Cardil ◽

Danilo Roberti Alves de Almeida ◽

...

Keyword(s):

Fixed Effects ◽

Tree Level ◽

Forest Plantations ◽

Forest Plantation ◽

Projected Area ◽

Individual Tree ◽

Lorenz Curves ◽

Fast Growing ◽

Crown Volume ◽

Individual Trees

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry efficiency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random effects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coefficient (GC). We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random effect variable, and canopy height, crown volume, and crown projected area as fixed effects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus—and similar forest plantations for carbon dynamics and forest product planning.

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Species Mixing Effects on Forest Productivity: A Case Study at Stand-, Species- and Tree-Level in the Netherlands

Forests ◽

10.3390/f9110713 ◽

2018 ◽

Vol 9 (11) ◽

pp. 713 ◽

Cited By ~ 3

Author(s):

Huicui Lu ◽

Godefridus Mohren ◽

Miren del Río ◽

Mart-Jan Schelhaas ◽

Meike Bouwman ◽

...

Keyword(s):

Forest Management ◽

Soil Fertility ◽

Basal Area ◽

Tree Level ◽

Stand Age ◽

Forest Plot ◽

Mixed Species ◽

Mixed Stands ◽

Individual Tree ◽

Species Mixtures

Many monoculture forests have been converted to mixed-species forests in Europe over the last decades. The main reasons for this conversion were probably to increase productivity, including timber production, and enhance other ecosystem services, such as conservation of biodiversity and other nature values. This study was done by synthesizing results from studies carried out in Dutch mixed forests compared with monoculture stands and evaluating them in the perspective of the current theory. Then we explored possible mechanisms of higher productivity in mixed stands, in relation to the combination of species, stand age and soil fertility, and discussed possible consequences of forest management. The study covered five two-species mixtures and their corresponding monoculture stands from using long-term permanent forest plots over multiple decades as well as two inventories (around 2003 and 2013) across the entire Netherlands. These forest plot data were used together with empirical models at total stand level, species level and tree level. Overyielding in Douglas-fir–beech and pine–oak mixtures was maintained over time, probably owing to the intensive thinning and was achieved on the poorer soils. However, this overyielding was not always driven by fast-growing light-demanding species. On individual tree level, intra-specific competition was not necessarily stronger than inter-specific competition and this competitive reduction was less seen at lower soil fertility and dependent on species mixtures. Moreover, size-asymmetric competition for light was more associated with tree basal area growth than size-symmetric competition for soil resources. Overall, this study suggests a substantial potential of species mixing for increasing productivity and implies developing forest management strategies to convert monospecific forests to mixed-species forests that consider the complementarity in resource acquisition of tree species.

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

Forests ◽

10.3390/f12050550 ◽

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.

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Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar

Remote Sensing ◽

10.3390/rs11212540 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2540 ◽

Cited By ~ 10

Author(s):

Qinan Lin ◽

Huaguo Huang ◽

Jingxu Wang ◽

Kan Huang ◽

Yangyang Liu

Keyword(s):

Forest Health ◽

Pine Forests ◽

Radiative Transfer Model ◽

Tree Level ◽

Lidar Data ◽

Transfer Model ◽

Combined Approach ◽

Individual Tree ◽

Pine Shoot Beetle ◽

Individual Trees

In recent years, the outbreak of the pine shoot beetle (PSB), Tomicus spp., has caused serious shoots damage and the death of millions of trees in Yunnan pine forests in southwestern China. It is urgent to develop a convincing approach to accurately assess the shoot damage ratio (SDR) for monitoring the PSB insects at an early stage. Unmanned airborne vehicles (UAV)-based sensors, including hyperspectral imaging (HI) and lidar, have very high spatial and spectral resolutions, which are very useful to detect forest health. However, very few studies have utilized HI and lidar data to estimate SDRs and compare the predictive power for mapping PSB damage at the individual tree level. Additionally, the data fusion of HI and lidar may improve the detection accuracy, but it has not been well studied. In this study, UAV-based HI and lidar data were fused to detect PSB. We systematically evaluated the potential of a hyperspectral approach (only-HI data), a lidar approach (only-lidar data), and a combined approach (HI plus lidar data) to characterize PSB damage of individual trees using the Random Forest (RF) algorithm, separately. The most innovative point is the proposed new method to extract the three dimensional (3D) shadow distribution of each tree crown based on a lidar point cloud and the 3D radiative transfer model RAPID. The results show that: (1) for the accuracy of estimating the SDR of individual trees, the lidar approach (R2 = 0.69, RMSE = 12.28%) performed better than hyperspectral approach (R2 = 0.67, RMSE = 15.87%), and in addition, it was useful to detect dead trees with an accuracy of 70%; (2) the combined approach has the highest accuracy (R2 = 0.83, RMSE = 9.93%) for mapping PSB damage degrees; and (3) when combining HI and lidar data to predict SDRs, two variables have the most contributions, which are the leaf chlorophyll content (Cab) derived from hyperspectral data and the return intensity of the top of shaded crown (Int_Shd_top) from lidar metrics. This study confirms the high possibility to accurately predict SDRs at individual tree level if combining HI and lidar data. The 3D radiative transfer model can determine the 3D crown shadows from lidar, which is a key information to combine HI and lidar. Therefore, our study provided a guidance to combine the advantages of hyperspectral and lidar data to accurately measure the health of individual trees, enabling us to prioritize areas for forest health promotion. This method may also be used for other 3D land surfaces, like urban areas.

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

Canadian Journal of Forest Research ◽

10.1139/cjfr-2019-0053 ◽

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.

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Rating Introgression between Lodgepole and Jack Pine at the Individual Tree Level Using Morphological Traits

Northern Journal of Applied Forestry ◽

10.1093/njaf/28.3.138 ◽

2011 ◽

Vol 28 (3) ◽

pp. 138-145 ◽

Cited By ~ 7

Author(s):

Katherine P. Bleiker ◽

Allan L. Carroll

Keyword(s):

Pinus Banksiana ◽

Pinus Contorta ◽

Morphological Traits ◽

Function Analysis ◽

Introgressive Hybridization ◽

Principal Component ◽

Jack Pine ◽

Tree Level ◽

Individual Tree ◽

Individual Trees

Abstract Introgressive hybridization between species generates novel gene combinations and phenotypes. We required an accessible, objective method of rating introgression between lodgepole pine (Pinus contorta var. latifolia [Engelm.] Critchfield) and jack pine (Pinus banksiana Lamb.) for individual trees where their ranges overlap in Canada for use in another study on host species effects on resistance to an eruptive herbivore that has recently expanded its range. We adapted, simplified, and fully quantified a morphological index developed to rate introgression of pine populations and applied it to individual trees. In addition to principal component analysis (PCA), we also used discriminant function analysis (DFA), a potentially more powerful method given a priori knowledge of parent taxa, to generate introgression ratings. Among-tree variation in morphological traits and introgression was high at sites within the hybrid zone but very low at pure parent sites. PCA and DFA produced similar introgression ratings at the stand level, but ratings differed substantially for some individual trees. Certain morphological traits may be omitted from both PCA and DFA with little impact on stand-level ratings. The discriminant functions presented here are based on easy-to-measure, fully quantifiable morphological traits and can be used by other researchers to produce relative introgression ratings for lodgepole and jack pine. The approach may also be applied to other plant hybrid systems.

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