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Special Issue "LiDAR and Other Remote Sensing Applications in Mapping and Monitoring of Forests Structure and Biomass"

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A special issue of Forests (ISSN 1999-4907).

Deadline for manuscript submissions: closed (28 February 2014)

Special Issue Editor

Guest Editor
Prof. Dr. L. Monika Moskal

School of Environmental and Forest Sciences, College of the Environment, University of Washington, Box 352100, Seattle WA 98195-2100, USA. Director, UW Precision Forestry Cooperative and Remote Sensing and Geospatial Analysis Laboratory
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Interests: ALS/TLS LiDAR; precision forestry; hyperspatial remote sensing; ecosystem services

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Published Papers (20 papers)

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Research

Open AccessArticle Cross-Correlation of Diameter Measures for the Co-Registration of Forest Inventory Plots with Airborne Laser Scanning Data
Forests 2014, 5(9), 2307-2326; doi:10.3390/f5092307
Received: 25 March 2014 / Revised: 3 July 2014 / Accepted: 15 September 2014 / Published: 19 September 2014
Cited by 2 | PDF Full-text (558 KB) | HTML Full-text | XML Full-text
Abstract
Continuous maps of forest parameters can be derived from airborne laser scanning (ALS) remote sensing data. A prediction model is calibrated between local point cloud statistics and forest parameters measured on field plots. Unfortunately, inaccurate positioning of field measures lead to a bad
[...] Read more.
Continuous maps of forest parameters can be derived from airborne laser scanning (ALS) remote sensing data. A prediction model is calibrated between local point cloud statistics and forest parameters measured on field plots. Unfortunately, inaccurate positioning of field measures lead to a bad matching of forest measures with remote sensing data. The potential of using tree diameter and position measures in cross-correlation with ALS data to improve co-registration is evaluated. The influence of the correction on ALS models is assessed by comparing the accuracy of basal area prediction models calibrated or validated with or without the corrected positions. In a coniferous, uneven-aged forest with high density ALS data and low positioning precision, the algorithm co-registers 91% of plots within two meters from the operator location when at least the five largest trees are used in the analysis. The new coordinates slightly improve the prediction models and allow a better estimation of their accuracy. In a forest with various stand structures and species, lower ALS density and differential Global Navigation Satellite System measurements, position correction turns out to have only a limited impact on prediction models. Full article
Open AccessArticle Correlating the Horizontal and Vertical Distribution of LiDAR Point Clouds with Components of Biomass in a Picea crassifolia Forest
Forests 2014, 5(8), 1910-1930; doi:10.3390/f5081910
Received: 18 December 2013 / Revised: 24 May 2014 / Accepted: 24 July 2014 / Published: 5 August 2014
Cited by 8 | PDF Full-text (30895 KB) | HTML Full-text | XML Full-text
Abstract
Light detection and ranging (LiDAR) has been widely used to estimate forest biomass. In this study, we aim to further explore this capability by correlating horizontal and vertical distribution of LiDAR data with components of biomass in a Picea crassifolia forest. Airborne small
[...] Read more.
Light detection and ranging (LiDAR) has been widely used to estimate forest biomass. In this study, we aim to further explore this capability by correlating horizontal and vertical distribution of LiDAR data with components of biomass in a Picea crassifolia forest. Airborne small footprint full-waveform data were decomposed to acquire higher density point clouds. We calculated LiDAR metrics at the tree level and subplot level and correlated them to biomass components, including branch biomass (BB), leaf biomass (LB) and above-ground biomass (AGB), respectively. A new metric (Horizcv) describing the horizontal distribution of point clouds was proposed. This metric was found to be highly correlated with canopy biomass (BB and LB) at the tree level and subplot level. Correlation between AGB and Horizcv at the subplot level is much lower than that at tree level. AGB for subplot is highly correlated with the mean height metric (Hmean), canopy cover index (CCI) and the product of them. On the other hand, the relationship between the vertical distribution of LiDAR point and biomass was explored by developing two types of vertical profiles, including LiDAR distribution profiles and a biomass profile. Good relationships were found between these two types of vertical profiles and assessed by Pearson’s correlation coefficient (R) and the area of overlap index (AOI). These good correlations possess potential in predicting the vertical distribution of canopy biomass. Overall, it is concluded that not only the vertical, but also the horizontal distribution of LiDAR points should be taken into account in estimating components of biomass by LiDAR. Full article
Open AccessArticle Estimation of the Timber Quality of Scots Pine with Terrestrial Laser Scanning
Forests 2014, 5(8), 1879-1895; doi:10.3390/f5081879
Received: 3 March 2014 / Revised: 1 July 2014 / Accepted: 23 July 2014 / Published: 31 July 2014
Cited by 6 | PDF Full-text (5363 KB) | HTML Full-text | XML Full-text
Abstract
Preharvest information on the quality of Scots pine (Pinus sylvestris) timber is required by the forest industry in Nordic countries, due to the strong association between the technical quality and product recovery of this species in particular. The objective of this
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Preharvest information on the quality of Scots pine (Pinus sylvestris) timber is required by the forest industry in Nordic countries, due to the strong association between the technical quality and product recovery of this species in particular. The objective of this study was to assess the accuracy of estimating external quality attributes and classifying the quality of mature Scots pine trees by terrestrial laser scanning (TLS). The tree quality was estimated using a random forest approach, based on both field and TLS measurements of stem diameters, tree height and branch heights. The relative root mean squared errors of the TLS measurements for tree height, diameter, diameter at 6 m and the lowest living and dead branch height were 7.1%, 5.9%, 8.9%, 9.6% and 42.9%, respectively. The highest errors of the branch heights were caused by the shadowing effect in the point cloud data. The quality classes were estimated accurately, based on both (field and TLS measured) tree attributes. Trees were classified with 95.0% and 83.6% accuracy into three operationally-important quality classes and with 87.1% and 76.4% accuracy into five classes using, field or TLS measurements, respectively. The obtained quality classification results were promising. The enhanced tree quality information could have a significant effect on planning forest management procedures, wood supply chains and optimizing the flow of raw materials. To fully integrate tree quality measurements in operational forestry, the methods used should be fully automated. Full article
Open AccessArticle Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups
Forests 2014, 5(7), 1565-1583; doi:10.3390/f5071565
Received: 30 December 2013 / Revised: 28 May 2014 / Accepted: 10 June 2014 / Published: 3 July 2014
PDF Full-text (832 KB) | HTML Full-text | XML Full-text
Abstract
Terrestrial laser scanning (TLS) data makes possible to directly characterize the three-dimensional (3D) distribution of canopy foliage elements. The scanned edges of these elements may result in incorrectly point measurements (i.e., “ghost points”) impacting the quality of point cloud
[...] Read more.
Terrestrial laser scanning (TLS) data makes possible to directly characterize the three-dimensional (3D) distribution of canopy foliage elements. The scanned edges of these elements may result in incorrectly point measurements (i.e., “ghost points”) impacting the quality of point cloud data. Therefore, estimation of the ghost points’ spatial visibilities, measurement of their characteristics and their removal are essential. In order to quantify the improvements on data quality, a method is developed in this study to efficiently correct for ghost points. Since the occurrence of ghost points is governed by a number of factors, (e.g., scanning resolution and distance, object properties, incident angle); the developed method is based on the analysis of the effects of these factors under controlled conditions where canopy-like objects (i.e., leaves, branches and layers of leaves) were scanned using a continuous-wave TLS system that employs phase-shift technology. Manual extraction of ghost points was done in order to calculate the relative amount of ghost points per scan, or ghost points ratio (gpr). The gpr values were computed in order to: (i) analyze their relationships with variables representing the above factors; and (ii) be used as a reference to evaluate the performance of filters used for extraction of ghost points. The ghost points’ occurrence was modeled by fitting regression models using different predictor variables that represent the variables under study. The obtained results indicated that reduced models with three predictors were suitable for gpr estimation in artificial leaves and in artificial branches, with a relative root mean squared error (RMSE) of 4.7% and 3.7%, respectively; while the full model with four predictors was appropriate for artificial layers of leaves, with relative RMSE of 4.5%. According to the statistical analysis, scanning distance was identified as the most important variable for modeling ghost points occurrence. Results indicated that optimized distance-based filters relative to the scanning distance have improved the outcomes in ghost points detection, in comparison to standard filtering criteria. These results suggest that more accurate characterization of forest canopy 3D structures can be achieved by removing ghost points using the new developed method. Full article
Open AccessArticle Quantifying Ladder Fuels: A New Approach Using LiDAR
Forests 2014, 5(6), 1432-1453; doi:10.3390/f5061432
Received: 28 February 2014 / Revised: 3 June 2014 / Accepted: 10 June 2014 / Published: 20 June 2014
Cited by 4 | PDF Full-text (4449 KB) | HTML Full-text | XML Full-text
Abstract
We investigated the relationship between LiDAR and ladder fuels in the northern Sierra Nevada, California USA. Ladder fuels are often targeted in hazardous fuel reduction treatments due to their role in propagating fire from the forest floor to tree crowns. Despite their importance,
[...] Read more.
We investigated the relationship between LiDAR and ladder fuels in the northern Sierra Nevada, California USA. Ladder fuels are often targeted in hazardous fuel reduction treatments due to their role in propagating fire from the forest floor to tree crowns. Despite their importance, ladder fuels are difficult to quantify. One common approach is to calculate canopy base height, but this has many potential sources of error. LiDAR may be a way forward to better characterize ladder fuels, but has only been used to address this question peripherally and in only a few instances. After establishing that landscape fuel treatments reduced canopy and ladder fuels at our site, we tested which LiDAR-derived metrics best differentiated treated from untreated areas. The percent cover between 2 and 4 m had the most explanatory power to distinguish treated from untreated pixels across a range of spatial scales. When compared to independent plot-based measures of ladder fuel classes, this metric differentiated between high and low levels of ladder fuels. These findings point to several immediate applications for land managers and suggest new avenues of study that could lead to possible improvements in the way that we model wildfire behavior across forested landscapes in the US. Full article
Open AccessArticle Interest of a Full-Waveform Flown UV Lidar to Derive Forest Vertical Structures and Aboveground Carbon
Forests 2014, 5(6), 1454-1480; doi:10.3390/f5061454
Received: 12 December 2013 / Revised: 22 April 2014 / Accepted: 10 June 2014 / Published: 20 June 2014
Cited by 5 | PDF Full-text (2004 KB) | HTML Full-text | XML Full-text
Abstract
Amongst all the methodologies readily available to estimate forest canopy and aboveground carbon (AGC), in-situ plot surveys and airborne laser scanning systems appear to be powerful assets. However, they are limited to relatively local scales. In this work, we have developed a full-waveform
[...] Read more.
Amongst all the methodologies readily available to estimate forest canopy and aboveground carbon (AGC), in-situ plot surveys and airborne laser scanning systems appear to be powerful assets. However, they are limited to relatively local scales. In this work, we have developed a full-waveform UV lidar, named ULICE (Ultraviolet LIdar for Canopy Experiment), as an airborne demonstrator for future space missions, with an eventual aim to retrieve forest properties at the global scale. The advantage of using the UV wavelength for a demonstrator is its low multiple scattering in the canopy. Based on realistic airborne lidar data from the well-documented Fontainebleau forest site (south-east of Paris, France), which is representative of managed deciduous forests in temperate climate zones, we estimate the uncertainties in the retrieval of forest vertical structures and AGC. A complete uncertainty study using Monte Carlo approaches is performed for both the lidar-derived tree top height (TTH) and AGC. Our results show a maximum error of 1.2 m (16 tC ha‑1) for the TTH (AGC) assessment. Furthermore, the study of leaf effect on AGC estimate for mid-latitude deciduous forests highlights the possibility for using calibration obtained during only one season to retrieve the AGC during the other, by applying winter and summer airborne measurements. Full article
Open AccessArticle Mapping Above- and Below-Ground Biomass Components in Subtropical Forests Using Small-Footprint LiDAR
Forests 2014, 5(6), 1356-1373; doi:10.3390/f5061356
Received: 4 April 2014 / Revised: 8 May 2014 / Accepted: 10 June 2014 / Published: 16 June 2014
Cited by 5 | PDF Full-text (1033 KB) | HTML Full-text | XML Full-text
Abstract
In order to better assess the spatial variability in subtropical forest biomass, the goal of our study was to use small-footprint, discrete-return Light Detection and Ranging (LiDAR) data to accurately estimate and map above- and below-ground biomass components of subtropical forests. Foliage, branch,
[...] Read more.
In order to better assess the spatial variability in subtropical forest biomass, the goal of our study was to use small-footprint, discrete-return Light Detection and Ranging (LiDAR) data to accurately estimate and map above- and below-ground biomass components of subtropical forests. Foliage, branch, trunk, root, above-ground and total biomass of 53 plots (30 × 30 m) were modeled using a range of LiDAR-derived metrics, with individual models built for each of the three dominant forest types using stepwise multi-regression analysis. A regular grid covered the entire study site with cell size 30 × 30 m corresponding to the same size of the plots; it was generated for mapping each biomass component. Overall, results indicate that biomass estimation was more accurate in coniferous forests, compared with the mixed and broadleaved plots. The coefficient of determination (R2) for individual models was significantly enhanced compared with an overall generic, or common, model. Using independent stand-level data from ground inventory, our results indicated that overall the model fit was significant for most of the biomass components, with relationships close to a 1:1 line, thereby indicating no significant bias. This research illustrates the potential for LiDAR as a technology to assess subtropical forest carbon accurately and to provide a better understanding of how forest ecosystems function in this region. Full article
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Open AccessArticle LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer
Forests 2014, 5(6), 1374-1390; doi:10.3390/f5061374
Received: 10 March 2014 / Revised: 10 May 2014 / Accepted: 10 June 2014 / Published: 16 June 2014
Cited by 8 | PDF Full-text (1166 KB) | HTML Full-text | XML Full-text
Abstract
The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the
[...] Read more.
The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the cold period in winter forces individuals to optimize their trade off in searching for food and shelter. We analyzed the winter habitat selection of roe deer (Capreolus capreolus) in a montane forest landscape combining estimates of vegetation cover in three different height strata, derived from high resolution airborne Laser-scanning (LiDAR, Light detection and ranging), and activity data from GPS telemetry. Specifically, we tested the influence of temperature, snow height, and wind speed on site selection, differentiating between active and resting animals using mixed-effects conditional logistic regression models in a case-control design. Site selection was best explained by temperature deviations from hourly means, snow height, and activity status of the animals. Roe deer tended to use forests of high canopy cover more frequently with decreasing temperature, and when snow height exceeded 0.6 m. Active animals preferred lower canopy cover, but higher understory cover. Our approach demonstrates the potential of LiDAR measures for studying fine scale habitat selection in complex three-dimensional habitats, such as forests. Full article
Open AccessArticle Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests
Forests 2014, 5(6), 1122-1142; doi:10.3390/f5061122
Received: 4 March 2014 / Revised: 8 April 2014 / Accepted: 14 May 2014 / Published: 28 May 2014
Cited by 5 | PDF Full-text (2606 KB) | HTML Full-text | XML Full-text
Abstract
Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in
[...] Read more.
Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in the strategy had to be empirically determined. This paper highlights a comprehensive study for the sensitivity analysis of 3D single tree detection from airborne LiDAR data. By varying key parameters, their influences on results are to be quantified. The aim of the study is to enlighten the optimal combination of parameter values towards new applications. For the experiment, a number of sample plots from two temperate forest sites in Europe were selected. LiDAR data with a point density of 25 pts/m2 over the first site in the Bavarian forest national park were captured with under both leaf-on and leaf-off conditions. Moreover, a Riegl scanner was used to acquire data over the Austrian Alps forest with four-fold point densities of 5 pts/m2, 10 pts/m2, 15 pts/m2 and 20 pts/m2, respectively, under leaf-off conditions. The study results proved the robustness and efficiency of the 3D segmentation approach. Point densities larger than 10 pts/m2 did not seem to significantly contribute to the improvement in the performance of 3D tree detection. The performance of the approach can be further examined and improved by optimizing the parameter settings with respect to different data properties and forest structures. Full article
Open AccessArticle Urban-Tree-Attribute Update Using Multisource Single-Tree Inventory
Forests 2014, 5(5), 1032-1052; doi:10.3390/f5051032
Received: 20 January 2014 / Revised: 13 May 2014 / Accepted: 14 May 2014 / Published: 22 May 2014
Cited by 7 | PDF Full-text (1488 KB) | HTML Full-text | XML Full-text
Abstract
The requirements for up-to-date tree data in city parks and forests are increasing, and an important question is how to keep the digital databases current for various applications. Traditional map-updating procedures, such as visual interpretation of digital aerial images or field measurements using
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The requirements for up-to-date tree data in city parks and forests are increasing, and an important question is how to keep the digital databases current for various applications. Traditional map-updating procedures, such as visual interpretation of digital aerial images or field measurements using tachymeters, are either inaccurate or expensive. Recently, the development of laser-scanning technology has opened new opportunities for tree mapping and attributes updating. For a detailed measurement and attributes update of urban trees, we tested the use of a multisource single-tree inventory (MS-STI) for heterogeneous urban forest conditions. MS-STI requires an existing tree map as input information in addition to airborne laser-scanning (ALS) data. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System (GNSS). Tree attributes were either measured from ALS or predicted by using metrics extracted from ALS data. Stem diameter-at-breast height (DBH) was predicted and compared to the field measures, and tree height and crown area were directly measured from ALS data at the two different urban-forest areas. The results indicate that MS-STI can be used for updating urban-forest attributes. The accuracies of DBH estimations were improved compared to the existing attribute information in the city of Helsinki’s urban-tree register. In addition, important attributes, such as tree height and crown dimensions, were extracted from ALS and added as attributes to the urban-tree register. Full article
Open AccessArticle Using VEGNET In-Situ Monitoring LiDAR (IML) to Capture Dynamics of Plant Area Index, Structure and Phenology in Aspen Parkland Forests in Alberta, Canada
Forests 2014, 5(5), 1053-1068; doi:10.3390/f5051053
Received: 4 March 2014 / Revised: 11 May 2014 / Accepted: 13 May 2014 / Published: 22 May 2014
Cited by 7 | PDF Full-text (1728 KB) | HTML Full-text | XML Full-text
Abstract
The use of ceptometers and digital hemispherical photographs to estimate Plant Area Index (PAI) often include biases and errors from instrument positioning, orientation and data analysis. As an alternative to these methods, we used an In-Situ Monitoring LiDAR system that provides indirect measures
[...] Read more.
The use of ceptometers and digital hemispherical photographs to estimate Plant Area Index (PAI) often include biases and errors from instrument positioning, orientation and data analysis. As an alternative to these methods, we used an In-Situ Monitoring LiDAR system that provides indirect measures of PAI and Plant Area Volume Density (PAVD) at a fixed angle, based on optimized principles and algorithms for PAI retrieval. The instrument was installed for 22 nights continuously from September 26 to October 17, 2013 during leaf-fall in an Aspen Parkland Forest. A total of 85 scans were performed (~4 scans per night). PAI measured decreased from 1.27 to 0.67 during leaf-fall, which is consistent with values reported in the literature. PAVD profiles allowed differentiating the contribution of PAI per forest strata. Phenological changes were captured in four ways: number of hits, maximum cumulative and absolute PAI values, time series of PAVD profiles and PAI values per forest strata. We also found that VEGNET IML Canopy PAI and MODIS LAI values showed a similar decreasing trend and differed by 2%–15%. Our results indicate that the VEGNET IML has great potential for rapid forest structural characterization and for ground validation of PAI/LAI at a temporal frequency compatible with earth observation satellites. Full article
Open AccessArticle Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description
Forests 2014, 5(5), 1069-1105; doi:10.3390/f5051069
Received: 4 March 2014 / Revised: 15 April 2014 / Accepted: 13 May 2014 / Published: 22 May 2014
Cited by 12 | PDF Full-text (7644 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a method for fitting cylinders into a point cloud, derived from a terrestrial laser-scanned tree. Utilizing high scan quality data as the input, the resulting models describe the branching structure of the tree, capable of detecting branches with a diameter
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This paper presents a method for fitting cylinders into a point cloud, derived from a terrestrial laser-scanned tree. Utilizing high scan quality data as the input, the resulting models describe the branching structure of the tree, capable of detecting branches with a diameter smaller than a centimeter. The cylinders are stored as a hierarchical tree-like data structure encapsulating parent-child neighbor relations and incorporating the tree’s direction of growth. This structure enables the efficient extraction of tree components, such as the stem or a single branch. The method was validated both by applying a comparison of the resulting cylinder models with ground truth data and by an analysis between the input point clouds and the models. Tree models were accomplished representing more than 99% of the input point cloud, with an average distance from the cylinder model to the point cloud within sub-millimeter accuracy. After validation, the method was applied to build two allometric models based on 24 tree point clouds as an example of the application. Computation terminated successfully within less than 30 min. For the model predicting the total above ground volume, the coefficient of determination was 0.965, showing the high potential of terrestrial laser-scanning for forest inventories. Full article
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Open AccessArticle Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden
Forests 2014, 5(5), 992-1010; doi:10.3390/f5050992
Received: 24 February 2014 / Revised: 22 April 2014 / Accepted: 14 May 2014 / Published: 21 May 2014
Cited by 6 | PDF Full-text (1729 KB) | HTML Full-text | XML Full-text
Abstract
Knowledge of the forest biomass and its change in time is crucial to understanding the carbon cycle and its interactions with climate change. LiDAR (Light Detection and Ranging) technology, in this respect, has proven to be a valuable tool, providing reliable estimates of
[...] Read more.
Knowledge of the forest biomass and its change in time is crucial to understanding the carbon cycle and its interactions with climate change. LiDAR (Light Detection and Ranging) technology, in this respect, has proven to be a valuable tool, providing reliable estimates of aboveground biomass (AGB). The overall goal of this study was to develop a method for assessing AGB using a synergy of low point density LiDAR-derived point cloud data and multi-spectral imagery in conifer-dominated forest in the southwest of Sweden. Different treetop detection algorithms were applied for forest inventory parameter extraction from a LiDAR-derived canopy height model. Estimation of AGB was based on the power functions derived from tree parameters measured in the field, while vegetation classification of a multi-spectral image (SPOT-5) was performed in order to account for dependences of AGB estimates on vegetation types. Linear regression confirmed good performance of a newly developed grid-based approach for biomass estimation (R2 = 0.80). Results showed AGB to vary from below 1 kg/m2 in very young forests to 94 kg/m2 in mature spruce forests, with RMSE of 4.7 kg/m2. These AGB estimates build a basis for further studies on carbon stocks as well as for monitoring this forest ecosystem in respect of disturbance and change in time. The methodology developed in this study can be easily adopted for assessing biomass of other conifer-dominated forests on the basis of low-density LiDAR and multispectral imagery. This methodology is hence of much wider applicability than biomass derivation based on expensive and currently still scarce high-density LiDAR data. Full article
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Open AccessArticle Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification
Forests 2014, 5(5), 1011-1031; doi:10.3390/f5051011
Received: 7 March 2014 / Revised: 5 May 2014 / Accepted: 14 May 2014 / Published: 21 May 2014
Cited by 13 | PDF Full-text (1192 KB) | HTML Full-text | XML Full-text
Abstract
The paper investigated the possible gains in using low density (average 1 pulse/m2) full-waveform (FWF) airborne laser scanning (ALS) data for individual tree detection and tree species classification and compared the results to the ones obtained using discrete return laser scanning.
[...] Read more.
The paper investigated the possible gains in using low density (average 1 pulse/m2) full-waveform (FWF) airborne laser scanning (ALS) data for individual tree detection and tree species classification and compared the results to the ones obtained using discrete return laser scanning. The aim is to approach a low-cost, fully ALS-based operative forest inventory method that is capable of providing species-specific diameter distributions required for wood procurement. The point data derived from waveform data were used for individual tree detection (ITD). Features extracted from segmented tree objects were used in random forest classification by which both feature selection and classification were performed. Experiments were conducted with 5532 ground measured trees from 292 sample plots and using FWF data collected with Leica ALS60 scanner over a boreal forest, mainly consisting of pine, spruce and birch, in southern Finland. For the comparisons, system produced multi-echo discrete laser data (DSC) were also analyzed with the same procedures. The detection rate of individual trees was slightly higher using FWF point data than DSC point data. Overall detection accuracy, however, was similar because commission error was increased when omission error was decreasing. The best overall classification accuracy was 73.4% which contains an 11 percentage points increase when FWF features were included in the classification compared with DSC features alone. The results suggest that FWF ALS data contains more information about the structure and physical properties of the environment that can be used in tree species classification of pine, spruce and birch when comparing with DSC ALS data. Full article
Open AccessArticle Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates
Forests 2014, 5(5), 936-951; doi:10.3390/f5050936
Received: 1 January 2014 / Revised: 3 March 2014 / Accepted: 13 May 2014 / Published: 16 May 2014
Cited by 10 | PDF Full-text (2100 KB) | HTML Full-text | XML Full-text
Abstract
This paper assesses the combined effect of field plot size and LiDAR density on the estimation of four forest structure attributes: volume, total biomass, basal area and canopy cover. A total of 21 different plot sizes were considered, obtained by decreasing the field
[...] Read more.
This paper assesses the combined effect of field plot size and LiDAR density on the estimation of four forest structure attributes: volume, total biomass, basal area and canopy cover. A total of 21 different plot sizes were considered, obtained by decreasing the field measured plot radius value from 25 to 5 m with regular intervals of 1 m. LiDAR data densities were simulated by randomly removing LiDAR pulses until reaching nine different density values. In order to avoid influence of the digital terrain model spatial resolution, eight different resolutions were considered (from 0.25 to 2 m grid size) and tested. A set of per-plot LiDAR metrics was extracted for each parameter combination. Prediction models of forest attributes were defined using forward stepwise ordinary least-square regressions. Results show that the highest R2 values are reached by combining large plot sizes and high LiDAR data density values. However, plot size has a greater effect than LiDAR point density. In general, minimum plot areas of 500–600 m2 are needed for volume, biomass and basal area estimates, and of 300–400 m2 for canopy cover. Larger plot sizes do not significantly increase the accuracy of the models, but they increase the cost of fieldwork. Full article
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Open AccessArticle Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data
Forests 2014, 5(3), 384-403; doi:10.3390/f5030384
Received: 14 November 2013 / Revised: 7 January 2014 / Accepted: 19 February 2014 / Published: 28 February 2014
Cited by 13 | PDF Full-text (1415 KB) | HTML Full-text | XML Full-text
Abstract
Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for
[...] Read more.
Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for a combined use of ALS and TLS in an inventory was tested and compared to a method without the use of TLS. Single-tree Norway spruce crown biomass was predicted using an ALS-model with training data obtained by TLS. ALS and TLS data were collected for sets of sample trees, including 68 trees with both ALS and TLS data. In total, 29 destructively sampled trees were used to fit a TLS crown biomass model, which then was used to predict crown biomass in a separate set of 68 trees. This dataset was subsequently used to fit an ALS crown biomass model. When validating the model, using a separate dataset with accurately measured crown biomass obtained through destructive sampling, the mean error was 32% of the observed mean biomass. Corresponding crown biomass predictions derived with ALS-predicted diameters and the use of conventional and existing allometric models resulted in a mean error of 35%. Thus, in the present study, a slight improvement, in terms of prediction accuracy, was found when using training data with ground reference values obtained by TLS. Full article
Open AccessArticle Assessing the Feasibility of Low-Density LiDAR for Stand Inventory Attribute Predictions in Complex and Managed Forests of Northern Maine, USA
Forests 2014, 5(2), 363-383; doi:10.3390/f5020363
Received: 20 December 2013 / Revised: 1 February 2014 / Accepted: 19 February 2014 / Published: 24 February 2014
Cited by 6 | PDF Full-text (2010 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study was to evaluate the applicability of using a low-density (1–3 points m−2) discrete-return LiDAR (Light Detection and Ranging) for predicting maximum tree height, stem density, basal area, quadratic mean diameter and total volume. The research was
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The objective of this study was to evaluate the applicability of using a low-density (1–3 points m−2) discrete-return LiDAR (Light Detection and Ranging) for predicting maximum tree height, stem density, basal area, quadratic mean diameter and total volume. The research was conducted at the Penobscot Experimental Forest in central Maine, where a range of stand structures and species composition is present and generally representative of northern Maine’s forests. Prediction models were developed utilizing the random forest algorithm that was calibrated using reference data collected in fixed radius circular plots. For comparison, the volume model used two sets of reference data, with one being fixed radius circular plots and the other variable radius plots. Prediction biases were evaluated with respect to five silvicultural treatments and softwood species composition based on the coefficient of determination (R2), root mean square error and mean bias, as well as residual scatter plots. Overall, this study found that LiDAR tended to underestimate maximum tree height and volume. The maximum tree height and volume models had R2 values of 86.9% and 72.1%, respectively. The accuracy of volume prediction was also sensitive to the plot type used. While it was difficult to develop models with a high R2, due to the complexities of Maine’s forest structures and species composition, the results suggest that low density LiDAR can be used as a supporting tool in forest management for this region. Full article
Open AccessArticle Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest
Forests 2013, 4(4), 984-1002; doi:10.3390/f4040984
Received: 8 August 2013 / Revised: 28 October 2013 / Accepted: 15 November 2013 / Published: 20 November 2013
Cited by 16 | PDF Full-text (2163 KB) | HTML Full-text | XML Full-text
Abstract
This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation
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This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation points. After, vegetation statistics, including height quantiles, mean height, and fractional cover were calculated. Stepwise multiple regression models were used to develop equations that relate the vegetation statistics from field inventory data with field-based estimates of biomass for each sample plot. The results showed that stem, branch, and above-ground biomass may be estimated with relatively higher accuracies; estimates have adjusted R2 values of 0.748, 0.749, and 0.727, respectively, root mean squared error (RMSE) values of 9.876, 1.520, and 15.237 Mg·ha−1, respectively, and relative RMSE values of 12.783%, 12.423%, and 14.163%, respectively. Moreover, fruit and crown biomass may be estimated with relatively high accuracies; estimates have adjusted R2 values of 0.578 and 0.648, respectively, RMSE values of 1.022 and 5.963 Mg·ha−1, respectively, and relative RMSE values of 23.273% and 19.665%, respectively. In contrast, foliage biomass estimates have relatively low accuracies; they had an adjusted R2 value of 0.356, an RMSE of 3.691 Mg·ha−1, and a relative RMSE of 26.953%. Finally, above-ground biomass and biomass component spatial maps were established using stepwise multiple regression equations. These maps are very useful for updating and modifying forest base maps and registries. Full article
Open AccessArticle A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery
Forests 2013, 4(4), 922-944; doi:10.3390/f4040922
Received: 26 September 2013 / Revised: 12 October 2013 / Accepted: 25 October 2013 / Published: 6 November 2013
Cited by 34 | PDF Full-text (6749 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on
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The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series. Full article
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Open AccessCommunication Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis
Forests 2013, 4(4), 808-829; doi:10.3390/f4040808
Received: 13 August 2013 / Revised: 12 September 2013 / Accepted: 23 September 2013 / Published: 11 October 2013
Cited by 2 | PDF Full-text (2232 KB) | HTML Full-text | XML Full-text
Abstract
The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT) in image segmentation, and to apply the object-based image analysis (OBIA) approach
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The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT) in image segmentation, and to apply the object-based image analysis (OBIA) approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial) imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping. Full article

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