Tree Height Estimation Research Articles

Application Of Unmanned Aerial Vehicle (UAV) Remote Sensing Technology for Estimation of Tree Height in Heterogeneous Forest

Tree height is an important piece of information in forest management. Cost, time, and effort are some of the limiting factors in extracting tree height values on a large scale. The canopy height model approach through aerial photography using UAV can be used to quickly estimate the height of large-scale trees combined with field measurements. CHM analysis was carried out using spatial statistics to get the maximum tree height value based on the tree canopy. Evaluation of accuracy in the form of statistical tests is used to assess the level of accuracy of the estimation. Photogrammetry results show that the obtained CHM has a resolution of 11.8 cm/pixel with the results of the evaluation of tree height accuracy having an RMSE of 2.4 m, MAE 2.0 m, SDE 3.8 m. The chi-square statistical test shows that the results of the tree height estimation accept H0 and there is a strong relationship between the observed tree height and the estimation through linear regression with an R2 value of 0.67. The broad estimation of height shows that Mansinam Island has a tree height in the range of 7 – 66 m. The dominant tree height is in the 19-30 m class with the number of individuals reaching 1,877 trees. This study shows that CHM obtained from aerial photography using low-cost UAVs is still able to estimate tree height well. For future studies, it is necessary to use a ground control point (GCP) to increase the accuracy of the elevation model and orthophoto.

ASSESSING RED PINE SEEDLINGS USING UAV POINT CLOUDS AND FIELD-VERIFIED DATA

Abstract. Accurate, reliable, and cost-efficient approaches to forest monitoring are critical for sustainable forest management. The use of digital photogrammetry for tree height estimation is well-known among forest managers and remote sensing researchers. Satellite remote sensing has not been very successful in providing detailed and reliable estimates of tree height. Unmanned Aerial Vehicles (UAVs) are one of the latest remote sensing platforms to get forest attributes information at very high temporal and spatial resolution. This study assessed the potential of using digital aerial photogrammetry point clouds and UAV acquired high-resolution imagery to estimate red pine seedlings' height in Adirondacks, New York. Seedling's location, height, crown width, and diameter were measured from 16 fixed area sample plots, and multispectral imagery was acquired with DJI Matrice 100- UAV fitted with Micasense RedEdge-M camera. UAV was flown under clear sky conditions at 93-meter height in a single grid pattern with 80% front and side overlap. PIX4D software was used to process UAV multispectral imagery and generate Digital Surface Model (DSM) and Orthomosiac at 6.08 cm/pixel resolution along with 3D Digital Terrain Model (DTM). 3D densified point cloud layers of regeneration canopy were generated at an average density of 1.54 per m3. Seedlings were differentiated from bare ground cover through supervised image classification methods. Preliminary results of this study highlight that multispectral imagery acquired from UAVs has the potential to characterize and provide detailed structural information to estimate red pine seedlings' height.

Measuring the Tree Height of Picea crassifolia in Alpine Mountain Forests in Northwest China Based on UAV-LiDAR

Forests in alpine mountainous regions are sensitive to global climate change. Accurate measurement of tree height is essential for forest aboveground biomass estimation. Unmanned aerial vehicle light detection and ranging (UAV-LiDAR) in tree height estimation has been extensively used in forestry inventories. This study investigated the influence of varying flight heights and point cloud densities on the extraction of tree height, and four flight heights (i.e., 85, 115, 145, and 175 m) were set in three Picea crassifolia plots in the Qilian Mountains. After point cloud data were classified, tree height was extracted from a canopy height model (CHM) on the basis of the individual tree segmentation. Through comparison with ground measurements, the tree height estimations of different flight heights and point cloud densities were analyzed. The results indicated that (1) with a flight height of 85 m, the tree height estimation achieved the highest accuracy (R2 = 0.75, RMSE = 2.65), and the lowest accuracy occurred at a height of 175 m (R2 = 0.65, RMSE = 3.00). (2) The accuracy of the tree height estimation decreased as the point cloud density decreased. The accuracies of tree height estimation from low-point cloud density (R2 = 0.70, RMSE = 2.75) and medium density (R2 = 0.69, RMSE = 2.80) were comparable. (3) Tree height was slightly underestimated in most cases when CHM-based segmentation methods were used. Consequently, a flight height of 145 m was more applicable for maintaining tree height estimation accuracy and assuring the safety of UAVs flying in alpine mountain regions. A point cloud density of 125–185 pts/m2 can guarantee tree height estimation accuracy. The results of this study could potentially improve tree height estimation and provide available UAV-LiDAR flight parameters in alpine mountainous regions in Northwest China.

Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil

The global monitoring of forest structure worldwide is increasingly being supported by refined and enhanced satellite mission datasets. Forest canopy height is a global metric to characterise and monitor dynamics in forest ecosystems worldwide. Satellite mapping missions as NASA's Global Ecosystem Dynamics Investigation (GEDI) are creating opportunities to refine global forest canopy height models adding forest structural information to time-series satellite imagery. A recent global canopy height model presented by Lang et al., (2022) using GEDI and 10-m Sentinel-2 and the map from Potapov et al., (2020) using GEDI and Landsat are both tested in this study using multi-temporal tree-level data collected over eucalypt plantations in Brazil. Our results at plot-level showed Lang et al., (2022)’s estimates of canopy height came short compared to 2020 maximum and mean tree height records in the plots, 7.6 and 3.6 m, respectively, but adding CHM standard deviation improves the agreement of ground records for maximum tree height. Higher errors were computed for the plots in 2019 using the Potapov's 30-m CHM: 14.2 and 9.5 m, respectively. Averaged stand values were more similar between the three sources tested. We report improvement from the 30-m CHM to the 10-m, but still height saturation problems were observed when accounting for height differences in tall eucalypt trees. As more global products for forest height and biomass are becoming available to users, more validation exercises as presented in this study are needed to assess the suitability of CHM products to forestry needs, and facilitate the uptake and actionability of the next generation of global height and biomass products. We provide recommendations and insights on the use of GEDI laser data for global mapping and on the potential of commercial forestry areas to benchmark the accuracy of satellite mapping missions focusing on tree height estimation in the tropics.

Use of Unoccupied Aerial Systems to Characterize Woody Vegetation across Silvopastoral Systems in Ecuador

The trees in pastures are recognized for the benefits they provide to livestock, farmers, and the environment; nevertheless, their study has been restricted to small areas, making it difficult to upscale this information to national levels. For tropical developing countries, it is particularly important to understand the contribution of these systems to national carbon budgets. However, the costs associated with performing field measurements might limit the acquisition of this information. The use of unoccupied aerial systems (UAS) for ecological surveys has proved useful for collecting information at larger scales and with significantly lower costs. This study proposes a methodology that integrates field and UAS surveys to study trees on pasture areas across different terrain conditions. Our overall objective was to test the suitability of UAS surveys to the estimation of aboveground biomass (AGB), relying mainly on open-source software. The tree heights and crown diameters were measured on 0.1-hectare circular plots installed on pasture areas on livestock farms in the Amazon and Coastal regions in Ecuador. An UAS survey was performed on 1-hectare plots containing the circular plots. Field measurements were compared against canopy-height model values and biomass estimates using the two sources of information. Our results demonstrate that UAS surveys can be useful for identifying tree spatial arrangements and provide good estimates of tree height (RMSE values ranged from 0.01 to 3.53 m), crown diameter (RMSE values ranged from 0.04 to 4.47 m), and tree density (density differences ranging from 21.5 to 64.3%), which have a direct impact on biomass estimates. The differences in biomass estimates between the UAS and the field-measured values ranged from 25 to 75%, depending on site characteristics, such as slope and tree coverage. The results suggest that UASs are reliable and feasible tools with which to study tree characteristics on pastures, covering larger areas than field methods only.

An automatic approach for tree species detection and profile estimation of urban street trees using deep learning and Google street view images

Tree species and canopy structural profile (‘tree profile’) are among the most critical environmental factors in determining urban ecosystem services such as climate and air quality control from urban trees. To accurately characterize a tree profile, the tree diameter, height, crown width, and height to the lowest live branch must be all measured, which is an expensive and time-consuming procedure. Recent advances in artificial intelligence aids to efficiently and accurately measure the aforementioned tree profile parameters. This can be particularly helpful if spatially extensive and accurate street-level images provided by Google (‘streetview’) or Kakao (‘roadview’) are utilized. We focused on street trees in Seoul, the capital city of South Korea, and suggested a novel approach to create a tree profile and inventory based on deep learning algorithms. We classified urban tree species using the YOLO (You Only Look Once), one of the most popular deep learning object detection algorithms, which provides an uncomplicated method of creating datasets with custom classes. We further utilized semantic segmentation algorithm and graphical analysis to estimate tree profile parameters by determining the relative location of the interface of tree and ground surface. We evaluated the performance of the model by comparing the estimated tree heights, diameters, and locations from the model with the field measurements as ground truth. The results are promising and demonstrate the potential of the method for creating urban street tree profile inventory. In terms of tree species classification, the method showed the mean average precision (mAP) of 0.564. When we used the ideal tree images, the method also reported the normalized root mean squared error (NRMSE) for the tree height, diameter at breast height (DBH), and distances from the camera to the trees as 0.24, 0.44, and 0.41.

Detecting contact in language trees: a Bayesian phylogenetic model with horizontal transfer

Phylogenetic trees are a central tool for studying language evolution and have wide implications for understanding cultural evolution as a whole. For example, they have been the basis of studies on the evolution of musical instruments, religious beliefs and political complexity. Bayesian phylogenetic methods are transparent regarding the data and assumptions underlying the inference. One of these assumptions—that languages change independently—is incompatible with the reality of language evolution, particularly with language contact. When speakers interact, languages frequently borrow linguistic traits from each other. Phylogenetic methods ignore this issue, which can lead to errors in the reconstruction. More importantly, they neglect the rich history of language contact. A principled way of integrating language contact in phylogenetic methods is sorely missing. We present contacTrees, a Bayesian phylogenetic model with horizontal transfer for language evolution. The model efficiently infers the phylogenetic tree of a language family and contact events between its clades. The implementation is available as a package for the phylogenetics software BEAST 2. We apply contacTrees in a simulation study and a case study on a subset of well-documented Indo-European languages. The simulation study demonstrates that contacTrees correctly reconstructs the history of a simulated language family, including simulated contact events. Moreover, it shows that ignoring contact can lead to systematic errors in the estimated tree height, rate of change and tree topology, which can be avoided with contacTrees. The case study confirms that contacTrees reconstructs known contact events in the history of Indo-European and finds known loanwords, demonstrating its practical potential. The model has a higher statistical fit to the data than a conventional phylogenetic reconstruction, and the reconstructed tree height is significantly closer to well-attested estimates. Our method closes a long-standing gap between the theoretical and empirical models of cultural evolution. The implications are especially relevant for less documented language families, where our knowledge of past contacts and linguistic borrowings is limited. Since linguistic phylogenies have become the backbone of many studies of cultural evolution, the addition of this integral piece of the puzzle is crucial in the endeavour to understand the history of human culture.

Effects of Driving Factors on Forest Aboveground Biomass (AGB) in China’s Loess Plateau by Using Spatial Regression Models

Forests are the main body of carbon sequestration in terrestrial ecosystems and forest aboveground biomass (AGB) is an important manifestation of forest carbon sequestration. Reasonable and accurate quantification of the relationship between AGB and its driving factors is of great importance for increasing the biomass and function of forests. Remote sensing observations and field measurements can be used to estimate AGB in large areas. To explore the applicability of the panel data models in AGB and its driving factors, we compared the results of panel data models (spatial error model and spatial lag model) with those of geographically weighted regression (GWR) and ordinary least squares (OLS) to quantify the relationship between AGB and its driving factors. Furthermore, we estimated the tree height, diameter at breast height, canopy cover (CC) and species diversity index (Shannon–Wiener index) of Robinia pseudoacacia plantations in Changwu on the Loess Plateau using field data and remote sensing images by a random forest model and estimated soil organic carbon (SOC) contents using laboratory data by ordinary kriging (OK) interpolation. We estimated AGB using the already estimated tree height and diameter at breast height combined with the allometric growth equation. In this study, we estimated SOC contents by OK interpolation, and the accuracy R2 values for each soil layer were greater than 0.81. We estimated diameter at breast height (DBH), CC, SW and tree height (TH) using the random forest, and the accuracy R2 values were 0.85, 0.82, 0.76 and 0.68, respectively. We estimated AGB with random forest and the allometric growth equation and found that the average AGB was 55.80 t/ha. The OLS results showed that the residuals of the OLS regression exhibited obvious spatial correlations and rejected OLS applications. GWR, SEM and SLM were used for spatial regression analysis, and SEM was the best model for explaining the relationship between AGB and its driving factors. We also found that AGB was significantly positively correlated with CC, SW, and 0–60 cm SOC content (p < 0.05) and significantly negatively correlated with slope aspect (p < 0.01). This study provides a new idea for studying the relationship between AGB and its driving factors and provides a basis for practical forest management, increasing biomass, and giving full play to the role of carbon sequestration.

Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests

Accurate individual tree segmentation (ITS) is fundamental to forest management and to the studies of forest ecosystem. Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) shows advantages for ITS and tree height estimation at stand and landscape scale. However, dense deciduous forests with tightly interlocked tree crowns challenge the performance for ITS. Available LiDAR points through tree crown and appropriate algorithm are expected to attack the problem. In this study, a new UAV-LiDAR dataset that fused leaf-off and leaf-on point cloud (FULD) was introduced to assess the synergetic benefits for ITS and tree height estimation by comparing different types of segmentation algorithms (i.e., watershed segmentation, point cloud segmentation and layer stacking segmentation) in the dense deciduous forests of Northeast China. Field validation was conducted in the four typical stands, including mixed broadleaved forest (MBF), Mongolian oak forest (MOF), mixed broadleaf-conifer forest (MBCF) and larch plantation forest (LPF). The results showed that the combination of FULD and the layer stacking segmentation (LSS) algorithm produced the highest accuracies across all forest types (F-score: 0.70 to 0.85). The FULD also showed a better performance on tree height estimation, with a root mean square error (RMSE) of 1.54 m at individual level. Compared with using the leaf-on dataset solely, the RMSE of tree height estimation was reduced by 0.22 to 0.27 m, and 12.3% more trees were correctly segmented by the FULD, which are mainly contributed by improved detection rate at nearly all DBH levels and by improved detection accuracy at low DBH levels. The improvements are attributed to abundant points from the bole to the treetop of FULD, as well as each layer point being included for segmentation by LSS algorithm. These findings provide useful insights to guide the application of FULD when more multi-temporal LiDAR data are available in future.

Estimation of mean dominant height using NAIP digital aerial photogrammetry and lidar over mixed deciduous forest in the southeastern USA

• Temperate deciduous tree heights estimated with point clouds from digital aerial photogrammetry. • Photogrammetrically-derived point clouds a possible surrogate for those derived from lidar data. • Lidar-derived DEM necessary to normalize point clouds derived from digital aerial photogrammetry. • A poor photogrammetric solution led to reduced estimation accuracy in some shadowed areas. In the absence of complete lidar coverage, digital surface models (DSMs) and point clouds produced from the United States Department of Agriculture National Agriculture Imagery Program (NAIP) are increasingly being analyzed for quality and application feasibility. This study compared canopy heights derived from NAIP DSMs (10 m) and point clouds to those derived from lidar data collected over Mountain Lake Biological Station and the Great Smoky Mountains Twin Creeks Site by the National Ecological Observatory Network (NEON) Airborne Observation Platform for 62 mixed deciduous tree plots. Mean dominant height (MDH) was estimated using lidar and the NAIP products using the 90th percentile of heights in a given plot as the independent variable for both the lidar- and NAIP-derived point clouds. The dependent variable was field-measured MDH, calculated using the four tallest trees for each 0.04-hectare plot based on the NEON woody vegetation structure dataset. All data (field and remotely sensed) were collected in 2018. Using maximum likelihood spatial error model for all analyses, the NAIP DSM (10 m resolution) resulted in a strong relationship with MDH (coefficient of determination ( R 2 ) = 0.90, standard error ( SE ) = 1.71 m). However, the 90th percentiles of heights derived from the point clouds were better at estimating MDH than was the comparatively coarse resolution DSM (NAIP point clouds: R 2 = 0.94, SE = 1.40 m; lidar: R 2 = 0.95, SE = 1.29 m, respectively) and are strongly correlated to each other ( R 2 = 0.99, SE = 0.68 m). The main limitation of the NAIP datasets was found to be where shadowing occurred due to steep terrain in the Great Smoky Mountain site. These areas resulted in erroneously high vegetation heights. Mean dominant heights estimated using NAIP DSMs and point clouds are thus comparable to those estimated using lidar data in these closed-canopy temperate deciduous forests where shadowing from steep terrain is not present. The utility of both the NAIP-derived 10 m DSM and the point clouds for estimating tree heights paves the way for statewide mapping of heights over the deciduous forests in Tennessee, Virginia, and possibly beyond.

UTILIZING SINGLE PHOTON LASER SCANNING DATA FOR ESTIMATING INDIVIDUAL TREE ATTRIBUTES

Abstract. Mapping and monitoring forest resources require collection of spatially explicit and timely remote sensing (RS) data. Although field measurements are still important, the RS-based forest inventory helps mapping large areas to be cheaper, faster, less labor intensive, and spatially more explicit. The single-photon laser (SPL) scanning data has been exploited for different forestry applications but lacks deep examination in mapping individual trees as well as being compared with ordinary laser scanning (Linear-mode, LML) data and different individual tree detection (ITD) methods. Hence, this research focuses on applying and comparing two datasets (SPL and LML) for extracting attributes of individual trees by applying two tree crown segmentation methods (local maxima and watershed segmentation) on both datasets. The results were validated over 49 field measured plots of different species, located in southern boreal forest.The SPL yielded more accurate results for both tree density and height estimation. Watershed segmentation method yielded more accurate results for tree density and height estimation in both LML and SPL datasets. Tree density was underestimated by 4.7% (rRMSE: 32.3%) for all species. Comparing tree density estimation of different species, it was most accurate in deciduous plots (rBias: −9.5, rRMSE: 17.0%). Tree height estimation with SPL explained the variations of field-measured height very well (R2 = 0.93), and was reliably accurate, underestimated by 3.4% (rRMSE: 7.0%). The mean tree height estimation was most accurate in pine plots (rBias: 4.9%, rRMSE: 1.1%). In this research, SPL represented reliable and usable point cloud data for estimating tree height and density.

Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation.

Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F1 score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.

Tree Height Estimation from Unmanned Aerial Vehicle Imagery and Its Sensitivity on Above Ground Biomass Estimation in Dry Afromontane Forest, Northern Ethiopia

Tree height is a parameter useful for calculating above-ground forest biomass and is mostly measured traditionally by ground survey. On the other hand, measuring the forest tree height and biomass estimation through field survey is labor-intensive and time-consuming. The application of remote sensing for forest above-ground biomass (AGB) estimation without forest destruction is important in order to estimate the carbon sequestration potential of the forest. The unmanned aerial vehicle (UAV) is an elating technology, which can help to estimate tree height and it is evolving at a rapid speed. Moreover, assessing the relationship between estimated and measured tree height is necessary for the future application of estimated tree height on AGB estimation. However, tree height estimation from photogrammetric UAV imagery in the dry Afromontane Forest and its sensitivity to AGB estimation are not investigated. Thus, this study aimed to assess the accuracy of tree height estimated from photogrammetric UAV imagery and the sensitivity of the estimated tree height on AGB estimation. Photogrammetric UAV acquired images and sample trees height measured on the ground were collected in Desa’a dry Afromontane Forest, Northern Ethiopia. Tree height was estimated from photogrammetric UAV acquired images and compared with tree heights measured on the ground. Moreover, the sensitivity of the estimated tree height on AGB estimation was investigated. The estimated tree height explained 89% of the tree height measured in the field. A considerable difference between estimated and measured tree height has an insignificant effect on AGB estimation. Thus, in the dry land Afromontane Forest the application of UAV aerial imagery for tree height estimation is promising to estimate AGB.
 

Integrating UAV-SfM and Airborne Lidar Point Cloud Data to Plantation Forest Feature Extraction

A low-cost but accurate remote-sensing-based forest-monitoring tool is necessary for regularly inventorying tree-level parameters and stand-level attributes to achieve sustainable management of timber production forests. Lidar technology is precise for multi-temporal data collection but expensive. A low-cost UAV-based optical sensing method is an economical and flexible alternative for collecting high-resolution images for generating point cloud data and orthophotos for mapping but lacks height accuracy. This study proposes a protocol of integrating a UAV equipped without an RTK instrument and airborne lidar sensors (ALS) for characterizing tree parameters and stand attributes for use in plantation forest management. The proposed method primarily relies on the ALS-based digital elevation model data (ALS-DEM), UAV-based structure-from-motion technique generated digital surface model data (UAV-SfM-DSM), and their derivative canopy height model data (UAV-SfM-CHM). Following traditional forest inventory approaches, a few middle-aged and mature stands of Hinoki cypress (Chamaecyparis obtusa) plantation forests were used to investigate the performance of characterizing forest parameters via the canopy height model. Results show that the proposed method can improve UAV-SfM point cloud referencing transformation accuracy. With the derived CHM data, this method can estimate tree height with an RMSE ranging from 0.43 m to 1.65 m, equivalent to a PRMSE of 2.40–7.84%. The tree height estimates between UAV-based and ALS-based approaches are highly correlated (R2 = 0.98, p < 0.0001), similarly, the height annual growth rate (HAGR) is also significantly correlated (R2 = 0.78, p < 0.0001). The percentage HAGR of Hinoki trees behaves as an exponential decay function of the tree height over an 8-year management period. The stand-level parameters stand density, stand volume stocks, stand basal area, and relative spacing are with an error rate of less than 20% for both UAV-based and ALS-based approaches. Intensive management with regular thinning helps the plantation forests retain a clear crown shape feature, therefore, benefitting tree segmentation for deriving tree parameters and stand attributes.

Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests

Biomass stored in young forests has enormous potential for the reduction of fossil fuel consumption. However, to ensure long-term sustainability, the measurement accuracy of tree height is crucial for forest biomass and carbon stock monitoring, particularly in young forests. Precise height measurement using traditional field measurements is challenging and time consuming. Remote sensing (RS) methods can, however, replace traditional field-based forest inventory. In our study, we compare individual tree height estimation from Light Detection and Ranging (LiDAR) and Digital Aerial Photogrammetry (DAP) with field measurements. It should be noted, however, that there was a one-year temporal difference between the field measurement and LiDAR/DAP scanning. A total of 130 trees (32 Scots Pine, 29 Norway Spruce, 67 Silver Birch, and 2 Eurasian Aspen) were selected for height measurement in a young private forest in south-east Finland. Statistical correlation based on paired t-tests and analysis of variance (ANOVA, one way) was used to compare the tree height measured with the different methods. Comparative results between the remote sensing methods and field measurements showed that LiDAR measurements had a stronger correlation with the field measurements and higher accuracy for pine (R2 = 0.86, bias = 0.70, RMSE = 1.44) and birch (R2 = 0.81, bias = 0.86, RMSE = 1.56) than DAP, which had correlation values of (R2 = 0.71, bias = 0.82, RMSE = 2.13) for pine and (R2 = 0.69, bias = 1.19, RMSE = 2.08) for birch. The correlation of the two remote sensing methods with the field measurements was very similar for spruce: LiDAR (R2 = 0.83, bias = 0.30, RMSE = 1.17) and DAP (R2 = 0.83, bias = 0.44, RMSE = 1.26). Moreover, the correlation was highly significant, with minimum error and mean difference (R2 = 0.79–0.98, MD = 0.12–0.33, RMSD = 0.45–1.67) between LiDAR and DAP for all species. However, the paired t-test suggested that there is a significant difference (p < 0.05) in height observation between the field measurements and remote sensing for pine and birch. The test showed that LiDAR and DAP output are not significantly different for pine and spruce. Presumably, the time difference in field campaign between the methods was the reason for these significant results. Additionally, the ANOVA test indicated that the overall means of estimated height from LiDAR and DAP were not significantly different from field measurements in all species. We concluded that utilization of LiDAR and DAP for estimating individual tree height in young forests is possible with acceptable error and comparable accuracy to field measurement. Hence, forest inventory in young forests can be carried out using LiDAR or DAP for height estimation at the individual tree level as an alternative to traditional field measurement approaches.

Effect of varied unmanned aerial vehicle laser scanning pulse density on accurately quantifying forest structure

ABSTRACT Airborne laser scanning (ALS) is increasingly used to estimate various forest characteristics. Technological improvements in unmanned aerial vehicles (UAVs) and drone laser scanning (DLS) sensors have permitted the acquisition of high pulse density datasets. There is an assumption that higher pulse densities yield greater accuracies in estimating forest characteristics. In this study, we investigated the effect of pulse density (.25, .5, 1, 5, 10, 50, 100 and 300 pulses m−2) on the ability to delineate individual tree crowns (ITCs) and estimate ITC height and crown horizontal diameter, in addition to plot-level leaf area index (LAI). The current study took place in an experimentally varied Pinus taeda L. forest, which included three stem densities: (i) 618; (ii) 1236; and (iii) 1853 trees per hectare (TPH). ITCs were classified directly from the DLS point cloud for each of the pulse densities. The correct delineation of ITCs relative to field tree-coordinates was relatively consistent (±5%) for pulse densities of 5 to 300 pulses m−2. ITC delineation accuracy decreased with lower pulse densities. Planting stem density did impact ITC delineation accuracy. Higher pulse densities, plots with 618 TPH correctly classified ~88% of ITCs, and plots with the 1853 TPH correctly classified ~50% of ITCs. Estimates of tree height were largely unaffected by changes in tree density. Root mean square error (RMSE) for tree height varied from .5 to 2.5 m at pulse densities of 300 to .25 pulses m−2, respectively. Estimates of crown horizontal diameter varied with regard to both pulse and stem density from 1.2 (300 ppm−2 and 1853 TPH) to 4.2 m (.25 ppm−2 and 618 TPH). RMSE varied among stem densities from .6 to 1.2 m as pulse density decreased. There was significant difference in ITC delineation accuracy, particularly when considering stem density, and the estimates of tree height and crown horizontal diameter among the DLS pulse densities used. The accuracy of predicted LAI was largely unaffected by changes in pulse density, when pulse density was above .5 pulses m−2. There was little or no difference in estimates of LAI at these pulse densities. Our results suggest that low-density DLS data may be capable of estimating plot-level forest metrics reliably in some situations, however once the analysis scale is reduced to the individual-tree-level, the influence of pulse density is more substantial. The results here provide guidance to forest managers who must balance metric estimation accuracy and price when planning new ALS or DLS acquisitions.

Species Mixing Proportion and Aridity Influence in the Height–Diameter Relationship for Different Species Mixtures in Mediterranean Forests

Estimating tree height is essential for modelling and managing both pure and mixed forest stands. Although height–diameter (H–D) relationships have been traditionally fitted for pure stands, attention must be paid when analyzing this relationship behavior in stands composed of more than one species. The present context of global change makes also necessary to analyze how this relationship is influenced by climate conditions. This study tends to cope these gaps, by fitting new H–D models for 13 different Mediterranean species in mixed forest stands under different mixing proportions along an aridity gradient in Spain. Using Spanish National Forest Inventory data, a total of 14 height–diameter equations were initially fitted in order to select the best base models for each pair species-mixture. Then, the best models were expanded including species proportion by area (mi) and the De Martonne Aridity Index (M). A general trend was found for coniferous species, with taller trees for the same diameter size in pure than in mixed stands, being this trend inverse for broadleaved species. Regarding aridity influence on H–D relationships, humid conditions seem to beneficiate tree height for almost all the analyzed species and species mixtures. These results may have a relevant importance for Mediterranean coppice stands, suggesting that introducing conifers in broadleaves forests could enhance height for coppice species. However, this practice only should be carried out in places with a low probability of drought. Models presented in our study can be used to predict height both in different pure and mixed forests at different spatio-temporal scales to take better sustainable management decisions under future climate change scenarios.

Predicting the Elevation of Canopy Occluded Ground Points in Dense Forest Regions

Forest regions are still considered as complex environments for measuring terrain information by aerial surveys. Tree canopies occluded most of the ground surface and limit sensors’ capabilities for capturing ground data. For instance, vision systems (e.g., cameras) cannot record any information about the ground below the canopy. This lack of knowledge might decrease the accuracy of aerial surveys’ products such as digital terrain models (DTMs). Therefore, to outperform the ground surface knowledge, we proposed a method for computing the elevation of occluded ground points using canopy data and estimated tree height. To this aim, individual tree crowns are identified from a 3-D point cloud, retrieved by discrete light detection and ranging (LiDAR) sensors (i.e., Riegl LMSQ560, ALTM 3100, and Riegl LMSQ5600). Then, ground elevation is predicted by taking advantage of the logarithmic relationship between crown diameter (CD) and tree height. Results have shown that the mean, minimum, and maximum ground elevation errors are about 3.01, 1.28, and 6.38, respectively. Conversely, if one attempts to determine a ground surface without the proposed method, the mean elevation difference is about 11.68.

Forest height mapping using inventory and multi-source satellite data over Hunan Province in southern China

Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging. It is essential for the estimation of forest aboveground biomass and the evaluation of forest ecosystems. Yet current regional to national scale forest height maps were mainly produced at coarse-scale. Such maps lack spatial details for decision-making at local scales. Recent advances in remote sensing provide great opportunities to fill this gap. In this study, we evaluated the utility of multi-source satellite data for mapping forest heights over Hunan Province in China. A total of 523 plot data collected from 2017 to 2018 were utilized for calibration and validation of forest height models. Specifically, the relationships between three types of in-situ measured tree heights (maximum-, averaged-, and basal area-weighted- tree heights) and plot-level remote sensing metrics (multispectral, radar, and topo variables from Landsat, Sentinel-1/PALSAR-2, and SRTM) were analyzed. Three types of models (multilinear regression, random forest, and support vector regression) were evaluated. Feature variables were selected by two types of variable selection approaches (stepwise regression and random forest). Model parameters and model performances for different models were tuned and evaluated via a 10-fold cross-validation approach. Then, tuned models were applied to generate wall-to-wall forest height maps for Hunan Province. The best estimation of plot-level tree heights (R2 ranged from 0.47 to 0.52, RMSE ranged from 3.8 to 5.3 ​m, and rRMSE ranged from 28% to 31%) was achieved using the random forest model. A comparison with existing forest height maps showed similar estimates of mean height, however, the ranges varied under different definitions of forest and types of tree height. Primary results indicate that there are small biases in estimated heights at the province scale. This study provides a framework toward establishing regional to national scale maps of vertical forest structure.

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Light detection and ranging (LiDAR) has contributed immensely to forest mapping and 3D tree modelling. From the perspective of data acquisition, the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels. This research develops a general framework to integrate ground-based and UAV-LiDAR (ULS) data to better estimate tree parameters based on quantitative structure modelling (QSM). This is accomplished in three sequential steps. First, the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy. Next, redundancy and noise were removed for the ground-based/ULS LiDAR data fusion. Finally, tree modeling and biophysical parameter retrieval were based on QSM. Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest, including poplar and dawn redwood species. Generally, ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data. The fusion-derived tree height, tree volume, and crown volume significantly improved by up to 9.01%, 5.28%, and 18.61%, respectively, in terms of r RMSE. By contrast, the diameter at breast height (DBH) is the parameter that has the least benefits from fusion, and r RMSE remains approximately the same, because stems are already well sampled from ground data. Additionally, particularly for dense forests, the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR. Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests, whereby the improvement owing to fusion is not significant. • Mitigation of the limitations of tree localization in case of ground-based/ULS LiDAR data co-registration using virtual keypoints. • Fusion of ground-based/ULS LiDAR data based on removal of redundant and noisy points. • Investigation and evaluation of the utility of ground-based/ULS LiDAR data fusion for tree parameter estimation. • Ground-based/ULS LiDAR data fusion improved estimation of tree height, tree volume, and crown volume. • Ground-based/ULS LiDAR data fusion has strong potential in QSM of trees particularly in high-stand-density forest.