Tree Height Measurements Research Articles

Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen

Tree height is a key indicator in forest ecology, reflecting tree growth status and ecosystem structure. Traditional methods of tree height measurement rely on ground-based measurements, which are limited by cost and time. In recent years, the development of machine learning and multi-source remotely sensed technologies has provided new ways to measure tree height. In this study, we utilized light detection and ranging and satellite data to extract spectral, vegetation, texture, polarization, terrain, and season features. By integrating these features with machine learning, deep learning, and optimization methods, we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability. It was found that (a) the seasonal tree height neural network demonstrated the highest prediction accuracy in tree height estimation ( R 2 = 0.72, mean absolute error = 1.89 m), and the optimization process of Shapley additive explanations reduced 23 features, which improved the prediction accuracy ( R 2 = 0.80, mean absolute error = 1.58 m) and saved computational resources; (b) the seasonal tree height neural network has a strong generalizability for estimating tree height across seasons and regions; and (c) during 2018 to 2023, tree heights in Shenzhen were mainly concentrated in 6 to 14 m, and the spatial distribution has a strong autocorrelation. Tree canopy heights in winter are generally lower than those in summer, and the tree growth rate shows spatial heterogeneity. Overall, this study uncovers the intricate interplay between tree growth and seasonal variations in its traits throughout the urbanization process in Shenzhen. It offers valuable data support and a theoretical foundation for urban greening management and ecological protection.

Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data.

The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor. A LiDAR sensor was used to collect 3D point cloud data from the apple orchard. Six samples of apple trees, representing a variety of shapes and sizes, were selected for data collection and validation. Commercial software and the python programming language were utilized to process the collected data. The data processing steps involved data conversion, radius outlier removal, voxel grid downsampling, denoising through filtering and erroneous points, segmentation of the region of interest (ROI), clustering using the density-based spatial clustering (DBSCAN) algorithm, data transformation, and the removal of ground points. Accuracy was assessed by comparing the estimated outputs from the point cloud with the corresponding measured values. The sensor-estimated and measured tree heights were 3.05 ± 0.34 m and 3.13 ± 0.33 m, respectively, with a mean absolute error (MAE) of 0.08 m, a root mean squared error (RMSE) of 0.09 m, a linear coefficient of determination (r2) of 0.98, a confidence interval (CI) of -0.14 to -0.02 m, and a high concordance correlation coefficient (CCC) of 0.96, indicating strong agreement and high accuracy. The sensor-estimated and measured canopy volumes were 13.76 ± 2.46 m3 and 14.09 ± 2.10 m3, respectively, with an MAE of 0.57 m3, an RMSE of 0.61 m3, an r2 value of 0.97, and a CI of -0.92 to 0.26, demonstrating high precision. For tree and row spacing, the sensor-estimated distances and measured distances were 3.04 ± 0.17 and 3.18 ± 0.24 m, and 3.35 ± 0.08 and 3.40 ± 0.05 m, respectively, with RMSE and r2 values of 0.12 m and 0.92 for tree spacing, and 0.07 m and 0.94 for row spacing, respectively. The MAE and CI values were 0.09 m, 0.05 m, and -0.18 for tree spacing and 0.01, -0.1, and 0.002 for row spacing, respectively. Although minor differences were observed, the sensor estimates were efficient, though specific measurements require further refinement. The results are based on a limited dataset of six measured values, providing initial insights into geometric feature characterization performance. However, a larger dataset would offer a more reliable accuracy assessment. The small sample size (six apple trees) limits the generalizability of the findings and necessitates caution in interpreting the results. Future studies should incorporate a broader and more diverse dataset to validate and refine the characterization, enhancing management practices in apple orchards.

Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data

ABSTRACT Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. However, complex forest stand structures due to dense canopy and abundant understory vegetation can seriously affect the extraction performance of tree parameters. This study selected Chinese fir plantations to examine how different forest stand structure conditions affected tree parameter extraction based on the integration of handheld laser scanning (HLS) and airborne laser scanning (ALS) data. This study also explored the ability of developing models using extracted tree height and crown width, or directly using the calculated variables from point clouds to estimate the diameter at breast height (DBH) and stem volume. The methods mainly consisted of two parts: (1) Selecting appropriate height to identify seed points for tree segmentation, and subsequently extracting tree parameters; (2) Taking tree height, crown width, and tree-level point cloud metrics as independent variables to establish regression and random forest models for estimating tree DBHs and stem volumes. Results showed that (1) tree DBHs were accurately extracted with relative root mean square error (RMSEr) of 7.1%−10.5% and stem volume with an RMSEr of 12.3%−16.8% under different understory conditions; (2) As stand structure became complex, direct extraction of tree DBHs from HLS data became a challenging task. However, developing a DBH estimation model using tree height and crown width proved feasible; (3) In the complex-understory condition, the volume estimation model utilizing point cloud variables achieved good performance with RMSEr of 28.0%. This research provides new insights for extracting tree parameters using HLS and ALS data, offering a potential replacement for field measurements of tree DBH, height, and stem volume for Chinese fir plantations.

Accuracy Assessment of Advanced Laser Scanner Technologies for Forest Survey Based on Three-Dimensional Point Cloud Data

Forests play a crucial role in carbon sequestration and climate change mitigation, offering ecosystem services, biodiversity conservation, and water resource management. As global efforts to reduce greenhouse gas emissions intensify, the demand for accurate spatial information to monitor forest conditions and assess carbon absorption capacity has grown. LiDAR (Light Detection and Ranging) has emerged as a transformative tool, providing high-resolution 3D spatial data for detailed analysis of forest attributes, including tree height, canopy structure, and biomass distribution. Unlike traditional manpower-intensive forest surveys, which are time-consuming and often limited in accuracy, LiDAR offers a more efficient and reliable solution. This study evaluates the accuracy and applicability of advanced LiDAR technologies—drone-mounted, terrestrial, and mobile scanners—for generating 3D forest spatial data. The results show that the terrestrial LiDAR achieved the highest precision for diameter at breast height (DBH) and tree height measurements, with RMSE values of 0.66 cm and 0.91 m, respectively. Drone-mounted LiDAR demonstrated excellent efficiency for large-scale surveys, while mobile LiDAR offered portability and speed but required further improvement in accuracy (e.g., RMSE: DBH 0.76 cm, tree height 1.83 m). By comparing these technologies, this study identifies their strengths, limitations, and optimal application scenarios, contributing to more accurate forest management practices and carbon absorption assessments.

Woody Species Composition, Structure, and Diversity of Parkland and Coffee-Based Agroforestry Systems, Habro District, Eastern Ethiopia

Abstract Agroforestry practices in eastern Ethiopia are renowned for their multifunctional landscapes, contributing significantly to biodiversity conservation and enhancement. However, there is a lack of comprehensive studies on the diversity and structure of woody species within coffee-based and parkland agroforestry systems documented in a single study. This research aimed to evaluate the composition, diversity, and structure of woody species in these agroforestry systems. Conducted in the Habro district of eastern Ethiopia, the study involved randomly selecting sixteen plots for each agroforestry system. Inventory assessments of woody species were carried out using 40×40 and 20 m*20-meter plots for parkland and coffee-based systems respectively, and 5×5-meter plots for coffee shrubs. For trees with a diameter at breast height (DBH) of ≥2.5 cm, measurements of tree heights were taken. The study computed basal area, stem density, diameter, and height class distribution to characterize the structure of woody communities, and species diversity was also assessed. The findings revealed a total of 57 woody species across 31 families. Specifically, 38 woody species were recorded in parkland agroforestry, while 43 species were found in coffee-based agroforestry systems. Significant differences were observed in species diversity indices and structural parameters between the two agroforestry systems. The Shannon diversity index and richness were higher in coffee-based agroforestry compared to parkland systems. Additionally, the density and basal area of woody species were greater in coffee-based systems than in parkland agroforestry. Overall, both agroforestry systems were found to conserve a significant number of woody species, highlighting their potential to contribute to biodiversity conservation and informing future agroforestry management strategies in national programs.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

Volume equations for Scots pine trees in Kastamonu region

The objective of this study is to develop tree volume equations for Scots pine trees in the Kastamonu region and to compare the prediction capability of these equations with existing tree volume equations. For this purpose, stump diameter, diameter at breast height, diameters in each meter up to the top of the tree, and tree height measurements were measured on 127 sample trees from the Kastamonu Forest Enterprise in the Kastamonu Regional Directorate of Forestry. Subsequently, tree volumes were calculated based on these measurements and using the sectional method. This method entailed volumizing the trees in three sections: the stump section, the top section and the one-meter sections between the stump and the top sections. To develop single- and double-entry tree volume equations, the parameters of eight volume equations were estimated. Four statistical criteria were employed to identify the best predictive models. The coefficients of determination for the best predictive single- and double-entry models were found to be 0.972 and 0.975, respectively. Additionally, the results of these models were compared with the volume predictions derived from four models presented in the literature. The results of this study will enhance the accuracy of tree volume estimation for Scots pine stands in the Kastamonu region.

A mixed-effects height-diameter model for Pinus douglasiana Martínez in temperate forests of Jalisco, Mexico

predicting tree-height of Pinus douglasiana in the natural forests of Jalisco, Mexico. Design/methodology/approach: For this study, we utilized 2,921 pairs of tree-height measurements collected from 65 permanent plots (each 50x50 m) in the study area. For each plot, we estimated the tree density as the number of trees per hectare (N, ha-1), the basal area per hectare (G, m2 ha-1) and the quadratic mean diameter (dg, cm). Subsequently, we tested several models from the literature and developed a generalized mixed-effects model for tree height prediction. Results: The Gompertz base model outperformed the other local models, achieving an R² of 0.75 and an RMSE of 3.13. Including the stand variables (N, G, and dg) and incorporating random effects significantly improved the model fit, resulting in an R² of 0.89 and an RMSE of 2.09 m. Calibration and validation steps revealed that selecting the three thickest trees is effective for estimating random effects in new plots or stands, with the RMSE and R² being 2.59 and 0.83, respectively. Limitations on study/implications: The present model can be applied in areas where P. douglasiana is naturally distributed. However, it is advisable to calibrate the model using a sub-sample to achieve more accurate predictions of tree heights in new plots or areas. Findings/conclusions: The Gompertz function was used as the base function to develop the final generalized mixed-effects model for predicting the height of Pinus douglasiana. The inclusion of stand variables (N, G, and dg) along with random effects improved the model fit. This model represents a new and more accurate tool for predicting the height of P. douglasiana in areas where it is naturally distributed.

Unoccupied-Aerial-Systems-Based Biophysical Analysis of Montmorency Cherry Orchards: A Comparative Study

With the global population on the rise and arable land diminishing, the need for sustainable and precision agriculture has become increasingly important. This study explores the application of unoccupied aerial systems (UAS) in precision agriculture, specifically focusing on Montmorency cherry orchards in Payson, Utah. Despite the widespread use of UAS for various crops, there is a notable gap in research concerning cherry orchards, which present unique challenges due to their physical structure. UAS data were gathered using an RTK-enabled DJI Mavic 3M, equipped with both RGB and multispectral cameras, to capture high-resolution imagery. This research investigates two primary applications of UAS in cherry orchards: tree height mapping and crop health assessment. We also evaluate the accuracy of tree height measurements derived from three UAS data processing software packages: Pix4D, Drone2Map, and DroneDeploy. Our results indicated that DroneDeploy provided the closest relationship to ground truth data with an R2 of 0.61 and an RMSE of 31.83 cm, while Pix4D showed the lowest accuracy. Furthermore, we examined the efficacy of RGB-based vegetation indices in predicting leaf area index (LAI), a key indicator of crop health, in the absence of more expensive multispectral sensors. Twelve RGB-based indices were tested for their correlation with LAI, with the IKAW index showing the strongest correlation (R = 0.36). However, the overall explanatory power of these indices was limited, with an R2 of 0.135 in the best-fitting model. Despite the promising results for tree height estimation, the correlation between RGB-based indices and LAI was underwhelming, suggesting the need for further research.

Comparison of Carbon Sequestration in Family Forest using Tree Height Measurement by UAV and Field Surveys

Comparison of Carbon Sequestration in Family Forest using Tree Height Measurement by UAV and Field Surveys

No Field Evidence of Grass Fuel Structure effects on Postfire Tree Mortality in Juniperus virginiana

Prescribed fires are an important management tool for containing woody plant encroachment in rangeland ecosystems. Grasses are the dominant fuel type in rangelands. Past work has shown that grass canopy architecture, which varies among grass species, can influence flammability. Whether variation in grass fuel structure can influence postfire plant responses has not yet been tested. To bridge this gap, we set up field burning experiments with different fuel treatments and examined postfire mortality of Juniperus virginiana L. in a tallgrass prairie in southwestern Missouri. We sampled 60 trees and measured tree height and diameter at breast height before the fire. Fuels surrounding each tree were manipulated to vary independently in both fuel load and fuel structure. Flame temperatures were measured during the fire, and both stem and canopy injuries were evaluated 1 d after the fire. We surveyed tree mortality 7 mo after the fire. We found no effects of either fuel load or fuel structure on postfire mortality or on canopy injury in J. virginiana. Canopy injury was a critical fire severity measurement determining postfire mortality in J. virginiana, and taller trees are more fire resilient. Despite laboratory-observed fuel structure effects on flammability, this study finds no evidence for the importance of grass fuel load and canopy architecture in influencing postfire tree response. This result might arise from the low crown depth and low canopy water content of J. virginiana, which can promote canopy fire and result in a high mortality rate across fuel treatments. Notwithstanding the negative results, testing laboratory-based findings in field settings is important for further examining laboratory observations and upscaling individual-level processes to ecosystems to help identify the key ecological processes determining population dynamics and community assembly. Our study also suggests that prescribed fire is an effective tool to remove encroaching J. virginiana in tallgrass prairies at an early stage.

New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests.

Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non-destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory-based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ-400(i) TLS-based D and H data, allowing accuracy assessment of local forest inventory-based H:D allometries. Additionally, TLS-based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory-based H:D allometries underestimated H compared with TLS-based allometries. For 30-metre-tall trees, these underestimations varied from -1.6 m (-5.3%) to -7.5 m (-25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as -31.7 m (-41.3%). We propose a TLS-based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of -4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries.

Restoring a dry tropical forest through assisted natural regeneration: enhancing tree diversity, structure, and carbon stock

Deforestation is a key factor in global climate change, severely impacting ecosystem services. Effective implementation of reducing emissions from deforestation and forest degradation (REDD+) initiatives is essential for mitigating this change. Reforestation is a sustainable way to mitigate deforestation effects. We studied the impact of assisted natural regeneration (ANR) on restoring dry forest land, increasing biodiversity, structure, and carbon stock. Reforestation units were established at a multiple row planting system in a dry tropic region. We selected 63 plots, each 20 m × 20 m, categorized into multiple row–mixed plantation (MRMP) and multiple row–unmixed plantation (MRUP). We measured tree height and diameter at breast height (DBH) and evaluated biodiversity indices, including the Shannon–Weiner Index (SWI), species richness, evenness, and Important Value Index (IVI). Carbon stock was estimated using allometric equations. We identified 931 individual trees representing 27 species, 24 genera, and 14 families. MRMP showed significantly higher biodiversity, species richness, and evenness than MRUP. Terminalia arjuna was the most dominant species (IVI=124.45), followed by Khaya senegalensis (53.84). Survival rates exceeded 90 % for T. arjuna, K. senegalensis, Madhuca longifolia, and Pongamia pinnata (p<0.001). No significant differences were observed in tree DBH, tree height, tree density, basal area, and carbon stock among the plantation categories. The species Albizia saman exhibited a high diameter increment rate (4.07 ± 1.55 cm/year), followed by K. senegalensis (3.83 ± 0.43 cm/year). A mean value of 5.63 ± 1.13 MgC/ha of carbon stock was stored, while 20.66 ± 4.13 Mg/ha of atmospheric CO2 was sequestered. The results highlight that promoting mixed tree species in multiple rows to enhance biodiversity and optimize carbon regulation. This technique should be popularized to restore the dry forest landscape across the region.

A Critical Evaluation of the Carbon Sequestration Capacity of Trees in Specific Regions within Kaduna State Nigeria

This research evaluated the capacity of trees to store carbon in two specific locations, namely Gamji Park and Kaduna Polytechnic in Kaduna State. This was done by conducting field measurements, and observations. To facilitate measurement, the research area was partitioned into quadrants. A grand total of 1555 trees were quantified, with 775 trees found in Gamji Park and 780 trees at Kaduna Polytechnic. A non-destructive approach was used, whereby the measurement of tree height and diameter at breast height was conducted without the need for tree felling. The diameter at breast height (DBH) and height of trees with a diameter at breast height of at least 5cm were measured in each plot. The pantropical allometric equations were used to determine the carbon stock and the quantity of carbon dioxide sequestered by the plants. A total of 6875.4 Megatons/ha of carbon dioxide (CO2) was calculated to be sequestered by trees in the two research locations. The tree species Eucalyptus tereticornis and Magnifera indica had a much greater capacity for sequestration in comparison to the other trees that were examined. The Pearson correlation coefficient was employed to examine the association between tree parameters and carbon sequestration. The correlation coefficients for height and DBH were 0.934 and 0.979, respectively. These results suggest a positive relationship between tree parameters, specifically tree height and DBH, and carbon sequestration. Notably, DBH exhibited a stronger correlation compared to other parameters. A statistically significant difference in the tree parameters was observed, as shown by a p-value of less than 0.05.

Performance Analysis of Forest Canopy Height Model Generated from UAV and InSAR

Forest canopy height is a crucial parameter in ecosystem process modelling, yet research on generating canopy height models (CHMs) using photogrammetry method from UAV data remains limited compared to methods such as light detection and ranging (LiDAR). This study investigates the performance of accuracy and effectiveness of CHM generated from low-cost Unmanned Aerial Vehicle (UAV) via the photogrammetry method together with the well-known long-established Interferometric Synthetic Aperture Radar (InSAR). Leveraging advancements in UAV technology for three-dimensional land surface modelling, this research focuses on the Pasoh Research Station (nearby the Arboretum area) of Pasoh Forest Reserve in Negeri Sembilan, Malaysia. A series of images were captured at ten different UAV flying altitudes ranging from 120m to 500m above the mean sea level. Subsequently, CHMs were derived from both UAV and InSAR data, and compared against field measurements of tree height. The results indicate that UAV with a flying height below 200m can generate much more accurate results (with average height difference less than 10m) than InSAR (average height difference of 10.532m). It is noticeable that when comparing the InSAR-generated CHM to field measurements at lower altitudes (below 150m), the canopy height obtained from InSAR data is less accurate (more than 10m difference with field measurement) than the CHM obtained via UAV photogrammetry processing (only 5.353 m difference with field measurement). However, when UAV flying height is above 200m, InSAR data is performed more reliably. A 120m UAV flying height has a good potential to generate canopy height that is closer to the field measurement.

An Accuracy Assessment of Field and Airborne Laser Scanning–Derived Individual Tree Inventories using Felled Tree Measurements and Log Scaling Data in a Mixed Conifer Forest

Abstract On-the-ground sample-based forest inventory methods have been the standard practice for more than a century, however, remote sensing technologies such as airborne laser scanning (ALS) are providing wall-to-wall inventories based on individual tree measurements. In this study, we assess the accuracy of individual tree height, diameter, and volume derived from field-cruising measurements and three ALS data-derived methods in a 1.1 ha stand using direct measurements acquired on felled trees and log-scale volume measurements. Results show that although height derived from indirect conventional field measurements and ALS were statistically equivalent to felled tree height measurements, ALS measured heights had lower root mean square error (RMSE) and bias. Individual tree diameters modeled using a height-to-diameter-at-breast-height model derived from local forest inventory data and the software ForestView had moderate RMSE (8.3–8.5 cm) and bias (-3.0 – -0.3 cm). The ALS-based methods underdetected trees but accounted for 78%–91% of the field reference harvested merchantable volume and 71%–99% of the merchantable volume scaled at the mill. The results also illustrate challenges of using mill-scaled volume estimates as validation data and highlight the need for more research in this area. Overall, the results provide key insights to forest managers on accuracies associated with conventional field-derived and ALS-derived individual tree inventories. Study Implications: Forest inventory data provide critical information for operational decisions and forest product supply chain planning. Traditionally, forest inventories have used field sampling of stand conditions, which is time-intensive and cost-prohibitive to conduct at large spatial scales. Remote sensing technologies such as airborne laser scanning (ALS) provide wall-to-wall inventories based on individual tree measurements. This study advances our understanding of the accuracy of conventional field-derived and ALS-derived individual tree inventories by evaluating these inventories with felled tree and log scaling data. The results provide key insights to forest managers on errors associated with conventional field and ALS-derived individual tree measurements.

Early treatment effects on plantation growth and biodiversity in mature ponderosa pine forest

We address the concerns that managed forest plantations may cause reductions of diversity of understory vegetation. We performed inventories of two mature ponderosa pine plantations in which multiple treatments were applied during plantation establishment. At stand age 35, we measured tree heights and diameters, understory plant cover and diversity, soil nutrients and chemical properties, and soil microbial biomass and diversity with phospholipid fatty acid. We found a significant, positive effect from both herbicide (H) and fertilizer (F) applications on subsequent overstory tree growth and development ( P &lt; 0.05); insecticide (I) effect was minimal. We observed negative effects on understory plant diversity decades later when herbicide was applied during stand establishment. However, lower plant diversity and ground cover appeared to have been caused primarily from overstory canopy closure, supported by the increased understory cover and diversity observed in the HI and HFI plots that had been thinned at age 12. Similarly, while fertilizer increased tree growth at both sites, it only negatively affected understory plants at the higher quality site. We did not find significant influence of treatment effects on soil nutrients and microbial communities. Therefore, to mitigate the potential loss of understory biodiversity in plantations, foresters can manage overstory trees with traditional pre-commercial thinning techniques and early tending.

Evaluation of Tree Diameter and Height Measurements in UAV Data by Integrating Remote Sensing and Machine Learning Methods

Evaluation of Tree Diameter and Height Measurements in UAV Data by Integrating Remote Sensing and Machine Learning Methods

Using bioacoustics to enhance the efficiency of spotted owl surveys and facilitate forest restoration

AbstractThe California spotted owl (Strix occidentalis occidentalis) is an older‐forest associated species that resides at the center of forest management planning in the Sierra Nevada and Southern California, USA, which are experiencing increasingly large and severe wildfires and drought‐related tree mortality. We leveraged advances in passive acoustic survey technologies to develop an acoustically assisted survey design that could increase the efficiency and effectiveness of project‐level surveys for spotted owls, allowing surveys to be completed in a single year instead of in multiple years. We deployed an array of autonomous recording units (ARUs) across a landscape and identified spotted owl vocalizations in the resulting audio using BirdNET. We then evaluated spatio‐temporal patterns in spotted owl vocalizations near occupied territories and the ability of a crew naïve to the location of occupied territories to locate spotted owls based on patterns of acoustic detections. After only 3 weeks of acoustic surveys, ≥1 ARU within 750 m of all 17 occupied territories obtained spotted owl detections across ≥2 nights. When active surveys using broadcast calling were conducted near ARUs with spotted owl detections by surveyors naïve to territory occupancy status and locations, surveyors located owls in 93% to 100% of occupied territories with ≤3 surveys. To further improve the efficiency of spotted owl surveys, we developed a statistical model to identify and prioritize areas across the Sierra Nevada for different survey methods (active only, acoustically assisted, no surveys) based on the expected probability of occupancy predicted from remotely sensed measurements of tree height and historical occupancy. Depending on managers' tolerance for false negatives, this model could help identify large areas that might not benefit from surveys based on low expected occupancy probabilities and areas where acoustically assisted surveys might enhance survey effectiveness and efficiency. Collectively, these findings can help managers streamline the survey process and thus increase the pace of forest restoration while minimizing potential near‐term adverse effects on California spotted owls.

Tree height and stem growth dynamics in a Scots pine dominated boreal forest

Tree growth is a key forest ecosystem service and essential for carbon sequestration and biomass production. However, the intra-seasonal dynamics of tree height and stem diameter growth have been difficult to measure hampering the understanding of the interplay between these processes. Here, we investigated the feasibility of a laser scanning system in monitoring tree height development, aiming to study how tree height and stem diameter growth dynamics vary and interact, and how environmental variables explain the tree growth dynamics within a growing season. The experimental design consisted of 40 boreal trees equipped with dendrometers measuring changes in the stem diameter at 15-minutes intervals while a laser scanner fixed to a 35-meter tower was used to measure tree height near-daily during the monitoring period from May to mid-August 2021. We found that vertical changes in the tree-segmented point clouds enabled monitoring of tree height increment and investigation of the temporal dynamics of changes in tree height and stem diameter, when coupled with dendrometer measurements. The experiments revealed that, on average, height increment occurred ahead of diameter increment and deviated more towards the late season. Norway spruce showed more delayed diameter increment than Scots pine during the late season. Silver birch experienced diameter increment ahead of height increment. Based on the dendrometer measurements, we computed a radial change response function that aimed at characterizing the current state of stem diameter development, whether it was increasing or decreasing from its past state. When these radial change responses were compared against environmental variables, we found that the radial change response was mostly controlled by balance between precipitation and evapotranspiration, soil water content, minimum daily temperature, and vapor pressure deficit. Our findings support the utilization of laser scanning time series for measuring intra-seasonal changes in tree height and increase our understanding of the interactions between tree height and stem diameter growth processes.