scholarly journals Tree Height Measurements in Degraded Tropical Forests Based on UAV-LiDAR Data of Different Point Cloud Densities: A Case Study on Dacrydium pierrei in China

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 328
Author(s):  
Xi Peng ◽  
Anjiu Zhao ◽  
Yongfu Chen ◽  
Qiao Chen ◽  
Haodong Liu
Keyword(s):  
Point Cloud ◽  
Mean Squared Error ◽  
Coniferous Forest ◽  
Tree Height ◽  
Forest Degradation ◽  
Forest Growth ◽  
Growth And Yield ◽  
Lidar Data ◽  
Tropical Rain Forests ◽  
Cloud Density

Tropical forest degradation is a major contributor to greenhouse gas emissions. Tree height can be used as an important predictor of forest growth, and yield models can provide basic data for forest degradation assessments. As an important parameter of unmanned aerial vehicle-light detection and ranging (UAV-LiDAR), it is not clear how the point cloud density affects the extraction accuracy of tree height in degraded tropical rain forests. To solve this problem, we collected UAV-LiDAR data at a flight altitude of 150 m, and then resampled the UAV-LiDAR data obtained according to the point cloud density percentage resampling method and obtained UAV-LiDAR data for five different point cloud densities, namely, 12, 17, 28, 64, and 108 points/m2. On the basis of the resampled LiDAR data, we generated a canopy height model (CHM) to extract the height of Dacrydium pierrei (D. pierrei). The results show that (1) With the increase in the point cloud density, the accuracy of tree height extraction gradually increased, with a maximum accuracy at 108 points/m2 (root mean squared error (RMSE)% = 22.78%, bias% = 14.86%). The accuracy (RMSE%) increased by 6.92% as the point cloud density increased from 12 points/m2 to 17 points/m2, but only increased by 0.99% as the point cloud density increased from 17 points/m2 to 108 points/m2, indicating that 17 points/m2 is a critical point for tree height extraction of D. pierrei. (2) Compared with the results from broad-leaved forests, the accuracy of D. pierrei height extraction from coniferous forest was higher. With the increase in point cloud density, the difference in the accuracy of D. pierrei height between two stands gradually increased. When the point cloud density was 108 points/m2, the differences in RMSE% and bas% were 3.55% and 6.22%, respectively. When the point cloud density was 12 points/m2, the differences in RMSE% and bias% were 2.71% and 4.69%, respectively. Our research identified the lowest LiDAR data point cloud density required to ensure a certain accuracy in tree height extraction, which will help scholars formulate UAV-LiDAR forest resource survey plans.

Predicting forest growth and yield in northeastern Ontario using the process-based model of TRIPLEX1.0

2005 ◽  
Vol 35 (9) ◽  
pp. 2268-2280 ◽  
Author(s):  
Xiaolu Zhou ◽  
Changhui Peng ◽  
Qing-Lai Dang ◽  
Jiaxin Chen ◽  
Sue Parton
Keyword(s):  
Black Spruce ◽  
Basal Area ◽  
Tree Height ◽  
Field Measurements ◽  
Forest Growth ◽  
Growth And Yield ◽  
Inventory Data ◽  
Yield Data ◽  
Yield Table ◽  
Forest Growth And Yield

Process-based carbon dynamic models are rarely validated against traditional forest growth and yield data and are difficult to use as a practical tool for forest management. To bridge the gap between empirical and process-based models, a simulation using a hybrid model of TRIPLEX1.0 was performed for the forest growth and yield of the boreal forest ecosystem in the Lake Abitibi Model Forest in northeastern Ontario. The model was tested using field measurements, forest inventory data, and the normal yield table. The model simulations of tree height and diameter at breast height (DBH) showed a good agreement with measurements for black spruce (Picea mariana (Mill.) BSP), jack pine (Pinus banksiana Lamb.), and trembling aspen (Populus tremuloides Michx.). The coefficients of determination (R2) between simulated values and permanent sample plot measurements were 0.92 for height and 0.95 for DBH. At the landscape scale, model predictions were compared with forest inventory data and the normal yield table. The R2 ranged from 0.73 to 0.89 for tree height and from 0.72 to 0.85 for DBH. The simulated basal area is consistent with the normal yield table. The R2 for basal area ranged from 0.82 to 0.96 for black spruce, jack pine, and trembling aspen for each site class. This study demonstrated the feasibility of testing the performance of the process-based carbon dynamic model using traditional forest growth and yield data and the ability of the TRIPLEX1.0 model for predicting growth and yield variables. The current work also introduces a means to test model accuracy and its prediction of forest stand variables to provide a complement to empirical growth and yield models for forest management practices, as well as for investigating climate change impacts on forest growth and yield in regions without sufficient established permanent sample plots and remote areas without suitable field measurements.

scholarly journals Quantitative Comparison of UAS-Borne LiDAR Systems for High-Resolution Forested Wetland Mapping

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4453
Author(s):  
Narcisa Gabriela Pricope ◽  
Joanne Nancie Halls ◽  
Kerry Lynn Mapes ◽  
Joseph Britton Baxley ◽  
James JyunYueh Wu
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Coastal Wetland ◽  
Coniferous Forest ◽  
Point Clouds ◽  
Atlantic Coastal Plain ◽  
Airborne Lidar ◽  
Forested Wetland ◽  
Variable Cost ◽  
Cloud Density

Wetlands provide critical ecosystem services across a range of environmental gradients and are at heightened risk of degradation from anthropogenic pressures and continued development, especially in coastal regions. There is a growing need for high-resolution (spatially and temporally) habitat identification and precise delineation of wetlands across a variety of stakeholder groups, including wetlands loss mitigation programs. Traditional wetland delineations are costly, time-intensive and can physically degrade the systems that are being surveyed, while aerial surveys are relatively fast and relatively unobtrusive. To assess the efficacy and feasibility of using two variable-cost LiDAR sensors mounted on a commercial hexacopter unmanned aerial system (UAS) in deriving high resolution topography, we conducted nearly concomitant flights over a site located in the Atlantic Coastal plain that contains a mix of palustrine forested wetlands, upland coniferous forest, upland grass and bare ground/dirt roads. We compared point clouds and derived topographic metrics acquired using the Quanergy M8 and the Velodyne HDL-32E LiDAR sensors with airborne LiDAR and results showed that the less expensive and lighter payload sensor outperforms the more expensive one in deriving high resolution, high accuracy ground elevation measurements under a range of canopy cover densities and for metrics of point cloud density and digital terrain computed both globally and locally using variable size tessellations. The mean point cloud density was not significantly different between wetland and non-wetland areas, but the two sensors were significantly different by wetland/non-wetland type. Ultra-high-resolution LiDAR-derived topography models can fill evolving wetlands mapping needs and increase accuracy and efficiency of detection and prediction of sensitive wetland ecosystems, especially for heavily forested coastal wetland systems.

scholarly journals A Novel Tree Height Extraction Approach for Individual Trees by Combining TLS and UAV Image-Based Point Cloud Integration

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 537 ◽  
Author(s):  
Jiarong Tian ◽  
Tingting Dai ◽  
Haidong Li ◽  
Chengrui Liao ◽  
Wenxiu Teng ◽  
...  
Keyword(s):  
Point Cloud ◽  
Coniferous Forest ◽  
Tree Height ◽  
High Density ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data ◽  
Tree Farms ◽  
Uav Image ◽  
Structure Evaluation

Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms.

scholarly journals Models for Measuring Height-Diameter Relationships for Agarwood (AquilariamalaccensisLamk) Plantations in Bangladesh

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
S.M.Z. Islam ◽  
M.A.M. Chowdhury ◽  
K. Misbahuzzaman
Keyword(s):  
Goodness Of Fit ◽  
Linear Models ◽  
Mean Squared Error ◽  
Tree Height ◽  
Information Criterion ◽  
Growth And Yield ◽  
Coefficient Of Determination ◽  
Regression Parameters ◽  
Forest Division ◽  
Watson Statistic

The relationship between tree height and diameter is an important element in growth and yield models, in carbon stock estimation and timber volume models, and in the description of stand dynamics.In this paper considered18 functional models and evaluated the performance that predict total tree height from diameter at breast height of agarwood. The models were applied to A.malaccensisLamk (Agarwood) which is economically important tree species planted in some potential forest areas of Bangladesh.A total of 5,866 tree heights and corresponding diameters at breast heights were extracted from many forest areas in Sylhet, Chittagong, Cox's Bazar and Chittagong Hill Tracts (Rangamati) forest division. The model goodness of fit values were evaluated in terms of adjusted coefficient of determination (R2), root mean squared error (RMSE), Akaike’s information criterion (AIC),Durbin-Watson statistic value,homogeneity of the residuals and significance of the regression parameters. The results of the study indicated that the height-diameter relationship can best be described by non-linear models. The best three models selected for the species with ranking in terms of goodness of fit. The Gompertz ; Parabolic and Logistic  with R2=0.91 were height-diameter models performed better than other models.

Estimation of Individual Tree Parameters Using Small-Footprint LiDAR with Different Density in a Coniferous Forest

2012 ◽  
Vol 518-523 ◽  
pp. 5320-5323 ◽  
Author(s):  
Qi Sheng He ◽  
Na Li
Keyword(s):  
Coniferous Forest ◽  
Tree Height ◽  
Gansu Province ◽  
Parameters Estimation ◽  
Western China ◽  
Lidar Data ◽  
Individual Tree ◽  
Crown Diameter ◽  
Crown Width ◽  
Point Density

In this paper, the effects of different LiDAR point density on individual tree parameters including tree height and crown diameter were investigated for the coniferous tree species in the Qilian Mountain area within Gansu province, western China. 10 different density data were acquired in field survey area, with the minimum density of 0.234 points/m2 and the maximum density of 0.6941 points/m2 for per flight. By summing up the different flight data, the different density LIDAR data from 0.234 points/m2 to 5.226 points/m2 for extracting tree height and crown diameter can be analyzed. The result showed that the number of extraction points and the extraction accuracy of tree height and crown width arrived at relative high level in point density of about 2.5 points per m2. When the point density increased, the increased extraction points and the extraction accuracy of tree height and crown width became slow. It means that about 2.5 points per m2 of LiDAR data density may provide relative high accurate individual tree parameters estimation.

Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

2024 ◽  
Vol 84 ◽  
Author(s):  
A. Yousafzai ◽  
W. Manzoor ◽  
G. Raza ◽  
T. Mahmood ◽  
F. Rehman ◽  
...  
Keyword(s):  
Climate Change ◽  
Mean Squared Error ◽  
Forest Growth ◽  
Growth And Yield ◽  
Silver Fir ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Yield Data ◽  
Root Mean Squared Error ◽  
Squared Error

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe’s (LM), Willmott’s index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

scholarly journals Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation

2021 ◽  
Vol 13 (18) ◽  
pp. 3569
Author(s):  
Daniel Tamburlin ◽  
Michele Torresani ◽  
Enrico Tomelleri ◽  
Giustino Tonon ◽  
Duccio Rocchini
Keyword(s):  
Species Diversity ◽  
Tree Species ◽  
Point Cloud ◽  
Forest Ecosystems ◽  
Coniferous Forest ◽  
Lidar Data ◽  
Height Distribution ◽  
Tree Species Diversity ◽  
Height Variation ◽  
Q Index

Forest biodiversity is a key element to support ecosystem functions. Measuring biodiversity is a necessary step to identify critical issues and to choose interventions to be applied in order to protect it. Remote sensing provides consistent quality and standardized data, which can be used to estimate different aspects of biodiversity. The Height Variation Hypothesis (HVH) represents an indirect method for estimating species diversity in forest ecosystems from the LiDAR data, and it assumes that the higher the variation in tree height (height heterogeneity, HH), calculated through the ’Canopy Height Model’ (CHM) metric, the more complex the overall structure of the forest and the higher the tree species diversity. To date, the HVH has been tested exclusively with CHM data, assessing the HH only with a single heterogeneity index (the Rao’s Q index) without making use of any moving windows (MW) approach. In this study, the HVH has been tested in an alpine coniferous forest situated in the municipality of San Genesio/Jenesien (eastern Italian Alps) at 1100 m, characterized by the presence of 11 different tree species (mainly Pinus sylvestris, Larix decidua, Picea abies followed by Betula alba and Corylus avellana). The HH has been estimated through different heterogeneity measures described in the new R rasterdiv package using, besides the CHM, also other LiDAR metrics (as the percentile or the standard deviation of the height distribution) at various spatial resolutions and MWs (ALS LiDAR data with mean point cloud density of 2.9 point/m2). For each combination of parameters, and for all the considered plots, linear regressions between the Shannon’s H′ (used as tree species diversity index based on field data) and the HH have been derived. The results showed that the Rao’s Q index (singularly and through a multidimensional approach) performed generally better than the other heterogeneity indices in the assessment of the HH. The CHM and the LiDAR metrics related to the upper quantile point cloud distribution at fine resolution (2.5 m, 5 m) have shown the most important results for the assessment of the HH. The size of the used MW did not influence the general outcomes but instead, it increased when compared to the results found in the literature, where the HVH was tested without MW approach. The outcomes of this study underline that the HVH, calculated with certain heterogeneity indices and LiDAR data, can be considered a useful tool for assessing tree species diversity in considered forest ecosystems. The general results highlight the strength and importance of LiDAR data in assessing the height heterogeneity and the related biodiversity in forest ecosystems.

scholarly journals STREET TREE INFORMATION EXTRACTION AND DYNAMICS ANALYSIS FROM MOBILE LIDAR POINT CLOUD

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 271-277
Author(s):  
Y. Q. Li ◽  
H. Y. Liu ◽  
Y. K. Liu ◽  
S. B. Zhao ◽  
P. P. Li ◽  
...  
Keyword(s):  
Dynamic Analysis ◽  
Point Cloud ◽  
Tree Height ◽  
Point Clouds ◽  
Lidar Data ◽  
Geometrical Parameters ◽  
Dynamics Analysis ◽  
Street Trees ◽  
Tree Model ◽  
Trunk Diameter

Abstract. Street trees are common features and important assets in urban scenes. They are huge in numbers and are constantly changing, thus are difficult to monitor on a regular basis. A method of automatic extraction and dynamic analysis of street trees based on mobile LiDAR data is proposed. First, ground and low objects are filtered from the point clouds. Then, based on a geometric tree model and semantic information, each tree point cloud is extracted, and geometrical parameters such as location, trunk diameter, trunk structure line, tree height, crown width, and crown volume of each tree is obtained. A dynamic analysis combined with the growing characteristics of trees is conducted to compare and analyse the street trees from different epochs, in order to understand whether the trees have grown or been pruned, replanted, or displaced. The proposed algorithm was tested on three epochs of mobile LiDAR data, obtained in 2010, 2016 and 2018, respectively. Experimental results showed that the proposed method was able to accurately detect trees and extract tree parameters for detailed dynamics analysis.

scholarly journals Predicting Height to Crown Base of Larix olgensis in Northeast China Using UAV-LiDAR Data and Nonlinear Mixed Effects Models

2021 ◽  
Vol 13 (9) ◽  
pp. 1834
Author(s):  
Xin Liu ◽  
Yuanshuo Hao ◽  
Faris Rafi Almay Widagdo ◽  
Longfei Xie ◽  
Lihu Dong ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Forest Growth ◽  
Growth And Yield ◽  
Medium Size ◽  
Scanning System ◽  
Lidar Data ◽  
Larix Olgensis ◽  
Mixed Effect

As a core content of forest management, the height to crown base (HCB) model can provide a theoretical basis for the study of forest growth and yield. In this study, 8364 trees of Larix olgensis within 118 sample plots from 11 sites were measured to establish a two-level nonlinear mixed effect (NLME) HCB model. All predictors were derived from an unmanned aerial vehicle light detection and ranging (UAV-LiDAR) laser scanning system, which is reliable for extensive forest measurement. The effects of the different individual trees, stand factors, and their combinations on the HCB were analyzed, and the leave-one-site-out cross-validation was utilized for model validation. The results showed that the NLME model significantly improved the prediction accuracy compared to the base model, with a mean absolute error and relative mean absolute error of 0.89% and 9.71%, respectively. In addition, both site-level and plot-level sampling strategies were simulated for NLME model calibration. According to different prediction scale and accuracy requirements, selecting 15 trees randomly per site or selecting the three largest trees and three medium-size trees per plot was considered the most favorable option, especially when both investigations cost and the model’s accuracy are primarily considered. The newly established HCB model will provide valuable tools to effectively utilize the UAV-LiDAR data for facilitating decision making in larch plantations management.

Sign in / Sign up

Export Citation Format

Share Document