scholarly journals AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds

2020 ◽  
Vol 12 (18) ◽  
pp. 3089
Author(s):  
Guangpeng Fan ◽  
Liangliang Nan ◽  
Yanqi Dong ◽  
Xiaohui Su ◽  
Feixiang Chen
Keyword(s):  
Reference Values ◽  
Measurement Data ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Relative Bias ◽  
Volume Estimation ◽  
Convex Polyhedra ◽  
Above Ground Biomass ◽  
Ground Biomass

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

Characterisation of a windbreak system on the south coast of Western Australia. 2. Crop growth

2002 ◽  
Vol 42 (6) ◽  
pp. 717 ◽  
Author(s):  
R. A. Sudmeyer ◽  
P. R. Scott
Keyword(s):  
Grain Yield ◽  
Western Australia ◽  
Tree Height ◽  
Crop Growth ◽  
Above Ground Biomass ◽  
Evaporative Demand ◽  
Yield Increase ◽  
Ground Biomass ◽  
Wind Events ◽  
Similar Area

This paper, which is the second in a series of three, describes dryland crop growth and yields in a windbreak bay in south-western Australia and relates changes to microclimate modification by the windbreaks. Over the 4 years of this trial, above ground biomass and the development rate of crops 3–20 times the tree height from the windbreak (H) were similar to crops growing in unsheltered conditions (more than 20 H from the windbreaks). Grain yield was 16–30% higher between 3 H and 20 H than at more than 20 H in 1994, the driest year on record for the district, in other years yield was largely unchanged. In contrast, above ground biomass growth was consistently less within 3 H than further from the windbreaks and grain yield within 3 H was 19–27% less than unsheltered yield. Water use by the trees is the most likely cause of reduced yield within 3 H. Over the 4 years, mean grain yield between 0.5 H and 20 H was 3.8% greater than yield at more than 20 H. This increase was largely due to the yield increase in 1994. As 5.4% of the paddock was directly occupied by, or uncropped next to, the windbreaks, there was a net yield decrease of 2.8% over 4 years compared to estimated production from a similar area with no windbreaks. The principle benefits of the windbreaks were reducing evaporative demand in extremely dry years and protection against extreme wind events. These benefits must be weighed against the costs of establishing and maintaining windbreak systems.

scholarly journals Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 905 ◽  
Author(s):  
Guerra-Hernández ◽  
Cosenza ◽  
Cardil ◽  
Silva ◽  
Botequim ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Point Clouds ◽  
Volume Estimation ◽  
P Value ◽  
Test Statistic ◽  
Individual Tree ◽  
Significant Difference

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

scholarly journals Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

2019 ◽  
Vol 11 (22) ◽  
pp. 2678 ◽  
Author(s):  
Zhu ◽  
Sun ◽  
Peng ◽  
Huang ◽  
Li ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Spatial Scales ◽  
Point Clouds ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
3D Point Clouds ◽  
Vehicle Data ◽  
Aerial Vehicle ◽  
Multispectral Camera

Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.

scholarly journals Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

2019 ◽  
Vol 11 (11) ◽  
pp. 1261 ◽  
Author(s):  
Yaxiao Niu ◽  
Liyuan Zhang ◽  
Huihui Zhang ◽  
Wenting Han ◽  
Xingshuo Peng
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Plant Height ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Point Clouds ◽  
Above Ground Biomass ◽  
Sensing System ◽  
Ground Biomass ◽  
Application Potential

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

scholarly journals Estimation of above-ground biomass in forest stands from regression on their basal area and height

2016 ◽  
Vol 64 (1) ◽  
pp. 70-92 ◽  
Author(s):  
Mait Lang ◽  
Ando Lilleleht ◽  
Mathias Neumann ◽  
Karol Bronisz ◽  
Samir G. Rolim ◽  
...  
Keyword(s):  
Regression Models ◽  
Measurement Data ◽  
Basal Area ◽  
Growth Conditions ◽  
Repeated Measurements ◽  
Carbon Accounting ◽  
Published Data ◽  
Above Ground Biomass ◽  
Forest Stands ◽  
Ground Biomass

Abstract A generic regression model for above-ground biomass of forest stands was constructed based on published data (R2 = 0.88, RSE = 32.8 t/ha). The model was used 1) to verify two allometric regression models of trees from Scandinavia applied to repeated measurements of 275 sample plots from database of Estonian Network of Forest Research (FGN) in Estonia, 2) to analyse impact of between-tree competition on biomass, and 3) compare biomass estimates made with different European biomass models applied on standardized forest structures. The model was verified with biomass measurements from hemiboreal and tropical forests. The analysis of two Scandinavian models showed that older allometric regression models may give biased estimates due to changed growth conditions. More biomass can be stored in forest stands where competition between trees is stronger. The tree biomass calculation methods used in different countries have also substantial influence on the estimates at stand-level. A common database of forest biomass measurements from Europe in similar to pan-tropical tree measurement data may be helpful to harmonise carbon accounting methods.

scholarly journals Use Basal Diameter to Establish Mixed Species Allometric Equations Predicting Woody Stand Biomass in the Sudano-guinea Savannahs of Ngaoundere, Cameroon

2020 ◽  
pp. 1-12
Author(s):  
Mamadou Laminou Mal Amadou ◽  
Halilou Ahmadou ◽  
Ahmadou Ibrahim ◽  
Tchindebe Alexandre ◽  
Massai Tchima Jacob ◽  
...  
Keyword(s):  
Tree Height ◽  
Allometric Equation ◽  
Information Criteria ◽  
Allometric Equations ◽  
Coefficient Of Determination ◽  
Leaf Biomass ◽  
Allometric Relationships ◽  
Above Ground Biomass ◽  
Basal Diameter ◽  
Ground Biomass

Little information on allometric relationships for estimating stand biomass in the savannah of Cameroon was available. Allometric relationships for estimating stand biomass were investigated in the sudano-guinea savannah of Ngaoundere, Cameroon. A total of 90 individual woody from sixteen (16) contrasting plant species belonging shrubs and trees were harvested in Dang savannah across a range of diameter classes, from 3 to 35 cm. Basal diameter (D), total height (H) and tree density were determined and considered as predictor variables, while total above-ground biomass, stem, branch and leaf biomass were the output variables of the allometric models. Among many models tested, the best ones were chosen according to the coefficient of determination adjusted (R2adj), the residual standard error (RSE) and the Akaike Information Criteria. The main results showed that the integration of tree height and density with basal diameter improved in the degree of fitness of the allometric equations. The fit allometric stand biomass model for leaf, branch, stem and above ground biomass were the following forms: Ln(LB) = -5.08 + 2.75*Ln(D) – 0.30*Ln(D2Hρ); Ln(BB) = -7.81 + 1.29*Ln(D2H) – 0.39*Ln(ρ); Ln(SB) = -5.08 + 2.40*Ln(D) +0.50*Ln(H) and Ln(TB) = -5.07 + 3.21*Ln(D) – 0.12*Ln(D2Hρ) respectively. It is concluded that the use of tree height and density in the allometric equation can be improved for these species, as far as the present study area is concerned. Therefore, for estimating the biomass of shrubs and small trees, the use of basal diameter as an independent variable in the allometric equation with a power equation would be recommended in the Sudano-guinea savannahs of Ngaoundere, Cameroon. The paper describes details of shrub biomass allometry, which is important in carbon stock and savannah management for the environmental protection.

scholarly journals Development of allometric relationships for estimate above ground biomass of trees in the tropical abandoned land

2019 ◽  
Vol 20 (12) ◽  
Author(s):  
Karyati Karyati ◽  
Kusno Yuli Widiati ◽  
Karmini Karmini ◽  
Rachmad Mulyadi
Keyword(s):  
Correlation Coefficients ◽  
Tree Height ◽  
Allometric Equation ◽  
Allometric Relationships ◽  
Above Ground Biomass ◽  
Study Objective ◽  
Land Type ◽  
Ground Biomass ◽  
Abandoned Land ◽  
Abandoned Lands

Abstract. Karyati, Widiati KY, Karmini, Mulyadi R. 2019. Development of allometric relationships for estimate above ground biomass of trees in the tropical abandoned lands. Biodiversitas 20: 3508-3516. The abandoned lands have important role in the ecological function as well as carbon sequestration. The allometric equations to estimate above ground biomass in abandoned land are still limited available. This study objective was to develop allometric relationships between tree size variables (diameter at breast height (DBH) and tree height) and leaf, branch, trunk, and total above ground biomass (TAGB) in abandoned land in East Kalimantan, Indonesia. The correlation coefficients between stem DBH and tree height to leaf and branch indicating a relatively weak relationship. The moderately strong relationships were showed by DBH and tree height to trunk and TAGB. The specific allometric equation of above ground biomass for different land use and land type is needed to estimate the accurate TAGB in the site.

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