# Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression  
[PERSON]  
1 Department of Engineering and Research Centre CIAMBITAL, University of Alberta, Carretera de Sacramento s/n, La Canada de San Urbano, 04120 Almeria, Spain; [EMAIL_ADDRESS] (A.N. ); [EMAIL_ADDRESS] (M.A.A.) [PERSON]  
1 Department of Engineering and Research Centre CIAMBITAL, University of Alberta, Carretera de Sacramento s/n, La Canada de San Urbano, 04120 Almeria, Spain; [EMAIL_ADDRESS] (A.N. ); [EMAIL_ADDRESS] (M.A.A.) [PERSON]  
1 Department of Engineering and Research Centre CIAMBITAL, University of Alberta, Carretera de Sacramento s/n, La Canada de San Urbano, 04120 Almeria, Spain; [EMAIL_ADDRESS] (A.N. ); [EMAIL_ADDRESS] (M.A.A.) [PERSON]  
2 Academie Editors: Hyuck-Jin Park, 2021 [PERSON], [PERSON] and [PERSON] [PERSON]  
Remote Sensing (RS) technology can help to solve the aforementioned drawback as we have witnessed an exponential increase in RS datasets derived from different sources (satellites, aircrafts, and UAV [Unmanned Aerial Vehicle]) at different resolutions based on different sensors (hyperspectral and multispectral cameras, LiDAR and SAR sensors, etc.) during the last decade. This has been accompanied by the fast development of computer-based processing techniques such as Structure from Motion stereo-photogrammetry to reconstruct 3D point clouds from low-cost UAV imagery [7; 8]. In fact, RS can be considered an exceptional source of data and powerful tools for monitoring forest dynamics at different spatial and temporal resolutions [9; 10]. For instance, and focusing on the tools applied in this study, terrestrial laser scanning (TLS) has proved to be an efficient and non-destructive measurement method that is becoming a new paradigm for implementing a tree-centric approach to deal with 3D forest modelling at plot scale [11; 12; 13; 14]. At the same time, parallel developments in Information Technology (IT) allow for the storage of very large datasets and their efficient processing. It has driven the development of many libraries and packages that implement supervised machine learning algorithms to investigate phenomena by automatically creating regression (and classification) models from labeled datasets in a very efficient way. It makes it possible to use machine learning methods in datasets derived from RS with the aim of increasing the level of automaticity in the extraction of valuable information [15; 16]. Current allometric models to estimate forest dry above-ground biomass (AGB) rely on stem diameter (diameter at breast height, DBH) and tree height (H) as key inputs [17; 18]. However, it is impossible to measure DBH from airborne or spaceborne sensors which, on the other hand, turn out to be the most suitable RS technologies for carrying out large-scale forest inventories [10]. In this regard, the classical linear regression techniques, after applying a logarithmic transformation to linearize the allometric model in a potential form that is usually the most used, are commonly applied to model allometric relationships between DBH (dependent variable) and predictor variables (H and crown diameter [CD]/crown area [CA]) [14; 18; 19]. It is important to highlight that linear models have the advantage that they are easy to fit, while generally offering adequate accuracy for tree allometric modeling [20]. Considering the above-mentioned background, this study uses TLS data collected at tree level in three teak plantations located in the Coastal Region of Ecuador (tropical dry forest) to compare the performance of several supervised machine learning regression methods with respect to traditional linear regression for modeling the local allometric relationships between DBH and H and CD. The underlying hypothesis is that learning-based models could outperform the results provided by traditional linear regression in the case of highly non-linear relationships found in tree allometry.