Estimating DBH of Trees Employing Multiple Linear Regression of the Best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines

Ibanez, C. A. G.; Carcellar, B. G.; Paringit, Enrico C.; Argamosa, R. J. L.; Faelga, R. A. G.; Posilero, M. A. V.; Zaragosa, G. P.; Dimayacyac, N. A.

doi:10.5194/isprsarchives-xli-b8-657-2016

Cited by 6 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) and (4), larger DBH also entails larger sapwood area, multiplying the effect of the sap velocity differences. Implementing spatial patterns of tree sizes into hydrological models could be attempted using mapped information from forest inventories, management plans or even lidar images (Ibanez et al, 2016;Rabadán et al, 2016;Vauhkonen and Mehtätalo, 2015). The stand density, expressed as the number of stems, explained on average 4 % of the variance in the daily models for sap velocity and 3 % for sap flow.…”

Section: Controls On Spatial Patterns In Daily Mean Sap Velocity and mentioning

confidence: 99%

Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration

Hassler

Weiler

Blume

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Transpiration is a key process in the hydrological cycle, and a sound understanding and quantification of transpiration and its spatial variability is essential for management decisions as well as for improving the parameterisation and evaluation of hydrological and soil–vegetation–atmosphere transfer models. For individual trees, transpiration is commonly estimated by measuring sap flow. Besides evaporative demand and water availability, tree-specific characteristics such as species, size or social status control sap flow amounts of individual trees. Within forest stands, properties such as species composition, basal area or stand density additionally affect sap flow, for example via competition mechanisms. Finally, sap flow patterns might also be influenced by landscape-scale characteristics such as geology and soils, slope position or aspect because they affect water and energy availability; however, little is known about the dynamic interplay of these controls. We studied the relative importance of various tree-, stand- and site-specific characteristics with multiple linear regression models to explain the variability of sap velocity measurements in 61 beech and oak trees, located at 24 sites across a 290 km2 catchment in Luxembourg. For each of 132 consecutive days of the growing season of 2014 we modelled the daily sap velocity and derived sap flow patterns of these 61 trees, and we determined the importance of the different controls. Results indicate that a combination of mainly tree- and site-specific factors controls sap velocity patterns in the landscape, namely tree species, tree diameter, geology and aspect. For sap flow we included only the stand- and site-specific predictors in the models to ensure variable independence. Of those, geology and aspect were most important. Compared to these predictors, spatial variability of atmospheric demand and soil moisture explains only a small fraction of the variability in the daily datasets. However, the temporal dynamics of the explanatory power of the tree-specific characteristics, especially species, are correlated to the temporal dynamics of potential evaporation. We conclude that transpiration estimates on the landscape scale would benefit from not only consideration of hydro-meteorological drivers, but also tree, stand and site characteristics in order to improve the spatial and temporal representation of transpiration for hydrological and soil–vegetation–atmosphere transfer models.

show abstract

Section: Controls On Spatial Patterns In Daily Mean Sap Velocity and mentioning

confidence: 99%

Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration

Hassler

Weiler

Blume

2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Multispectral imagery provides additional information regarding the individual tree species which can be classified using machine learning methods or analysed for diseases or insect invasions (Ibanez et al, 2016).…”

Section: Post-processing For the Multispectral Datamentioning

confidence: 99%

Optimized Post-Processing of Multiple Uav Images for Forestry Inspections

Raeva

Pavelka

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The following paper discusses possible optimized post-processing and data tracking of UAV imagery for forestry inspection. The survey took place in the National Natural Reserve Božídarské rašeliniště – The Wetland of Božídar from 2015 till now. The purpose of this study is to provide with a suitable post-processing method of UAV images in a protected area with no necessity of human interaction with the species. The authors used UAV imagery from RGB and multispectral sensors. The focus of the paper is the post-processing which relies solely on open-source tools. The results of the paper are a script for automatic computation of vegetation indices, a script for canopy height model in a certain part of the mapped area a possible GIS solution for storing and tracking the data.

show abstract

“…Many studies used LiDAR data from conventional ALS to measure predictor variables, such as total height and crown diameter, aiming at an estimation of DBH (response variable) with a linear regression model. For instance, Ibanez et al (2016) used linear regression models with LiDAR data to estimate DBH at plot levels of tropical forests. The r² of the adjusted model was 0.70; 0.83 and 0.72 for the three plots tested with 5x5 m², 10x10 m² and 2x20m³ respectively.…”

Section: Assessment Of the Linear Regression Modelmentioning

confidence: 99%

Evaluation of Multiple Linear Regression Model to Obtain DBH of Trees Using Data From a Lightweight Laser Scanning System on-Board a Uav

Machado

Tommaselli

Tachibana

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Vegetation mapping requires information about trees and underlying vegetation to ensure proper management of the urban and forest environments. This information can be obtained using remote sensors. For instance, lightweight systems composed of Unmanned Aerial Vehicles (UAVs) as a platform, low-cost laser units and the recent miniaturized navigation sensors (positioning and orientation) have become a very feasible and flexible alternative. Low-cost UAV-ALS systems usually provide centimetric accuracy in altimetry, according to flight data configuration and quality of observations. This paper presents a feasibility study of a lightweight ALS system on-board a UAV to estimate the diameters at breast height (DBH) of urban trees using LiDAR data and linear regression model. A mathematical model correlating the crown diameter and height of the tree to estimate the DBH was developed based on a linear regression with stepwise method. The stepwise linear regression method enables the addition and the removal of predictor variables through statistical tests. The tree samples were separated in two classes (A and B), according to the diametric distribution. These sample classes were used to define two linear regression models. The regression models that best fit the samples achieved an R2 adj value above 94% for class A and B, which demonstrates the closeness between the samples and the developed mathematical models. The quality control of the proposed regression models was performed comparing the DBH values estimated and directly measured (reference). DBH of the trees were estimated with an average discrepancy of 8.7&thinsp;cm.

show abstract

Estimating DBH of Trees Employing Multiple Linear Regression of the Best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines

Cited by 6 publications

References 1 publication

Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration

Tree-, stand- and site-specific controls on landscape-scale patterns of transpiration

Optimized Post-Processing of Multiple Uav Images for Forestry Inspections

Evaluation of Multiple Linear Regression Model to Obtain DBH of Trees Using Data From a Lightweight Laser Scanning System on-Board a Uav

Contact Info

Product

Resources

About