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Predicting tree sap flux and stomatal conductance from drone-recorded surface temperatures in a mixed agroforestry system — a machine learning approach

Please always quote using this URN: urn:nbn:de:bvb:20-opus-220059
  • Plant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measuredPlant transpiration is a key element in the hydrological cycle. Widely used methods for its assessment comprise sap flux techniques for whole-plant transpiration and porometry for leaf stomatal conductance. Recently emerging approaches based on surface temperatures and a wide range of machine learning techniques offer new possibilities to quantify transpiration. The focus of this study was to predict sap flux and leaf stomatal conductance based on drone-recorded and meteorological data and compare these predictions with in-situ measured transpiration. To build the prediction models, we applied classical statistical approaches and machine learning algorithms. The field work was conducted in an oil palm agroforest in lowland Sumatra. Random forest predictions yielded the highest congruence with measured sap flux (r\(^2\) = 0.87 for trees and r\(^2\) = 0.58 for palms) and confidence intervals for intercept and slope of a Passing-Bablok regression suggest interchangeability of the methods. Differences in model performance are indicated when predicting different tree species. Predictions for stomatal conductance were less congruent for all prediction methods, likely due to spatial and temporal offsets of the measurements. Overall, the applied drone and modelling scheme predicts whole-plant transpiration with high accuracy. We conclude that there is large potential in machine learning approaches for ecological applications such as predicting transpiration.show moreshow less

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Metadaten
Author: Florian Ellsäßer, Alexander Röll, Joyson Ahongshangbam, Pierre-André Waite, Hendrayanto, Bernhard Schuldt, Dirk Hölscher
URN:urn:nbn:de:bvb:20-opus-220059
Document Type:Journal article
Faculties:Fakultät für Biologie / Julius-von-Sachs-Institut für Biowissenschaften
Language:English
Parent Title (English):Remote Sensing
ISSN:2072-4292
Year of Completion:2020
Volume:12
Issue:24
Article Number:4070
Source:Remote Sensing (2020) 12:24, 4070. https://doi.org/10.3390/rs12244070
DOI:https://doi.org/10.3390/rs12244070
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 58 Pflanzen (Botanik) / 580 Pflanzen (Botanik)
Tag:UAV; artificial neural network; method comparison; multiple linear regression; oil palm; random forest; support vector machine; transpiration
Release Date:2022/07/07
Date of first Publication:2020/12/12
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International