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Remote Sensing
Volume 12
Issue 24
10.3390/rs12244070
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Article

Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach

by
Florian Ellsäßer
1,*,
Alexander Röll
1,
Joyson Ahongshangbam
1,
Pierre-André Waite
2,
Hendrayanto
3,
Bernhard Schuldt
4 and
Dirk Hölscher
1,5
1
Tropical Silviculture and Forest Ecology, University of Goettingen, Büsgenweg 1, 37077 Göttingen, Germany
2
Plant Ecology and Ecosystems Research, University of Goettingen, Untere Karspüle 2, 37073 Göttingen, Germany
3
Forest Management, Kampus IPB Darmaga, Bogor Agricultural University, Bogor 16680, Indonesia
4
Julius-von-Sachs-Institute for Biological Sciences, Chair of Ecophysiology and Vegetation Ecology, University of Wuerzburg, Julius-von-Sachs-Platz 3, 97082 Wuerzburg, Germany
5
Centre of Biodiversity and Sustainable Land Use, University of Goettingen, Platz der Göttinger Sieben 5, 37073 Göttingen, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(24), 4070; https://doi.org/10.3390/rs12244070
Submission received: 25 October 2020 / Revised: 22 November 2020 / Accepted: 9 December 2020 / Published: 12 December 2020
(This article belongs to the Special Issue UAVs for Vegetation Monitoring)
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Abstract

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 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 (r2 = 0.87 for trees and r2 = 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.
Keywords: transpiration; method comparison; UAV; oil palm; multiple linear regression; support vector machine; random forest; artificial neural network transpiration; method comparison; UAV; oil palm; multiple linear regression; support vector machine; random forest; artificial neural network
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MDPI and ACS Style

Ellsäßer, F.; Röll, A.; Ahongshangbam, J.; Waite, P.-A.; Hendrayanto; Schuldt, B.; Hölscher, D. Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach. Remote Sens. 2020, 12, 4070. https://doi.org/10.3390/rs12244070

AMA Style

Ellsäßer F, Röll A, Ahongshangbam J, Waite P-A, Hendrayanto, Schuldt B, Hölscher D. Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach. Remote Sensing. 2020; 12(24):4070. https://doi.org/10.3390/rs12244070

Chicago/Turabian Style

Ellsäßer, Florian, Alexander Röll, Joyson Ahongshangbam, Pierre-André Waite, Hendrayanto, Bernhard Schuldt, and Dirk Hölscher. 2020. "Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach" Remote Sensing 12, no. 24: 4070. https://doi.org/10.3390/rs12244070

APA Style

Ellsäßer, F., Röll, A., Ahongshangbam, J., Waite, P.-A., Hendrayanto, Schuldt, B., & Hölscher, D. (2020). Predicting Tree Sap Flux and Stomatal Conductance from Drone-Recorded Surface Temperatures in a Mixed Agroforestry System—A Machine Learning Approach. Remote Sensing, 12(24), 4070. https://doi.org/10.3390/rs12244070

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