Using machine learning to model long term effects of thinning and climate change on transpiration and water use efficiency
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Earth, Environmental and Geographic Sciences, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Liu, Wanyi
issued: 2023-02-23T17:09:33Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Forest thinning is a method to reduce competition in overstocked young forests. While the short-term effect of thinning is often studied by field thinning experiments, the long-term effect of thinning is rarely studied and remains unclear, particularly in the context of climate change. In this study, we used data from field experiment to train two Artificial Intelligence models: artificial neural network (ANN) and deep neural network (DNN) for assessing the long term effect of thinning on transpiration and water use efficiency (WUE) in young lodgepole pine forests at both tree and stand levels. We showed that DNN is more accurate than ANN in terms of model performance measures. We then used the trained DNN model to predict the transpiration and WUE under different thinning and climate change scenarios. The simulations showed that thinning has a long-lasting effect on increasing tree-level transpiration and both tree- and stand-level WUE but decreasing stand-level transpiration. Thinning effect decreases as thinning intensity decreases and trees age. Climate change reduces both tree- and stand-level WUE. Thinning mitigates the impact of climate change on stand-level transpiration. Based on the simulated long-term effects, we recommend that the thinning with the remaining of 2500–3500 stem/ha as suitable densities for a better balancing or trade-off between total carbon and water (stand transpiration) for young and overstocked lodgepole pine stands in the southern interior of British Columbia. However, selection of the final thinning densities must consider broader ecological processes and functions such as wood quality, biodiversity as well as their interactions with climate change.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83839
note: Using machine learning to model longterm effects of thinning and climate changeon transpiration and water use efficiencybyWanyi LiuB.Sc. in Agriculture, Beijing Forestry University, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Earth and Environmental Sciences)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)Feburary 2023© Wanyi Liu, 2023The following individuals certify that they have read, and recommend to theCollege of Graduate Studies for acceptance, the thesis entitled:USING MACHINE LEARNING TO MODEL LONG TERM EFFECTS OFTHINNING AND CLIMATE CHANGE ON TRANSPIRATION AND WATER USEEFFICIENCYsubmitted by WANYI LIU in partial fulfillment of the requirements of the degreeof MASTER OF SCIENCEAdam Wei, I. K. Barber School of Arts & SciencesSupervisorDavid Scott, I. K. Barber School of Arts & SciencesSupervisory Committee MemberRita Winkler, Ministry of Forests, Lands, Natural Resource Operations and RuralDevelopmentSupervisory Committee MemberGuangyu Wang, Faculty of Forestry, University of British Columbia VancouverUniversity ExamineriiAbstractForest thinning is a method to reduce competition in overstocked young forests.While the short-term effect of thinning is often studied by field thinning exper-iments, the long-term effect of thinning is rarely studied and remains unclear,particularly in the context of climate change. In this study, we used data fromfield experiment to train two Artificial Intelligence models: artificial neural net-work (ANN) and deep neural network (DNN) for assessing the long term effectof thinning on transpiration and water use efficiency (WUE) in young lodgepolepine forests at both tree and stand levels. We showed that DNN is more accuratethan ANN in terms of model performance measures. We then used the trainedDNN model to predict the transpiration and WUE under different thinning and cli-mate change scenarios. The simulations showed that thinning has a long-las…
has type: Thesis/Dissertation
date issued: 2023-05
is shown at: 10.14288/1.0427252
language: eng
degree discipline: Earth and Environmental Sciences
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Wei, Xiaohua
type: Text
identifier URI: http://hdl.handle.net/2429/83839