Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.

Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change differ. The objective of this research is to anal...

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Autores principales:	Taninnuch Lamjiak, Rungnapa Kaewthongrach, Booncharoen Sirinaovakul, Phongthep Hanpattanakit, Amnat Chithaisong, Jumpol Polvichai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/28883e1ca91644ae8484ea197e8e3393
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spelling	oai:doaj.org-article:28883e1ca91644ae8484ea197e8e33932021-12-02T20:17:33ZCharacterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.1932-620310.1371/journal.pone.0255962https://doaj.org/article/28883e1ca91644ae8484ea197e8e33932021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255962https://doaj.org/toc/1932-6203Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change differ. The objective of this research is to analyze the impact of climate variability on the leaf phenology in Thailand's tropical forests. Machine learning approaches were applied to model how leaf phenology in dry dipterocarp forest in Thailand responds to climate variability and El Niño. First, we used a Self-Organizing Map (SOM) to cluster mature leaf phenology at the species level. Then, leaf phenology patterns in each group along with litterfall phenology and climate data were analyzed according to their response time. After that, a Long Short-Term Memory neural network (LSTM) was used to create model to predict leaf phenology in dry dipterocarp forest. The SOM-based clustering was able to classify 92.24% of the individual trees. The result of mapping the clustering data with lag time analysis revealed that each cluster has a different lag time depending on the timing and amount of rainfall. Incorporating the time lags improved the performance of the litterfall prediction model, reducing the average root mean square percent error (RMSPE) from 14.35% to 12.06%. This study should help researchers understand how each species responds to climate change. The litterfall prediction model will be useful for managing dry dipterocarp forest especially with regards to forest fires.Taninnuch LamjiakRungnapa KaewthongrachBooncharoen SirinaovakulPhongthep HanpattanakitAmnat ChithaisongJumpol PolvichaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255962 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Taninnuch Lamjiak Rungnapa Kaewthongrach Booncharoen Sirinaovakul Phongthep Hanpattanakit Amnat Chithaisong Jumpol Polvichai Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.
description	Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change differ. The objective of this research is to analyze the impact of climate variability on the leaf phenology in Thailand's tropical forests. Machine learning approaches were applied to model how leaf phenology in dry dipterocarp forest in Thailand responds to climate variability and El Niño. First, we used a Self-Organizing Map (SOM) to cluster mature leaf phenology at the species level. Then, leaf phenology patterns in each group along with litterfall phenology and climate data were analyzed according to their response time. After that, a Long Short-Term Memory neural network (LSTM) was used to create model to predict leaf phenology in dry dipterocarp forest. The SOM-based clustering was able to classify 92.24% of the individual trees. The result of mapping the clustering data with lag time analysis revealed that each cluster has a different lag time depending on the timing and amount of rainfall. Incorporating the time lags improved the performance of the litterfall prediction model, reducing the average root mean square percent error (RMSPE) from 14.35% to 12.06%. This study should help researchers understand how each species responds to climate change. The litterfall prediction model will be useful for managing dry dipterocarp forest especially with regards to forest fires.
format	article
author	Taninnuch Lamjiak Rungnapa Kaewthongrach Booncharoen Sirinaovakul Phongthep Hanpattanakit Amnat Chithaisong Jumpol Polvichai
author_facet	Taninnuch Lamjiak Rungnapa Kaewthongrach Booncharoen Sirinaovakul Phongthep Hanpattanakit Amnat Chithaisong Jumpol Polvichai
author_sort	Taninnuch Lamjiak
title	Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.
title_short	Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.
title_full	Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.
title_fullStr	Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.
title_full_unstemmed	Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.
title_sort	characterizing and forecasting the responses of tropical forest leaf phenology to el nino by machine learning algorithms.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/28883e1ca91644ae8484ea197e8e3393
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Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.

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