Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China

Mangrove forests have important social, ecological, and economic value in coastal ecosystems. However, they are one of the most vulnerable ecosystems in the world and are widely threatened due to their unique location along the land-sea interface. Thus, mangrove conservation based on scientific and...

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Autores principales:	Bin Zhu, Jingjuan Liao, Guozhuang Shen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Mangrove forests Remote sensing Spatio-temporal changes Time series analysis Land cover data Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/a202bfff1a904d1b9b9725cf045de56d
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spelling	oai:doaj.org-article:a202bfff1a904d1b9b9725cf045de56d2021-12-01T04:59:35ZCombining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China1470-160X10.1016/j.ecolind.2021.108135https://doaj.org/article/a202bfff1a904d1b9b9725cf045de56d2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21008001https://doaj.org/toc/1470-160XMangrove forests have important social, ecological, and economic value in coastal ecosystems. However, they are one of the most vulnerable ecosystems in the world and are widely threatened due to their unique location along the land-sea interface. Thus, mangrove conservation based on scientific and effective monitoring methods is essential. In this study, a method for analyzing the spatio-temporal changes in mangrove forests was proposed in Qinglangang Nature Reserve, Hainan Province, China. This method combined multiple time series analysis (including Theil-Sen median trend analysis, Mann-Kendall test, and Hurst exponent) with land cover data from Landsat and Sentinel-1/2 to provide a clearer geographic explanation for changes in mangrove forests. The study showed that high-resolution data performs better than low-resolution data, optical indices are more ideal than SAR indices, and EVI is more advantageous than NDVI for mangrove time series analysis. The results revealed that mangroves in Qinglangang have experienced a severe degradation stage (1987–2003), a slow improvement stage (2003–2013), and a coexistence of improvement and degradation stage (2013–2020). In all stages, the main causes of mangrove degradation are anthropogenic factors, such as development of aquaculture ponds and building land, and natural factors caused by typhoons and sea-level rise. The anthropogenic factors have a broader and longer impact and should be a focus of future studies. The improvement in mangrove forests is due to habitat quality restoration and artificial planting, which is shown to be quite effective.Bin ZhuJingjuan LiaoGuozhuang ShenElsevierarticleMangrove forestsRemote sensingSpatio-temporal changesTime series analysisLand cover dataEcologyQH540-549.5ENEcological Indicators, Vol 131, Iss , Pp 108135- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mangrove forests Remote sensing Spatio-temporal changes Time series analysis Land cover data Ecology QH540-549.5
spellingShingle	Mangrove forests Remote sensing Spatio-temporal changes Time series analysis Land cover data Ecology QH540-549.5 Bin Zhu Jingjuan Liao Guozhuang Shen Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China
description	Mangrove forests have important social, ecological, and economic value in coastal ecosystems. However, they are one of the most vulnerable ecosystems in the world and are widely threatened due to their unique location along the land-sea interface. Thus, mangrove conservation based on scientific and effective monitoring methods is essential. In this study, a method for analyzing the spatio-temporal changes in mangrove forests was proposed in Qinglangang Nature Reserve, Hainan Province, China. This method combined multiple time series analysis (including Theil-Sen median trend analysis, Mann-Kendall test, and Hurst exponent) with land cover data from Landsat and Sentinel-1/2 to provide a clearer geographic explanation for changes in mangrove forests. The study showed that high-resolution data performs better than low-resolution data, optical indices are more ideal than SAR indices, and EVI is more advantageous than NDVI for mangrove time series analysis. The results revealed that mangroves in Qinglangang have experienced a severe degradation stage (1987–2003), a slow improvement stage (2003–2013), and a coexistence of improvement and degradation stage (2013–2020). In all stages, the main causes of mangrove degradation are anthropogenic factors, such as development of aquaculture ponds and building land, and natural factors caused by typhoons and sea-level rise. The anthropogenic factors have a broader and longer impact and should be a focus of future studies. The improvement in mangrove forests is due to habitat quality restoration and artificial planting, which is shown to be quite effective.
format	article
author	Bin Zhu Jingjuan Liao Guozhuang Shen
author_facet	Bin Zhu Jingjuan Liao Guozhuang Shen
author_sort	Bin Zhu
title	Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China
title_short	Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China
title_full	Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China
title_fullStr	Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China
title_full_unstemmed	Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China
title_sort	combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: a case study of qinglangang nature reserve, hainan, china
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/a202bfff1a904d1b9b9725cf045de56d
work_keys_str_mv	AT binzhu combiningtimeseriesandlandcoverdataforanalyzingspatiotemporalchangesinmangroveforestsacasestudyofqinglangangnaturereservehainanchina AT jingjuanliao combiningtimeseriesandlandcoverdataforanalyzingspatiotemporalchangesinmangroveforestsacasestudyofqinglangangnaturereservehainanchina AT guozhuangshen combiningtimeseriesandlandcoverdataforanalyzingspatiotemporalchangesinmangroveforestsacasestudyofqinglangangnaturereservehainanchina
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Combining time series and land cover data for analyzing spatio-temporal changes in mangrove forests: A case study of Qinglangang Nature Reserve, Hainan, China

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