Analysis of the Response of Long-Term Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing Data: A Case Study in Guangdong Province (China)

The dynamic change and spatial–temporal distribution of vegetation coverage are of great significance for regional ecological evolution, especially in the subtropics and tropics. Identifying the heterogeneity in vegetation activities and its response to climate factors is crucial for projecting ecos...

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Main Authors: Sai Wang, Fenglei Fan
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Remote Sensing
Subjects:
EVI
Online Access:https://www.mdpi.com/2072-4292/13/10/1873
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spelling doaj-15da0ff1bcfe40648617e0cada00f0572021-05-31T23:42:22ZengMDPI AGRemote Sensing2072-42922021-05-01131873187310.3390/rs13101873Analysis of the Response of Long-Term Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing Data: A Case Study in Guangdong Province (China)Sai Wang0Fenglei Fan1School of Geography, South China Normal University, Guangzhou 510631, ChinaSchool of Geography, South China Normal University, Guangzhou 510631, ChinaThe dynamic change and spatial–temporal distribution of vegetation coverage are of great significance for regional ecological evolution, especially in the subtropics and tropics. Identifying the heterogeneity in vegetation activities and its response to climate factors is crucial for projecting ecosystem dynamics. We used long-term (2001–2018) satellite-derived enhanced vegetation index (EVI) datasets and climatic factors to analyze the spatiotemporal patterns of vegetation activities in an experimental area in Guangdong Province (China), as well as their links to changes in temperature (TEM), relative humidity (HUM), precipitation (PRE), sunshine duration (SUN), and surface runoff. The pruned exact linear time change point detection method (PELT) and the disturbance lag model (DLM) were used to understand the detailed ecological coverage status and time lag relationships between the EVI and climatic factors. The results indicate the following. (1) At the whole regional scale, a significant overall upward trend in the EVI variation was observed in 2001–2018. More specifically, there were two distinct periods with different trends, which were split by a turning point in 2005. PRE was the main climate-related driver of the rising EVI pre-2005, and the increase in TEM was the main climate factor influencing the forest EVI variation post-2006. (2) A three-month time lag effect was observed in the EVI response to relative humidity. The same phenomenon was found in the sunshine duration factor. (3) The EVI of farmlands (one type of land use) exhibited the largest lags between relative humidity and the sunshine duration factor, followed by grasslands and forests. (4) The comprehensive index of surface runoff could explain the time lags of vegetation activities, and the surface runoff value showed an apparently negative relationship with the vegetation coverage change.https://www.mdpi.com/2072-4292/13/10/1873EVIclimate factorsPELTdisturbance lag modelsurface runoff
collection DOAJ
language English
format Article
sources DOAJ
author Sai Wang
Fenglei Fan
spellingShingle Sai Wang
Fenglei Fan
Analysis of the Response of Long-Term Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing Data: A Case Study in Guangdong Province (China)
Remote Sensing
EVI
climate factors
PELT
disturbance lag model
surface runoff
author_facet Sai Wang
Fenglei Fan
author_sort Sai Wang
title Analysis of the Response of Long-Term Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing Data: A Case Study in Guangdong Province (China)
title_short Analysis of the Response of Long-Term Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing Data: A Case Study in Guangdong Province (China)
title_full Analysis of the Response of Long-Term Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing Data: A Case Study in Guangdong Province (China)
title_fullStr Analysis of the Response of Long-Term Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing Data: A Case Study in Guangdong Province (China)
title_full_unstemmed Analysis of the Response of Long-Term Vegetation Dynamics to Climate Variability Using the Pruned Exact Linear Time (PELT) Method and Disturbance Lag Model (DLM) Based on Remote Sensing Data: A Case Study in Guangdong Province (China)
title_sort analysis of the response of long-term vegetation dynamics to climate variability using the pruned exact linear time (pelt) method and disturbance lag model (dlm) based on remote sensing data: a case study in guangdong province (china)
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-05-01
description The dynamic change and spatial–temporal distribution of vegetation coverage are of great significance for regional ecological evolution, especially in the subtropics and tropics. Identifying the heterogeneity in vegetation activities and its response to climate factors is crucial for projecting ecosystem dynamics. We used long-term (2001–2018) satellite-derived enhanced vegetation index (EVI) datasets and climatic factors to analyze the spatiotemporal patterns of vegetation activities in an experimental area in Guangdong Province (China), as well as their links to changes in temperature (TEM), relative humidity (HUM), precipitation (PRE), sunshine duration (SUN), and surface runoff. The pruned exact linear time change point detection method (PELT) and the disturbance lag model (DLM) were used to understand the detailed ecological coverage status and time lag relationships between the EVI and climatic factors. The results indicate the following. (1) At the whole regional scale, a significant overall upward trend in the EVI variation was observed in 2001–2018. More specifically, there were two distinct periods with different trends, which were split by a turning point in 2005. PRE was the main climate-related driver of the rising EVI pre-2005, and the increase in TEM was the main climate factor influencing the forest EVI variation post-2006. (2) A three-month time lag effect was observed in the EVI response to relative humidity. The same phenomenon was found in the sunshine duration factor. (3) The EVI of farmlands (one type of land use) exhibited the largest lags between relative humidity and the sunshine duration factor, followed by grasslands and forests. (4) The comprehensive index of surface runoff could explain the time lags of vegetation activities, and the surface runoff value showed an apparently negative relationship with the vegetation coverage change.
topic EVI
climate factors
PELT
disturbance lag model
surface runoff
url https://www.mdpi.com/2072-4292/13/10/1873
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