Corrigendum to “Mann-Kendall Monotonic Trend Test and Correlation Analysis using Spatio-temporal Dataset

the case of Asia using vegetation greenness and climate factors” (MethodsX (2018) 5 (803–807), (S2215016118301134), (10.1016/j.mex.2018.07.006))

Munkhnasan Lamchin, Woo-Kyun Lee, Seong Woo Jeon, Sonam Wangyel Wang, Chul Hee Lim, Cholho Song, Minjun Sung

Research output: Contribution to journalComment/debate

Abstract

The Earth Trends Modeler (ETM) is an earth observation software tool that allows for modeling environmental changes and trend analyses of earth observation data. We used Global Inventory Modeling and Mapping Studies (GIMMS)-Normalized Difference Vegetation Index-3rd generation (NDVI3g) and Climatic Research Unit Time Series (CRU-TS) for climate data. We applied Mann-Kendall Monotonic Trend (MKMT) test using the ETM for changing trend analyses, correlation and multiple regression for analyzing relationship between vegetation greenness and climate factors. These methods are effective approaches for conducting long-term monitoring and correlation analyses in broad area using satellite data. These methods were used to analyze the long term data, but mostly focused on national scale study. Our study expanded the methodological applicability over the whole Asia during the last 33 years. In addition, we used spatio-temporal data such as vegetation greenness, rainfall, temperature, and potential evapotranspiration in order to estimate changing trends and relationship analysis of vegetation greenness and climate factors. • MKMT test was an applicable method for broad area and analyzed the increasing or decreasing trends using time series dataset with a predetermined level of significance. • The correlation and regression analysis were suitable and useful methods to estimate spatial relationships between vegetation greenness and climate factors in the long term period.

Original languageEnglish
Pages (from-to)1379-1383
Number of pages5
JournalMethodsX
Volume6
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Spatio-Temporal Analysis
Climate
Earth (planet)
Time series
Observation
Evapotranspiration
Regression analysis
Rain
Software
Regression Analysis
corrigendum
Datasets
Satellites
Equipment and Supplies
Temperature
Monitoring
Research

Keywords

  • Application of Earth Trend Modeler (ETM) for long term spatio-temporal data analysis
  • Changing trend analysis
  • Earth trend modeler
  • Mann-Kendall monotonic trend test
  • Relationship analysis
  • Spatio-temporal analysis

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Medical Laboratory Technology

Cite this

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title = "Corrigendum to “Mann-Kendall Monotonic Trend Test and Correlation Analysis using Spatio-temporal Dataset: the case of Asia using vegetation greenness and climate factors” (MethodsX (2018) 5 (803–807), (S2215016118301134), (10.1016/j.mex.2018.07.006))",
abstract = "The Earth Trends Modeler (ETM) is an earth observation software tool that allows for modeling environmental changes and trend analyses of earth observation data. We used Global Inventory Modeling and Mapping Studies (GIMMS)-Normalized Difference Vegetation Index-3rd generation (NDVI3g) and Climatic Research Unit Time Series (CRU-TS) for climate data. We applied Mann-Kendall Monotonic Trend (MKMT) test using the ETM for changing trend analyses, correlation and multiple regression for analyzing relationship between vegetation greenness and climate factors. These methods are effective approaches for conducting long-term monitoring and correlation analyses in broad area using satellite data. These methods were used to analyze the long term data, but mostly focused on national scale study. Our study expanded the methodological applicability over the whole Asia during the last 33 years. In addition, we used spatio-temporal data such as vegetation greenness, rainfall, temperature, and potential evapotranspiration in order to estimate changing trends and relationship analysis of vegetation greenness and climate factors. • MKMT test was an applicable method for broad area and analyzed the increasing or decreasing trends using time series dataset with a predetermined level of significance. • The correlation and regression analysis were suitable and useful methods to estimate spatial relationships between vegetation greenness and climate factors in the long term period.",
keywords = "Application of Earth Trend Modeler (ETM) for long term spatio-temporal data analysis, Changing trend analysis, Earth trend modeler, Mann-Kendall monotonic trend test, Relationship analysis, Spatio-temporal analysis",
author = "Munkhnasan Lamchin and Woo-Kyun Lee and Jeon, {Seong Woo} and Wang, {Sonam Wangyel} and Lim, {Chul Hee} and Cholho Song and Minjun Sung",
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language = "English",
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T2 - the case of Asia using vegetation greenness and climate factors” (MethodsX (2018) 5 (803–807), (S2215016118301134), (10.1016/j.mex.2018.07.006))

AU - Lamchin, Munkhnasan

AU - Lee, Woo-Kyun

AU - Jeon, Seong Woo

AU - Wang, Sonam Wangyel

AU - Lim, Chul Hee

AU - Song, Cholho

AU - Sung, Minjun

PY - 2019/1/1

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N2 - The Earth Trends Modeler (ETM) is an earth observation software tool that allows for modeling environmental changes and trend analyses of earth observation data. We used Global Inventory Modeling and Mapping Studies (GIMMS)-Normalized Difference Vegetation Index-3rd generation (NDVI3g) and Climatic Research Unit Time Series (CRU-TS) for climate data. We applied Mann-Kendall Monotonic Trend (MKMT) test using the ETM for changing trend analyses, correlation and multiple regression for analyzing relationship between vegetation greenness and climate factors. These methods are effective approaches for conducting long-term monitoring and correlation analyses in broad area using satellite data. These methods were used to analyze the long term data, but mostly focused on national scale study. Our study expanded the methodological applicability over the whole Asia during the last 33 years. In addition, we used spatio-temporal data such as vegetation greenness, rainfall, temperature, and potential evapotranspiration in order to estimate changing trends and relationship analysis of vegetation greenness and climate factors. • MKMT test was an applicable method for broad area and analyzed the increasing or decreasing trends using time series dataset with a predetermined level of significance. • The correlation and regression analysis were suitable and useful methods to estimate spatial relationships between vegetation greenness and climate factors in the long term period.

AB - The Earth Trends Modeler (ETM) is an earth observation software tool that allows for modeling environmental changes and trend analyses of earth observation data. We used Global Inventory Modeling and Mapping Studies (GIMMS)-Normalized Difference Vegetation Index-3rd generation (NDVI3g) and Climatic Research Unit Time Series (CRU-TS) for climate data. We applied Mann-Kendall Monotonic Trend (MKMT) test using the ETM for changing trend analyses, correlation and multiple regression for analyzing relationship between vegetation greenness and climate factors. These methods are effective approaches for conducting long-term monitoring and correlation analyses in broad area using satellite data. These methods were used to analyze the long term data, but mostly focused on national scale study. Our study expanded the methodological applicability over the whole Asia during the last 33 years. In addition, we used spatio-temporal data such as vegetation greenness, rainfall, temperature, and potential evapotranspiration in order to estimate changing trends and relationship analysis of vegetation greenness and climate factors. • MKMT test was an applicable method for broad area and analyzed the increasing or decreasing trends using time series dataset with a predetermined level of significance. • The correlation and regression analysis were suitable and useful methods to estimate spatial relationships between vegetation greenness and climate factors in the long term period.

KW - Application of Earth Trend Modeler (ETM) for long term spatio-temporal data analysis

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KW - Earth trend modeler

KW - Mann-Kendall monotonic trend test

KW - Relationship analysis

KW - Spatio-temporal analysis

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