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 language | English |
|---|---|
| Pages (from-to) | 1379-1383 |
| Number of pages | 5 |
| Journal | MethodsX |
| Volume | 6 |
| DOIs |
|
| Publication status | Published - 2019 Jan 1 |
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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
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)). / Lamchin, Munkhnasan; Lee, Woo-Kyun; Jeon, Seong Woo; Wang, Sonam Wangyel; Lim, Chul Hee; Song, Cholho; Sung, Minjun.
In: MethodsX, Vol. 6, 01.01.2019, p. 1379-1383.Research output: Contribution to journal › Comment/debate
}
TY - JOUR
T1 - Corrigendum to “Mann-Kendall Monotonic Trend Test and Correlation Analysis using Spatio-temporal Dataset
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
Y1 - 2019/1/1
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
KW - Changing trend analysis
KW - Earth trend modeler
KW - Mann-Kendall monotonic trend test
KW - Relationship analysis
KW - Spatio-temporal analysis
UR - http://www.scopus.com/inward/record.url?scp=85067194333&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067194333&partnerID=8YFLogxK
U2 - 10.1016/j.mex.2019.05.030
DO - 10.1016/j.mex.2019.05.030
M3 - Comment/debate
AN - SCOPUS:85067194333
VL - 6
SP - 1379
EP - 1383
JO - MethodsX
JF - MethodsX
SN - 2215-0161
ER -