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 5 (2018) 803–807

摘要

class="kwd-title">Method name: Application of Earth Trend Modeler (ETM) for long term spatio-temporal data analysis class="kwd-title">Keywords: Mann-Kendall monotonic trend test, Spatio-temporal analysis, Earth trend modeler, Changing trend analysis, Relationship analysis class="head no_bottom_margin" id="abs0010title">AbstractThe 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. class="first-line-outdent" id="lis0005">

• 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.