Habitat suitability modeling based on remote sensing to realize time synchronization of species and environmental variables. (29th November 2020) - Wang, Da-Ju; Wei, Hai-Yan; Zhang, Xu-Hui; Fang, Ya-Qin; Gu, Wei
Abstract: Aims: Remote sensing (RS) is a technical method for effectively capturing real-world data on a large scale. We aimed to (i) realize the time synchronization of species and environmental variables, and extract variables related to the actual growth of species based on RS in habitat suitability modeling, and (ii) provide a reference for species management. Methods: Taking invasive species Ambrosia artemisiifolia in China as an example for habitat suitability modeling. Temperature and precipitation variables were calculated from the land surface temperature provided by the moderate-resolution imaging spectroradiometer (MODIS), and climate station data, respectively. Besides, other variables that directly affect the growth or reproduction of A. artemisiifolia were also included, such as the relative humidity of the previous year's flowering period (RHPFP), and the effective UV irradiance reaching the Earth's surface (UVI). The random forest method was selected to model the habitat suitability. The environmental variables and samples were divided into four-time periods (i.e. 1990–2000, 2001–2005, 2006–2010 and 2011–2016) based on sampling time. Variables from the long-time series of RS (1990–2016) and WorldClim (1960–1990) were also modeled. Important Findings: It was feasible to extract environmental variables from RS for habitat suitability modeling, and was more accurate than that based on the variables from WorldClim. The potential distribution of A. artemisiifolia in 1990–2000 and 2006–2010 was smaller than that in 2001–2005 and 2011–2016. The precipitation of driest months (bio14), precipitation coefficient of variation (bio15), RHPFP and UVI were the important environmental variables that affect the growth and reproduction of A. artemisiifolia . The results indicated that the time synchronization of species and environmental variables improved the prediction accuracy of A. artemisiifolia, which should be considered in habitat suitability modeling (especially for annual species). This study can provide an important reference for the management and prevention of the spread of A. artemisiifolia .