We aim to develop a QGIS plugin (KARI-SDM) that can perform regional modeling using high-frequency and high-resolution imagery, such as Sentinel and PlanetScope data, to reduce temporal mismatches between occurrence data and environmental variables and to more accurately reflect habitat conditions. Important elements of regional analysis are topography, land cover, and optical indices, which are processed using Python, such as land cover processed with the Random Forest algorithm. The model is run using MaxEnt to effectively reflect the nonlinear relationship between occurrence data and environmental variables. By simplifying the SDM workflow with efficient image processing and an intuitive interface, KARI-SDM enhances accessibility for researchers across various taxa. The plugin enables quantitative habitat analysis, supports time-series SDM, and provides valuable insights for species-specific conservation planning by incorporating micro-topographic and local environmental factors.
Oh et al. (Thu,) studied this question.