Extrapolating the Spatial Distribution of Endemic fir Reversely from the Windbreak Effect of Terrain-Shelterbelt on Red Cypress

• Main Authors: Shao, Bao-Hua, Li, Hung, Lo, Nan-Chang, Huang, Kai-Yi
• Format: Conference Proceeding
• Language: English
• Subjects: Biological system modeling; Climate change; Graphical models; impermeable shelterbelt; machine learning; Machine learning algorithms; Moisture; Predictive models; Radio frequency; Soil moisture; Species Distribution Model (SDM); terrain-related variable; topographic sheltering index (TSI)
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS46834.2022.9883267

As climate change getting severer, finding an effective way to establish specie distribution model (SDM) becomes vital, and collecting terrain-related variable is efficient. Chamaecyparis formosensis (Taiwan red cypress, TRC) grow around ridges above 1,800m covered with dry soil or gravel. Abies kawakamii (Taiwan fir, TF) grow near ridges and peaks over 3,000m with impermeable "terrain-shelterbelt" that block away overwhelming moisture. We used digital elevation models (DEM) with four different grid sizes (1, 5, 20, and 40m) to derive variables. We used several types of machine learning algorithms to model SDMs. The most accurate algorithms are decision tree and random forest, and the most suitable resolution is 5m. Elevation and topographic sheltering index are necessary for developing SDMs, and models can be more precise and accurate with using multiple layers of TSIs. As for different species, TRC tends to be majorly influenced by main high ridgeline, and TF is mostly affected by micro terrain.