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.

A novel watermarking method is proposed to hide a binary watermark into image files compressed by fractal block coding. This watermarking method utilizes a special type of orthogonalization fractal coding method where the fractal affine transform is determined by the range block mean and contrast scaling. Such orthogonalization fractal decoding is a mean-invariant iteration. In contrast, the fractal parameters of classical fractal compression are very sensitive to any change of domain block pool and to common signal and geometric distortion. Hence, it is impossible to directly place a watermark in fractal parameters. The proposed watermark embedding procedure inserts a permutated pseudo-random binary sequence into the quantized range block means. The watermark is detected by computing the correlation coefficient between the original and the extracted watermark. Experimental results show that the proposed fractal watermarking scheme is robust against common signal and geometric distortion such as JPEG compression, low-pass filtering, rescaling, and clipping.