We developed a reservoir-based spiking neural network (r-SNN) to classify three terrain types in a botanical garden. It included a recurrent layer and a supervised layer. The input spike trains to the recurrent layer were generated from linear accelerometer and gyroscope sensor values as well as camera frames from a ground robot. Compared to an SVM model and a 3L logistic regression model, our r-SNN method generated better prediction accuracy without reliance on a time window of data. Because the r-SNN is compatible with neuromorphic hardware, our proposed method could be part of a biologically-inspired power-efficient autonomous robot navigation system.