USING LONG SHORT-TERM MEMORY MODEL FOR CLOUD FOREST VEGETATION GROWTH STATUS PREDICTION – A CASE STUDY IN SHEI-PA NATIONAL PARK
Keywords: Cloud Forest, NDVI, LSTM, Climate Change, time series
Abstract. Cloud Forests (CFs) are characterized by their persistent foggy environment, in which fog can save two times the amount of precipitation in the dry season and increase water storage by 10% in the rainy season. CFs play an important role in ecosystems as high biodiversity and abundant endemic species live within CFs. However, CFs are sensitive to environmental changes, especially in current global climate warming conditions. Therefore, a typical cloud forest in Taiwan, Shei-Pa National park, was chosen as the study area. Specifically, the Normalized Difference Vegetation Index (NDVI) with meteorological factors including rainfall, average temperature, maximum temperature, and minimum temperature were obtained to assess the overall CFs trend from 2001 to 2017. Moreover, the Long Short-Term Memory neural network model (LSTM) was implemented to predict the future vegetation status. Preliminary results have shown that vegetation condition in Shei-Pa National park was getting better; rainfall, average temperature, and minimum temperature represented an upward trend while maximum temperature showed a downward trend. Furthermore, the LSTM- maximum temperature model displayed the highest prediction power with the MAPE index of 4.84%. The results provide a valuable reference for forest resource conservation and future climate adaptation strategies in Taiwan.