REMOTE SENSING ESTIMATION OF LITTERFALL DYNAMICS

摘要

Litterfall represents the major losses of aboveground carbon and nutrients to forest floors, which are pivotal components in terrestrial biogeochemical cycles. It is also an indicator to assess the impacts of perturbations on forest ecosystems such as tropical cyclones and insect infestation. The objective of this study is to conduct a large scale research to estimate the litterfall by combining field observations and estimates from multi-scale remote sensing techniques in a subtropical forest in Taiwan. Litterfall traps across a wide range of topo-edaphic gradients were installed and periodically collected data in Lienhuachi experimental forest (n = 18) and in Chilan mountain (n = 10). Enhance vegetation index (EVI), photochemical reflectance index (PRI) and photosynthetic vegetation (PV) were used to estimate time-series dynamics of litterfall from Landsat TM/ETM+ and MODIS images. Results showed that there had no significant correlations between litterfall data and the differences of monthly EVI (r = -0.17), monthly mean EVI (r = 0.08) and sPRI (r = 0.10). But there was a tricky positive relationship between APV and litterfall (r = 0.63, p-value = 0.012). If the values of litterfall induced by typhoon were took out, the relationship between ΔPV and litterfall was not significant (r = -0.25, p-value > 0.05). No significant correlations between vegetation indices and litterfall could be due to that the study sites were located at evergreen broadleaf and conifer forests where the seasonal dynamics of EVI (0.30 - 0.57) and sPRI (0.42 - 0.51) were small compared to deciduous forest, and it was difficult for remote sensing technique to capture the high variable dynamics of litterfall (90 - 1800 kg/ha). If the changes of vegetation phonology were not significant, the signal of litterfall will be easily mixed with noises in the images. Based on preliminary observation, finding a more sensitive index to capture the slight dynamics of litterfall and vegetation phonology is important in the future.