Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing

Koffi Dodji Noumonvi,Urša Vilhar,Ana Žust,Gal Oblišar

doi:10.3390/rs13153015

Abstract

Phenological events are good indicators of the effects of climate change, since phenological phases are sensitive to changes in environmental conditions. Although several national phenological networks monitor the phenology of different plant species, direct observations can only be conducted on individual trees, which cannot be easily extended over large and continuous areas. Remote sensing has often been applied to model phenology for large areas, focusing mostly on pure forests in which it is relatively easier to match vegetation indices with ground observations. In mixed forests, phenology modelling from remote sensing is often limited to land surface phenology, which consists of an overall phenology of all tree species present in a pixel. The potential of remote sensing for modelling the phenology of individual tree species in mixed forests remains underexplored. In this study, we applied the seasonal midpoint (SM) method with MODIS GPP to model the start of season (SOS) and the end of season (EOS) of six different tree species in Slovenian mixed forests. First, substitute locations were identified for each combination of observation station and plant species based on similar environmental conditions (aspect, slope, and altitude) and tree species of interest, and used to retrieve the remote sensing information used in the SM method after fitting the best of a Gaussian and two double logistic functions to each year of GPP time series. Then, the best thresholds were identified for SOS and EOS, and the results were validated using cross-validation. The results show clearly that the usual threshold of 0.5 is not best in most cases, especially for estimating the EOS. Despite the difficulty in modelling the phenology of different tree species in a mixed forest using remote sensing, it was possible to estimate SOS and EOS with moderate errors as low as <8 days (Fagus sylvatica and Tilia sp.) and <10 days (Fagus sylvatica and Populus tremula), respectively.

Highlights

Reliable phenological observations, achieved through either direct visual human observations in phenology networks, near-surface measurements using phenology cameras and unmanned aerial vehicles, or remote sensing via satellites [1], are important for anticipating the effects of climate change on tree phenology in forests
Tree phenological phases mirror environmental conditions and can respond to variations in climate, making them good indicators of the effects of climate change when long series of phenological observations exist, as they can reveal some trends in the timing of spring and autumn phenological events [2,3]
At the end of season in autumn, pixels with significant cover of coniferous tree species would probably lead to a slow decrease in vegetation indices as compared to pixels with pure deciduous broadleaved species, which makes the estimation of the date of EOS more uncertain, as it was the case in our study

Summary

Introduction

Reliable phenological observations, achieved through either direct visual human observations in phenology networks, near-surface measurements using phenology cameras and unmanned aerial vehicles, or remote sensing via satellites [1], are important for anticipating the effects of climate change on tree phenology in forests. Many national phenological monitoring networks have their phenological stations distributed over altitude, slope, and aspect, most of the stations are either outside the forest or on the forest edge due to accessibility and other practical reasons [4,9]. This could have implications for the observed tree phenology and the generalisations resulting from these observations at national or regional scales [10] Remote Sens. 2021, 13, 3015 determinant of water and CO2 fluxes, and several studies clearly showed that growing season length controls net ecosystem primary productivity and that phenological shifts already modified the annual carbon cycle of terrestrial ecosystems [7,8].

Methods

Results

Discussion

Conclusion