Assessment of Spatio-Temporal Patterns of Black Spruce Bud Phenology across Quebec Based on MODIS-NDVI Time Series and Field Observations

Siddhartha Khare,Maxime Charles Paré,Guillaume Drolet,Sergio Rossi,Jean-Daniel Sylvain

doi:10.3390/rs11232745

Abstract

Satellite remote sensing is a widely accessible tool to investigate the spatiotemporal variations in the bud phenology of evergreen species, which show limited seasonal changes in canopy greenness. However, there is a need for precise and compatible data to compare remote sensing time series with field observations. In this study, fortnightly MODIS-NDVI was fitted using double-logistic functions and calibrated using ordinal logit models with the sequential phases of bud phenology collected during 2015, 2017 and 2018 in a black spruce stand. Bud break and bud set were spatialized for the period 2009–2018 across 5000 stands in Quebec, Canada. The first phase of bud break and the last phase of bud set were observed in the field in mid-May and at the beginning of September, when NDVI was 80.5% and 92.2% of its maximum amplitude, respectively. The NDVI rate of change was estimated at 0.07 in spring and 0.04 in autumn. When spatialized on the black spruce stands, bud break was detected earlier in the southwestern regions (April–May), and later in the northeastern regions (mid to end of June). No clear trend was observed for bud set, with different patterns being detected among the years. Overall, the process bud break and bud set lasted 51 and 87 days, respectively. Our results demonstrate the potential of satellite remote sensing for providing reliable timings of bud phenological events using calibrated NDVI time series on wide regions that are remote or with limited access.

Highlights

Plant phenology is the study of recurring life cycle events, such as growth reactivation and dormancy, leaf emergence and senescence, and flowering
Calibration of bud phenological events using Normalized Difference Vegetation Index (NDVI) time series on the one hand indicates the limitations of this approach for spatio-temporal pattern analyses, but on the other hand, demonstrates the ability of remote sensing to upscale bud phenology over larger forested areas
The most confounding issues are related to the selection of vegetation index suitable for the estimation of phenological metrics during the start and end of the growing season in evergreen forests, which exhibit smaller seasonal variations in canopy optical properties compared to deciduous forests [22,74]

Summary

Introduction

Plant phenology is the study of recurring life cycle events, such as growth reactivation and dormancy, leaf emergence and senescence, and flowering. Satellite data provide wide coverage with varying temporal, spectral and spatial resolutions. Remote sensing is a flexible, reliable and widely recognized tool to study phenology in a number of ecosystems, from forests to grasslands, which may be too difficult using other data collection methods [8,9,10]. Current sensors on board satellite platforms record spectral signals that can be used to monitor seasonal and interannual variations in vegetation cover and determine the timings of phenological events and the growing season across the landscape [11,12]

Methods

Results

Discussion

Conclusion