A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

Abstract

Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes.

Compound climate extremes driving recent sub-continental tree mortality in northern Australia have no precedent in recent centuries

“…Factors such as tidal regime 9 and wave energy 37 are also likely to be relevant: the far north of Australia experiences a diurnal mesotidal regime with low wave energy which plays an important role in sediment dynamics and mangrove distribution. Additionally, phenological differences by site and species 35 , 38 that mediate the impact of climate extremes and non-climatic factors (e.g., geomorphological factors such as soil depth) may also be relevant. Our use of an average mangrove forest recovery time of 10 years may not represent all mangrove forests, as recovery time will be dependent on localised environmental factors.…”

Uncertainty in parameterizing a flux‐based model of vegetation carbon phenology using ecosystem respiration

“…This effect may be exacerbated with higher average air temperatures in subtropical regions where the evergreen canopy is active most of the year (Whelan et al, 2013), which causes uncertainty in the signal of phenophase transition. Furthermore, warmer winters and higher water availability in summer had a significant impact on estimating VCP metrics for Re, as variation in Re leads to prolonged growing season and multi‐peak behavior in summer (Younes et al, 2020). In addition, we found that the date of fall senescence derived from the 2‐band EVI was often mismatched with modeled Re ( p < 0.01) (Appendix S1: Figure S4), in agreement with Wu et al (2014).…”

“…These day‐to‐day carbon exchange anomalies will be reflected in phenological modeling, that is, causing multiple peaks in the phenophases during the growing season (Appendix S1: Figures S1–S2). This will make the traditional functions assuming single‐peak seasonal dynamics unable to adequately characterize phenological patterns, that is, overestimating or underestimating the length of the active season (Younes et al, 2020; Zhou, 2018). While the functional form and phenological metric extraction algorithms tested in this study assume unimodal behavior, extraction methods based on curvature, fixed thresholds, or dynamic thresholds may be more suitable for stable ecosystems (Younes et al, 2020; Zhou, 2018).…”

“…This effect may be exacerbated with higher average air temperatures in subtropical regions where the evergreen canopy is active most of the year (Whelan et al, 2013), which causes uncertainty in the signal of phenophase transition. Furthermore, warmer winters and higher water availability in summer had a significant impact on estimating VCP metrics for Re, as variation in Re leads to prolonged growing season and multi-peak T A B L E 5 Pearson correlation coefficient between time series of environmental variables: T a (air temperature), PAR (photosynthetically active radiation), and Pptn (precipitation) versus vegetation carbon phenology metrics (DOP, day of peak; EOP, end of peak; EOR, end of respiration; LOP, length of peak; LOR, length of respiration; SOP, the start of peak; SOR, start of respiration) behavior in summer (Younes et al, 2020). In addition, we found that the date of fall senescence derived from the 2band EVI was often mismatched with modeled Re (p < 0.01) (Appendix S1: Figure S4), in agreement with Wu et al (2014).…”

Recent Advancement in Mangrove Forests Mapping and Monitoring of the World Using Earth Observation Satellite Data

“…Younes [2] explored and developed a novel, data-driven approach to extract plant phenology of six different mangrove forests across Australia. They used Landsat imagery and Generalized Additive Models (GAMs) to derive phenology.…”