Research on Mangrove Phenology
Introduction
Mangrove forests are an intertidal ecosystem made up of a variety of tree species that have adapted to live in a saline, hypoxic environment through common morphological, physiological, biochemical, and reproductive traits. Mangrove canopy has been a focus of attention in recent years as it is essential to improve the survival rate of restoration planting.
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Mangrove phenology, which includes stages like new leaf growth, flowering, fruiting, and leaf fall, varies by species and environmental conditions. Understanding these patterns is crucial, as shifts inmangrove phenology can impact the productivity of the ecosystem (Songsom et al., 2021). Time series of satellite reflectance data have been used to model and extract phenological information, producing phenological metrics that are known to be highly sensitive to the algo rithm selection (Duncan et al., 2015). When specific threshold values on the ascending and descending portions, as well as the peak values on the reflectance or vegetation index (VI) time series, are reached, respec tively, the day of the year that corresponds to the start of season (SOS), end of season (EOS), and peak of season (POS) are usually used to es timate Land Surface Phenology (LSP) metrics (Tan et al., 2010). With varying degrees of success, the various LSP metrics are then connected to different ground observations despite their sensitivity to changes in canopy level, like leaf unfolding and defoliation, than to bud-break events. Despite their shortcomings and lack of direct correlation to ground observations, a variety of LSP metrics have been able to provide significant insights into vegetation seasonality and their various controls at temporal and spatial scales (Misra et al., 2016).
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Mangroves, blue carbon forest, play a vital role in the environment by protecting the coast, stabilizing the environment, and managing the ecosystem. Analysis of mangrove phenology is required to understand the dynamics of mangrove ecosystems and for restoration measures. Remote sensing technology has been widely employed to monitor vegetation phenology, yet there is a dearth of research addressing the remotely sensed phenology of mangrove forests in coastal Gujarat, India. In this study, we investigated the mangrove phenology dynamics in the Gulf of Kutch region of Gujarat using satellite-derived Sentinel-1&2 time series during 2017–2022, compared the ability of vegetation indices to influence mangrove phenology, and later explored the impact of environmental stressors on mangrove phenology. Eco-climatic stressors show a negative correlation between mean sea level. temperature, precipitation, relative humidity, wind speed, with the increasing impact but for salinity which also shows negative correlation with phenological events, its impact weakens. Shifts were observed in phenological metrics over our study period, highlighting the influence of environmental factors on mangrove dynamics. The findings can enhance our knowledge of the phenological attributes of mangrove forests, which further provides vision to formulate policies for the restoration and sustainable management of the mangrove ecosystem.​
Highlights
Mangroves in the Gulf of Kutch are experiencing increasing pressure from climatic variability and anthropogenic influences, leading to observable changes in their phenological patterns. Understanding these shifts is critical for ecosystem resilience, biodiversity conservation, and informed coastal resource management. This study leverages advanced Earth Observation tools to assess mangrove phenology in a dynamic coastal environment.
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Integrated Sentinel-1 (SAR) and Sentinel-2 (optical) time-series data to monitor mangrove phenology across eco-climatic gradients in the Gulf of Kutch.
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Detected phenological shifts in mangrove species, including altered greening, flowering, and senescence phases, in response to climatic anomalies such as extreme temperature, salinity fluctuations, and rainfall variability.
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Identified spatial heterogeneity in phenological responses driven by proximity to industrial zones, tidal dynamics, and freshwater inflow patterns.
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Applied synergistic modeling techniques to link remote sensing indicators (NDVI, backscatter coefficients) with eco-climatic drivers over multi-year observations (2017–2023).
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Findings support the development of climate-resilient mangrove management strategies and early warning systems for phenological disruption under future climate scenarios.
Results and Analysis
Study area map of mangrove region in Gulf of Kutch ranging from Vadinar to Jamnagar.
Phenological metrics derived from time-series NDVI index (a) SOS, (b) MaxGreen, and (c) EOS estimated for the year 2017 & (d) SOS, (e) MaxGreen, and (f) EOS estimated for the year 2022 for Gulf of Kutch region.
Seasonal variations in Sentinel 1 & 2 VI time series during 2017–2022 in the Gulf of Kutch region. The analysis of vegetation indices reveals consistent seasonal trends across all indices, demonstrating considerable changes associated with different seasons.
Seasonal variations in (a) Temperature, (b) Precipitation, (c) Relative Humidity, (d) Mean Sea Level, (e) Salinity & (f) Wind speed for year 2017–18 and 2021–22 with the time-series curve of NDVI and phenological metrics.
Research Team
Ms. Ishita Kariya is a researcher specializing in remote sensing and GIS applications in forest ecology, with a focus on mangrove ecosystems. Their PhD research explores spatio-temporal synergistic modeling of mangrove phenology under climatic variability along Gujarat’s coast. Passionate about ecological monitoring and sustainable coastal management, she integrates advanced geospatial tools to study phenological patterns and climate impacts on biodiversity.