Spatiotemporal Characterization of Mangrove Phenology and Disturbance Response: The Bangladesh Sundarban

Christopher Small,Daniel Sousa

doi:10.3390/rs11172063

Christopher Small, Daniel Sousa

Open Access

https://doi.org/10.3390/rs11172063

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Abstract

This work presents a spatiotemporal analysis of the phenology and disturbance response in the Sundarban mangrove forest on the Ganges-Brahmaputra Delta in Bangladesh. The methodological approach is based on an Empirical Orthogonal Function (EOF) analysis of the new Harmonized Landsat Sentinel-2 (HLS) BRDF and atmospherically corrected reflectance time series, preceded by a Robust Principal Component Analysis (RPCA) separation of Low Rank and Sparse components of the image time series. Low Rank components are spatially and temporally pervasive while Sparse components are transient and localized. The RPCA clearly separates subtle spatial variations in the annual cycle of monsoon-modulated greening and senescence of the mangrove forest from the spatiotemporally complex agricultural phenology surrounding the Sundarban. A 3 endmember temporal mixture model maps spatially coherent differences in the 2018 greening-senescence cycle of the mangrove which are both concordant and discordant with existing species composition maps. The discordant patterns suggest a phenological response to environmental factors like surface hydrology. On decadal time scales, a standard EOF analysis of vegetation fraction maps from annual post-monsoon Landsat imagery is sufficient to isolate locations of shoreline advance and retreat related to changes in sedimentation and erosion, as well as cyclone-induced defoliation and recovery.

Highlights

In addition to informing our understanding of forest ecology and function, mapping vegetation phenology can provide information on plants’ response to environmental influences
4.RRoebsuulsttsPrincipal Component Analysis (RPCA, [9]) considers an image time series M to be the sum of aOlnocwe -trhaenvkemgeattartiixonL farnacdtiaonspmaraspesmhaavtreixbeSe:n estimated for each Harmonized Landsat Sentinel-2 (HLS) tile date, the vegetation fraction map time series is constructed for exploratory data analysis before the spatiotemporal analysis is done
Once the vegetation fraction maps have been estimated for each HLS tile date, the vegetation fraction map time series is constructed for exploratory data analysis before the spatiotemporal analysis is done

Summary

Introduction

In addition to informing our understanding of forest ecology and function, mapping vegetation phenology can provide information on plants’ response to environmental influences. Direct observations are required to quantify flowering, fruiting and seed drop, but remotely sensed observations can provide an important complement in the form of synoptic constraints on the processes of leaf emergence, growth, and shedding. This can be especially important in biodiverse environments where phenological heterogeneity can violate simplistic assumptions about species’ response to environmental factors influencing phenology. Most approaches to phenology mapping using remotely sensed image time series are based on some form of cyclic curve fitting (e.g., logistic function) or event detection (e.g., Start Of Season) Both of these approaches require the analyst to make assumptions about the form and magnitude of the expected phenological cycle(s). In areas where processes are less well-understood or multiple processes can influence observations, it is important to characterize the observations before making the assumptions on which models are built

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