A history of the rehabilitation of mangroves and an assessment of their diversity and structure using Landsat annual composites (1987–2019) and transect plot inventories

Enhancing monitoring of mangrove spatiotemporal tree diversity and distribution patterns

“…Indeed, distinct spectral and structural heterogeneity of forest landscapes could be helpful prior to sampling for determining the study design, identifying possible locations of transect lines or sampling plots (Pimple et al, 2022). The spectral response fluctuation in LANDSAT and SENTINEL‐2 time‐series can reflect the various phenomena that occur at specific sampling locations (Otero et al, 2019; Pimple et al, 2020; Pimple et al, 2022). Furthermore, quantification or statistical testing of distribution (Clarke et al, 2008; Ellison et al, 2000) based on systematic sampling designs and inclusion of environmental factors can yield more reliable outcomes than traditional random sampling approaches (Pimple et al, 2022).…”

“…Spectral variability of time‐series imagery can serve as a powerful proxy for temporal changes or species succession. Here, the focus has been placed on reviewing recent literature containing time‐series analyses for deriving a set of possible conditions characterizing the time and magnitude of various natural and anthropogenic disturbances (Chazdon et al, 2016; Otero et al, 2019; Pimple et al, 2020; Pimple et al, 2022; Ryo et al, 2019; Wulder et al, 2019). Observed changes across time could be identified, studied, and used as a proxy for interpreting the observed diversity and structural differences as they relate to underlying stressors or processes.…”

“…Remote sensing data have the potential to be used in either parameterizing or evaluating mechanistic mangrove stand models. Developments in remote sensing and drone‐based methods are providing an increased potential for measurements of forest structure, species composition, biomass and productivity patterns over time and space, and the data on the assessment of performance measurements in mangrove rehabilitation projects to improve the prediction of mangrove forest diversity and dynamics at a large spatiotemporal scale (Pimple et al, 2020; Pimple et al, 2022; Shugart et al, 2015; Shugart et al, 2018). Several studies have described remote sensing applications such as SAR and multispectral data for mangrove structure and diversity monitoring (Fatoyinbo et al, 2008; Rahman et al, 2019; Simard et al, 2006; Wang et al, 2019).…”

“…Peters et al (2020) indicated another benefit of coupling modelling with remote sensing approaches. Furthermore, Pimple et al (2020) noted the possibility of using LANDSAT and SENTINEL‐2 MSI time‐series data to validate the performance of landscape and individual‐based models for rehabilitation projects. Despite the advantages of remote sensing technology, uncertainties remain in parameterizing or evaluating the mechanistic models and directly influence the vegetation properties if above‐mentioned data issues are not addressed.…”

“…Accordingly, the challenges of cloud cover and shadows over mangrove habitats have been well-documented in T A B L E 1 Species diversity studies conducted at the high-diversity regions that were reviewed in the frame of this study previous studies (Cárdenas et al, 2017;Kuenzer et al, 2011). Despite these challenges, numerous methods to automatically screen clouds and cloud shadows from optical images have been developed recently with the support of cloud computing platforms such as Google Earth Engine (Pimple et al, 2020;Pimple et al, 2022;Simonetti et al, 2021;Zhu & Helmer, 2018).…”