# Spatio-temporal dynamics of mangrove forest diversity in Eastern Thailand

Uday Pimple, Dario Simonetti, Kumron Leadprathom, Ronny Peters, Sukan Pungkul, Tamanai Pravinvongvuthi, Uta Berger, Erika Podest, Poonsri Wanthongchai, Valéry Gond

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## ► To cite this version:

Uday Pimple, Dario Simonetti, Kumron Leadprathom, Ronny Peters, Sukan Pungkul, et al., Spatio-temporal dynamics of mangrove forest diversity in Eastern Thailand. Mangrove Macrobenthos and Management Meeting, Jul 2023, Cartagena, Colombia. Corporation Center of Excellence in Marine Sciences; Red Colombiana de Estuarios y Manglares, 1 poster, 2023. hal-05181053

HAL Id: hal-05181053

https://hal.science/hal-05181053v1

Submitted on 23 Jul 2025

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# SPATIO-TEMPORAL DYNAMICS OF MANGROVE FOREST DIVERSITY IN EASTERN THAILAND

Uday Pimple*<sup>a</sup>, Dario Simonetti<sup>b</sup>, Kumron Leadprathom<sup>c</sup>, Ronny Peters<sup>d</sup>, Sukan Pungkul<sup>e</sup>, Tamanai Pravinvongvuthi<sup>f</sup>, Uta Berger<sup>d</sup>, Erika Podest<sup>g</sup>, Poonsri Wanthongchai<sup>f</sup>, Valery Gond<sup>g</sup>

<sup>a</sup>The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi, Thailand; <sup>b</sup> GIS and Remote Sensing Consultant, Italy; <sup>c</sup>Royal Forest Department Bangkok Thailand; <sup>d</sup> Technische Universität Dresden, Tharandt, Germany; <sup>e</sup>Department of National Parks, Wildlife and Plant Conservation, Thailand; <sup>f</sup>Department of Marine and Coastal Resources, Thailand; <sup>g</sup>Jet Propulsion Laboratory, National Aeronautics and Space Administration, California Institute of Technology, USA; <sup>g</sup>CIRAD, UPR Forests and Societies (F&S), France

Correspondence: upimp@gmail.com, uday.pim@kmutt.ac.th

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## BACKGROUND

* Spatiotemporal information on mangrove species assemblage of natural, regenerated, and rehabilitated is an essential prerequisite for effective strategies for biodiversity conservation and management. Appropriate linkage of field-based sampling strategies and remote sensing approaches of spatial heterogeneity still hamper the detection of the species distribution and its temporal development.

* To address these concerns, we must improve our ability to gather reliable forest inventory measurements, spatial scale biodiversity predictions, and good practices for using Earth observation data.

## OBJECTIVES

* We aim to investigate the knowledge gaps considering potential spatial diversity, sea-to-land species distribution, and the historic state of mangrove forest species, and tested the role of environmental settings such as topography and anthropogenic (rehabilitation) settings on diversification.

* In addition, explore the full potential of freely available Landsat and a combination of Sentinel-1 SAR and -2 MSI satellite imagery to discriminate the spatiotemporal patterns of mangrove species diversity.

## STUDY SITE

<figure><img src="image_4.png" /><figcaption>**Figure 1** Location of the study site in the Trat Province, Thailand. Stratified samples across the landscape and three transects perpendicular to shoreline 257 plots are 10 x 10 m, 69 Ground points, 134 using high resolution database)</figcaption></figure>

## METHODS

<figure><img src="image_5.png" /><figcaption>**Figure 2** Decision-support-system for monitoring species diversity and management aspects of mangrove forestry. Key component of decision-support-system for ecosystem baseline</figcaption></figure>

**Funding:** This research was funded by the Southeast Asia-Europe Joint Funding Scheme for Research and Innovation (SEA-EU-NET), 2018–2022 as a part of a project entitled, “Monitoring and optimizing the design quality of mangrove restoration towards a sustainable coastal ecosystem management in Thailand and Mekong delta of Vietnam” [Project code: Thailand (NSTDA): P-18-51184, France (ANR): N0 ANR- 17-ASIE-0003- 01, Germany (BMBF): 01DQ18004].

<figure><img src="image_6.png" /><figcaption>**Figure 3** Practical implementation of decision-support-system. ARMA: automatic regrowth monitoring algorithm; SAR: synthetic aperture radar; MSI: multi-spectral instrument</figcaption></figure>

## RESULTS

The key findings include: (1) the mangrove forest made a significant recovery over time (1987-2022), due to local community's awareness for mangrove conservation. (2) the rehabilitated mangroves (34 years old) at the study site consist of monocultures of Rhizophoraceae, however, reached heights comparable to adjacent natural stands. (3) the absence of any single species zonation patterns within transects along the elevation gradient from sea to land. (4) final classification identified six groups of species. Class 1 and 7 was monoculture of natural *Rhizophoraceae*. The remaining Classes were an association of relevant species (Figure 4)

<figure><img src="image_7.png" /><figcaption>**Figure 4** Spatiotemporal dynamics of mangrove species diversity (1987-2022). Class 1: natural *Rhizophoraceae* (Ra and Rm); Class 2: association of Xg, Xm, Bg, Ea and Ra; Class 3: contains Aa or Am or Sa or So; Class 4: association of Ll, Lr, Ct and Ra (scrub stands); Class 5: association Bruguiera spp.(Bg, Bc, Bs and Bh), Ct, Ea and Ra; Class 6: water and mud; Class 7: rehabilitated and *Rhizophoraceae* (Rap and Ram). *Sonneratia alba* (Sa), *Avicennia alba* (Aa), *Avicennia marina* (Am), *Bruguiera cylindrica* (Bc), *Bruguiera gymnorrhiza* (Bg), *Bruguiera sexangula* (Bs), *Bruguiera hainesii* (Bh), *Ceriops tagal* (Ct), *Excoecaria agallocha* (Ex), *Intsia bijuga* (Ib), *Lumnitzera littorea* (LI), *Lumnitzera racemosa* (Lr), *Rhizophora apiculata* (Ra), *Rhizophora mucronata* (Rm), *Rhizophora apiculata* planted (Rap), *Rhizophora mucronata* planted (Rmp), *Xylocarpus granatum* (Xg), and *Xylocarpus moluccensis* (Xm). Note: *Sonneratia ovata Backer* (So), *Ceriops decandra* (Cd), *Bruguiera hainesii* (Bh) and *Intsia bijuga* (Ib) rarely occurred.</figcaption></figure>

## Conclusion:

The proposed decision-support-system demonstrates several potential applications for restoration management planning, and therefore will be a useful tool to measure and evaluate spatial scale species biodiversity.

## References:

Pimple, U., Simonetti, D., Sitthi, A., Pungkul, S., Leadprathom, K., Skupek, H., Som-ard, J., Gond, V., Towprayoon, S., 2018. Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thailand. J. Comput. Commun. 06, 247–264. https://doi.org/10.4236/jcc.2018.61025

Pimple, U., Simonetti, D., Hinks, I., Oszwald, J., Berger, U., Pungkul, S., Leadprathom, K., Pravinvongvuthi, T., Maprasoap, P., Gond, V., 2020. 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. For. Ecol. Manage. 462, 118007. https://doi.org/10.1016/j.foreco.2020.118007

Pimple, U., 2020. Dataset on plot inventories of species diversity and structural parameters of natural and rehabilitated mangrove forest in the Trat province of Thailand. Data Br. 30. https://doi.org/10.1016/j.dib.2020.105500

Pimple, U., Leadprathom, K., Simonetti, D., Sitthi, A., Peters, R., Berger, U., Siri-on, K., Kemacheevakul, P., Gond, V., 2022a. Assessing mangrove species diversity, zonation and functional indicators in response to natural, regenerated, and rehabilitated succession. J. Environ. Manage. 318, 115507. https://doi.org/10.1016/j.jenvman.2022.115507

Pimple, U., Simonetti, D., Peters, R., Berger, U., Podest, E., Gond, V., 2022b. Enhancing monitoring of mangrove spatiotemporal tree diversity and distribution patterns. L. Degrad. Dev. 1–18. https://doi.org/10.1002/ldr.4537

Pimple, U., 2022 Spatio-temporal dynamics of mangrove forest in Trat province of Thailand, BOIS & FORETS DES TROPIQUES, 353, 93-94. https://doi.org/10.19182/bft2022.353.a36999