Enhancing monitoring of mangrove spatiotemporal tree diversity and distribution patterns

Research output: Contribution to journalReview articleContributedpeer-review



Spatiotemporal information on mangrove species assemblage of natural, disturbed, and rehabilitated is an essential prerequisite for effective strategies for biodiversity conservation and management. However, appropriate field-based sampling strategies of spatial heterogeneity still hamper the detection of the species distribution and its temporal development. An increasing amount of remote sensing data seems the perfect way to tackle these challenges. With this article, we fill this gap by presenting a review of the challenges and limitations to assess the current status of species diversity. We conclude that species discrimination based on remote sensing techniques is still limited by atmospheric contamination and tidal fluctuations. The lack of accurate information on the spatiotemporal development of species diversity and forest structure further curtails an understanding of functional indicators and the predictive power of modeling approaches. Nevertheless, multi-source remote-sensing techniques could seemingly capture the landscape heterogeneity and support systematic sampling designs. Spatially balanced (systematic) training and validation data are necessary to compile robust spatiotemporal information, supporting reliable predictions for optimizing restoration efforts. A systematic sampling of spatiotemporal ecological information is vital to derive the historical state of mangroves, detecting their degradation, and predicting future patterns of species distribution that are generally crucial for restoration, and particularly to rehabilitate species diversity.


Original languageEnglish
Pages (from-to)1265-1282
Number of pages18
JournalLand Degradation and Development
Issue number5
Publication statusPublished - Mar 2023

External IDs

Scopus 85144088257
Mendeley 5078b3d0-eab2-365e-b62b-6e3a1559482c
WOS 000898372100001



  • diversity prediction, field inventory, mangrove species diversity, systematic sampling, tidal influence, trajectories, Diversity prediction, Systematic sampling, Mangrove species diversity, Tidal influence, Field inventory, Trajectories