Mapping mangrove multi-trait functional diversity from satellite observations across dense and fragmented stands using spectral-biophysical derivatives


  • IPBES. IPBES (2019): Global assessment report on biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services. In: E. S. Brondizio, J. Settele, S. Díaz, and H. T. Ngo (editors). IPBES secretariat, Bonn, Germany. 1148 pages. https://doi.org/10.5281/zenodo.3831673 (2019).

  • Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).

    Article 

    Google Scholar
     

  • Cavender-Bares, J., Gamon, J. A. & Townsend, P. A. Remote Sensing of Plant Biodiversity. Remote Sens. Plant Biodivers. https://doi.org/10.1007/978-3-030-33157-3 (2020).

    Article 

    Google Scholar
     

  • Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants https://doi.org/10.1038/NPLANTS.2016.24 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Xiong, Y. et al. Machine learning-based examination of recent mangrove forest changes in the Western Irrawaddy River Delta, Southeast Asia. Catena (Amst) 234, 107601 (2024).

    Article 

    Google Scholar
     

  • Hauser, L. T., Binh, N. A., Hoa, P. V., Quan, N. H. & Timmermans, J. Gap-free monitoring of annual mangrove forest dynamics in ca mau province, vietnamese mekong delta, using the landsat-7-8 archives and post-classification temporal optimization. Remote Sens. (Basel) 12, 1–16 (2020).


    Google Scholar
     

  • Zhou, Y., Dai, Z., Liang, X. & Cheng, J. Machine learning-based monitoring of mangrove ecosystem dynamics in the Indus Delta. For. Ecol. Manag. 571, 122231 (2024).

    Article 

    Google Scholar
     

  • Binh, N. et al. Monitoring mangrove traits through optical Earth observation: Towards spatio-temporal scalability using cloud-based Sentinel-2 continuous time series. ISPRS J. Photogramm. Remote. Sens. 214, 135–152 (2024).

    Article 

    Google Scholar
     

  • Pettorelli, N. et al. Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sens. Ecol. Conserv. 2, 122–131 (2016).

    Article 

    Google Scholar
     

  • Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-019-0826-1 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • CBD. Monitoring framework for the Kunming-Montreal Global Biodiversity Framework. Conference Of the Parties to the Convention on Biological Diversity Fifteenth meeting (2022).

  • Hauser, L. T. Satellite Remote Sensing of Plant Functional Diversity (Leiden University, 2022).


    Google Scholar
     

  • Khare, S., Latifi, H. & Rossi, S. Forest beta-diversity analysis by remote sensing: How scale and sensors affect the Rao’s Q index. Ecol. Indic. 106, 105520 (2019).

    Article 

    Google Scholar
     

  • Cerrejón, C., Valeria, O. & Fenton, N. J. Estimating lichen α- and β-diversity using satellite data at different spatial resolutions. Ecol. Indic. 149, 110173 (2023).

    Article 

    Google Scholar
     

  • Rossi, C. et al. Remote sensing of spectral diversity: A new methodological approach to account for spatio-temporal dissimilarities between plant communities. Ecol. Indic. 130, 108106 (2021).

    Article 

    Google Scholar
     

  • Anderson, C. B. Biodiversity monitoring, earth observations and the ecology of scale. Ecol. Lett. https://doi.org/10.1111/ele.13106 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Butler, D. Earth observation enters next phase. Nature https://doi.org/10.1038/508160a (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Palmer, M. W., Earls, P. G., Hoagland, B. W., White, P. S. & Wohlgemuth, T. Quantitative tools for perfecting species lists. Environmetrics 13, 121–137 (2002).

    Article 

    Google Scholar
     

  • Torresani, M. et al. Reviewing the spectral variation hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing. Ecol. Inform. 82, 102702 (2024).

    Article 

    Google Scholar
     

  • Rossi, C. et al. From local to regional: Functional diversity in differently managed alpine grasslands. Remote Sens. Environ. 236, 111415 (2020).

    Article 

    Google Scholar
     

  • Zheng, Z. et al. Remotely sensed functional diversity and its association with productivity in a subtropical forest. Remote Sens. Environ. 290, 113530 (2023).

    Article 

    Google Scholar
     

  • Zheng, Z. et al. Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest. Remote Sens. Environ. 252, 112170 (2021).

    Article 

    Google Scholar
     

  • Hauser, L. T. et al. Towards scalable estimation of plant functional diversity from Sentinel-2: In-situ validation in a heterogeneous (semi-)natural landscape. Remote Sens. Environ. 262, 112505 (2021).

    Article 

    Google Scholar
     

  • Nagelkerken, I. et al. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat. Bot. https://doi.org/10.1016/j.aquabot.2007.12.007 (2008).

    Article 

    Google Scholar
     

  • Sunkur, R., Kantamaneni, K., Bokhoree, C. & Ravan, S. Mangroves’ role in supporting ecosystem-based techniques to reduce disaster risk and adapt to climate change: A review. J. Sea Res. 196, 102449 (2023).

    Article 

    Google Scholar
     

  • Choudhary, B., Dhar, V. & Pawase, A. S. Blue carbon and the role of mangroves in carbon sequestration: Its mechanisms, estimation, human impacts and conservation strategies for economic incentives. J. Sea Res. 199, 102504 (2024).

    Article 

    Google Scholar
     

  • Hauser, L. T. et al. Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004–2013). Appl. Geogr. 86, 197–207 (2017).

    Article 

    Google Scholar
     

  • Schweiger, A. K. et al. Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat. Ecol. Evol. 2, 113021 (2018).

    Article 

    Google Scholar
     

  • Wang, D., Qiu, P., Wan, B., Cao, Z. & Zhang, Q. Mapping α- and β-diversity of mangrove forests with multispectral and hyperspectral images. Remote Sens. Environ. 275, 113021 (2022).

    Article 

    Google Scholar
     

  • Hauser, L. T. et al. Explaining discrepancies between spectral and in-situ plant diversity in multispectral satellite earth observation. Remote Sens. Environ. 265, 112684 (2021).

    Article 

    Google Scholar
     

  • Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).

    Article 
    PubMed 

    Google Scholar
     

  • Bunting, P. et al. Global mangrove extent change 1996–2020: Global mangrove watch Version 30. Remote Sens. (Basel) 14, 3657 (2022).

    Article 

    Google Scholar
     

  • Le, Q. T. & Tong, S. S. Monitoring mangrove forest changes in vietnam using cloud-based geospatial analysis and multi-temporal satellite images. Environ. Sci. Eng. 1, 543–560 (2023).

    Article 

    Google Scholar
     

  • Nguyen, L. T., Hoang, H. T., Ta, H. V. & Park, P. S. Comparison of mangrove stand development on accretion and erosion sites in Ca Mau, Vietnam. Forests https://doi.org/10.3390/f11060615 (2020).

    Article 

    Google Scholar
     

  • Van, T. T. et al. Changes in mangrove vegetation area and character in a war and land use change affected region of Vietnam (Mui Ca Mau) over six decades. Acta Oecol. 63, 71–81 (2015).

    Article 

    Google Scholar
     

  • Quoc Vo, T., Kuenzer, C. & Oppelt, N. How remote sensing supports mangrove ecosystem service valuation: A case study in Ca Mau province, Vietnam. Ecosyst. Serv. 14, 67–75 (2015).

    Article 

    Google Scholar
     

  • Truong, T. D. & Do, L. H. Mangrove forests and aquaculture in the Mekong river delta. Land Use Policy 73, 20–28 (2018).

    Article 

    Google Scholar
     

  • Ha, T. T. P., van Dijk, H. & Visser, L. Impacts of changes in mangrove forest management practices on forest accessibility and livelihood: A case study in mangrove-shrimp farming system in Ca Mau Province, Mekong Delta, Vietnam. Land Use Policy 36, 89–101 (2014).

    Article 

    Google Scholar
     

  • Lymburner, L., Beggs, P. J. & Jacobson, C. R. Estimation of canopy-average surface-specific leaf area using landsat TM data. Photogrammet. Eng. Remote Sens. 66, 183–192 (2000).


    Google Scholar
     

  • Wilson, E. H. & Sader, S. A. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens. Environ. 80, 385–396 (2002).

    Article 

    Google Scholar
     

  • Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B. & McMurtrey, J. E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74, 229–239 (2000).

    Article 

    Google Scholar
     

  • Schneider, F. D. et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. https://doi.org/10.1038/s41467-017-01530-3 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Durán, S. M. et al. Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient. Sci. Adv. https://doi.org/10.1126/sciadv.aaw8114 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hauser, L. T., Timmermans, J., Soudzilovskaia, N. A. & Van Bodegom, P. M. Linking land use and plant functional diversity patterns in Sabah, Borneo, through large-scale spatially continuous Sentinel-2 inference. Land (Basel) 11, 572 (2022).


    Google Scholar
     

  • Karadimou, E. K., Kallimanis, A. S., Tsiripidis, I. & Dimopoulos, P. Functional diversity exhibits a diverse relationship with area, even a decreasing one. Sci. Rep. https://doi.org/10.1038/srep35420 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schleuter, D., Daufresne, M., Massol, F. & Argillier, C. A user’s guide to functional diversity indices. Ecol. Monogr. 80, 469–484 (2010).

    Article 

    Google Scholar
     

  • Wang, Z., Rahbek, C. & Fang, J. Effects of geographical extent on the determinants of woody plant diversity. Ecography 35, 1160–1167 (2012).

    Article 

    Google Scholar
     

  • Rossi, C. et al. Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity. Environ. Res. Lett. 19, 074023 (2024).

    Article 

    Google Scholar
     

  • Ez-zahouani, B. et al. Remote sensing imagery segmentation in object-based analysis: A review of methods, optimization, and quality evaluation over the past 20 years. Remote Sens. Applic. Soc. Environ. https://doi.org/10.1016/j.rsase.2023.101031 (2023).

    Article 

    Google Scholar
     

  • Yan, L., Roy, D. P., Promkhambut, A., Fox, J. & Zhai, Y. Automated extraction of aquaculture ponds from Sentinel-2 seasonal imagery—A validated case study in central Thailand. Sci. Remote Sens. 6, 100063 (2022).

    Article 

    Google Scholar
     

  • Thi Huyen, N., Hoang, T. L. & Kim Loi, N. Applying landscape approach in assessing effectiveness of mangrove conservation in Ca Mau Cape National Park, Vietnam. J. Forest Res. 27, 371–378 (2022).

    Article 
    CAS 

    Google Scholar
     

  • Tran, L. X. & Fischer, A. Spatiotemporal changes and fragmentation of mangroves and its effects on fish diversity in Ca Mau Province (Vietnam). J. Coast Conserv. 21, 355–368 (2017).

    Article 

    Google Scholar
     

  • Kacic, P. & Kuenzer, C. Forest biodiversity monitoring based on remotely sensed spectral diversity—A review. Remote Sens. https://doi.org/10.3390/rs14215363 (2022).

    Article 

    Google Scholar
     

  • Bergen, K. M. et al. Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2008JG000883 (2009).

    Article 

    Google Scholar
     

  • Nagendra, H. et al. Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecol. Indic. 33, 45–59 (2013).

    Article 

    Google Scholar
     

  • Bae, S. et al. Radar vision in the mapping of forest biodiversity from space. Nat. Commun. https://doi.org/10.1038/s41467-019-12737-x (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pacheco-Labrador, J. et al. Challenging the link between functional and spectral diversity with radiative transfer modeling and data. Remote Sens. Environ. 280, 113170 (2022).

    Article 

    Google Scholar
     

  • Alvarez-Vanhard, E., Houet, T., Mony, C., Lecoq, L. & Corpetti, T. Can UAVs fill the gap between in situ surveys and satellites for habitat mapping?. Remote Sens. Environ. 243, 111780 (2020).

    Article 

    Google Scholar
     

  • Torresani, M. et al. Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data. Ecol. Indic. 117, 106520 (2020).

    Article 

    Google Scholar
     

  • Younes Cárdenas, N., Joyce, K. E. & Maier, S. W. Monitoring mangrove forests: Are we taking full advantage of technology?. Int. J. Appl. Earth Observ. Geoinformat. 63, 1–14 (2017).

    Article 

    Google Scholar
     

  • de Almeida, D. R. A. et al. A new era in forest restoration monitoring. Restor. Ecol. 28, 8–11 (2020).

    Article 

    Google Scholar
     

  • Khare, S., Latifi, H. & Rossi, S. A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index. Ecol. Indic. 121, 107105 (2021).

    Article 

    Google Scholar
     

  • Zupanc, A. Improving cloud detection with machine learning. https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13 (2017).

  • Skakun, S. et al. Cloud mask intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2. Remote Sens. Environ. 274, 112990 (2022).

    Article 

    Google Scholar
     

  • Son, N. T. et al. Mangrove mapping and change detection in ca mau peninsula, vietnam, using landsat data and object-based image analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 503–510 (2015).

    Article 

    Google Scholar
     

  • Pham, M. H., Do, T. H., Pham, V. M. & Bui, Q. T. Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system. PLoS ONE 15, e0233110 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baloloy, A. B., Blanco, A. C., Raymund Rhommel, R. R. C. & Nadaoka, K. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS J. Photogramm. Remote Sens. 166, 95–117 (2020).

    Article 

    Google Scholar
     

  • Quadros, A. F. & Zimmer, M. Dataset of ‘true mangroves’ plant species traits. Biodivers. Data J. https://doi.org/10.3897/BDJ.5.e22089 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Weiher, E. et al. Challenging theophrastus: A common core list of plant traits for functional ecology. J. Vegetat. Sci. 10, 609–620 (1999).

    Article 

    Google Scholar
     

  • Lichtenthaler, H. K. & Buschmann, C. Chlorophylls and carotenoids: Measurement and characterization by UV-VIS spectroscopy. Curr. Protocols Food Anal. Chem. https://doi.org/10.1002/0471142913.faf0403s01 (2001).

    Article 

    Google Scholar
     

  • Asner, G. P. et al. Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests. Ecol. Applic. 21, 85–98 (2011).

    Article 

    Google Scholar
     

  • Damm, A. et al. Remote sensing of plant-water relations: An overview and future perspectives. J. Plant Physiol. 227, 3–19 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Loureiro, N., Mantuano, D., Manhães, A. & Sansevero, J. Use of the trait-based approach in ecological restoration studies: a global review. Trees Struct. Funct. https://doi.org/10.1007/s00468-023-02439-9 (2023).

    Article 

    Google Scholar
     

  • Sun, W. et al. Monitoring wetland plant diversity from space: Progress and perspective. Int. J. Appl. Earth Obs. Geoinf. 130, 103943 (2024).


    Google Scholar
     

  • Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: The primary components of functional diversity. Oikos 111, 112–118 (2005).

    Article 

    Google Scholar
     

  • Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: Convex hull volume. Ecology 87, 1465–1471 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Scott, D. W. Multivariate Density Estimation: Theory, Practice, and Visualization 2nd edn. (Wiley, 2015).

    Book 

    Google Scholar
     



  • Source link

    More From Forest Beat

    Spatial distribution of exotic lumbricid earthworm Octolasion tyrtaeum in endangered Taxus...

    Pandit, M. K., Sodhi, N. S., Koh, L. P., Bhaskar, A. & Brook, B. W. Unreported yet massive deforestation driving loss of endemic...
    Biodiversity
    15
    minutes

    Unlocking the African bioeconomy and strengthening biodiversity conservation through genomics and...

    Ebenezer, T. E. et al. Africa: sequence 100,000 species to safeguard biodiversity. Nature 603, 388–392 (2022).CAS  ...
    Biodiversity
    19
    minutes

    Cryptobenthic crab assemblages are more distinct across a 90 m depth gradient...

    Graham, N. A. et al. Managing resilience to reverse phase shifts in coral reefs. Front. Ecol. Environ. 11, 541–548 (2013). ...
    Biodiversity
    13
    minutes

    Terrestrial land cover shapes fish diversity in a major subtropical river...

    The study was conducted in the Chao Phraya River catchment located in Northern and Central Thailand, covering rivers in both mountainous and plain...
    Biodiversity
    16
    minutes
    spot_imgspot_img