Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

Aguirre‐Gutiérrez, Jesús; Rifai, Sami W.; Shenkin, Alexander; Oliveras, Imma; Bentley, Lisa Patrick; Svátek, Martin; Girardin, Cécile; Both, Sabine; Riutta, Terhi; Berenguer, Erika; Kissling, W. Daniel; Bauman, David; Raab, Nicolas; Moore, Sam; Farfán-Ríos, William; Figueiredo, Axa Emanuelle Simões; Reis, Simone Matias; Ndong, Josué Edzang; Ondo, Fidèle Evouna; Bengone, Natacha Nssi; Mihindou, Vianet; Seixas, Marina Maria Moraes de; Adu‐Bredu, Stephen; Abernethy, Katharine; Asner, Gregory P.; Barlow, Jos; Burslem, David F. R. P.; Coomes, David A.; Cernusak, Lucas A.; Dargie, Greta C.; Enquist, Brian J.; Ewers, Robert M.; Ferreira, Joice; Jeffery, Kathryn J.; Joly, Carlos A.; Lewis, Simon L.; Marimon‐Junior, Ben Hur; Martin, Roberta E.; Morandi, Paulo S.; Phillips, Oliver L.; Quesada, Carlos Alberto; Salinas, Norma; Silman, Miles R.; Teh, Yit Arn; White, Lee; Malhi, Yadvinder

doi:10.1016/j.rse.2020.112122

Cited by 56 publications

(37 citation statements)

References 90 publications

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“…By modelling phenology and the factors that affect it, users can take preventive or corrective measurements before the plant (or crop) fails or dies. More importantly, high-resolution imagery could potentially be used to create models at the same scale as the data collection plots, could be used to monitor restoration projects [63], and couple phenology to functional traits [64].…”

Section: Moving Forwardmentioning

confidence: 99%

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

et al. 2020

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Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes.

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Section: Moving Forwardmentioning

confidence: 99%

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

et al. 2020

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“…While other studies have had success applying hyperspectral, high-resolution data to alpha-diversity estimates through remote sensing [54], here the spatial and spectral resolution may be too coarse to capture the fine variation of spectral signatures of the species variability at a local scale (Figure 3) [89]. RS analyses at this scale may be more closely related to functional diversity of photosynthetic traits at the community scale, [43,90,91] or to forest structure, [57,92] rather than to tree species diversity. Furthermore, in this case, it may also be possible that the species present are not spectrally different enough to be accurately detected by Sentinel-2 [93].…”

Section: Alpha and Beta-diversity Comparisonmentioning

confidence: 95%

A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest

et al. 2021

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Biodiversity monitoring and understanding ecological processes on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity.

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“…As is evident from Section 2 of this review, there has been substantial remote sensing research on the structure, composition, and ecophysiology of savannas. Traditional land-cover classification of savannas should be enhanced to include ecologically relevant information such as land surface phenology [189], soil properties [190], rainfall seasonality [191], fire activity [192], and even plant canopy traits and functional diversity [193,194].…”

Section: A Consistent Definition Of Savannas In Remote Sensingmentioning

confidence: 99%

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

et al. 2022

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Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.

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Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

Cited by 56 publications

References 90 publications

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest

Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities

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