Towards scalable estimation of plant functional diversity from Sentinel-2: In-situ validation in a heterogeneous (semi-)natural landscape

Hauser, Leon T.; Féret, Jean‐Baptiste; Bình, Nguyễn An; Windt, Niels van der; Sil, Ângelo; Timmermans, J.; Soudzilovskaia, Nadejda A.; Bodegom, Peter M. van

doi:10.1016/j.rse.2021.112505

Cited by 45 publications

(24 citation statements)

References 114 publications

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“…Féret et al [23], by comparison, calibrated partial least squares (PLS) models based on PROSPECT-5 simulations and generated RMSE ranging between 0.002 and 0.0025 g cm −2 on synthetic data and 0.0007 g cm −2 on an experimental dataset. Using Sentinel-2 multispectral data, Hauser et al [110] successfully demonstrated the utility of PROSAIL-D/support vector regression hybrid technique in retrieving LMA (R 2 = 0.59, RMSE = 5.77 mg cm −2 ) in a forest and shrubland ecosystem at canopy scale. It is worth noting that very few studies have evaluated the utility of hybrid modelling in the retrieval of LMA, particularly at the canopy level in forest ecosystems.…”

Section: Hybrid Modellingmentioning

confidence: 99%

“…At an operational level, the European Space Agency's SNAP (Sentinel Application Platform) toolbox inverts PROSAIL simulations based on Sentinel-2 MSI using ANN to retrieve several traits that include LMA, etc. For example, Hauser et al [110] recently used SNAP to retrieve LMA (R 2 = 0.51, RMSE = 5.5 mg cm −2 ) in a forest and shrubland ecosystem in a temperate forest in Portugal using Sentinel-2 data. More research is required to determine the utility of the SNAP toolbox in LMA retrieval in forest ecosystems.…”

Section: Hybrid Modellingmentioning

confidence: 99%

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Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

2021

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Quantitative remote sensing of leaf traits offers an opportunity to track biodiversity changes from space. Augmenting field measurement of leaf traits with remote sensing provides a pathway for monitoring essential biodiversity variables (EBVs) over space and time. Detailed information on key leaf traits such as leaf mass per area (LMA) is critical for understanding ecosystem structure and functioning, and subsequently the provision of ecosystem services. Although studies on remote sensing of LMA and related constituents have been conducted for over three decades, a comprehensive review of remote sensing of LMA—a key driver of leaf and canopy reflectance—has been lacking. This paper reviews the current state and potential approaches, in addition to the challenges associated with LMA estimation/retrieval in forest ecosystems. The physiology and environmental factors that influence the spatial and temporal variation of LMA are presented. The scope of scaling LMA using remote sensing systems at various scales, i.e., near ground (in situ), airborne, and spaceborne platforms is reviewed and discussed. The review explores the advantages and disadvantages of LMA modelling techniques from these platforms. Finally, the research gaps and perspectives for future research are presented. Our review reveals that although progress has been made, scaling LMA to regional and global scales remains a challenge. In addition to seasonal tracking, three-dimensional modeling of LMA is still in its infancy. Over the past decade, the remote sensing scientific community has made efforts to separate LMA constituents in physical modelling at the leaf level. However, upscaling these leaf models to canopy level in forest ecosystems remains untested. We identified future opportunities involving the synergy of multiple sensors, and investigated the utility of hybrid models, particularly at the canopy and landscape levels.

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Section: Hybrid Modellingmentioning

confidence: 99%

Section: Hybrid Modellingmentioning

confidence: 99%

Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

2021

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“…Despite the availability of very high spatial resolution imagery (VHSRI), its use for estimating forest's plant diversity over large spatial scales is limited because of the time-consuming nature and the cost associated to the instrumentation used for image analysis [26,32]. Meanwhile, these VHSRI datasets are often not current due to the lack of high spectral or temporal information [33,34].…”

Section: Introductionmentioning

confidence: 99%

“…They are freely available and operating in twin mode allowing to acquire high temporal (2-5 days), high spatial (10-20 m) and high radiometric (12-bit) resolution data sets across diverse wavelengths [42,43]. The satellites might be an enormously powerful tool since they allow for coverage of large geographical scales in a short period of time, having the potential for ecologists to provide a critical information about the drivers of the spatial and temporal distribution of biodiversity [34,44,45]. For example, S-2 imagery has provided valuable results in the monitoring of forest ecosystems distribution [46,47], plant species classification [48], mapping of forest vegetation dominant leaf types [49], monitoring plant phenology [50], predicting above-ground biomass [51,52] as well as in distinguishing temperate tree species [53].…”

Section: Introductionmentioning

confidence: 99%

Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

et al. 2022

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Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ2 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector.

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“…That could lead to the formulation of more hybrid spatial models. Mechanistic [26] or statistical modeling approaches [8,9,11] can be followed towards a spatially explicit monitoring of grassland plant diversity as a function of satellite and local spatial environmental factors. Statistical modeling represents a bridge that has been commonly used to link field observations and predictor variables [17].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Plant Diversity to Support Agri-Environmental Schemes: Evaluating Statistical Models Informed by Satellite and Local Factors in Southern European Mountain Pastoral Systems

Monteiro

Alves²,

Carvalho-Santos

et al. 2021

Diversity

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The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Gerês National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species–area (P1), species–energy (P2) and species–spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species–energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, ∆AIC = 0.0, wi = 0.97). Species–area and species–spectral heterogeneity pathways (P1 and P3) were less statistically supported (ΔAICc values in the range 5.7–10.0). The underlying support of the species–energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Greenspring) and on its ratio of change between spring and summer (NIR/Greenchange). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species–energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species–energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

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Towards scalable estimation of plant functional diversity from Sentinel-2: In-situ validation in a heterogeneous (semi-)natural landscape

Cited by 45 publications

References 114 publications

Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

Forest Leaf Mass per Area (LMA) through the Eye of Optical Remote Sensing: A Review and Future Outlook

Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

Monitoring Plant Diversity to Support Agri-Environmental Schemes: Evaluating Statistical Models Informed by Satellite and Local Factors in Southern European Mountain Pastoral Systems

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