Radiative transfer modelling reveals why canopy reflectance follows function

Optical remote sensing is potentially highly informative to track Earth’s plant functional diversity. Yet, causal explanations of how and why plant functioning is expressed in canopy reflectance remain limited. Variation in canopy reflectance can be described by radiative transfer models (here PROSAIL) that incorporate plant traits affecting light transmission in canopies. To establish causal links between canopy reflectance and plant functioning, we investigate how two plant functional schemes, i.e. the Leaf Economic Spectrum (LES) and CSR plant strategies, are related to traits with relevance to reflectance. These traits indeed related to both functional schemes, whereas only traits describing leaf properties correlated with the LES. In contrast, traits related to canopy structure showed no correlation to the LES, but to CSR strategies, as the latter integrates both plant economics and size traits, rather than solely leaf economics. Multiple optically relevant traits featured comparable or higher correspondence to the CSR space than those traits originally used to allocate CSR scores. This evidences that plant functions and strategies are directly expressed in reflectance and entails that canopy ‘reflectance follows function’. This opens up new possibilities to understand differences in plant functioning and to harness optical remote sensing data for monitoring Earth´s functional diversity

The retrieval of plant functional traits from canopy spectra through RTM-inversions and statistical models are both critically affected by plant phenology

Plant functional traits play a key role in the assessment of ecosystem processes and properties. Optical remote sensing is ascribed a high potential in capturing those traits and their spatiotemporal patterns. In vegetation remote sensing, reflectance-based retrieval methods are either statistical (relying on empirical observations) or physically-based (based on inversions of a radiative transfer model, RTM). Both trait retrieval approaches remain poorly investigated regarding phenology. However, within the phenology of a plant, its leaf constituents, canopy structure, and the presence of phenology-related organs (i.e., flowers or inflorescence) vary considerably – and so does its reflectance. We, therefore, addressed the question of how plant phenology affects the predictive performance of both statistical and RTM-based methods and how this effect differs between traits. For a complete growing season, we weekly measured traits of 45 herbaceous plant species together with hyperspectral canopy reflectance (ASD FieldSpec III). Plants were grown in an experimental setup. The investigated traits comprised Leaf Area Index (LAI) and the leaf traits chlorophyll, anthocyanins, carotenoids, equivalent water thickness, and leaf mass per area. We compared the predictive performances of PLSR models and three variants of PROSAIL inversions based on (1) all observations and based on (2) a phenological subset where flowering plants were excluded and only those observations most suitable for modeling were kept. Our results show that both statistical and RTM-based trait retrievals were largely affected by phenology. For carotenoids for example, R$^{2}$ decreased from 0.58 at non-flowering canopies to 0.25 at 100% flowering canopies. Temporal trends were diverse. LAI and equivalent water thickness were best estimated earlier in the growing season; chlorophyll and carotenoids towards senescence. PLSR models showed generally higher bias than the PROSAIL-based retrieval approaches. Lookup-table inversion of PROSAIL in combination with a continuous wavelet transformation of reflectance showed highest accuracies. We found RTM-based retrieval not to be as accurate and transferable as previously indicated. Our results suggest that phenology is essential for accurate retrieval of plant functional traits and varies depending on the studied species and functional traits, respectively

How are spectrally relevant plant traits distributed across plant functional gradients?

Various plant traits that affect the spectral properties of plant canopies can be retrieved using optical earth observation data and thus enable to map plant functional properties (Kattenborn 2017). From a remote sensing perspective, the mechanistic response of these optically relevant plant traits is already quite well understood and formulated in process-based models, i.e. canopy radiative transfer models (RTMs). The latter model the reflectance of plant properties using the sun and observer (sensor) orientation and defined plant traits. However, the relationship of these traits towards plant functioning was not systematically assessed. Thus, the present study examines how spectrally relevant traits (those implemented in PROSAIL) are related to two established plant functional schemes, i.e. the leaf economic spectrum (LES) and CSR plant strategies. The trait expressions were measured in-situ in 42 cultivated herbaceous plants. As expected these plant traits indeed relate to the assessed gradients of plant functioning (LES and CSR). As expected traits related to leaf properties (e.g. pigments and dry matter content) show clear correspondence to the LES. Traits related to the canopy structure show no or very little correspondence to the LES but clearly relate to CSR plant strategies which reflect plant functioning at the level of plant individuals or communities. Multiple trait expressions such as LAI, canopy foliage mass (LMA * LAI), faPAR and fAPAR integrated over a growing season feature comparable or even higher correlations to the CSR space than traits that were originally used to allocate CSR scores (e.g. LMA or LDMC). Our results therefore highlight that spectrally relevant plant traits are a valuable alternative or addition to traits traditionally used in trait-based ecology. These traits might not only enrich the suite of potential indicators to characterize plant functional gradients using EO data; they also allow to establish physical and therefore explicit relationships which advance our theoretical understanding as well as the operationalization of such knowledge into mapping and monitoring approaches. References 1. Kattenborn, T., Fassnacht, F. E., Pierce, S., Lopatin, J., Grime, J. P., Schmidtlein, S. 2017. Linking plant strategies and plant traits derived by radiative transfer modelling. Journal of Vegetation Science, 28(4)

Deep learning and citizen science enable automated plant trait predictions from photographs

Plant functional traits (‘traits’) are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth’s plant functional diversity

About the link between biodiversity and spectral variation

Aim: The spectral variability hypothesis (SVH) suggests a link between spectral varia -tion and plant biodiversity. The underlying assumptions are that higher spectral vari-ation in canopy reflectance (depending on scale) is caused by either (1) variation in habitats or linked vegetation types or plant communities with their specific optical community traits or (2) variation in the species themselves and their specific optical traits.Methods: The SVH was examined in several empirical remote-sensing case studies, which often report some correlation between spectral variation and biodiversity- related variables (mostly plant species counts); however, the strength of the observed correlations varies between studies. In contrast, studies focussing on understanding the causal relationship between (plant) species counts and spectral variation remain scarce. Here, we discuss these causal relationships and support our perspectives through simulations and experimental data.Results: We reveal that in many situations the spectral variation caused by species or functional traits is subtle in comparison to other factors such as seasonality and physiological status. Moreover, the degree of contrast in reflectance has little to do with the number but rather with the identity of the species or communities involved. Hence, spectral variability should not be expressed based on contrast but rather based on metrics expressing manifoldness. While we describe cases where a certain link between spectral variation and plant species diversity can be expected, we be -lieve that as a scientific hypothesis (which suggests a general validity of this assumed relationship) the SVH is flawed and requires refinement.Conclusions: To this end we call for more research examining the drivers of spectral variation in vegetation canopies and their link to plant species diversity and biodiver-sity in general. Such research will allow critically assessing under which conditions spectral variation is a useful indicator for biodiversity monitoring and how it could be integrated into monitoring network

Medicinal service supply by wild plants in Samburu, Kenya: Comparisons among medicinal plant assemblages

Supply of medicinal plants from African landscapes is crucial because of their widespread use. Rapid climate change and land use change are potential threats to this resource but knowledge about the ecological needs of many of these plants is still rather limited. More knowledge about potential threats to medicinal plants supply and options to prevent future losses are desirable. Therefore, the objectives of the study were to examine (1) the effects of environmental drivers on the occurrence of medicinal plant species, (2) how different vegetation formations contribute to the provision of plants used for the treatment of diseases and (3) how these contributions are secured by redundancy. The analysis was based on a sample of 130 sampling plots in Samburu County, Kenya. We identified patterns in medicinal plants co-occurrences using classification and ordination analyses and analyzed these pattern in terms of environmental drivers, service diversity and service security. The pattern in medicinal plants co-occurrences reflected the distribution of broad formations (bushed grassland, forest, wooded grassland, savanna) driven by differences in grazing pressure, drought, slope and fraction of sand in soils. Each of the formations brought with it its own characteristic endowment with medicinal plants. The formations differed in the diversity and security of medicinal services provided. All resulted as fulfilling unique services with diseases treated by plants occurring exclusively in one or another formation. Forests featured the highest diversity of medicinal services, with medicinal plants used against 67 diseases. The supply security in forests, resulting from redundancy in supply provision, was moderate. In contrast to this, savanna grasslands featured plants with uses against 49 diseases, some of them were treated exclusively by plants from savanna grasslands. This formation also showed the highest redundancy. Wooded grasslands showed very little redundancy and is likely to be adversely affected by climate change. Whereas savannas feature the largest pool of medicinal plants and should receive due attention, urgent and highest conservation priority should, presently and in future, go towards the wooded grassland that had the lowest supply redundancy for traditional medicine

Differentiating plant functional types using reflectance: which traits make the difference?

Abiotic ecosystem properties together with plant species interaction create differences in structural and physiological traits among plant species. Certain plant traits cause a spatial and temporal variation in canopy reflectance that enables the differentiation of plant functional types, using earth observation data. However, it often remains unclear which traits drive the differences in reflectance between plant functional types, since the spectral regions in which electromagnetic radiation is influenced by certain plant traits are often overlapping. The present study aims to assess the relative (statistical) contributions of plant traits to the separability of plant functional groups using their reflectance. We apply the radiative transfer model PROSAIL to simulate optical canopy reflectance of 38 herbaceous plant species based on field‐measured traits such as leaf area index, leaf inclination distribution, chlorophyll content, carotenoid content, water and dry matter content. These traits of the selected grassland species were measured in an outdoor plant experiment. The 38 species differed in growth form and strategy types according to Grime′s CSR model and hence represented a broad range of plant functioning. We determined the relative (statistical) contribution of each plant trait for separating plant functional groups via reflectance. Therein we used a separation into growth forms, that is graminoids and herbs, and into CSR strategy types. Our results show that the relative contribution of plant traits to differentiate between the examined plant functional types (PFT) groups using canopy reflectance depends on the PFT scheme applied. Plant traits describing the canopy structure were more important in this regard than leaf traits. Accordingly, leaf area index (LAI) and leaf inclination showed consistently high importance for separating the examined PFT groups. This indicates that the role of canopy structure for spectrally differentiating PFT might have been underestimated

Climate change impacts on the availability of anti-malarial plants in Kenya

In many rural East African areas, anti-malarial plants are commonly used as first-line treatment against malaria. However, spatially explicit information about the future availability of anti-malarial plant species and its relation to future suitable habitat for malaria vectors is limited. In this study we (1) model the distribution of anti-malarial plant and malaria vector species and assess the drivers of their distributions taking the example of the Samburu dryland in Kenya, (2) map the modeled overlap in this area, (3) assess the impact of future climate change on anti-malarial plant and malaria vector species and (4) report their future overlaps. Our results show that mean temperature of warmest quarter, precipitation of wettest quarter and mean temperature of coldest quarter were the most important environmental variables that affected the distribution of anti-malarial species. The effects of climate change will vary, with some areas characterized by huge losses in anti-malarial species habitat while others gained or remained stable under both SSP2-4.5 and SSP5-8.5 climate change scenarios by 2050s and 2070s. According to most of our scenarios, more than half of the anti-malarial species will become vulnerable or threatened by 2050s and 2070s. A comparison between distribution patterns of future anti-malarial species richness and malaria vector species suitable habitat suggests that the former will decrease considerably while the later will increase. Because the availability of anti-malarial species will decrease in the areas affected by malaria vectors, geographically targeted conservation strategies and further control measures against malaria vectors are all the more important