Biodiversity science is entering a new era. Years of effort by the scientific community are culminating in recent and upcoming launches of satellite systems specifically designed for global biodiversity assessment and monitoring. In addition, the Kunming-Montréal Global Biodiversity Framework, the most ambitious international agreement addressing biodiversity loss, is pushing for biodiversity and ecosystem protection, restoration and better management to occupy more prominent positions on global political agendas. It is clear by now that we need remote sensing to assess the status and monitor biodiversity globally and repeatedly. However, global biodiversity observatory systems need to combine remote sensing with ground observations to develop reliable and intepretable products. In this talk, I will try to summarize the current status and potential future directions of remote sensing of biodiversity across spatial, temporal and biological scales. The focus of my talk will be plant spectroscopy, which is based on the physical and physiological connections between plants and light. I will discuss the ways in which integrating ecological theory with measurements across spatial and temporal scales allow for a better understanding of what aspects of biodiversity global satellite systems are capable of detecting on the ground. I will also provide examples of remote sensing studies investigating the diversity of taxonomic groups other than plants through their connection with particular vegetation characteristics. Future advances in the field of remote sensing of biodiversity will benefit more than ever from diverse teams, global cooperation and collaborations across disciplines, including biology, geography, computer science and robotics. Now is the time to do our best work to help prevent and mitigate the negative consequences of biodiversity loss.

How to cite: Schweiger, A. K.: Remote Sensing of Biodiversity – Current Challenges and Future Prospects, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15068, https://doi.org/10.5194/egusphere-egu23-15068, 2023.

Technological advances in optical remote sensing, which measures the electromagnetic radiation reflected by an object at various wavelengths, allow for efficient and relatively inexpensive collection of baseline data related to biodiversity. In particular, spectral diversity—the variability in remotely sensed spectral reflectance data obtained from plant communities—has emerged as a valuable proxy for different facets of biodiversity, such as plant taxonomic, phylogenetic and functional diversity. However, successful estimation of plant diversity using spectral diversity is negatively impacted by several factors, including: i) limited or coarse spatial resolution of remote sensing data, ii) changes in remotely sensed reflectance data over time, and iii) weak linkages between species counts and spectral diversity in agricultural landscapes. To overcome these limitations, we present three novel spectral diversity approaches: i) a subpixel spectral diversity approach, ii) a multi-temporal spectral diversity approach, and iii) an object-based spectral diversity approach. Here, we provide different case studies using these three spectral diversity approaches to quantify plant diversity in two distinct grassland ecosystems: an agricultural landscape in the Swiss Alps and a tallgrass prairie in Oklahoma, U.S.

How to cite: Rossi, C., Hauser, L., and Gholizadeh, H.: How to overcome different limitations in estimating plant diversity via spectral diversity?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2872, https://doi.org/10.5194/egusphere-egu23-2872, 2023.

Increasing land use and climatic change lead to a global decline of biodiversity at alarming rates. To counteract this massive loss, global aims such as the convention of biological diversity’s Aichi targets and related conservation programs have been launched. These programs require a detailed monitoring of biodiversity across large areas, which raises high expectation with respect to biodiversity assessments from Earth Observation data. 

One frequently discussed approach is an application of the so called Spectral Variation Hypothesis (SVH). This approach aims to link the spectral variation of remotely-sensed image data to environmental heterogeneity as the main driver of species diversity in a given area. According to the SVH, diversity in leaf and canopy optical properties and habitat structures increase with increasing species diversity what in turn drives variations in the spectral signature of the plant communities. 
Various studies that explore these correlations in terrestrial ecosystems come to promising conclusions. However, the transferability of the proposed relations between spectral and taxonomic diversity to other ecosystem types and across different spatial resolutions remains unclear. Especially for grasslands where the mismatch between pixel and individual plant size is heavily pronounced, no comprehensive study has systematically tested the SVH yet. 

To fill this gap, we developed a theoretical framework that enables the simulation of realistic reflectance patterns of grassland vegetation. Thereby, we can mimic the spectral signal hypothetically reaching different sensor systems. Moreover, it allows us to test the relationships between spectral variation and taxonomic diversity for a high number of simulated plant communities. 

We created spatial distributions of artificial grassland units based on species inventories and trait data that we sampled in the field. We further simulated the spectral signature of these artificial communities using the leaf and canopy RTM PROSAIL. By including in-situ plant traits (species-, site- and season-specific, sampled on the individual plant level) we 1) simulate realistic reflection profiles which also incorporate seasonal dynamics, 2) modify species composition and species richness, and 3) use this as the basis to assess scaling effects. 

The modelling framework will be presented as well as the results of the spatial plant community simulations including the generated spectral patterns for three sites and seasons. Further, first results regarding the spectral-to-taxonomic diversity relationship will be discussed. 

How to cite: Ludwig, A., Doktor, D., and Feilhauer, H.: Using simulated grassland communities and radiative transfer models to test the Spectral Variation Hypothesis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11742, https://doi.org/10.5194/egusphere-egu23-11742, 2023.

The last decade has brought increased scientific interest in the use of remote sensing to map and monitor diversity patterns. While theory predicts that spectral diversity is an indicator of biodiversity (the spectral variation hypothesis), there is mixed empirical support for this relationship in herbaceous ecosystems, impeding the operational use of remote sensing in monitoring programs. It remains unclear why the strength of the biodiversity-spectral diversity relationships varies so much among herbaceous ecosystems. Scale is one recognized influence on this relationship, but the spatial resolution of spectral campaigns is typically predetermined. Therefore, we investigated three biological characteristics that may also affect the strength of the relationship between taxonomic and spectral diversity: vegetation density, spatial species turnover (beta-diversity) and invasion by non-native species.

For nine herbaceous sites in the National Ecological Observatory Network, we calculated taxonomic diversity from field surveys of 20 m × 20 m plots, and derived spectral diversity for those same plots from airborne hyperspectral imagery with a spatial resolution of 1 m. We found a significant relationship between taxonomic and spectral diversity at some, but not all, sites. Spectral diversity was a better proxy for taxonomic diversity in sites where within-plot spatial species turnover is high and invasion is low. The strength of the taxonomic diversity-spectral diversity relationship was indifferent to variation in vegetation density.

In this study, we demonstrated that, even when the spatial resolution of pixels does not match the spatial scale of plant individuals, certain biological characteristics may enable a positive relationship between taxonomic and spectral diversity. With this, we provide insight into when and why spectral diversity may serve as an indicator of taxonomic diversity in herbaceous ecosystems and be useful for monitoring.

How to cite: Van Cleemput, E., Adler, P., and Nash Suding, K.: Making remote sense of biodiversity in herbaceous ecosystems: Biological characteristics moderate the strength of the relationship between taxonomic and spectral diversity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9992, https://doi.org/10.5194/egusphere-egu23-9992, 2023.

Assisted migration programs, introducing new better adapted species at critical locations in our forests, have the potential to mitigate the adverse effects of climate change through the increase of forest diversity and resilience. While such measures entail ecological risks related with invasiveness of exotic species or outbreaks of new diseases, introducing close relatives of native species or populations from different parts of the species range is seen as the ecologically safer option. However, due to the similar appearance of closely related species, monitoring based on the external phenotype becomes difficult and leaves genetic screening as the only reliable, yet expensive option, limiting our ability to monitor large geographic areas. Reflectance spectroscopy has emerged as an important tool to assess plant functional trait distributions and taxonomic diversity, representing a rapid, scalable and integrated measure of the plant external and internal phenotype.

Here, we examine the potential of leaf-level reflectance spectroscopy to discriminate between the subspecies European beech (Fagus sylvatica L.), and Oriental beech (Fagus sylvatica spp Orientalis (Lipsky) Greut. & Burd), which has been proposed as a potential candidate for assisted migration in European forests due to its greater genetic diversity and potentially higher drought tolerance. We investigated two European beech forests in France and Switzerland where Oriental beech from the Caucasus was introduced over 100 years ago next to European beech. Over the summers of 2021 and 2022, we measured leaf spectral reflectance and leaf morphological and biochemical traits from previously genotyped adult trees.

Using least squares discriminant analysis (PLS-DA), we found that leaf spectral reflectance allowed the accurate discrimination of the two beech subspecies. In particular, we found that the short-wave-infrared (SWIR) region between 1450-1750 nm from top-of-canopy leaves provided the most accurate subspecies discrimination (BA = 0.86±0.08, k = 0.72±0.15). To provide a mechanistic basis of our findings, we estimated a suite of leaf traits based on spectra-derived indices and standard field and lab protocols. Phenotyping confirmed significant subspecies differences between traits that are known to govern light-plant interactions in the SWIR, including lignin, nitrogen in proteins, leaf mass per area and leaf thickness.

Our study provides a basis for crown-level subspecies classifications from airborne or satellite-based imagery in the genus Fagus. Our findings provide an important starting point for the interpretation of variability in tree crown reflectance and the superior discrimination capacity we found for leaves at the top as compared to leaves at the bottom of the canopy, holds promise for the upscaling of the method using remote sensing.

How to cite: D'Odorico, P., Schuman, M. C., Kurz, M., and Csilléry, K.: Can spectral phenotypes discriminate subspecies? A case study at two European and Oriental beech forest stands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12954, https://doi.org/10.5194/egusphere-egu23-12954, 2023.

To overcome threats to agro-ecosystems, such as a dramatic species decline, an ecological intensification in crop production is needed. One possible strategy is the simultaneous cultivation of legume and cereal plants in a mixed arrangement, namely mixed cropping. Cereal-legume crop mixtures may benefit from diversity effects, i.e. improved use of environmental resources such as light, water and nitrogen. Thus, mixtures have shown to result in higher land productivity with respect to grain yield compared to sole cropping. However, mixture systems are complex and difficult to study due to dynamic species interactions and their heterogeneous canopy structures. 
To better understand structural and functional diversity effects in a mixed cropping system, we non-invasively studied two crops in a field trial in 2021 and 2022. Here, different genotypes of faba bean (Vicia faba L.) and spring wheat (Triticum aestivum L.) were combined in six legume-cereal mixtures. The 1:1 mixtures were compared to each other and against the respective sole crops. To study structural and functional diversity effects in mixtures, we applied proximal and remote sensing tools. We characterized photosynthesis-related plant traits derived from hyperspectral and solar-induced fluorescence (SIF) data recorded with ground-based and airborne sensors. The high-performance airborne spectrometer HyPlant was used to acquire SIF image data with 1 m spatial resolution. Additionally, we collected hyperspectral and SIF point measurements with the mobile field sensor system FloX on different dates during the two growing seasons. We found that HyPlant and FloX datasets of different mixtures and crop types collected in mid-June showed significantly different levels of far-red SIF emission efficiency (ε_F) (p<0.05), while the same was not observed for the absolute far-red SIF measurements. Wheat provided higher ε_F values in comparison to beans. Furthermore, differences between mixture combinations could be observed. This was more prominent in data collected in 2021 compared to 2022. In order to identify seasonal dynamics of mixture performance we extracted photosynthesis-related variables by combining radiative transfer modelling (RTM) with machine-learning regression algorithms (MLRAs) in a hybrid manner. First, we simulated reflectance and SIF data using the ‘Soil Canopy Observation, Photochemistry and Energy fluxes’ (SCOPE) RTM. Next, we calibrated different regression methods (e.g. Gaussian Process Regression, Kernel ridge Regression) with simulated data in order to retrieve relevant variables to characterize the photosynthetic performance, such as absorbed photosynthetically active radiation (APAR) and ε_F. Results for mixed cropping plots were corrected for the species composition calculated using spectral mixture analysis based on multispectral UAV data with high spatial resolution.
In our study we explore how crop performance driven by diversity effects can be explained by hyperspectral and SIF information. We believe that such data will facilitate new insights into the complex relationship underlying the mixture of two species in a diversified legume-cereal system.

How to cite: Krämer, J., Siegmann, B., Stephany, C., Muller, O., Döring, T., and Rascher, U.: Exploring photosynthetic dynamics in diverse crop canopies by using hyperspectral and solar-induced fluorescence (SIF) data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11577, https://doi.org/10.5194/egusphere-egu23-11577, 2023.

Northern peatlands provide key climate regulating services by sequestering and storing atmospheric carbon as peat, but they are also habitat for highly specialised plant and animal species. Habitat suitability and peat accumulation rate in peatlands are strongly related to vegetation structure (species composition, biomass) and its spatial organisation (microforms). Diversity in vegetation patterns therefore act as an ecological indicator for peatland functioning.

Although microforms and their associated plant species only occur at fine spatial scales (varying from 1–10m to 0.01–1m respectively), their patterning is often repeated on the scale of whole peatlands. Consequently, remote sensing applications have recently gained much attention in this ecosystem for their potential role in landscape-scale mapping and monitoring of fine-scale vegetation patterns and functions. However, standardized methods to optimize such approaches are currently lacking or non-existent. For this reason, we set out to develop remote sensing methodology that can accurately, efficiently, flexibly, and cheaply map the distribution of microforms and plant functional types for a variety of peatland types, spatial scales, and research goals. To this end, we collected very high-resolution drone imagery (spectral and topographical) from eight Irish peatlands in 2021 and 2022 (from 1–250ha) using a consumer-grade drone with RGB camera sensor. Hereafter, we thoroughly evaluated to what extent classification accuracy and total processing time from imagery capture to final map was affected by various flight parameters (flight altitude, image overlap), image processing parameters (spatial resolution, segmentation scale, training/testing sample size), and pattern complexity (spatial patch characteristics).

The results of our study show that peatland vegetation patterns could both accurately and efficiently be classified and mapped using drone imagery, independent of pattern complexity. We also found that flying at the maximum legal flight altitude of 120m is significantly more efficient than flying at any lower altitudes because the spatial resolution of drone imagery at 120m is most often much higher than the size of peatland vegetation patterns. Flying at lower altitudes thus introduces more internal heterogeneity within plants.  However, our results also indicate that minimum spatial resolution requirements for mapping microforms and plant functional types varied notably among the studied peatlands (ranging from 0.1–1m), and showed strong relationships with spatial patch characteristics of microforms. This suggest that spatial resolution requirements in heterogeneous landscapes are not only simply driven by the types of vegetation that are present, but also by their spatial organisation.

Taken together, our results show that peatlands lend themselves very well for drone-based, landscape-scale mapping and monitoring of vegetation patterns because of the affordability, flexibility, and ease by which drones can collect and process very high-resolution spectral and topographical data. Yet, given the tremendous scale at which peatlands can in the landscape, we urge development of nested drone-satellite approaches to further improve upscaling of fine-scale vegetation patterns and their functions to the regional and global scale.

How to cite: Steenvoorden, J. and Limpens, J.: Mapping fine-scale vegetation patterns as ecological indicators for peatland biodiversity and carbon sequestration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1210, https://doi.org/10.5194/egusphere-egu23-1210, 2023.

Forest biodiversity is one of the seven thematic programmes established by the Conference of the Parties within the Convention on Biological Diversity. The topic of identification, monitoring, definition of indicators and assessment of biodiversity is one of the cross-cutting issues  of the Convention to collect, maitain and organize biodiversity information.

The huge amount, spectral diversity, regular and dense acquisition plan of current Earth Observation spaceborne missions provides a means to monitor and evaluate the vegetation biodiversity. In this work we present the results of an application of multispectral, hyperspectral and SAR satellite images to map the vegetation biodiversity in National Parks of Gargano, Alta Murgia, Cilento-Vallo di Diano-Alburni, Appennino Lucano Val D’Agri Lagonegrese and Pollino, all located in Southern Italy. For each of the aforementioned parks, study areas have been selected. Sentinel-2 and PRISMA images have been used to compute different vegetation indeces to analyze the different phenological properties of plants and the impact of the interaction soil-vegetation on the reflection coefficient measured by the sensors. Furthermore, Sentinel-1 images have been used to compute the radar vegetation index and the interferometric Synthetic Aperture Radar (SAR) coherence.

The maps of all the above multi- and hypespectral indeces and SAR products have been analyzed in terms of two abundance-based metrics and used within a agent-based model to quantify vegetation biodiversity. The Shannon entropy and Rao’s Q metrics haven been implemented and applied to the matrices of vegetation indeces and SAR products. These two computational tools are compared in terms of their ability to describe the diversity of the agro-forestry landspace. Furthermore, the landscape heterogeneity has been modelled by intelligent agents that move through the selected areas in a simulated environment and collect information on vegetation indices in order to measure their diversity. The output of the agent-based model has been compared to the results obtained by the abundance-based metrics to identify mathematical tools useful for the conservation planning of critical habitats.

How to cite: Nico, G., Monaco, M., and Masci, O.: Analysis of vegetation biodiversity by means of abundance-based metrics and agent-based models applied to spaceborne multispectral, hyperspectral and SAR images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4477, https://doi.org/10.5194/egusphere-egu23-4477, 2023.

We present the methods designed for the NaturaSat software devoted to the identification, classification, monitoring, and evaluation of Natura 2000 habitats by Sentinel-2 multispectral data. The NaturaSat software contains various image processing techniques based on novel mathematical models, and together with vegetation data, it makes a suitable facility for all requirements of habitat exploration. The semi-automatic and automatic segmentation methods are implemented to identify the habitat areas. The novel deep learning algorithm, a natural numerical network, is implemented for habitat classification but can also be used in various research tasks or nature conservation practices, such as identifying ecosystem services and conservation value. Moreover, based on the natural numerical network, relevancy maps are created, and it can improve many further vegetation and landscape ecology studies. The relevancy map tells us about the relevancy of the classification of the segmented area into the chosen habitat. NaturaSat provides direct access to multispectral Sentinel-2 data provided by the European Space Agency and thereby allows monitoring of Natura 2000 habitats. The monitoring process is based on calculating the statistical characteristics in the protected areas and analyzing them in time. The software was developed through the intensive cooperation of botany field scientists, mathematicians, and software developers, which means that the implemented methods have a mathematical basis and are validated in field research. The NaturaSat has a user-friendly environment for, e.g., vegetation scientists, fieldwork experts, and nature conservationists, and it is robust enough to accurately extract target unit borders, even at the habitat level. The accuracy is close to the pixel resolution; in the case of Sentinel-2 images, it is 10 m. If unmanned aerial vehicles or air-borne images are used in the software, the accuracy is rapidly pushed.

How to cite: Ozvat, A. A., Mikula, K., Kollár, M., Ambroz, M., Šibíková, M., Šibík, J., and Čahojová, L.: NaturaSat – a software for the identification, classification, and monitoring of Natura 2000 habitats, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14203, https://doi.org/10.5194/egusphere-egu23-14203, 2023.