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Sentinel-2 data, as part of the Copernicus Sentinel missions, have ushered in a new era for retrieving vegetation's biophysical and biochemical properties. As a result, several vegetation variables can be accurately retrieved due to the band configuration as well as spatial and temporal resolutions of the Sentinel-2 imagery. Several of these satellite-derived variables have been proposed as essential biodiversity variables (EBVs) by GEOBON or considered as remote sensing enabled-essential biodiversity variables (RS-enabled EBVs) by remote sensing and ecology experts. The leaf area index (LAI) and canopy chlorophyll content (CCC) received considerable attention among proposed RS-enabled EBV candidates. As a critical biophysical vegetation parameter, LAI gives important information about vegetative structure and function. It serves a crucial role in climate modelling and monitoring biodiversity. In this respect, the LAI was suggested as a prioritized RS-enabled EBV candidate for "Ecosystem Function" and "Ecosystem Structure" EBV classes. On the other hand, precise estimation of the CCC is significant for understanding terrestrial ecosystem dynamics such as carbon and water flux, productivity, and light use efficiency. As a result, CCC was recently proposed as a prioritized RS-enabled EBV candidate for "Ecosystem Function" and "Species Traits" EBV classes. In this respect, the Faculty of geoinformation and Earth observation of the University of Twente, as part of its commitment to the e-shape initiative, under the "myECOSYSTEM" showcase, established the portal that enables the user to generate the LAI and CCC products through empirical approaches and using Sentinel-2 data with 20m resolution at the European scale. The generated products will be stored on the server for 48 hours and removed accordingly, enabling the end-users to download and apply them in their investigation or research studies. In addition, some of the CCC products for pilot sites have been permanently populated on the GEOBON EBVs portal in order to provide easy access to regularly scaled products for pilot sites (e.g., the Netherlands and Bavarian Forest Nation Park).

Natural and anthropogenic stressors such as increasing drought, pests, and diseases exert increasing pressure on the condition of forests. Forest health assessment, mapping and monitoring are crucial for targeted management interventions and conservation. Direct forest health assessment in the field, although accurate, is a labour-intensive approach. Remote sensing (RS) is widely used in forest health assessment to create standardized methods that reduce subjectiveness, extrapolate observations in unvisited, inaccessible areas and reduce labour and costs. Unmanned Aerial Systems (UAS) have gained popularity in many forest-related management activities and research. Stress in trees causes a change in their physiological process, resulting in a temperature rise of the canopy. Thermal infrared (TIR, 7.5 -13.5 μm) remote sensing data can detect such canopy temperature changes. Previous research has confirmed the ability of UAS imagery to detect plants' health status. This study aims to investigate whether UAS-TIR imagery can be used to accurately map the health status of Pine trees (Pinus brutia). The usefulness of UAS acquired thermal data was examined in an open Mediterranean Pine forest in west Crete, Greece. The UAS campaign was conducted between 1 and 2 September 2021 over two sites covering 105.9 hectares. During fieldwork, the defoliation and decolouration of individual trees were recorded, and preliminary analysis was done using 56 observation data. Average canopy temperature was calculated for the delineated crowns and used to classify trees health status. In line with past research in other ecosystems, the present study results indicate a positive but weak correlation between defoliation and canopy temperature (R= 0.38). Further investigation is needed to assess the performance of thermal data in combination with multispectral and hyperspectral imagery.

Land cover, which is an essential climate variable and a remote sensing-enabled essential biodiversity variable is important for understanding terrestrial ecosystems functioning. Many studies have investigated forest land cover classification using remote sensing data from the visible, near, and short-wave infrared (VNIR-SWIR, 0.4- 2.5 μm) regions. However, to our knowledge, no study has addressed forest land cover classification using thermal infrared (TIR, 8-14 μm) hyperspectral data. In this study, for the first time, we present the preliminary assessment of vegetation classification using TIR hyperspectral data. TIR hyperspectral images (7.5 – 12.5 μm) were acquired by EUFAR aircraft using the AISA Owl sensor in July 2017 in Bavaria Forest National Park, Germany. In addition, fieldwork was conducted in 2017, concurrent to the flight campaign as well as in 2020 and 2021, and vegetation types were recorded in 92 plots. Canopy emissivity spectra were extracted for three vegetation classes namely, coniferous, broadleaves, and mixed classes. The extracted emissivity spectra were further used to classify three vegetation classes by means of a supervised Random Forest classifier. The results confirmed the expected capabilities of hyperspectral TIR data to produce an acceptable land cover map with an overall accuracy of 66%. The study showed that for coniferous class the most important spectral bands for classification were wavelengths 8.9 μm, between 9.7 – 9.9 μm and 10.3 μm. While for broadleaves there were,10.2 μm, 10.8 μm, and between 11.0 – 11.4 μm bands. The findings of this study show the possibility of using airborne hyperspectral TIR data for forest land cover classification. However, further investigation should be done applying other machine learning and deep learning techniques to examine the potential of TIR hyperspectral data for land cover classification.

The leaf area index (LAI)- as one of the most important vegetation biophysical variables, has been retrieved in vegetation canopies using data from different remote sensing platforms. LAI was recently proposed as a remote sensing-enabled essential biodiversity variable. To our knowledge, however, the retrieval of the LAI using hyperspectral thermal infrared (i.e., TIR 8-14 m) data has been addressed only under controlled laboratory conditions and has not yet been accomplished using thermal infrared hyperspectral data acquired from an airborne platform. Therefore, the primary goal of this study is to determine the accuracy of LAI prediction using thermal infrared hyperspectral data acquired from an airborne platform. The field campaign was conducted in July 2017 in the Bavarian Forest National Park in southeast Germany, and biophysical parameters, including LAI, were measured for 36 plots. Concurrently, thermal hyperspectral data were obtained using the Twin Otter aircraft operated by NERC-ARF (i.e., the U.K. Natural Environment Research Council- Airborne Research Facility) and the AISA Owl sensor. LAI was retrieved using an artificial neural network Levenberg-Marquardt algorithm. The results indicated that thermal infrared hyperspectral data could estimate LAI with relatively high accuracy (R= 0.734, RMSE=0.554). The study showed the significance of using an artificial neural network. It proved the possibility of using hyperspectral thermal infrared data to estimate vegetation biophysical properties at the canopy level and over a large forest area.

Canopy biophysical properties play an important role in understanding forest health and productivity. Among these parameters, forest leaf area index (LAI), canopy cover fraction, and canopy chlorophyll content describe the vegetation abundance, photosynthetic capacity and primary productivity of forest stands. The new generation of remote sensing satellites such as Sentinel-2 with high spatial and temporal resolutions has provided vast opportunities for monitoring these parameters and assessing their interrelationships over vast forest landscapes. In this research, temporal Sentinel-2 data between 2017-2019 in the temperate mixed forest ecosystem of the Bavarian Forest National Park, Germany, was used to retrieve forest canopy biophysical variables. INFORM radiative transfer model was used to retrieve LAI and canopy chlorophyll content while the fraction of vegetation functional types were calculated using phenological parameters and empirical approaches. A recent landcover map of the Bavarian Forest National Park was applied to retrieve considered variables pursuant to the different land cover classes. The retrieved variables were validated using in situ measurements of LAI and canopy chlorophyll content. Primary productivity was then calculated using (i) vegetation index universal pattern decomposition approach and (ii) the process-based dynamic vegetation-terrestrial ecosystem model LPJ-GUESS model. The relationships between calculated productivities and estimated biophysical variables were then studied. Our results showed that there is a good agreement between primary productivities calculated from LPG GUESS and the decomposition approach. Among studied parameters, canopy chlorophyll content, which represents pigments and vegetation abundance within the canopy, showed a strong direct relationship with both calculated primary productivities and hence may be used to explain plant functioning. Our results also revealed that remotely sensed vegetation biophysical parameters- that are becoming more and more readily available due to the availability of Earth observation data- can be used as proxies for estimation of the primary productivity calculated using either approach. Calculation of primary productivity usually needs information about canopy life-cycle and geometry, which are often not available at large scales. The results of our study support our findings in the myVARIABLE pilot of the EuroGEOSS Showcases initiative (e-shape) on developing primary productivity as a remotely sensed- essential biodiversity variable describing ‘Ecosystem function.’

In the version of this Perspective initially published, there was an error in units reported in the main text. Specifically, in the first sentence of the sixth paragraph under the heading “A critical review of EBVs retrieved by remote sensing,” in the text now reading “Finally, when harmonizing the terminology used by ecological and remote sensing communities, it is important to emphasize that utilizing broadband optical wavelengths (for example, for PlanetScope, approximately 400-700 nm) at very high spatial resolution,” 400-700 nm originally appeared as “60-90 nm.” The error has been corrected in the online version of the article.

Primary productivity is recognized as an Essential Biodiversity Variables (EBVs) under EBVs ‘Ecosystem Function’ class by GEOBON. According to existing literature, primary productivity has been one of the most important applications attempted by satellite remote sensing. The wide array of canopy geometry and life-cycle dynamics at large scales makes the estimation of primary production from remote sensing data very challenging. Primary productivity is either directly or indirectly linked to a number of other remote sensing- enabled biodiversity products including canopy chlorophyll content, leaf fraction exposed to light, absorbed photosynthetic active radiation, leaf area index and land use/cover change which are critical to understanding plant functioning. In the myVARIABLE pilot of the EuroGEOSS Showcases initiative (e-shape), we aim to develop primary productivity as an RS-EBV describing ‘Ecosystem Physiology’ and ‘Species Physiology’, being calibrated and validated by European observation networks including eLTER and other in situ data to support delivery at European level. Estimation of primary productivity involves using process-based models, semi-empirical light use efficiency (LUE) models or statistical models. The complexity and uncertainty of parameterization of process-based models, underlying assumptions in LUE models and dependency of statistical models to altering environmental conditions will be evaluated and assessed in order to propose and select the best approach for estimation of primary productivity at the European level using Sentinel-2 data.

Earth observation data is an ideal platform for capturing change in biodiversity at various resolutions, both spatially and temporally, while GEOBON creates an explicit structure for monitoring biodiversity by proposing EBV candidates. It is not only important to generate remote sensing (RS)-enabled biodiversity products using high-resolution data, but more than ever, it is necessary to address the biodiversity loss at the global scale from satellite acquisitions. In this regard, under the biodiversity pilot of the NextGEOSS initiative, the ITC biodiversity community portal (http://nextgeoss.itc.utwente.nl/ebv/) provides a self-service framework to generate RS-enabled biodiversity products for better understanding of biodiversity loss and ecosystem changes for the remote sensing and biodiversity communities. In addition, the WENR Biodiversity community portal (https://www.synbiosys.alterra.nl/nextgeoss) applies the RS-enabled biodiversity products as predictors, as well as in situ vegetation plot data for EUNIS habitat suitability modelling. Different users, including research and development institutions, public and private stakeholders, and decision-makers, are also making use of these resources. Currently, users can access the ITC Biodiversity community portal to generate Leaf Area Index as an RS-enabled biodiversity product in GEOBON EBV class ‘Ecosystem Function’, and also as one of the most important vegetation biophysical variable on a global scale using high-resolution satellite data (Sentinel-2, 20m) processed online using Cloud services (Terradue Cloud service). Also, under the EuroGEOSS project, additional remote sensing biodiversity products (Net primary productivity, chlorophyll content, habitat type, and fragmentation) are being moved and mirrored from the ITC biodiversity community portal to the GEOBON biodiversity portal, where they will be permanently available for use by the biodiversity and remote sensing communities.

EBVs have been proposed as a layer between biodiversity observation and biodiversity indicators, used in policy. More specifically, EBV classes – such as species traits, species populations, ecosystem functions as well as ecosystem structure – are being implemented by ecologists to identify global monitoring priorities. To support this there is an urgent need for remote sensing enabled EBVs to fill the spatial and temporal gaps between in situ observation data of biodiversity. In other words, without remotely sensed synoptic, systematic and continuous observations, a global framework for monitoring biodiversity cannot exist. Several RS-EBVs are anticipated to be derived from satellite remote sensing, because satellite remote sensing is the only methodology able to provide a global coverage and continuous measures across space at relatively high spatial and temporal resolutions. Habitats are very significant as an indicator for biodiversity and habitats have a strong links to species of which many are not being monitored at all. The NextGEOSS habitat mapping suitability interactive web facility (https://www.synbiosys.alterra.nl) uses more than 1 million European in-situ vegetation plot data in combination with climate, topographic, soil data, next to RS-enabled EBVs to produce European habitat suitability maps for each EUNIS habitat type (at level 3) using the MAXENT habitat distribution model (HDM). In situ plot observation data (derived from the EVA database; http://euroveg.org/eva-database) are available for 160 EUNIS terrestrial habitats . The model can be executed by end-users by making a aselection of currently 30 predictors, comprising 7 climate parameters, 7 soil parameters, and 13 RS-EBVs (LULC, vegetation height, Inundation, Phenology, LAI). For the modelling Maxent version 3.4.1 is used. The habitat suitability model is running in the cloud on Terradue servers. Model raster output can be downloaded by the client for further processing. End-users are invited not only to use the NextGeoss community portal for finetuning European habitat suitability maps but also to give their feedback.

In NextGEOSS Biodiversity Pilot WP 6.2.1, we focus on creating the NextGEOSS European remote sensing-enabled EBVs (RS enabled-EBVs) data-hub by identifying and populating available RS-enabled EBVs products. 123 variables were compiled as EBV candidates for five out of six EBV classes, as the genetic composition cannot be measured using remote sensing data. All EBV candidates were prioritized based on different criteria and observation requirements including relevancy to Aichi biodiversity targets, availability through remote sensing data (i.e., feasibility), and a measure of accuracy and maturity of remote sensing technologies and techniques. The 30 highest-prioritized RS-enabled EBVs were selected, and from these available RS-enabled EBVs products were identified with special consideration to their spatial resolution and scales. Metadata was created for each considered RS- enabled EBVs products with respect to the data provider and inserted in the NextGEOSS data-hub

The poster summarizes the implementation of the Innovative Pilot on Biodiversity; WP 6.2.1 developed within NextGEOSS project. The pilot focuses on generating remote sensing –enabled essential biodiversity variables (RS-enabled EBVs) by means of high- resolution satellite data using an empirical approach. From the RS-enabled EBVs, which were initially proposed to be derived from high-resolution satellite data, leaf area index (LAI) was selected as one of the most important vegetation biophysical parameters as well as the EBVs. Sentinel-2 data (Level-2A product) was used and further LAI was retrieved using the relationship between LAI and Enhanced Vegetation Index

Although the retrieval of leaf area index (LAI) as one of the essential biodiversity variable from remote sensing data has shown to be successful over visible/near-infrared (VNIR, 0.3-1.0 μm), shortwave infrared (SWIR, 1.0-2.5 μm), and TIR (8-14 μm) domains, integration of VNIR/SWIR with the TIR data for LAI estimation has not been addressed yet. Despite the importance, maturity, and availability of the remotely sensed data over VNIR and SWIR regions, TIR remote sensing data (i.e., emissivity spectra) has a number of advantages for LAI estimation. As such, it is known that the emissivity spectra over the TIR domain do not saturate even at relatively high values of LAI. In this respect, the utility of Landsat-8 TIR data together with its optical spectral data was examined to quantify LAI over Bavarian Forest National Park (Mixed temperate forest) in Germany. A field campaign was conducted in August 2015 in the National Park concurrent with the time of the Landsat-8 overpass. LAI was measured in the field for 37 plots. In this study, a number of vegetation indices, which have been widely applied in the literature were used to estimate LAI using VNIR/SWIR data. Furthermore, land surface emissivity (i.e., LSE) was derived from the band 10 of TIRS sensor using the normalized difference vegetation index threshold method. LSE was integrated with the reflectance data as the input layers to examine the LAI retrieval accuracy using the artificial neural network as a machine learning approach. The levenberg-marquardt algorithm was used for network training. LAI was predicted with modest accuracy using vegetation indices (R2CV=0.63, RMSECV=1.56 m2m-2, and R2CV=0.65, RMSECV=1.56 m2m-2 for NDI, and SR respectively). However, when the VNIR/SWIR bands and TIR data (LSE) were integrated, the prediction accuracy of LAI increased significantly (R2CV=0.79, RMSECV=0.75, m2m-2). Our results demonstrate that the combination of LSE and VNIR/SWIR satellite data can lead to higher retrieval accuracy for LAI. This finding has implication for retrieval of other vegetation parameters through the integration of TIR and optical satellite remote sensing data as well as regional mapping of LAI when coupled with a canopy radiative transfer model. 3

In NextGEOSS Biodiversity Pilot WP 6.2.1, we focus on creating the NextGEOSS European remote sensing-enabled EBVs (RS enabled-EBVs) data-hub by identifying and populating available RS-enabled EBVs products. 123 variables were compiled as EBV candidates for five out of six EBV classes, as the genetic composition cannot be measured using remote sensing data. All EBV candidates were prioritized based on different criteria and observation requirements including relevancy to Aichi biodiversity targets, availability through remote sensing data (i.e., feasibility), and a measure of accuracy and maturity of remote sensing technologies and techniques. The 30 highest-prioritized RS-enabled EBVs were selected, and from these available RS-enabled EBVs products were identified with special consideration to their spatial resolution and scales. Metadata was created for each considered RS-enabled EBVs products with respect to the data provider and inserted in the NextGEOSS data-hub.

Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R2 < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.