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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).

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.

Machine learning algorithms, and specifically kernel-based methods such as Gaussian processes regression (GPR), have been shown to outperform traditional empirical methods for retrieving vegetation traits. GPR is attractive for its property of automatically generating uncertainty estimates for predicted traits. GPR has been increasingly used for the estimation of canopy traits from hyperspectral remote sensing data in agricultural fields and grassland ecosystems. However, to our knowledge, the application of GPR using full-spectrum airborne hyperspectral data in forest ecosystems remains under-explored. Therefore, in this study, we evaluated the performance of GPR as a representative of kernel-based machine learning algorithms in estimating two essential forest canopy traits (i.e., LAI and canopy chlorophyll content) using airborne hyperspectral data. The performance of GPR was compared with partial least square regression (PLSR) which is widely used for retrieving vegetation traits in spectroscopic studies. Field measurements of LAI and leaf chlorophyll content were collected in the Bavarian Forest National Park (BFNP) in Germany, concurrent with the acquisition of the Fenix airborne hyperspectral data (400−2500 nm) in July 2017 in the framework of the EUFAR summer school RS4forestEBV. The cross-validated coefficient of determination (R2) and normalised root mean square error (nRMSE) between the field-measured and retrieved traits were used to examine the accuracy of the respective methods. The results indicated that GPR somewhat outperformed PLSR in producing accurate estimations for LAI (GRP nRMSE = 16.7%; PLSR nRMSE = 23.0%) and canopy chlorophyll content (GPR nRMSE = 16.2%; PLSR nRMSE = 22.5%). The uncertainty maps generated by GPR showed that the retrieval uncertainties were generally low across the map, whereas higher uncertainties mainly corresponded with regions with low vegetation cover or under-represented in our field sampling. The capability to generate accurate predictions and associated uncertainty estimates suggest the GPR may be a promising candidate for the retrieval of vegetation traits.

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.

Two case studies are introduced. First, a quantitative method for assessing the need for ecological networks through modeling the potential geographic distributions of species based on a case study of local populations of Asiatic black bear is presented. Second, genetic variation of Asiatic black bear in Tohoku region, Japan, are reported. To determine how population subdivision relates to management units proposed by the Ministry of the Environment, genetic variation in the mitochondrial DNA control region and seven autosomal microsatellite loci was assessed in bears captured in northern Japan. Geographic distribution of the subpopulations was assessed using landscape analyses to find the best-fit model based on maximum entropy prediction and cost of movement. Finally, how human–bear conflicts, nuisance control, and traditional hunting may affect conservation and management of Asiatic black bears in southern Tohoku area, where large suitable habitats for this species exist, are also shown.

The ESA funded GlobDiversity project was the first large-scale project explicitly designed to develop and engineer Remote Sensing enabled Essential Biodiversity Variables (RS-enabled EBVs) and ended in June 2020. The project also aimed to contribute with the documents and procedures generated to the development of a workflow starting from user requirements to final products that can be used for policy relevant global biodiversity monitoring and assessments. As a final step, the project developed a road map discussing the project’s outputs, e.g., strategic documents, processing chain and data products derived when focusing on particular RS-enabled EBVs, in the context of the overall EBV framework with and in context of relevant players such as the Group on Earth Observations Biodiversity Observation Network (GEO BON), CBD, IPBES, CEOS, the EBV user community and decision makers, Copernicus Services and the space agencies. We will thus present this RS-enabled EBV road map strategic document with the aim to put in place the project’s output into the EBV frame work. The proposed workflow includes discussions about the involvement of different users from the very beginning, to the development of any EBV data set, as well as to the implementation and use in the framework of the indictors. In addition, we will discuss the project’s experiences gained while developing biodiversity products based on remote sensing. In particular, we will present knowledge gaps and recommendations when evaluating the proposed road map. Thus, we will present this strategic document, so that the biodiversity community can most benefit from GlobDiversity’s outcome.

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.

Assessment of canopy chlorophyll content (CCC) is an essential variable in developing indicators for biodiversity monitoring and climate change studies. The Sentinel-2 Multi-Spectral Imager (MSI) is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving CCC from Sentinel-2 in Bavarian forest national park, Germany. Fourteen statistical-based methods, including 13 different vegetation indices (VIs) and a non-parametric statistical approach, and two physical-based methods such as INFORM and PROSAIL radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field data collected in July 2017, and cloud-free Sentinel-2 image acquired on 13 July 2017 were used for evaluating the performance of the methods. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. The modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE = 0.22 g/m2 than mSR3. Further, the physical-based approach-INFORM inversion using look-up table resulted in an RMSE = 0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for large scale mapping.

Quantifying Canopy chlorophyll content (CCC) is fundamental for the understanding of terrestrial ecosystems through monitoring and evaluating terrestrial ecosystem properties such as carbon and water fluxes, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects of biodiversity. However, robust and rigorous methods for regional and global mapping of CCC from remote sensing data is not well defined. This study aimed at evaluating the spatiotemporal consistency and scalability retrieval methods for large scale mapping of CCC. Four methods (i.e., Radiative transfer models (RTMs) inversion using look-up table (LUT), the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR), were investigated for their performance across biomes. Statistical measures were computed and spatiotemporal consistency pairwise comparison applied to evaluate the similarities and differences among CCC products generated by the four methods in four biomes (Temperate forest, Tropical forest, wetland, and Arctic Tundra). All the tested methods, except PLSR showed similar patterns and no significant difference in the spatial distribution in temperate forests. The CCC products obtained using the SRVI and the SNAP toolbox approach result in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent predictions of CCC with range closer to expectations. Therefore, the RTM inversion using LUT approaches, particularly INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems are recommended for CCC mapping from Sentinel-2 data for regional and global mapping of CCC. Further validation of the two RTMs using in situ CCC data in different terrestrial biomes is required in the future.

The European bark beetle (Ips typographus, L.) is a potentially severe invasive species in the UK and North America. It is resulting in a high degree of fragmentation, forest productivity, and phenology. Understanding its biology, as well as developing early detection based on its behavior, is an important aspect of its successful management and eradication. Bark beetle infestation causes changes biochemical and biophysical characteristics such as chlorophyll water and nitrogen content. This study showcases the potential of the Canopy Chlorophyll Content (CCC) product derived from remote sensing datasets to detect early bark beetle infestation in Bavarian forest national park. We generated time series CCC maps from RapidEye and Sentinel-2 images of the study area through Radiative transfer model inversion. The CCC products were then classified into infested and healthy using CCC mean and variance collected in 2015 and 2016 from infested and healthy Norway spruce trees in the Park. Reference data obtained from processing and interpretation of high resolution (0.1m) color aerial photographs were used to validate the accuracy of the infestation maps. Our results demonstrated that CCC products as derived from remote sensing data were a rigorous proxy to early detect bark beetle infestation. Validation of the infestation maps revealed > 70% classification accuracy throughout the time-space. Hence, CCC products play a significant role to understand the dynamics of the infestation and improve the management of bark beetle outbreaks in forest ecosystem. Despite these promising results, other plant traits such as dry matter content and Nitrogen content will need to be investigated as additional predictors, which may considerably improve the accuracy of early detection of bark beetle infestation using remote sensing derived products.

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

A new era in the retrieval of plant traits has started by emerging the new satellites such as the Copernicus Sentinel families. Among these traits, leaf area index (LAI) is a key indicator of vegetation growth and an essential variable in biodiversity studies. Numerous literature has shown that radiative transfer approach has been a successful method to retrieve LAI from remote sensing data. However, suitability and adaptability of this approach largely depend on the type of remote sensing data, and the ecosystem studied. In this regard, the retrieval of leaf area index in a saltmarsh ecosystem is examined in this study using Sentinel-2 and RapidEye data through inversion of PROSAILH radiative transfer model. Field measurements of LAI and a number of other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 in the saltmarsh of Schiermonnikoog, the Netherlands. Sentinel-2 (2016) and RapidEye (2015) data were acquired concurrent to the time of field campaigns. The broadly employed PROSAILH model was inverted using a look-up table (LUT) which contained 500, 000 records. Different scenarios of band combinations, as well as different solutions, were considered to obtain the LAI estimates. The R2 and RMSE between measured and estimated LAI were used then to evaluate the retrieval accuracy. The removal of dead materials from the measured LAI improved the estimation accuracies. Our results showed that generally the LAI retrieved using the Sentinel-2 data had higher accuracy compared to RapidEye data. In particular, the SWIR bands of Sentinel were modeled best using the PROSAILH. Leaf area index was best retrieved using the NIR and SWIR bands of Sentinel-2 (R2=0.56, RMSE=1.7). Our results highlight the importance of proper parametrization of radiative transfer models and capacity of Sentinel-2 data, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.

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

Forest management requires the use of comprehensive remote sensing data which enable monitoring of biodiversity changes. Biophysical and biochemical vegetation parameters can characterize changes in biodiversity through changes in ecosystem structure and function. To address this need the University of Twente, Faculty ITC (the Netherlands) in collaboration with the Bavarian Forest National Park and the German Aerospace Center, (DLR) in Oberpfaffenhofen coordinated a summer school in July 2017. The two weeks of summer school was funded by EUFAR and was hosted by the Bavarian Forest National Park and DLR. The 19 participants of the summer school were PhD students and post-docs from 10 EU member states. The summer school offered the field expertise as well as the technical skills to understand and measure a number of essential biodiversity variables (EBVs) in forest ecosystems. Further, the students learned how to process the hyperspectral, thermal , and LiDAR data for the estimation of EBVs. The course contained two days of fieldwork in the Bavarian Forest National Park, and the participants of different themes (Hyperspectral, Thermal, LiDAR) were trained how to perform field measurements of various EBVs in a forest ecosystem. Further, the course participants were taught how to conduct field spectroscopy, thermal spectrometry and terrestrial LiDAR measurements. Concurrent to the time of field measurements an airborne campaign with the NERC Airborne Research Facility (NERC-ARF) was organized that simultaneously acquired hyperspectral as well as thermal-infrared imaging data using the Specim AISA Fenix and Owl systems, respectively. The second half of the summer school was parallel with the ICARE 2017 conference, and the course participants visited the aircraft exhibition and were welcomed by the airborne research and operator community.

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.

Seasonal changes in leaf chlorophyll across the canopy vertical profile provide information on ecosystem structure and functioning. However, studies on the retrieval of leaf chlorophyll content (Cab) using radiative transfer models such as PROSPECT across the canopy vertical profile throughout the growing season are lacking. In this regard, we sought to evaluate the performance of the PROSPECT in modeling leaf optical properties and retrieving Cab across the canopy position throughout the growing season. We collected 588 leaf samples from the upper and lower canopies of deciduous stands over three seasons in Bavaria Forest National Park, Germany. PROSPECT input parameters were measured for all the samples, and their respective reflectance spectra were obtained using an ASD FieldSpec-3 Pro FR spectroradiometer coupled with an Integrating Sphere. To retrieve Cab, we inverted the PROSPECT using a look-up-table (LUT) approach. Our results consistently revealed a strong agreement between the measured and PROSPECT simulated reflectance spectra for the lower canopy compared to the upper canopy, especially in the NIR. This observation concurred with the pattern of Cab retrieval accuracies across the canopy i.e. the Cab retrieval accuracy for the lower canopy was consistently higher (NRMSE = 0.1-0.2) when compared to the upper canopy (NRMSE = 0.122 - 0.269) across all seasons. Results of this study demonstrate that although the PROSPECT model provides acceptable inversion of Cab, subtle seasonal variations in leaf biochemistry and morphology across the canopy potentially affect the performance of the model.

Plant traits are used to define species, but also to evaluate the health status of forests, plantations and crops. Conventional methods of measuring plant traits (e.g. wet chemistry), although accurate, are inefficient and costly when applied over large areas or with intensive sampling. Spectroscopic methods, as used in the food industry and mineralogy, are nowadays applied to identify plant traits, however, most studies analysed visible to near infrared, while infrared spectra of longer wavelengths have been little used for identifying the spectral differences between plant species. This study measured the infrared spectra (1.4–16.0 mm) on individual, fresh leaves of 19 species (from herbaceous to woody species), as well as 14 leaf traits for each leaf. The results describe at which wavelengths in the infrared the leaves’ spectra can differentiate most effectively between these plant species. A Quadratic Discrimination Analysis (QDA) shows that using five bands in the SWIR or the LWIR is enough to accurately differentiate these species (Kappa: 0.93, 0.94 respectively), while the MWIR has a lower classification accuracy (Kappa: 0.84). This study also shows that in the infrared spectra of fresh leaves, the identified speciesspecific features are correlated with leaf traits as well as changes in their values. Spectral features in the SWIR (1.66, 1.89 and 2.00 mm) are common to all species and match the main features of pure cellulose and lignin spectra. The depth of these features varies with changes of cellulose and leaf water content and can be used to differentiate species in this region. In the MWIR and LWIR, the absorption spectra of leaves are formed by key species-specific traits including lignin, cellulose, water, nitrogen and leaf thickness. The connection found in this study between leaf traits, features and spectral signatures are novel tools to assist when identifying plant species by spectroscopy and remote sensing.

Both agricultural area expansion and intensification are necessary to cope with the growing demand for food, and the growing threat of food insecurity which is rapidly engulfing poor and under-privileged sections of the global population. Therefore, it is of paramount importance to have the ability to accurately estimate crop area and spatial distribution. Remote sensing has become a valuable tool for estimating and mapping cropland areas, useful in food security monitoring. This work contributes to addressing this broad issue, focusing on the comparative performance analysis of two mapping approaches (i) a hyper-temporal Normalized Difference Vegetation Index (NDVI) analysis approach and (ii) a Landscape-ecological approach. The hyper-temporal NDVI analysis approach utilized SPOT 10-day NDVI imagery from April 1998–December 2008, whilst the Landscape-ecological approach used multitemporal Landsat-7 ETM+ imagery acquired intermittently between 1992 and 2002. Pixels in the time-series NDVI dataset were clustered using an ISODATA clustering algorithm adapted to determine the optimal number of pixel clusters to successfully generalize hyper-temporal datasets. Clusters were then characterized with crop cycle information, and flooding information to produce an NDVI unit map of rice classes with flood regime and NDVI profile information. A Landscape-ecological map was generated using a combination of digitized homogenous map units in the Landsat-7 ETM+ imagery, a Land use map 2005 of the Mekong delta, and supplementary datasets on the regions terrain, geo-morphology and flooding depths. The output maps were validated using reported crop statistics, and regression analyses were used to ascertain the relationship between land use area estimated from maps, and those reported in district crop statistics. The regression analysis showed that the hyper-temporal NDVI analysis approach explained 74% and 76% of the variability in reported crop statistics in two rice crop and three rice crop land use systems respectively. In contrast, 64% and 63% of the variability was explained respectively by the Landscape-ecological map. Overall, the results indicate the hyper-temporal NDVI analysis approach is more accurate and more useful in exploring when, why and how agricultural land use manifests itself in space and time. Furthermore, the NDVI analysis approach was found to be easier to implement, was more cost effective, and involved less subjective user intervention than the landscape-ecological approach.

Remote sensing (RS)—taking images or other measurements of Earth from above—provides a unique perspective on what is happening on the Earth and thus plays a special role in biodiversity and conservation applications. The periodic repeat coverage of satellite-based RS is particularly useful for monitoring change and so is essential for understanding trends, and also provides key input into assessments, international agreements, and conservation management. Historically, RS data have often been expensive and hard to use, but changes over the last decade have resulted in massive amounts of global data being available at no cost, as well as significant (if not yet complete) simplification of access and use. This chapter provides a baseline set of information about using RS for conservation applications in three realms: terrestrial, marine, and freshwater. After a brief overview of the mechanics of RS and how it can be applied, terrestrial systems are discussed, focusing first on ecosystems and then moving on to species and genes. Marine systems are discussed next in the context of habitat extent and condition and including key marine-specific challenges. This is followed by discussion of the special considerations of freshwater habitats such as rivers, focusing on freshwater ecosystems, species, and ecosystem services.

This chapter covers the questions of ecosystem definition and the organisation of a monitoring system. It treats where and how ecosystems should be measured and the integration between in situ and RS observations. Ecosystems are characterised by composition, function and structure. The ecosystem level is an essential link in biodiversity surveillance and monitoring between species and populations on the one hand and land use and landscapes on the other. Ecosystem monitoring requires a clear conceptual model that incorporates key factors influencing ecosystem dynamics to base the variables on that have to be monitored as well as data collection methods and statistics. Choices have to be made on the scale at which monitoring should be carried out and eco-regionalisation or ecological stratification are approaches for identification of the units to be sampled. This can be done on expert judgement but nowadays also on stratifications derived from multivariate statistical clustering. Data should also be included from individual research sites over the entire world and from organically grown networks covering many countries. An important added value in the available monitoring technologies is the integration of in situ and RS observations, as various RS technologies are coming into reach of ecosystem research. For global applications this development is essential. We can employ an array of instruments to monitor ecosystem characteristics, from fixed sensors and in situ measurements to drones, planes and satellite sensors. They allow to measure biogeochemical components that determine much of the chemistry of the environment and the geochemical regulation of ecosystems. Important global databases on sensor data are being developed and frequent high resolution RS scenes are becoming available. RS observations can complement field observations as they deliver a synoptic view and the opportunity to provide consistent information in time and space especially for widely distributed habitats. RS has a high potential for developing distribution maps, change detection and habitat quality and composition change at various scales. Hyperspectral sensors have greatly enhanced the possibilities of distinguishing related habitat types at very fine scales. The end-users can use such maps for estimating range and area of habitats, but they could also serve to define and update the sampling frame (the statistical ‘population’) of habitats for which field sample surveys are in place. Present technologies and data availability allow us to measure fragmentation through several metrics that can be calculated from RS data. In situ data have been collected in several countries over a longer term and these are fit for statistical analysis, producing statistics on species composition change, habitat richness and habitat structure. It is now possible to relate protocols for RS and in situ observations based on plant life forms, translate them and provide direct links between in situ and RS data.