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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 vertical distribution of leaf water content (LWC) within plant canopy plays an important role in light penetration and scattering, thus affecting reflectance simulation with radiative transfer models. Although passive remote sensing techniques have been widely applied to estimate LWC, they are unable to retrieve the LWC vertical distribution within canopy. By providing vertical information, terrestrial LiDAR can potentially overcome this limitation. In this paper we investigated the applicability of the terrestrial full-waveform LiDAR to estimate the LWC vertical profile within the canopy of individual plants. A standard radiometric calibration was applied to convert the amplitude and the echo width to a physically well-defined radiometric quantity, namely the backscatter coefficient. However, the backscatter coefficient is strongly affected by the incidence angle between the laser beam and the leaf normal. In order to compensate for incidence angle effects, reference reflectors (Spectralon from Labsphere, Inc.) were used to build a look-up table to calibrated the backscatter coefficient. Our results showed that the backscatter coefficient had a strong correlation (R 2 = 0.66) with LWC after correcting for the incidence angle effect. Good agreements were achieved between the predicted vertical profile of LWC and the measured vertical profile of LWC with a mean RMSE (root mean square error) value of 0.001 g/cm 2 and a mean MAPE (mean absolute percent error) value of 4.46 %. Our study successfully demonstrated the feasibility of retrieving LWC vertical distribution within plant canopy from a terrestrial full-waveform LiDAR.

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.

Vegetation indices (VI) combine mathematically a few selected spectral bands to minimize undesired effects of soil background, illumination conditions and atmospheric perturbations. In this way, the relation to vegetation biophysical variables is enhanced. Albeit numerous experiments found close relationships between vegetation indices and several important vegetation biophysical variables, well known shortcomings and drawbacks remain. Important limitations of VIs are illustrated and discussed in this paper. As most of the limitations can be overcome using physically-based radiative transfer models (RTM), advantages and limits of RTM are also presented. © 2011 SPIE.

Voor vele agrarische, ecologische en meteorologische toepassingen, is een nauwkeurige kwantitative meting van de biochemische en bio-fysische karakteristieken van vegetatie van belang. Vanwege zijn wereldwijde dekking, relatief goedkope eigenschappen is Remote Sensing erkend als een betrouwbare en praktische methode om diverse bio-fysische en biochemische variabelen in vegetatie te meten. Door het ingebruik nemen van hyper-spectrale Remote Sensing is het nu mogelijk specifieke vegetatie variabelen te meten, die voorheen met conventionele multi-spectralesensoren niet waren te meten