Predicting leaf traits of herbaceous species from their spectral characteristics

Trait predictions from leaf spectral properties are mainly applied to tree species, while herbaceous systems received little attention in this topic. Whether similar trait–spectrum relations can be derived for herbaceous plants that differ strongly in growing strategy and environmental constraints is therefore unknown. We used partial least squares regression to relate key traits to leaf spectra (reflectance, transmittance, and absorbance) for 35 herbaceous species, sampled from a wide range of environmental conditions. Specific Leaf Area and nutrient-related traits (N and P content) were poorly predicted from any spectrum, although N prediction improved when expressed on a per area basis (mg/m2 leaf surface) instead of mass basis (mg/g dry matter). Leaf dry matter content was moderately to good correlated with spectra. We explain our results by the range of environmental constraints encountered by herbaceous species; both N and P limitations as well as a range of light and water availabilities occurred. This weakened the relation between the measured response traits and the leaf constituents that are truly responsible for leaf spectral behavior. Indeed, N predictions improve considering solely upper or under canopy species. Therefore, trait predictions in herbaceous systems should focus on traits relating to dry matter content and the true, underlying drivers of spectral properties

Hoe met remote sensing via de vegetatie bodem en water kunnen worden gekarteerd

Remote sensing is de verzamelterm voor diverse vormen van aardobservatie. Zoals observaties van het aardoppervlak door satellieten (spaceborne remote sensing). Observaties vanuit vliegtuigen (airborne remote sensing) zijn eveneens mogelijk. De vraag wat remote sensing kan betekenen voor hydrologische toepassingen stond centraal tijdens de NHV voorjaarsbijeenkomst, in mei 2012. Daar betoogden wij dat diverse hydrologische grootheden afgeleid kunnen worden van remote sensing beelden. Dit artikel is een verdere uitwerking van dit betoog. We willen met deze bijdrage aantonen dat de natuurlijke vegetatie een nuttige vertaalsleutel kan vormen tussen remote sensing observaties enerzijds en eigenschappen van de ondergrond anderzijds

Quadrupole collectivity in neutron-rich Cd isotopes

4 pags., 2 figs. -- INPC 2013 – International Nuclear Physics ConferenceThe investigation of the excitation energies of the 21+ –states in the neutron-rich Cd isotopes shows an irregular behaviour when approaching the neutron shell-closure at N = 82. The energy of the 21+–state in 128Cd is lower than the one in 126Cd. The transition strength B(E2, 0gs+ → 21+) in the even isotopes 122−128Cd was measured in Coulomb excitation experiments with the high-purity germanium detector array MINIBALL at REXISOLDE (CERN). The values for 122,124Cd coincide with beyond-mean-field calculations with a resultant prolate deformation, whereas 126,128Cd are better described by shell-model calculations.This project is supported by BMBF (No. 06 DA 9036I, No. 05 P12 RDCIA, No. 05 P12 RDCIB and No. 05 P12 PKFNE), HIC for FAIR, EU through EURONS (No. 506065) and ENSAR (No. 262010) and the MINIBALL and REX-ISOLDE collaborations

Hoe met remote sensing via de vegetatie bodem en water kunnen worden gekarteerd

Remote sensing is de verzamelterm voor diverse vormen van aardobservatie. Zoals observaties van het aardoppervlak door satellieten (spaceborne remote sensing). Observaties vanuit vliegtuigen (airborne remote sensing) zijn eveneens mogelijk. De vraag wat remote sensing kan betekenen voor hydrologische toepassingen stond centraal tijdens de NHV voorjaarsbijeenkomst, in mei 2012. Daar betoogden wij dat diverse hydrologische grootheden afgeleid kunnen worden van remote sensing beelden. Dit artikel is een verdere uitwerking van dit betoog. We willen met deze bijdrage aantonen dat de natuurlijke vegetatie een nuttige vertaalsleutel kan vormen tussen remote sensing observaties enerzijds en eigenschappen van de ondergrond anderzijds

An evaluation of remote sensing derived soil pH and average spring groundwater table for ecological assessments

Ecological assessments such as species distribution modelling and benchmarking site quality towards regulations often rely on full spatial coverage information of site factors such as soil acidity, moisture regime or nutrient availability. To determine if remote sensing (RS) is a viable alternative to traditional data sources of site factor estimates, we analysed the accuracy (using ground truth validation measurements) of traditional and RS sources of pH and mean spring groundwater level (MSL, in m) estimates. Traditional sources were a soil map and hydrological model. RS estimates were obtained using vegetation indicator values (IVs) from a Dutch national system as an intermediate between site factors and spectral response. IVs relate to those site factors that dictate vegetation occurrence, whilst also providing a robust link to canopy spectra. For pH, the soil map and the RS estimate were nearly as accurate. For MSL, the RS estimates were much closer to the observed groundwater levels than the hydrological model, but the error margin of the estimates still exceeded the tolerance range of moisture sensitive vegetation. The relatively high accuracy of the RS estimates was made possible by the availability of local calibration points and large environmental gradients in the study site. In addition, the error composition of the RS estimates could be analysed step-by-step, whereas the traditional sources had to be accepted ‘as-is’. Also considering that RS offers high spatial and temporal resolution at low costs, RS offered advantages over traditional sources. This will likely hold true for any other situation where prerequisites of accurate RS estimates have been met

An evaluation of remote sensing derived soil pH and average spring groundwater table for ecological assessments

Ecological assessments such as species distribution modelling and benchmarking site quality towards regulations often rely on full spatial coverage information of site factors such as soil acidity, moisture regime or nutrient availability. To determine if remote sensing (RS) is a viable alternative to traditional data sources of site factor estimates, we analysed the accuracy (using ground truth validation measurements) of traditional and RS sources of pH and mean spring groundwater level (MSL, in m) estimates. Traditional sources were a soil map and hydrological model. RS estimates were obtained using vegetation indicator values (IVs) from a Dutch national system as an intermediate between site factors and spectral response. IVs relate to those site factors that dictate vegetation occurrence, whilst also providing a robust link to canopy spectra. For pH, the soil map and the RS estimate were nearly as accurate. For MSL, the RS estimates were much closer to the observed groundwater levels than the hydrological model, but the error margin of the estimates still exceeded the tolerance range of moisture sensitive vegetation. The relatively high accuracy of the RS estimates was made possible by the availability of local calibration points and large environmental gradients in the study site. In addition, the error composition of the RS estimates could be analysed step-by-step, whereas the traditional sources had to be accepted 'as-is'. Also considering that RS offers high spatial and temporal resolution at low costs, RS offered advantages over traditional sources. This will likely hold true for any other situation where prerequisites of accurate RS estimates have been met

Mapping a priori defined plant associations using remotely sensed vegetation characteristics

Incorporation of a priori defined plant associations into remote sensing products is a major challenge that has only recently been confronted by the remote sensing community. We present an approach to map the spatial distribution of such associations by using plant indicator values (IVs) for salinity, moisture and nutrients as an intermediate between spectral reflectance and association occurrences. For a 12 km2 study site in the Netherlands, the relations between observed IVs at local vegetation plots and visible and near-infrared (VNIR) and short-wave infrared (SWIR) airborne reflectance data were modelled using Gaussian Process Regression (GPR) (R2 0.73, 0.64 and 0.76 for salinity, moisture and nutrients, respectively). These relations were applied to map IVs for the complete study site. Association occurrence probabilities were modelled as function of IVs using a large database of vegetation plots with known association and IVs. Using the mapped IVs, we calculated occurrence probabilities of 19 associations for each pixel, resulting in both a crisp association map with the most likely occurring association per pixel, as well as occurrence probability maps per association. Association occurrence predictions were assessed by a local vegetation expert, which revealed that the occurrences of associations situated at frequently predicted indicator value combinations were over predicted. This seems primarily due to biases in the GPR predicted IVs, resulting in associations with envelopes located in extreme ends of IVs being scarcely predicted. Although the results of this particular study were not fully satisfactory, the method potentially offers several advantages compared to current vegetation classification techniques, like site-independent calibration of association probabilities, site-independent selection of associations and the provision of IV maps and occurrence probabilities per association. If the prediction of IVs can be improved, this method may thus provide a viable roadmap to bring a priori defined plant associations into the domain of remote sensing