Leaf Mass per Area of Wetland Vegetation under Water Stress Analyzed with Imaging Spectroscopy

Plant and community traits of wetland vegetation show a high intra-specific plasticity, originating from the high variability of environmental conditions. Remote sensing approaches promise to be able to retrieve some of these traits and their plasticity from the spectral reflectance signal of the canopy. In the present study, we evaluate a remote-sensing based approach for an analysis of spatial patterns of leaf mass per area (LMA), a key trait for ecosystem functioning and good negative correlate of potential growth rate. The test was conducted in Las Tablas de Daimiel, a National Park in Central Spain. This wetland was affected by a long-term drought, which introduced pronounced trait plasticity as part of the adaptation mechanisms of the vegetation to reduced water availability as well as a decrease in photosynthetic activity. Imaging spectroscopy (HyMap) data of the wetland were acquired in 2009 at peak drought intensity. At the same time, a field campaign was conducted. We applied an inversion of the PROSAIL model on these data to map the LMA distribution across the wetland. PROSAIL is a radiative transfer model that simulates the physical principles of light absorption and scattering in a vegetation canopy. The inversion enables the retrieval of trait information from the spectral signal. Furthermore, we assessed trends in photosynthetic activity and changing species composition across the wetland by analyzing time series of the normalized difference vegetation index (NDVI) as determined from various multispectral sensors. The mapped LMA values were analyzed within and between stands of different species and communities along a gradient of changing photosynthetic activity and species composition. LMA values retrieved for stands of species with high photosynthetic activity at peak drought intensity closely met values reported in trait data bases. The observed intra-specific LMA variability is in line with the expected plasticity of this trait along a moisture gradient that is reflected in a change in photosynthetic activity and species composition. We thus conclude that remote sensing approaches provide sufficient detail to trace the LMA-response of wetland vegetation to long-term drought stress

Differences between recent and historical records of upperspecies limits in the northern European Alps

Are there differences in historical and recent upper range limits of vascular plants and are such differences more pronounced in individual species groups? The limits of 1103 plants of the Northern Alps are compared to range limits in the mid-19th century. The comparison is based on two surveys. The first survey was conducted by Otto Sendtner in 1848–1853, the second in 1991–2008 during a habitat inventory. To our knowledge this is the first comparative studies reaching back to the end of the “Little Ice Age” and comprising an almost entire regional flora covering the complete range of habitats. During the recent survey, most species were found at higher elevations. Even though the differences fit well with the expected shifts due to climate warming we cannot exclude effects of sampling bias. However, we assume that the relative differences between species groups can be safely interpreted. The differences in upper limits between both surveys were significantly larger among forest species. The most important reason is probably discontinued pasture and mowing, which may have amplified possible warming effects. Nitrogen deposits may have contributed to this effect by placing competitive species in a more advantageous position

Are remotely sensed traits suitable for ecological analysis? A case study of long-term drought effects on leaf mass per area of wetland vegetation

Plant and community traits provide valuable insights in ecosystem functioning. Yet, such traits are costly to sample. Existing trait data bases give access to species-specific traits and help to reduce the sampling effort and costs. However, many traits show a high intra-specific plasticity due to the variability of environmental conditions, which is not fully accounted for in the data bases. As an alternative approach, remote sensing is able to retrieve some traits from the spectral reflectance signal of the canopy. Here we test whether remotely sensed traits provide the level of detail to trace the trait plasticity in response to changing environmental conditions. For this proof of concept study, we selected the example of leaf mass per area (LMA), a key trait for ecosystem functioning and good negative correlate of potential growth rate, in our study site Las Tablas de Daimiel National Park (Spain). This wetland was affected by a long-term drought, which introduced a pronounced trait plasticity as part of the adaptation mechanisms of the vegetation to reduced water availability as well as a decrease in photosynthetic activity. Imaging spectroscopy (HyMap) data of the wetland at peak drought intensity were acquired in 2009 simultaneous to a field campaign. We applied an inversion of the PROSAIL radiative transfer model on these data to map the LMA distribution across the wetland. Further, we quantified trends in photosynthetic activity and changing species composition across the wetland by analyzing time series of the normalized difference vegetation index (NDVI) as calculated for multispectral remote sensing data. The retrieved LMA values were analyzed within and between stands of different species and communities along a gradient of changing photosynthetic activity and species composition. Results show that LMA values retrieved for stands of species with high photosynthetic activity at peak drought intensity closely meet the values reported in data bases. The observed intra-specific LMA variability is in line with the expected plasticity of this trait along a moisture gradient that is reflected in a change in photosynthetic activity and species composition. We thus conclude that LMA values retrieved from imaging spectroscopy data provide sufficient detail to trace the response of wetland vegetation to long-term drought stress.Funding for this study was granted by the German Ministry of Economics and Technology (BMWI) within the framework of the project of the 2009 EnMAP preparation campaign, the DECAMERON project 1/2008 of the Agency for National Parks of the Spanish Ministry of Agriculture, Food and Environment, and by the German Research Foundation (DFG grant FE1331/2-1).Peer Reviewe

Assessing floristic composition with multispectral sensors: a comparison based on monotemporal and multiseasonal field spectra

62013 FEI 1reservedInternationalInternational coauthor/editorAssessing and mapping patterns of (semi-)natural vegetation types at a large spatial scale is a difficult task. The challenge increases if the floristic variation within vegetation types (i.e., subtype variation of species composition) is the target. A desirable way to deal with this task may be to address such vegetation patterns with remote-sensing approaches. In particular data from multispectral sensors are easy to obtain, globally accessible, and often provide a high temporal resolution. They hence offer a comprehensive basis for vegetation mapping. The potential of such sensors for vegetation mapping has, however, never been thoroughly investigated. In particular, a systematic test regarding the spectral capabilities of these data for an assessment of detailed floristic variation has not been implemented to date. We thus addressed in this study the question how the ability of optical sensors to map floristic variation is affected by their respective spectral coverage and number of bands. To answer this question, we simulated monotemporal and multiseasonal data of eleven multispectral sensors. These data were used to model gradual transitions of the species composition (i.e., floristic gradients) within three types of spontaneous vegetation typical for Central Europe using Partial Least Squares regression. Comparison of the model fits (ranging up to R2 = 0.76 in cross-validation) illustrated the potential of multispectral data for detailed vegetation mapping. The results show that spectral coverage of the entire solar-reflective domain is the most important sensor characteristic for a successful assessment of floristic variation. Model and sensor performances as well as limitations are thoroughly discussed, and recommendations for sensor development are made based on the final conclusions of this study.restrictedFeilhauer, H.; Thonfeld, F.; Faude, U.; He, K.S.; Rocchini, D.; Schmidtlein, S.Feilhauer, H.; Thonfeld, F.; Faude, U.; He, K.S.; Rocchini, D.; Schmidtlein, S