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The poster presents the development of the remote sensing forest vitality literature published between 1986-2022.

Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaved, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.

Background and ObjectiveThe spectrum of myelin oligodendrocyte glycoprotein (MOG) antibody–associated disorder (MOGAD) comprises monophasic diseases such as acute disseminated encephalomyelitis (ADEM), optic neuritis (ON), and transverse myelitis and relapsing courses of these presentations. Persistently high MOG antibodies (MOG immunoglobulin G [IgG]) are found in patients with a relapsing disease course. Prognostic factors to determine the clinical course of children with a first MOGAD are still lacking. The objective of the study is to assess the clinical and laboratory prognostic parameters for a risk of relapse and the temporal dynamics of MOG‐IgG titers in children with MOGAD in correlation with clinical presentation and disease course.MethodsIn this prospective multicenter hospital-based study, children with a first demyelinating attack and complete data set comprising clinical and radiologic findings, MOG-IgG titer at onset, and clinical and serologic follow-up data were included. Serum samples were analyzed by live cell-based assay, and a titer level of ≥1:160 was classified as MOG-IgG–positive.ResultsOne hundred sixteen children (f:m = 57:59) with MOGAD were included and initially diagnosed with ADEM (n = 59), unilateral ON (n = 12), bilateral ON (n = 16), myelitis (n = 6), neuromyelitis optica spectrum disorder (n = 8) or encephalitis (n = 6). The median follow-up time was 3 years in monophasic and 5 years in relapsing patients. There was no significant association between disease course and MOG-IgG titers at onset, sex, age at presentation, or clinical phenotype. Seroconversion to MOG-IgG–negative within 2 years of the initial event showed a significant risk reduction for a relapsing disease course. Forty-two/one hundred sixteen patients (monophasic n = 26, relapsing n = 16) had serial MOG-IgG testing in years 1 and 2 after the initial event. In contrast to relapsing patients, monophasic patients showed a significant decrease of MOG-IgG titers during the first and second years, often with seroconversion to negative titers. During the follow-up, MOG-IgG titers were persistently higher in relapsing than in monophasic patients. Decrease in MOG-IgG of ≥3 dilution steps after the first and second years was shown to be associated with a decreased risk of relapses. In our cohort, no patient experienced a relapse after seroconversion to MOG-IgG–negative.DiscussionIn this study, patients with declining MOG-IgG titers, particularly those with seroconversion to MOG-IgG–negative, are shown to have a significantly reduced relapse risk.

Although gravitropism forces trees to grow vertically, stems have shown to prefer specific orientations. Apart from wind deforming the tree shape, lateral light can result in prevailing inclination directions. In recent years a species dependent interaction between gravitropism and phototropism, resulting in trunks leaning down-slope, has been confirmed, but a terrestrial investigation of such factors is limited to small scale surveys. ALS offers the opportunity to investigate trees remotely. This study shall clarify whether ALS detected tree trunks can be used to identify prevailing trunk inclinations. In particular, the effect of topography, wind, soil properties and scan direction are investigated empirically using linear regression models. 299.000 significantly inclined stems were investigated. Species-specific prevailing trunk orientations could be observed. About 58% of the inclination and 19% of the orientation could be explained by the linear models, while the tree species, tree height, aspect and slope could be identified as significant factors. The models indicate that deciduous trees tend to lean down-slope, while conifers tend to lean leeward. This study has shown that ALS is suitable to investigate the trunk orientation on larger scales. It provides empirical evidence for the effect of phototropism and wind on the trunk orientation.

Reliable identification of clouds is necessary for any type of optical remote sensing image analysis, especially in operational and fully automatic setups. One of the most elaborated and widespread algorithms, namely Fmask, was initially developed for the Landsat suite of satellites. Despite their similarity, application to Sentinel-2 imagery is currently hampered by the unavailability of a thermal band, and although results can be improved when taking the cirrus band into account, Sentinel-2 cloud detections are unsatisfactory in two points. (1) Low altitude clouds can be undetectable in the cirrus band, and (2) bright land surfaces – especially built-up structures – are often misclassified as clouds when only considering spectral information. In this paper, we present the Cloud Displacement Index (CDI), which makes use of the three highly correlated near infrared bands that are observed with different view angles. Hence, elevated objects like clouds are observed under a parallax and can be reliably separated from bright ground objects. We compare CDI with the currently used cloud probabilities, and propose how to integrate this new functionality into the Fmask algorithm. We validate the approach using test images over metropolitan areas covering a wide variety of global environments and climates, indicating the successful separation of clouds and built-up structures (overall accuracy 95%, i.e. an improvement in overall accuracy of 0.29–0.39 compared to the previous Fmask versions over the 20 test sites), and hence a full compensation for a missing thermal band.

Background New-generation, cell-based assays have demonstrated a robust association of serum autoantibodies to full-length human myelin oligodendrocyte glycoprotein (MOG-IgG) with (mostly recurrent) optic neuritis, myelitis, and brainstem encephalitis, as well as with neuromyelitis optica (NMO)-like or acute-disseminated encephalomyelitis (ADEM)-like presentations. However, only limited data are yet available on cerebrospinal fluid (CSF) findings in MOG-IgG-associated encephalomyelitis (MOG-EM; also termed MOG antibody-associated disease, MOGAD). Objective To describe systematically the CSF profile in children with MOG-EM. Material and methods Cytological and biochemical findings (including white cell counts [WCC] and differentiation; frequency and patterns of oligoclonal bands; IgG/IgM/IgA and albumin concentrations and CSF/serum ratios; intrathecal IgG/IgM/IgA fractions; locally produced IgG/IgM/IgA concentrations; immunoglobulin class patterns; IgG/IgA/IgM reibergrams; Link index; measles/rubella/zoster [MRZ] reaction; other anti-viral and anti-bacterial antibody indices; CSF total protein; CSF l-lactate) from 108 lumbar punctures in 80 pediatric patients of mainly Caucasian descent with MOG-EM were analyzed retrospectively. Results Most strikingly, CSF-restricted oligoclonal IgG bands, a hallmark of multiple sclerosis (MS), were absent in 89% of samples (N = 96), and the MRZ reaction, the most specific laboratory marker of MS known so far, in 100% (N = 29). If present at all, intrathecal IgG synthesis was low, often transient and mostly restricted to acute attacks. Intrathecal IgM synthesis was present in 21% and exclusively detectable during acute attacks. CSF WCC were elevated in 54% of samples (median 40 cells/μl; range 6–256; mostly lymphocytes and monocytes; > 100/μl in 11%). Neutrophils were present in 71% of samples; eosinophils, activated lymphocytes, and plasma cells were seen only rarely (all N = 79) and at least once in 48% of all patients (N = 67) tested. CSF alterations were significantly more frequent and/or more pronounced in patients with acute spinal cord or brain disease than in patients with acute ON and varied strongly depending on attack severity. CSF l-lactate levels correlated significantly with the spinal cord lesions load (measured in vertebral segments) in patients with acute myelitis (p = 0.0099). An analysis of pooled data from the pediatric and the adult cohort showed a significant relationship of QAlb (p < 0.0005), CST TP (p < 0.0001), and CSF l-lactate (p < 0.0003) during acute attacks with age. Conclusion MOG-IgG-associated EM in children is characterized by CSF features that are distinct from those in MS. With regard to most parameters, no marked differences between the pediatric cohort and the adult cohort analyzed in Part 1 were noted. Our findings are important for the differential diagnosis of pediatric MS and MOG-EM and add to the understanding of the immunopathogenesis of this newly described autoimmune disease.

Summary For the estimation of spruce timber reserves in individual forest districts of the German federal state Rhineland-Palatinate, small area methods are applied. A model using stock values of the state forest inventory and a canopy height model derived by airborne laser scanning is used to provide adequate estimates. Since the interaction between the variables is non-linear and must fulfil further constraints, a new spline-based small area estimation method is proposed, formulated as a quadratic programming problem. This method enables providing realistic estimates via including specialized constraints which are especially important in practice as well as more stable estimates. The applicability of the new method and the related mean-squared-error estimators is shown in a simulation study. Further, spruce timber reserves in Rhineland-Palatinate are estimated by using the new approach compared with already existing methods.

Objective To evaluate disease symptoms, and clinical and magnetic resonance imaging (MRI) findings and to perform longitudinal volumetric MRI analyses in a European multicenter cohort of pediatric anti-N-methyl-D-aspartate receptor encephalitis (NMDARE) patients. Methods We studied 38 children with NMDARE (median age = 12.9 years, range =1-18) and a total of 82 MRI scans for volumetric MRI analyses compared to matched healthy controls. Mixed-effect models and brain volume z scores were applied to estimate longitudinal brain volume development. Ordinal logistic regression and ordinal mixed models were used to predict disease outcome and severity. Results Initial MRI scans showed abnormal findings in 15 of 38 (39.5%) patients, mostly white matter T2/fluid-attenuated inversion recovery hyperintensities. Volumetric MRI analyses revealed reductions of whole brain and gray matter as well as hippocampal and basal ganglia volumes in NMDARE children. Longitudinal mixed-effect models and z score transformation showed failure of age-expected brain growth in patients. Importantly, patients with abnormal MRI findings at onset were more likely to have poor outcome (Pediatric Cerebral Performance Category score > 1, incidence rate ratio = 3.50, 95% confidence interval [CI] = 1.31-9.31, p = 0.012) compared to patients with normal MRI. Ordinal logistic regression models corrected for time from onset confirmed abnormal MRI at onset (odds ratio [OR] = 9.90, 95% CI = 2.51-17.28, p = 0.009), a presentation with sensorimotor deficits (OR = 13.71, 95% CI = 2.68-24.73, p = 0.015), and a treatment delay > 4 weeks (OR = 5.15, 95% CI = 0.47-9.82, p = 0.031) as independent predictors of poor clinical outcome. Interpretation Children with NMDARE exhibit significant brain volume loss and failure of age-expected brain growth. Abnormal MRI findings, a clinical presentation with sensorimotor deficits, and a treatment delay > 4 weeks are associated with worse clinical outcome. These characteristics represent promising prognostic biomarkers in pediatric NMDARE. ANN NEUROL 2020 ANN NEUROL 2020;88:148-159.

Die Effizienz moderner Verfahren der Datenerhebung sowie deren zugehorige Auswertung hangen immer mehr von der Gute der Vor- oder Zusatzinformationen ab. Die Verfugbarkeit von Big Data liefert heutzutage ganz neue und andersartige Moglichkeiten, Schatzungen in der amtlichen und institutionellen Statistik zu verbessern, stellt aber auch Herausforderungen an die Qualitat der Resultate auf, die diskutiert werden mussen. In der Forstinventur wird schon seit einiger Zeit die Verwendung von Fernerkundungsdaten diskutiert und sogar umgesetzt. Im Rahmen dieser Arbeit werden die aktuell diskutierten Verfahren vorgestellt und konkrete Schatzungen fur Rheinland-Pfalz durchgefuhrt. Abschliesend werden die Herausforderungen an zukunftige Anwendungen vorgestellt, die sich im Rahmen von Big Data durch die allgemeine Verfugbarkeit von Satellitendaten ergeben.

Given the importance of forest ecosystems, the availability of reliable, spatially explicit information about the site-specific climate sensitivity of tree species is essential for implementing suitable adaptation strategies. In this study, airborne hyperspectral data were used to assess the response of deciduous species (dominated by European beech and Sessile and Pedunculate oak) to water stress during a summery dry spell. After masking canopy gaps, shaded crown areas and non-deciduous species, potentially indicative spectral indices, the Photochemical Reflectance Index (PRI), Moisture Stress Index (MSI), Normalized Difference Water Index (NDWI), and Chlorophyll Index (CI), were analyzed with respect to available maps of site-specific soil moisture regimes. PRI provided an important indication of site-specific photosynthetic stress on leaf level in relation to limitations in soil water availability. The CI, MSI and NDWI revealed statistically significant differences in total chlorophyll and water concentration at the canopy level. However, after reducing the canopy effects by normalizing these indices with respect to the structure-sensitive simple ratio (SR) vegetation index, it was not yet possible to identify site-specific concentration differences in leaf level at this early stage of the drought. The selected indicators were also tested with simulated EnMAP and Sentinel-2 data (derived from the original airborne data set). While PRI proved to be useful also in the spatial resolution of EnMAP (GSD = 30 m), this was not the case with Sentinel-2, owing to the lack of adequate spectral bands; the remaining indicators (MSI, CI, SR) were also successfully produced with Sentinel-2 data at superior spatial resolution (GSD = 10 m). The study confirms the importance of using earth observation systems for supplementing traditional ecological site classification maps, particularly during dry spells and heat waves when ecological gradients are increasingly reflected in the spectral response at the tree crown level. It also underlined the importance of using Sentinel-2 and EnMAP in synergy, as soon as both systems become available.

Drought stress is expected to become a recurrent problem for central European forests due to regional climate change. In order to study the effects on one of the most common tree species in Germany, the European beech Fagus sylvatica, young potted beech trees were exposed to drought stress in a controlled experiment and their reaction was observed using visible/near-infrared VNIR and shortwave infrared SWIR field imaging spectroscopy cameras mounted on a platform. Equivalent water thickness EWT and leaf chlorophyll content LCC were measured and partial least squares PLS regression models were trained using these reference measurements and reflectance spectra of the trees. The models were applied to create maps of these properties with a spatial resolution in the millimetre range. These maps can be used to study the spatial distribution of EWT and LCC for single leaves or even for intra-leaf variability. Both properties can be estimated using only the VNIR sensor, but EWT estimation improves considerably by also incorporating SWIR data. LCC estimations with SWIR data alone do not work satisfactorily.

Remote Sensing, 9 (7), ISSN:2072-4292

Butterflies are strongly declining on grassland habitats of Central Europe. Therefore, the success of conservation measures on high quality grassland habitats is controversially discussed. We compared the changes in butterfly diversity and community structure on six managed calcareous grasslands with eight unmanaged vineyard fallows. We obtained strong losses of species diversity and remarkable shifts of community compositions on both habitat types. However, the changes on vineyard fallows were only slightly more severe but more stochastic than on the calcareous grasslands. The shifts in community composition with respect to functional species traits were rather similar between the two different grassland types so that complex butterfly communities evolved into generalist-dominated ones. Connectivity was higher among vineyard fallows than among calcareous grasslands. Consequently, conservation measures on calcareous grasslands only partly achieved their goal to maintain the high species diversity and functional complexity still observed in the 1970s. The negative impacts of eutrophication and monotonisation of the landscape as well as climate change are affecting all habitats, independently from management concepts. Therefore, management on conservation sites can buffer against these effects, but is not sufficient for a full compensation.

In order to monitor dryness stress under controlled conditions, we set up an experiment with beech seedlings in plant pots and built a platform for observing the seedlings with field imaging spectroscopy. This serves as a preparation for multi-temporal hyperspectral air- and space-borne data expected to be available in coming years. Half of the trees were watered throughout the year, the other half were cut off from water supply for a five-week period in late summer. Plant health and soil, as well as leaf water status, were monitored. Moreover, hyperspectral images of the trees were acquired four times during the experiment. Results show that the experimental imaging setup is well suited for recording hyperspectral images of objects, like the beech pots, under natural illumination conditions. The high spatial resolution makes it feasible to discern between background, soil, wood, green leaves and brown leaves. Furthermore, it could be shown that dryness stress is detectable from an early stage even in the limited spectral range considered. The decline of leaf chlorophyll over time was also well monitored using imaging spectroscopy data.

Objective To determine the frequency and clinical-radiological associations of antibodies to myelin oligodendrocyte glycoprotein (MOG) and aquaporin-4 (AQP4) in children presenting with neuromyelitis optica (NMO) and limited forms. Methods Children with a first event of NMO, recurrent (RON), bilateral ON (BON), longitudinally extensive transverse myelitis (LETM) or brainstem syndrome (BS) with a clinical follow-up of more than 12 months were enrolled. Serum samples were tested for MOG- and AQP4-antibodies using live cell-based assays. Results 45 children with NMO (n=12), LETM (n=14), BON (n=6), RON (n=12) and BS (n=1) were included. 25/45 (56%) children had MOG-antibodies at initial presentation (7 NMO, 4 BON, 8 ON, 6 LETM). 5/45 (11%) children showed AQP4-antibodies (3 NMO, 1 LETM, 1 BS) and 15/45 (33%) were seronegative for both antibodies (2 NMO, 2 BON, 4 RON, 7 LETM). No differences were found in the age at presentation, sex ratio, frequency of oligoclonal bands or median EDSS at last follow-up between the three groups. Children with MOG-antibodies more frequently (1) had a monophasic course (p=0.018) after one year, (2) presented with simultaneous ON and LETM (p=0.004) and (3) were less likely to receive immunosuppressive therapies (p=0.0002). MRI in MOG-antibody positive patients (4) less frequently demonstrated periependymal lesions (p=0.001), (5) more often were unspecific (p=0.004) and (6) resolved more frequently (p=0.016). Conclusions 67% of all children presenting with NMO or limited forms tested positive for MOG- or AQP4-antibodies. MOG-antibody positivity was associated with distinct features. We therefore recommend to measure both antibodies in children with demyelinating syndromes.

Due to high variation in forest communities, forest structure and the fragmentation of the forested area in Central Europe, satellite-based forest inventory methods have to meet particularly high-quality requirements. This study presents an innovative method to combine official forest inventory information at stand level with multidate satellite imagery using a spatially adaptive classification approach for producing wall-to-wall forest cover maps of important tree species and management classes across multiple ownership regions in a heterogeneous low mountain range in Germany. The classification approach was applied to a 5,200-km2 area (about 2,080 km2 of forest land, mostly mixed forests) located in the Eifel mountain range in Central Europe. In comparison with conventional classifiers, our results demonstrate a significant increase in classification accuracy in the order of 12%. The method was tested with ASTER images but holds the potential to be used for regular state forest inventories based on standard and novel earth observation data supplied for instance from the SPOT-5 and RapidEye sensors.

One of the most common applications of remote sensing in forestry is the production of thematic maps, depicting e.g. tree species or stand age, by means of image classification. Nevertheless, the absolute quantification of stand variables is even more essential for forest inventories. For both issues, satellite data are attractive for their large-area and up-to-date mapping capacities. This study followed two steps, and at first a supervised parametric classification was performed for a German test site based on a radiometrically corrected Landsat-5 TM scene. There, eight forest classes were identified with an overall accuracy of 87.5%. In the following, the study focused on the estimation of one key stand variable, the stem number per hectare (SN), which was carried out for a number of Norway spruce stands that had been clearly identified in the multispectral classification. For the estimation of SN, the approach of Linear Spectral Mixture Analysis (LSMA) was found to be clearly more effective than spectral indices. LSMA is based on the premise that measured reflectances can be linearly modelled from a set of so-called endmember spectra. In this study, the endmember sets were held variable to decompose pixel values to abundances of a vegetation, a background (soil, litter, bark) and a shade fraction. Forest structure determines the visible portions of these fractions, and therefore, a multiple regression using them as predictor variables provided the best SN estimates. LSMA allows a pixel-by-pixel quantification of SN for complete satellite images. This opens the view to exploit these data for an improved calibration of large-scale multi-parameter assessment strategies (e.g. statistical modelling or the kNN method for satellite data interpretation).