The Role of Early-Life Conditions in the Cognitive Decline due to Adverse Events Later in Life

Cognitive functioning of elderly individuals may be affected by events such as the loss of a (grand)child or partner or the onset of a serious chronic condition, and by negative economic shocks such as job loss or the reduction of pension benefits. It is conceivable that the impact of such events is stronger if conditions early in life were adverse. In this paper we address this using a Dutch longitudinal database that follows elderly individuals for more than 15 years and contains information on demographics, socio-economic conditions, life events, health, and cognitive functioning. We exploit exogenous variation in early-life conditions as generated by the business cycle. We also examine to what extent the cumulative effect of consecutive shocks later in life exceeds the sum of the separate effects, and whether economic and health shocks later in life reinforce each other in their effect on cognitive functioning.cognitive functioning, business cycle, bereavement, developmental origins, retirement, health, long-run effects, dementia

Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories. Remote Sens

Abstract: The cost effective monitoring of habitats and their biodiversity remains a challenge to date. Earth Observation (EO) has a key role to play in mapping habitat and biodiversity in general, providing tools for the systematic collection of environmental data. The recent GEO-BON European Biodiversity Observation Network project (EBONE) established a framework for an integrated biodiversity monitoring system. Underlying this framework is the idea of integrating in situ with EO and a habitat classification scheme based on General Habitat Categories (GHC), designed with an Earth Observation-perspective. Here we report on EBONE work that explored the use of NDVI-derived phenology metrics for the identification and mapping of Forest GHCs. Thirty-one phenology metrics were extracted from MODIS NDVI time series for Europe. Classifications to discriminate forest types were performed based on a Random Forests ™ classifier in selected regions. Results indicate that date phenology metrics are generally more significant for forest type discrimination. The achieved class accuracies are generally not satisfactory, except for coniferous forests in homogeneous stands (77–82%). The main causes of low classification accuracies were identified as (i) the spatial resolution of the imagery (250 m) which led t

An econometric analysis of the mental-health effects of major events in the life of older individuals

Major events in the life of an older individual, such as retirement, a significant decrease in income, death of the spouse, disability, and a move to a nursing home, may affect the mental health status of the individual. For example, the individual may enter a prolonged depression. We investigate this using unique longitudinal panel data that track labor market behavior, health status, and major life events, over time. To deal with endogenous aspects of these events we apply fixed effects estimation methods. We find some strinkingly large effects of certain events on the occurence of depression. We relate the results to the health care and labor market policy towards older individuals.Death; retirement; income loss; disease; depression; health indicators; widowhood; care; panel data; endogeneity; fixed effects

Leaf phenology amplitude derived from MODIS NDVI and EVI: maps of leaf phenology synchrony for Meso‐ and South America

The leaf phenology (i.e. the seasonality of leaf amount and leaf demography) of ecosystems can be characterized through the use of Earth observation data using a variety of different approaches. The most common approach is to derive time series of vegetation indices (VIs) which are related to the temporal evolution of FPAR, LAI and GPP or alternatively used to derive phenology metrics that quantify the growing season. The product presented here shows a map of average ‘amplitude’ (i.e. maximum minus minimum) of annual cycles observed in MODIS‐derived NDVI and EVI from 2000 to 2013 for Meso‐ and South America. It is a robust determination of the amplitude of annual cycles of vegetation greenness derived from a Lomb–Scargle spectral analysis of unevenly spaced data. VI time series pre‐processing was used to eliminate measurement outliers, and the outputs of the spectral analysis were screened for statistically significant annual signals. Amplitude maps provide an indication of net ecosystem phenology since the satellite observations integrate the greenness variations across the plant individuals within each pixel. The average amplitude values can be interpreted as indicating the degree to which the leaf life cycles of individual plants and species are synchronized. Areas without statistically significant annual variations in greenness may still consist of individuals that show a well‐defined annual leaf phenology. In such cases, the timing of the phenology events will vary strongly within the year between individuals. Alternatively, such areas may consist mainly of plants with leaf turnover strategies that maintain a constant canopy of leaves of different ages. Comparison with in situ observations confirms our interpretation of the average amplitude measure. VI amplitude interpreted as leaf life cycle synchrony can support model evaluation by informing on the likely leaf turn over rates and seasonal variation in ecosystem leaf age distribution

Intra-annual taxonomic and phenological drivers of spectral variance in grasslands

According to the Spectral Variation Hypothesis (SVH), spectral variance has the potential to predict taxonomic composition in grasslands over time. However, in previous studies the relationship has been found to be unstable. We hypothesise that the diversity of phenological stages is also a driver of spectral variance and could act to confound the species signal. To test this concept, intra-annual repeat spectral and botanical sampling was performed at the quadrat scale at two grassland sites, one displaying high species diversity and the other low species diversity. Six botanical metrics were used, three taxonomy based and three phenology based. Using uni-temporal linear permutation models, we found that the SVH only held at the high diversity site and only for certain metrics and at particular time points. We also tested the seasonal influence of phenological stage dominance, alongside the taxonomic and phenological diversity metrics on spectral variance using linear mixed models. A term of percentage mature leaves, alongside an interaction term of percentage mature leaves and species diversity, explained 15-25% of the model variances, depending on the spectral region used. These results indicate that the dominant canopy phenology stage is a confounding variable when examining the spectral variance-species diversity relationship. We emphasise the challenges that exist in tracking species or phenology-based metrics in grasslands using spectral variance but encourage further research that contextualises spectral variance data within seasonal plant development alongside other canopy structural and leaf traits

Post-fire vegetation phenology in Siberian burn scars

Softwarefirma startext entwickelt Lösung zur Unterstützung der Bergungsarbeiten. "..... In einem ersten Schritt wird der Inhalt der Bergungskisten erfasst und deren Lagerort verzeichnet. Anschließend werden die einzelnen Inhalte, sogenannte Einheiten, detailliert beschrieben und zusätzlich durch ein Digitalfoto belegt. Für diese Arbeitsschritte hat startext gemeinsam mit den verantwortlichen Archivaren aus Köln innerhalb von nur zwei Wochen ein webbasiertes Softwareprogramm konzipiert und en..

Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (Carea) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for Parea and Narea. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology

The feasibility of leaf reflectance-based taxonomic inventories and diversity assessments of species-rich grasslands: a cross-seasonal evaluation using waveband selection

Hyperspectral leaf-level reflectance data may enable the creation of taxonomic inventories and diversity assessments of grasslands, but little is known about the stability of species-specific spectral classes and discrimination models over the course of a growing season. Here, we present a cross-seasonal dataset of seventeen species that are common to a temperate, dry and nutrient-poor calcareous grassland, which spans thirteen sampling dates, a week apart, during the spring and summer months. By using a classification model that incorporated waveband selection (a sparse partial least squares discriminant analysis), most species could be classified, irrespective of the sampling date. However, between 42 and 95% of the available spectral information was required to obtain these results, depending on the date and model run. Feature selection was consistent across time for 70 out of 720 wavebands and reflectance around 1410 nm, representing water features, contributed the most to the discrimination. Model transferability was higher between neighbouring sampling dates and improved after the “green-up” period. Some species were consistently easy to classify, irrespective of time point, when using up to six latent variables, which represented about 99% of the total spectral variance, whereas other species required many latent variables, which represented very small spectral differences. We concluded that it did seem possible to create reliable taxonomic inventories for combinations of certain grassland species, irrespective of sampling date, and that the reason for this could lie in their distinctive morphological and/or biochemical leaf traits. Model transferability, however, was limited across dates and cross-seasonal sampling that captures leaf development would probably be necessary to create a predictive framework for the taxonomic monitoring of grasslands. In addition, most variance in the leaf reflectance within this system was driven by a subset of species and this finding implies challenges for the application of spectral variance in the estimation of biodiversity

Prediction of grassland biodiversity using measures of spectral variance: a meta-analytical review

Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess test results of the hypothesis across studies using several moderating variables, relating to the botanical and spectral sampling strategies, and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatment due to their dynamic phenology and the taxonomic complexity of their canopies over small scales. Across studies, results suggest an overall positive relationship between spectral variation and species diversity (mean correlation co-efficient = 0.36). However, high levels of both within study and between study heterogeneity was found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf level studies displaying a stronger relationship compared to canopy level studies. We highlight the challenges facing synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multi-temporal assessments to resolve controversy in the field

Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning

The identification and counting of plant individuals is essential for environmental monitoring. UAV based imagery offer ultra-fine spatial resolution and flexibility in data acquisition, and so provide a great opportunity to enhance current plant and in-situ field surveying. However, accurate mapping of individual plants from UAV imagery remains challenging, given the great variation in the sizes and geometries of individual plants and in their distribution. This is true even for deep learning based semantic segmentation and classification methods. In this research, a novel Scale Sequence Residual U-Net (SS Res U-Net) deep learning method was proposed, which integrates a set of Residual U-Nets with a sequence of input scales that can be derived automatically. The SS Res U-Net classifies individual plants by continuously increasing the patch scale, with features learned at small scales passing gradually to larger scales, thus, achieving multi-scale information fusion while retaining fine spatial details of interest. The SS Res U-Net was tested to identify and map frailejones (all plant species of the subtribe Espeletiinae), the dominant plants in one of the world’s most biodiverse high-elevation ecosystems (i.e. the páramos) from UAV imagery. Results demonstrate that the SS Res U-Net has the ability to self-adapt to variation in objects, and consistently achieved the highest classification accuracy (91.67% on average) compared with four state-of-the-art benchmark approaches. In addition, SS Res U-Net produced the best performances in terms of both robustness to training sample size reduction and computational efficiency compared with the benchmarks. Thus, SS Res U-Net shows great promise for solving remotely sensed semantic segmentation and classification tasks, and more general machine intelligence. The prospective implementation of this method to identify and map frailejones in the páramos will benefit immensely the monitoring of their populations for conservation assessments and management, among many other applications