Suchergebnisse

Der Autor setzt sich mit den Vorschlägen von Scharpf zur Subventionierung niedriger Erwerbseinkommen auseinander. Er entwickelt ein eigenes Modell einer teilzeitrelevant gestalteten negativen Einkommenssteuer. (IAB)

Objective: A series of experiments assessed biases in perceived distance that occur while driving as a function of the backlight position of the car ahead and fog density. Background: V. Cavallo, M. Colomb, and J. Doré (2001) have shown that smaller horizontal backlight separation and fog may result in increased estimates of the distance between an observer and a car of which only the backlights are visible. They also predicted that raising the height of the car backlights would lead to increasing distance estimates. Method: Distance perception was assessed in both static and dynamic computer-simulated scenarios in which the distance estimates were performed using a familiarized analog scale or using time-to-collision judgments for both pairs of backlights and single backlights. Results: In a series of five experiments, the horizontal separation and fog density effects were replicated. In addition, distance estimates were consistently larger with higher than with lower vertical backlight positions. Conclusion: There is reason to believe that biases in distance perception may be augmented by car backlight positions and by low-visibility weather conditions. Application: Car designers should take backlight placement seriously. Speed-dependent car-to-car distance control systems seem desirable to counteract biases in distance perception.

The assessment of land degradation and the quantification of its effects on land productivity have been both a scientific and political challenge. After four decades of Earth Observation (EO) applications, little agreement has been gained on the magnitude and direction of land degradation in the Sahel. The large number of EO datasets and methods associated with the complex interactions among biophysical and social drivers of ecosystem changes make it difficult to apply aggregated EO indices for these non-linear processes. Hence, while many studies stress that the Sahel is greening, others indicate no trend or browning. The different generations of sensors, the granularity of studies, the study period, the applied indices and the assumptions and/or computational methods impact these trends. Consequently, many uncertainties exist in regression models between rainfall, biomass and various indices that limit the ability of EO science to adequately assess and develop a consistent message on the magnitude of land degradation. We suggest several improvements: (1) harmonize time-series data, (2) promote knowledge networks, (3) improve data-access, (4) fill data gaps, (5) agree on scales and assumptions, (6) set up a denser network of long-term field-surveys and (7) consider local perceptions and social dynamics. To allow multiple perspectives and avoid erroneous interpretations, we underline that EO results should not be interpreted without contextual knowledge.

To alleviate the severe rocky desertification and improve the ecological conditions in Southwest China, the national and local Chinese governments have implemented a series of Ecological Restoration Projects since the late 1990s. In this context, remote sensing can be a valuable tool for conservation management by monitoring vegetation dynamics, projecting the persistence of vegetation trends and identifying areas of interest for upcoming restoration measures. In this study, we use MODIS satellite time series (2001-2013) and the Hurst exponent to classify the study area (Guizhou and Guangxi Provinces) according to the persistence of future vegetation trends (positive, anti-persistent positive, negative, anti-persistent negative, stable or uncertain). The persistence of trends is interrelated with terrain conditions (elevation and slope angle) and results in an index providing information on the restoration prospects and associated uncertainty of different terrain classes found in the study area. The results show that 69% of the observed trends are persistent beyond 2013, with 57% being stable, 10% positive, 5% anti-persistent positive, 3% negative, 1% anti-persistent negative and 24% uncertain. Most negative development is found in areas of high anthropogenic influence (low elevation and slope), as compared to areas of rough terrain. We further show that the uncertainty increases with the elevation and slope angle, and areas characterized by both high elevation and slope angle need special attention to prevent degradation. Whereas areas with a low elevation and slope angle appear to be less susceptible and relevant for restoration efforts (also having a high uncertainty), we identify large areas of medium elevation and slope where positive future trends are likely to happen if adequate measures are utilized. The proposed framework of this analysis has been proven to work well for assessing restoration prospects in the study area, and due to the generic design, the method is expected to be applicable for other areas of complex landscapes in the world to explore future trends of vegetation.

Abstract
Forests are attracting attention as a promising avenue to provide nutritious and "free" food without damaging the environment. Yet, we lack knowledge on the extent to which this holds in areas with sparse tree cover, such as in West Africa. This is largely due to the fact that existing methods are poorly designed to quantify tree cover in drylands. In this study, we estimate how various levels of tree cover across West Africa affect children's (aged 12–59 months) consumption of vitamin A–rich foods. We do so by combining detailed tree cover estimates based on PlanetScope imagery (3 m resolution) with Demographic Health Survey data from >15,000 households. We find that the probability of consuming vitamin A–rich foods increases from 0.45 to 0.53 with an increase in tree cover from the median value of 8.8 to 16.8% (which is the tree cover level at which the predicted probability of consuming vitamin A–rich foods is the highest). Moreover, we observe that the effects of tree cover vary across poverty levels and ecoregions. The poor are more likely than the non-poor to consume vitamin A–rich foods at low levels of tree cover in the lowland forest-savanna ecoregions, whereas the difference between poor and non-poor is less pronounced in the Sahel-Sudan. These results highlight the importance of trees and forests in sustainable food system transformation, even in areas with sparse tree cover.

Abstract
Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.