Dead or Alive: Drivers of Wind Mortality Initiate Multiple Disturbance Regime in a Temperate Primeval Mountain Forest

The driving forces of tree mortality following wind disturbances of mountain mixed European temperate forests belongs among issues not comprehensively resolved. Hence, we aimed to elucidate the key factors of tree resistance to historical severe disturbance events in the Boubínský Primeval Forest, one of the oldest forest reserves in the Czech Republic. By using spatially explicit tree census, dendrochronological and soil data, we study spatial and temporal patterns of past disturbances and mathematically compared selected characteristics of neighboring trees that were killed by a severe storm in 2017 and those that remained undisturbed. The tendency of trees toward falling was primarily driven edaphically, limiting severe events non-randomly to previously disturbed sites occupied by hydromorphic soils and promoting the existence of two spatially-separated disturbance regimes. While disturbed trees usually recruited in gaps and experienced only one severe release event, surviving trees characteristically regenerated under the canopy and were repeatedly released. Despite the fact that disturbed trees tended to reach both lower ages and dimensions than survivors, they experienced significantly higher growth rates. Our study indicates that slow growth with several suppression periods emerged as the most effective tree strategy for withstanding severe windstorms, dying of senescence in overaged life stage. Despite the selective impact of the Herwart storm on conifer population, we did not find any difference in species sensitivity for most characteristics studied. We conclude that the presence of such ancient, high-density wood trees contributes significantly to the resistance of an entire stand to severe storms.

Heterogeneous Study of Multiple Disturbance Factors Outside Residential Electricity Consumption: A Case Study of Beijing

Residential electricity consumption is an important part of the electricity consumption of the whole society. The systematic analysis of the influence mechanism of the external complex factors of residential electricity consumption is significant for scientific and effective power demand side optimization management. From the socio-economic and climate perspectives, Spearman’s correlation was used to analyze external multiple disturbance indicators, and principal component analysis (PCA) was used to reduce data dimensionality. The multi-factor residential electricity measurement model (PCA-MCA) was established to explore the heterogeneity of influence mechanisms. Taking Beijing as a case study, the results show that the sensitivity of residential electricity consumption of Beijing to socio-economic indicators is greater than that of climate indicators, and the two influencing factors are obviously heterogeneous. The impact of socio-economic factors on residential electricity consumption appears to have continuous and stable characteristics, but climate factors are more volatile. This paper discusses factors and disturbance mechanisms of regional residential electricity consumption, fully considering the actual situation in Beijing. Taking the realization of regional power demand lateral optimization management as the idea, the paper proposes some optimization strategies to achieve regional power availability. This provides an analysis basis and practical reference for sustainable development of regional power.

Disturbance History and Dynamics of an Old-Growth Nothofagus Forest in Southern Patagonia

The identification of disturbance events using disturbance chronologies has become a valuable tool in reconstructing disturbance history in temperate forests worldwide; yet detailed reconstructions of disturbance history and their effect on the structure and dynamics of the old-growth Nothofagus forests in the southern Patagonia are scarce. We reconstructed forest dynamics and disturbance history of an old-growth N. pumilio forest in the Toro River Valley, Santa Cruz, Argentina using dendroecological techniques. Since a variation in the disturbance regimes was expected with changing elevation, we sampled at different elevations. We found distinct differences in forest structure, dynamics, and disturbance history with changes in the elevation. The disturbance chronologies provided robust evidence that forests in the study area have been subjected to multiple disturbance events over the last 200 years. Yet, recognizing the agent of disturbance could be difficult in these montane forests and further studies are required. Moreover, disturbances might have varied from frequent, moderate- to high-severity events to less frequent and more severe events. This study represents the first of its kind for the temperate forests of Patagonia.

Lane Marking Detection Based on Segments with Upper and Lower Structure

An effective and accurate lane marking detection algorithm is a fundamental element of the intelligent vehicle system and the advanced driver assistant system, which can provide important information to ensure the vehicle runs in the lane or warn the driver in case of lane departure. However, in the complex urban environment, lane markings are always affected by illumination, shadow, rut, water, other vehicles, abandoned old lane markings and non-lane markings, etc. Meanwhile, the lane markings are weak caused by hard use over time. The dash and curve lane marking detection is also a challenge. In this paper, a new lane marking detection algorithm for urban traffic is proposed. In the low-level phase, an iterative adaptive threshold method is used for image segmentation, which is especially suitable for the blurred and weakened lane markings caused by low illumination or wear. In the middle-level phase, the algorithm clusters the candidate pixels into line segments, and the upper and lower structure is used to cluster the line segments into candidate lanes, which is more suitable for curve and dashed lane markings. In the high-level phase, we compute the highest scores to get the two optimal lane markings. The optimal strategy can exclude interference similar to lane markings. We test our algorithm on Future Challenge TSD-Lane dataset and KITTI UM dataset. The results show our algorithm can effectively detect lane markings under multiple disturbance, occlusions and sharp curves.