A Rupestrian Algorithm

Deciphering recently discovered cave paintings by the Astracinca, an egalitarian leaderless society flourishing in the 3rd millennium BCE, we present and analyze their shamanic ritual for forming new colonies. This ritual can actually be used by systems of anonymous mobile finite-state computational entities located and operating in a grid to solve the line recovery problem, a task that has both self-assembly and flocking requirements. The protocol is totally decentralized, fully concurrent, provably correct, and time optimal

A new protocol for texture mapping process and 2d representation of rupestrian architecture

The development of the survey techniques for architecture and archaeology requires a general review in the methods used for the representation of numerical data. The possibilities offered by data processing allow to find new paths for studying issues connected to the drawing discipline. The research project aimed at experimenting different approaches for the representation of the rupestrian architecture and the texture mapping process. The nature of the rupestrian architecture does not allow a traditional representation of sections and projections of edges and outlines. The paper presents a method, the Equidistant Multiple Sections (EMS), inspired by cartography and based on the use of isohipses generated from different geometric plane. A specific paragraph is dedicated to the texture mapping process for unstructured surface models. One of the main difficulty in the image projection consists in the recognition of homologous points between image and point cloud, above all in the areas with most deformations. With the aid of the “virtual scan” tool a different procedure was developed for improving the correspondences of the image. The result show a sensible improvement of the entire process above all for the architectural vaults. A detailed study concerned the unfolding of the straight line surfaces; the barrel vault of the analyzed chapel has been unfolded for observing the paintings in the real shapes out of the morphological context

Temporal Turnover of Species Maintains Ant Diversity but Transforms Species Assemblage Recovering from Fire Disturbance

The rupestrian complex is a montane transitional vegetation type between the Brazilian Savanna (‘Cerrado’) and the Atlantic Forest, frequently threatened by human activities. In this study, we evaluated the recovery to fire disturbance of ant fauna in an environment evolved under fire regime. We confirmed that the ant diversity recovers quickly after the fire. However, our results show that ant assemblage in burned areas presented greater ant’s foraging activity, here detected as higher abundance. The ant composition changed over time, being that species turnover lead to a strikingly different species composition comparing burned to unburned areas after 16 months of recovering. In fact, both areas changed their ant composition through species turnover, but we believe that the mechanisms that act in species turnover are different in each area. Along time, in burned areas the fauna maintained a constant species diversity but dramatically changed species assemblage due to appearance of several species not found in the unburned area

Determining temporal sampling schemes for passive acoustic studies in different tropical ecosystems

Among different approaches to exploring and describing the ecological complexity of natural environments, soundscape analyses have recently provided useful proxies for understanding and interpreting dynamic patterns and processes in a landscape. Nevertheless, the study of soundscapes remains a new field with no internationally accepted protocols. This work provides the first guidelines for monitoring soundscapes in three different tropical areas, specifically located in the Atlantic Forest, Rupestrian fields, and the Cerrado (Brazil). Each area was investigated using three autonomous devices recording for six entire days during a period of 15 days in both the wet and dry seasons. The recordings were processed via a specific acoustic index and successively subsampled in different ways to determine the degree of information loss when reducing the number of minutes of recording used in the analyses. We describe for the first time the temporal and spectral soundscape features of three tropical environments. We test diverse programming routines to describe the costs and the benefits of different sampling designs, considering the pressing issue of storing and analyzing extensive data sets generated by passive acoustic monitoring. Schedule 5 (recording one minute of every five) appeared to retain most of the information contained in the continuous recordings from all the study areas. Less dense recording schedules produced a similar level of information only in specific portions of the day. Substantial sampling protocols such as those presented here will be useful to researchers and wildlife managers, as they will reduce time- and resource-consuming analyses, whilst still achieving reliable results

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland

Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland

High-Resolution 3D FEM Stability Analysis of the Sabereebi Cave Monastery, Georgia

This study assesses the static stability of the artificial Sabereebi Cave Monastery southeast of Georgia's capital, Tbilisi. The cliff into which these Georgian-Orthodox caverns, chapels, and churches were carved consists of a five-layered sequence of weak sedimentary rock—all of which bear a considerable failure potential and, consequently, pose the challenge of preservation to geologists, engineers, and archaeologists. In the first part of this study, we present a strategy to process point cloud data from drone photogrammetry as well as from laser scanners acquired in- and outside the caves into high-resolution CAD objects that can be used for numerical modeling ranging from macro- to micro-scale. In the second part, we explore four distinct series of static elasto-plastic finite element stability models featuring different levels of detail, each of which focuses on specific geomechanical scenarios such as classic landsliding due to overburden, deformation of architectural features as a result of stress concentration, material response to weathering, and pillar failure due to vertical load. With this bipartite approach, the study serves as a comprehensive 3D stability assessment of the Sabereebi Cave Monastery on the one hand; on the other hand, the established procedure should serve as a pilot scheme, which could be adapted to different sites in the future combining non-invasive and relatively cost-efficient assessment methods, data processing and hazard estimation