Oportunidades e desafios para lidar com as barreiras à adoção de práticas de green supply chain management: guidelines à luz de um estudo de múltiplos casos no Brasil

This article aims to discuss how leading companies of the automotive battery industry installed in Brazil are dealing with barriers to the adoption of green supply chain management (GSCM) practices. From these discussions, we list the opportunities and challenges to the automotive battery sector in incorporating GSCM actions. We conducted a multiple-case study with the top five companies in this sector. The highlight factors are “organizational values” and “human resources” for internal barriers, and “suppliers” and “consumers” for external barriers.

Raman Lidar monitors emissions from sugar cane fires in the State of São Paulo: a pilot-project integrating radar, sodar, aerosol and gas observations

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Aplicação de técnicas de processamento e análise de imagens para detecção de edificações e vegetação a partir de dados LiDAR

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aerosol profiling with lidar in the Amazon Basin during the wet and dry season

For the first time, multiwavelength polarization Raman lidar observations of optical and microphysical particle properties over the Amazon Basin are presented. The fully automated advanced Raman lidar was deployed 60 km north of Manaus, Brazil (2.5 degrees S, 60 degrees W) in the Amazon rain forest from January to November 2008. The measurements thus cover both the wet season (Dec-June) and the dry or burning season (July-Nov). Two cases studies of young and aged smoke plumes are discussed in terms of spectrally resolved optical properties (355, 532, and 1064 nm) and further lidar products such as particle effective radius and single-scattering albedo. These measurement examples confirm that biomass burning aerosols show a broad spectrum of optical, microphysical, and chemical properties. The statistical analysis of the entire measurement period revealed strong differences between the pristine wet and the polluted dry season. African smoke and dust advection frequently interrupt the pristine phases during the wet season. Compared to pristine wet season conditions, the particle scattering coefficients in the lowermost 2 km of the atmosphere were found to be enhanced, on average, by a factor of 4 during periods of African aerosol intrusion and by a factor of 6 during the dry (burning) season. Under pristine conditions, the particle extinction coefficients and optical depth for 532 nm wavelength were frequently as low as 10-30 Mm(-1) and <0.05, respectively. During the dry season, biomass burning smoke plumes reached to 3-5 km height and caused a mean optical depth at 532 nm of 0.26. On average during that season, particle extinction coefficients (532 nm) were of the order of 100 Mm(-1) in the main pollution layer (up to 2 km height). Angstrom exponents were mainly between 1.0 and 1.5, and the majority of the observed lidar ratios were between 50-80 sr.

Misure di caratterizzazione su un prototipo di LiDAR basato su modulazione pseudocasuale

Un LiDAR è uno strumento di misura che sta vedendo uno sviluppo enorme negli ultimi decenni e sta dando risultati di grande utilità pratica. Abbiamo svolto alcune misure di distanza utilizzando uno strumento realizzato con materiale di recupero e un semplice software scritto da noi. In una prima parte del lavoro, più teorica, si illustrerà il funzionamento dello strumen- to, basato sull’invio di fasci laser su bersagli opachi e sulla ricezione della loro riflessione. Si presterà particolare attenzione ai metodi sviluppati per poter sfruttare laser continui piuttosto che impulsati, che risulterebbero più costosi: le sequenze pseudocasuali di bit. Nella parte sperimentale invece si mostrerà l’analisi dei dati effettuata e si commen- teranno i risultati ottenuti osservando le misure, con lo scopo di verificare alcune ipotesi, fra cui si darà particolare attenzione al confronto delle diverse sequenze. Lo scopo di questo lavoro è caratterizzare lo strumento tramite l’analisi delle misure e verificare l’asserzione dell’articolo [1] in bibliografia secondo cui particolari sequenze di bit (A1 e A2) darebbero risultati migliori se utilizzate al posto della sequenza pseudocasuale di lunghezza massima, M-sequence.

Debris-flow activity in abandoned channels of the Manival torrent reconstructed with LiDAR and tree-ring data

Hydrogeomorphic processes are a major threat in many parts of the Alps, where they periodically damage infrastructure, disrupt transportation corridors or even cause loss of life. Nonetheless, past torrential activity and the analysis of areas affected during particular events remain often imprecise. It was therefore the purpose of this study to reconstruct spatio-temporal patterns of past debris-flow activity in abandoned channels on the forested cone of the Manival torrent (Massif de la Chartreuse, French Prealps). A Light Detecting and Ranging (LiDAR) generated Digital Elevation Model (DEM) was used to identify five abandoned channels and related depositional forms (lobes, lateral levees) in the proximal alluvial fan of the torrent. A total of 156 Scots pine trees (Pinus sylvestris L.) with clear signs of debris flow events was analyzed and growth disturbances (GD) assessed, such as callus tissue, the onset of compression wood or abrupt growth suppression. In total, 375 GD were identified in the tree-ring samples, pointing to 13 debris-flow events for the period 1931–2008. While debris flows appear to be very common at Manival, they have only rarely propagated outside the main channel over the past 80 years. Furthermore, analysis of the spatial distribution of disturbed trees contributed to the identification of four patterns of debris-flow routing and led to the determination of three preferential breakout locations. Finally, the results of this study demonstrate that the temporal distribution of debris flows did not exhibit significant variations since the beginning of the 20th century.

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Landscape and forest structure at moose mortality sites on Isle Royale National Park. A LiDAR based assessment

Landscape structure and heterogeneity play a potentially important, but little understood role in predator-prey interactions and behaviourally-mediated habitat selection. For example, habitat complexity may either reduce or enhance the efficiency of a predator's efforts to search, track, capture, kill and consume prey. For prey, structural heterogeneity may affect predator detection, avoidance and defense, escape tactics, and the ability to exploit refuges. This study, investigates whether and how vegetation and topographic structure influence the spatial patterns and distribution of moose (Alces alces) mortality due to predation and malnutrition at the local and landscape levels on Isle Royale National Park. 230 locations where wolves (Canis lupus) killed moose during the winters between 2002 and 2010, and 182 moose starvation death sites for the period 1996-2010, were selected from the extensive Isle Royale Wolf-Moose Project carcass database. A variety of LiDAR-derived metrics were generated and used in an algorithm model (Random Forest) to identify, characterize, and classify three-dimensional variables significant to each of the mortality classes. Furthermore, spatial models to predict and assess the likelihood at the landscape scale of moose mortality were developed. This research found that the patterns of moose mortality by predation and malnutrition across the landscape are non-random, have a high degree of spatial variability, and that both mechanisms operate in contexts of comparable physiographic and vegetation structure. Wolf winter hunting locations on Isle Royale are more likely to be a result of its prey habitat selection, although they seem to prioritize the overall areas with higher moose density in the winter. Furthermore, the findings suggest that the distribution of moose mortality by predation is habitat-specific to moose, and not to wolves. In addition, moose sex, age, and health condition also affect mortality site selection, as revealed by subtle differences between sites in vegetation heights, vegetation density, and topography. Vegetation density in particular appears to differentiate mortality locations for distinct classes of moose. The results also emphasize the significance of fine-scale landscape and habitat features when addressing predator-prey interactions. These finer scale findings would be easily missed if analyses were limited to the broader landscape scale alone.

Applications of LiDAR remote sensing of forest structure in the Upper Great Lakes region, USA

We used active remote sensing technology to characterize forest structure in a northern temperate forest on a landscape- and local-level in the Upper Peninsula of Michigan. Specifically, we used a form of active remote sensing called light detection and ranging (e.g., LiDAR) to aid in the depiction of current forest structural stages and total canopy gap area estimation. On a landscape-level, LiDAR data are shown not only to be a useful tool in characterizing forest structure, in both coniferous and deciduous forest cover types, but also as an effective basis for data-driven surrogates for classification of forest structure. On a local-level, LiDAR data are shown to be a benchmark reference point to evaluate field-based canopy gap area estimations, due to the highly accurate nature of such remotely sensed data. The application of LiDAR remote sensed data can help facilitate current and future sustainable forest management.

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.

Introduction to the Maïdo Lidar Calibration Campaign dedicated to the validation of upper air meteorological parameters

The first operations at the new High-altitude Maïdo Observatory at La Réunion began in 2013. The Maïdo Lidar Calibration Campaign (MALICCA) was organized there in April 2013 and has focused on the validation of the thermodynamic parameters (temperature, water vapor, and wind) measured with many instruments including the new very large lidar for water vapor and temperature profiles. The aim of this publication consists of providing an overview of the different instruments deployed during this campaign and their status, some of the targeted scientific questions and associated instrumental issues. Some specific detailed studies for some individual techniques were addressed elsewhere. This study shows that temperature profiles were obtained from the ground to the mesopause (80 km) thanks to the lidar and regular meteorological balloon-borne sondes with an overlap range showing good agreement. Water vapor is also monitored from the ground to the mesopause by using the Raman lidar and microwave techniques. Both techniques need to be pushed to their limit to reduce the missing range in the lower stratosphere. Total columns obtained from global positioning system or spectrometers are valuable for checking the calibration and ensuring vertical continuity. The lidar can also provide the vertical cloud structure that is a valuable complementary piece of information when investigating the water vapor cycle. Finally, wind vertical profiles, which were obtained from sondes, are now also retrieved at Maïdo from the newly implemented microwave technique and the lidar. Stable calibrations as well as a small-scale dynamical structure are required to monitor the thermodynamic state of the middle atmosphere, ensure validation of satellite sensors, study the transport of water vapor in the vicinity of the tropical tropopause and study their link with cirrus clouds and cyclones and the impact of small-scale dynamics (gravity waves) and their link with the mean state of the mesosphere.

Study of mineral dust entrainment in the planetary boundary layer by lidar depolarisation technique

Measurements on 27 June 2011 were performed over the Southern Iberian Peninsula at Granada EARLINET station, using active and passive remote sensing and airborne and surface in-situ data in order to study the entrainment processes between aerosols in the free troposphere and those in the planetary boundary layer (PBL). To this aim the temporal evolution of the lidar depolarisation, backscatter-related Angström exponent and potential temperature profiles were used in combination with the PBL contribution to the aerosol optical depth (AOD). Our results show that the mineral dust entrainment in the PBL was caused by the convective processes which ‘trapped’ the lofted mineral dust layer, distributing the mineral dust particles within the PBL. The temporal evolution of ground-based in-situ data evidenced the impact of this process at surface level. Finally, the amount of mineral dust in the atmospheric column available to be dispersed into the PBL was estimated by means of POLIPHON (Polarizing Lidar Photometer Networking). The dust mass concentration derived from POLIPHON was compared with the coarse-mode mass concentration retrieved with airborne in-situ measurements. Comparison shows differences below 50 µg/m³ (30% relative difference) indicating a relative good agreement between both techniques.