ICWIM8 - 8th Conference on Weigh-in-Motion - Book of proceedings

ICWIM8, 8th International Conference on Weigh-in-Motion, PRAGUE, TCHÈQUE, RÉPUBLIQUE, 20-/05/2019 - 24/05/2019The conference addresses the broad range of topics related to on-road and in-vehicle WIM technology, its research, installation and operation and use of mass data across variable end-uses. Innovative technologies and experiences of WIM system implementation are presented. Application of WIM data to infrastructure, mainly bridges and pavements, is among the main topics. However, the most demanding application is now WIM for enforcement, and the greatest challenge is WIM for direct enforcement. Most of the countries and road authorities should ensure a full compliance of heavy vehicle weights and dimensions with the current regulations. Another challenging objective is to extend the lifetimes of existing road assets, despite of increasing heavy vehicle loads and flow, and without compromising with the structural safety. Fair competition and road charging also require accurately monitoring commercial vehicle weights by WIM. WIM contributes to a global ITS (Intelligent Transport System) providing useful data on heavy good vehicles to implement Performance Based Standards (PBS) and Intelligent Access Programme (IAP, Australia) or Smart Infrastructure Access Programme (SIAP). The conference reports the latest research and developments since the last conference in 2016, from all around the World. More than 150 delegates from 33 countries and all continents are attending ICWIM8, mixing academics, end users, decision makers and WIM vendors. An industrial exhibition is organized jointly with the conference

Consumer Goods and Deforestation: An Analysis of the Extent and Nature of Illegality in Forest Conversion for Agriculture and Timber Plantations

A comprehensive new analysis released earlier this month says that nearly half (49%) of all recent tropical deforestation is the result of illegal clearing for commercial agriculture. The study also finds that around half of this illegal destruction was driven by overseas demand for agricultural commodities including palm oil, beef, soy, and wood products. In addition to devastating impacts on forest-dependent people and biodiversity, the illegal conversion of tropical forests for commercial agriculture is estimated to produce 1.47 gigatonnes of carbon each year -- equivalent to 25% of the EU's annual fossil fuel-based emissions. The world must wake up to the scale of how much of this agricultural production is taking place on land that has been illegally cleared. According to the study 90% of the deforestation in Brazil from 2000 to 2012 was illegal, primarily due to the failure to conserve a percentage of natural forests in large-scale cattle and soy plantations, as required by Brazilian law. (Much of this occurred prior to 2004, when the Brazilian government took steps to successfully reduce deforestation.) And in the forests of Indonesia, 80% of deforestation was illegal -- mostly for large-scale plantations producing palm oil and timber, 75% of which is exported. While other countries also experience high levels of illegal deforestation, Brazil and Indonesia produce the highest level of agricultural commodities destined for global markets, many of which wind up in cosmetics or household goods (palm oil), animal feed (soy), and packaging (wood products)

Deforestation, degradation, and natural disturbance in the Amazon: using a new monitoring approach to estimate area and carbon loss

Forest degradation causes environmental damage and carbon emissions, but its extent and magnitude are not well understood. New methods for monitoring forest degradation and deforestation show that more disturbance has occurred in the Amazon in recent decades than previously realized, indicating an unaccounted for source of carbon emissions and damage to Amazon ecosystems. Forest degradation and natural disturbance change a landscape, but the visible damage apparent in satellite images may be temporary and difficult to differentiate from undisturbed forests. Time series analysis of Landsat data used in a spectral mixture analysis improves monitoring of forest degradation and natural disturbance. In addition, the use of statistical inference accounts for classification bias and provides an estimate of uncertainty. Application of the methodology developed in this dissertation to the Amazon Ecoregion found that forest degradation and natural disturbance were more prevalent than deforestation from 1995 to 2017. Of consequence, the total area of forest in the Amazon that has been recently disturbed is greater than previously known. Overall, deforestation affected 327,900 km2 (±15,500) of previously undisturbed forest in the Amazon while degradation and natural disturbance affected 434,500 km2 (±22,100). Forest degradation and natural disturbance occur more frequently during drought years, which have increased in frequency and severity in recent years. Deforestation has largely decreased since 2004, while forest degradation and natural disturbance have remained consistent. Previously disturbed forests are lower in biomass than undisturbed forests, yet regeneration after disturbance gradually sequesters carbon. A carbon flux model shows that gross aboveground carbon loss from forest degradation and natural disturbance and deforestation from 1996 to 2017 in the Amazon were 2.2-2.8 Pg C and 3.3-4.3 Pg C, respectively. Since 2008, however, carbon loss from degradation and natural disturbance has been approximately the same as from deforestation. The methodologies developed in this dissertation are useful for monitoring deforestation and degradation throughout the world’s forest ecosystems. By leveraging dense data time series, statistical inference, and carbon modeling it is possible to quantify areas of deforestation and forest degradation in addition to the resulting carbon emissions. The results of this dissertation stress the importance of degradation and natural disturbance in the global carbon cycle and information valuable for climate science and conservation initiatives

Mitigating Zoonotic Disease Threats to Prevent Future Pandemics: A Critical Analysis of Policy Favoring the Closures of Wildlife Markets in Latin America

The Preventing Future Pandemics Act was introduced to mitigate zoonotic disease threats around the world by focusing policy efforts on the closure of wildlife markets that gave rise to COVID–19. This Note challenges the efficacy of wildlife market closure policy by considering cultural, socioeconomic, and legal factors for the existence of wildlife market within megadiverse countries in Latin America. Based on scientific research on the animal-to-human interface and zoonotic disease transmission, this Note suggests effective policy should incorporate a targeted species ban for reservoir species, improved sanitary measures and disease surveillance, and wildlife trafficking prevention. Ultimately, this Note calls for policymakers to take into account the context of a historically undervalued Global South, the realities of human behavior, culture, and society, and the science on disease transmission

Essays on sustainable agriculture: Studies on water, deforestation and family farming

This cumulative dissertation comprises three independent studies concerning fundamental topics of sustainable agriculture. Chapter 2 draws from the absence of water data, which limits the development of economic models gauging water scarcity impacts. The paper makes a central contribution by developing an in-depth description of national statistics, international and global water databases. Chapter 3 concerns the relationship between deforestation data and widely used institutional indices. The paper offers empirical-based evidence about the relationship between governmental performance, public corruption perception and forest resources. Moreover, computer-intensive data management was employed to convert georeferenced raster data into a format compatible with economic statistical software and enable sample replications of large original data. Chapter 4 investigates the presence of spatial spillovers as providing beneficial opportunities to family farming credit in the Brazilian Amazon. Credit rationing is argued to target wealthier farmers engaged in livestock production while neglecting those producing crops. The paper employs a spatial Durbin error model of credit acquisition for husbandry and agricultural systems in 103 microregions. To enhance the paper’s discussion, 35 semi-structured interviews with key informants were conducted

Modelling land cover change in tropical rainforests

Tropical deforestation is one of the most important drivers of biodiversity loss and carbon emissions. This thesis seeks to analyse the dynamics of tropical deforestation and develop a probabilistic model that predicts land cover change (LCC) in the tropics. The main findings from the analysis of the Brazilian Amazon deforestation dynamics are that large clearings comprised progressively smaller amounts of total annual deforestation while the number of smaller clearings remained unchanged over time. These changes were coincident with the implementation of conservation policies by the government. The review of LCC models presented here showed that this modelling community would benefit from improving: the openness to share model inputs, code and outputs; model validations; and standardised frameworks to be used for model comparisons. The modelling framework developed aimed to tackle the limitations found before and two scenarios of deforestation in the Brazilian Amazon were simulated. For both scenarios forest next to roads and areas already deforested were found to be more likely to be deforested. States in the south and east of the region showed high predicted probability of losing nearly all forest outside of protected areas by 2050. The release of carbon to the atmosphere is an important consequence of tropical deforestation. Even if deforestation had ended in 2010 there would still be large quantities of carbon to be released. The amount of carbon released immediately is higher than the one committed for future release in the first few years of analysis, but presently these accounted for at least two-thirds of total carbon emissions. Finally, the drivers of LCC were found to vary among transition types, but less so through time. The accuracy of the model predictions was heavily dependent on the year calibrated, suggesting that a widespread reliance on single calibration time period may be providing biased predictions of future LCC

Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al