A GEOBIA approach for multitemporal land-cover and land-use change analysis in a Tropical Watershed in the southeastern Amazon

The southeastern Amazon region has been intensively occupied by human settlements over the past three decades. To evaluate the effects of human settlements on land-cover and land-use (LCLU) changes over time in the study site, we evaluated multitemporal Landsat images from the years 1984, 1994, 2004, 2013 and Sentinel to the year 2017. Then, we defined the LCLU classes, and a detailed “from-to” change detection approach based on a geographic object-based image analysis (GEOBIA) was employed to determine the trajectories of the LCLU changes. Three land-cover (forest, montane savanna and water bodies) and three land-use types (pasturelands, mining and urban areas) were mapped. The overall accuracies and kappa values of the classification were higher than 0.91 for each of the classified images. Throughout the change detection period, ~47% (19,320 km2) of the forest was preserved mainly within protected areas, while almost 42% (17,398 km2) of the area was converted from forests to pasturelands. An intrinsic connection between the increase in mining activity and the expansion of urban areas also exists. The direct impacts of mining activities were more significant throughout the montane savanna areas. We concluded that the GEOBIA approach adopted in this study combines the advantages of quality human interpretation and the capacities of quantitative computing

Canga biodiversity, a matter of mining

Brazilian name canga refers to the ecosystem associated with superficial iron crusts typical for the Brazilian state of Minas Gerais (MG) and some parts of Amazon (Flona de Carajas). Iron stone is associated with mountain plateaux and so, in addition to high metal concentrations (particularly iron and manganese), canga ecosystems, as other rock outcrops, are characterized by isolation and environmental harshness. Canga inselbergs, all together, occupy no more than 200km2 of area spread over thousands of km2 of the Iron Quadrangle (MG) and the Flona de Carajas, resulting in considerable beta biodiversity. Moreover, the presence of different microhabitats within the iron crust is associated with high alpha biodiversity. Hundreds of angiosperm species have been reported so far across remote canga inselbergs and different micro-habitats. Among these are endemics such as the cactus Arthrocereus glaziovii and the medicinal plant Pilocarpus microphyllus. Canga is also home to iron and manganese metallophytes; species that evolved to tolerate high metal concentrations. These are particularly interesting to study metal homeostasis as both iron and manganese are essential plant micro-elements. Besides being models for metal metabolism, metallophytes can be used for bio-remediation of metal contaminated sites, and as such are considered among priority species for canga restoration.Biodiversity mining is not the only mining business attracted to canga. Open cast iron mining generates as much as 5-6% of Brazilian gross domestic product (GDP) and dialogue between mining companies, government, society and ecologists, enforced by legal regulation, is ongoing to find compromise for canga protection, and where mining is unavoidable for ecosystem restoration. Environmental factors that shaped canga vegetation, canga biodiversity , physiological mechanisms to play a role, and ways to protect and restore canga will be reviewed

Optimization of the AZO dyes decoloration process through neural networks: Determination of the H2O2 addition critical point

The concentration of hydrogen peroxide is an important parameter in the azo dyes decoloration process through the utilization of advanced oxidizing processes, particularly by oxidizing via UV/H2O2. It is pointed out that, from a specific concentration, the hydrogen peroxide works as a hydroxyl radical self-consumer and thus a decrease of the system`s oxidizing power happens. The determination of the process critical point (maximum amount of hydrogen peroxide to be added) was performed through a ""thorough mapping"" or discretization of the target region, founded on the maximization of an objective function objective (constant of reaction kinetics of pseudo-first order). The discretization of the operational region occurred through a feedforward backpropagation neural model. The neural model obtained presented remarkable coefficient of correlation between real and predicted values for the absorbance variable, above 0.98. In the present work, the neural model had, as phenomenological basis the Acid Brown 75 dye decoloration process. The hydrogen peroxide addition critical point, represented by a value of mass relation (F) between the hydrogen peroxide mass and the dye mass, was established in the interval 50 < F < 60. (C) 2007 Elsevier B.V. All rights reserved

Prediction via neural networks of the residual hydrogen peroxide used in photo-fenton processes for effluent treatment

This communication proposes the use of neural networks in the prediction of residual concentrations of hydrogen peroxide from the treatment of effluents through Advanced Oxidative Processes (AOP's), in particular, the photo-Fenton process. To verify the efficiency of the oxidative process, the Chemical Oxygen Demand (COD) parameter, the values of which may be modified by the presence of oxidizing agents such as residual hydrogen peroxide, is frequently taken in account. The analysis of the H2O2 interference was performed by spectrophotometry at 450 nm wavelength, via the monitoring of the reaction of ammonia with metavanadate. The results of the hydrogen peroxide residual concentration were modeled via a feedforward neural network, with the correlation coefficients between actual and predicted values above 0.96, indicating good prediction capacity

Discoloration process modeling by neural network

The photo-oxidation of acid orange 52 dye was performed in the presence of H2O2, utilizing UV light, aiming the discoloration process modeling and the process variable influence characterization. The discoloration process was modeled by the use of feedforward neural network. Each sample was characterized by five independent variables (dye concentration, pH, hydrogen peroxide volume, temperature and time of operation) and a dependent variable (absorbance). The neural model has also provided, through Garson Partition coefficients and the Pertubation method, the independent variable influence order determination. The results indicated that the time of operation was the predominant variable and reaction mean temperature was the lesser influent variable. The neural model obtained presented coefficients of correlation on the order 0.98, for sets of trainability, validation and testing, indicating the power of prediction of the model and its character of generalization. (c) 2007 Elsevier B.V. All rights reserved

Biogeochemical processes in canga ecosystems: armoring of iron ore against erosion and importance in iron duricrust restoration in Brazil

Supergene enriched iron ore deposits in Brazil are typically blanketed by goethite-cemented breccias that form a protective duricrust known as canga. Moderately hard, well consolidated, permeable and resistant to erosion and chemical weathering, the canga blanket protects the relatively friable iron ore below. The protective canga horizons in the Carajás and Quadrilátero Ferrífero mineral provinces represent some of the longest-lived, continuously exposed land surfaces on Earth, and their formation is essential to supergene iron ore enrichment and preservation. Remarkably, the iron-rich duricrusts that have developed in Brazilian tropical rainforest environments, i.e, Carajás, yield geochronological results that indicate that these ancient erosion-resistant surfaces continue to evolve today. Active biogeochemical iron cycling is essential for the ‘self-healing’ cementation/re-cementation occurring in canga, suggesting that recurrent iron reduction and subsequent oxidation are responsible for canga evolution. Macroscopic biological features in canga including ferruginised plant roots and termite tracks have been linked to the biogeochemical cycling of iron. The ‘organic’ textures in canga can be traced to the microscopic scale, preserving fossilised bacterial cell envelopes and permineralised biofilms. At the canga surface, naturally rare and endemic rupestrian plant species carve out an existence, commonly in the absence of soil. Growth of grasses also promotes metal cycling highlighting that the rhizosphere contributes to canga evolution. The fossilisation of microbial biofilms and rhizosphere horizons consolidates canga, affecting its permeability, limiting water transport and enhancing biogeochemical cycling. The development of canga has been essential for the formation, preservation, and discovery of iron ore deposits, and its restoration will ultimately be required for mined land remediation of these unique ecosystems