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Displaying 1261 - 1265 of 1605artificial immune network approach to multi-sensor land use/land cover classification
An optimized artificial immune network-based classification model, namely OPTINC, was developed for remote sensing-based land use/land cover (LULC) classification.
Manganese recovery from secondary resources: A green process for carbothermal reduction and leaching of manganese bearing hazardous waste
During the hydrometallurgical extraction of zinc by electrowinning process, a hazardous solid waste called anode mud is generated. It contains large quantity of manganese oxides (55–80%) and lead dioxide (6–16%). Due to the presence of a large quantity of lead, the anode mud waste is considered hazardous and has to be disposed of in secure landfills, which is costly, wastes available manganese and valuable land resources.
integrated approach to modelling land-use change on continental and global scales
Land-use and land-cover change are important drivers of global environmental change, affecting the state of biodiversity, the global carbon cycle, and other aspects of the earth system. In this article we describe the development of the land-use model LandSHIFT, which aims to simulate land-use and land-cover change on the continental and global scale. The model is based on a “land-use systems” approach, which describes the interplay between anthropogenic and environmental system components as drivers of land-use change.
Soil carbon storage and stratification under different tillage systems in a semi-arid region
Changes in the agricultural management can potentially increase the accumulation rate of soil organic carbon (SOC), thereby sequestering CO₂ from the atmosphere. In a long-term experiment (1992-2008) we examined the effects of various tillage intensities: no-tillage (NT), minimum tillage with chisel plow (MT), and conventional tillage with mouldboard plow (CT), on the topsoil profile distribution (0-30cm) of SOC, on a semi-arid loamy soil from Central Spain. The crop sequence established was cheap pea (Cicer arietinun L.) cv. Inmaculada/barley (Hordeum vulgare L.) cv. Volley.
integrative model of human-influenced fire regimes and landscape dynamics
Fire regimes depend on climate, vegetation structure and human influences. Climate determines the water content in fuel and, in the longer term, the amount of biomass. Humans alter fire regimes through increased ignition frequency and by hindering the spread of fire through fire suppression and fuel fragmentation. Here, we present FIRE LADY (FIre REgime and LAndscape DYnamics), a spatially explicit fire regime model that takes into account daily weather data, topography, vegetation growth, fire behaviour, fire suppression and land use changes.