Suchergebnisse

Grasslands occupy 40% of the world's land surface (excluding Antarctica and Greenland) and support diverse groups, from traditional extensive nomadic to intense livestock-production systems. Population pressures mean that many of these grasslands are in a degraded state, particularly in less-productive areas of developing countries, affecting not only productivity but also vital environmental services such as hydrology, biodiversity, and carbon cycles; livestock condition is often poor and household incomes are at or below poverty levels. The challenge is to optimize management practices that result in "win-win" outcomes for grasslands, the environment, and households. A case study is discussed from northwestern China, where it has been possible to reduce animal numbers considerably by using an energy-balance/market-based approach while improving household incomes, providing conditions within which grassland recovery is possible. This bottom-up approach was supported by informing and working with the six layers of government in China to build appropriate policies. Further policy implications are considered. Additional gains in grassland rehabilitation could be fostered through targeted environmental payment schemes. Other aspects of the livestock production system that can be modified are discussed. This work built a strategy that has implications for many other grassland areas around the world where common problems apply.

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net‐water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics‐based entropy method. The weighted indices were input into the WNB‐based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image‐based sampling and validation, cell‐by‐cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood‐related environmental hazard studies.