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Displaying 396 - 400 of 661role of social learning for soil conservation: the case of Amba Zuria land management, Ethiopia
Social learning plays key roles in sustainable natural resource management; however, studies on its role show mixed results. Even though most current studies highlight positive outcomes, there are also negative effects of social learning with respect to natural resource management. This paper explores the influence of social learning outcomes on the adoption of soil conservation practices in Amba Zuria, Ethiopia. Data were collected through semi-structured interviews, group discussions and in workshops.
Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA
The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random forests) and a non-ensemble learning algorithm (classification trees) using multiple criteria related to algorithm and training data characteristics.
Linking poverty, HIV/AIDS and climate change to human and ecosystem vulnerability in southern Africa: consequences for livelihoods and sustainable ecosystem management
People in southern Africa are facing escalating levels of risk, uncertainty and consequently vulnerability as a result of multiple interacting stressors, including HIV/AIDS, poverty, food insecurity, weak governance, climate change and land degradation, to name but a few. Vulnerability or livelihood insecurity emerges when poor people as individuals or social units have to face harmful threats or shocks with inadequate capacity to respond effectively. In such situations, people often have no choice but to turn to their immediate environment for support.
Using Advanced Land Imager (ALI) and Landsat Thematic Mapper (TM) for the Detection of the Invasive Shrub Lonicera maackii in Southwestern Ohio Forests
We tested how accurately image data from the Advanced Land Imager (ALI) sensor vs. the Landsat Thematic Mapper (TM) predict the land cover of Lonicera maackii in the forest understory, taking advantage of this invasive shrub's extended leaf retention in the fall when the canopy is leafless. Percent cover of L. maackii in 20 woodlots in southwestern Ohio was regressed on values for spectral vegetation indices (SVIs) derived for each image. The land cover of L. maackii was best explained by the Simple Ratio (SR) using TM data (R ² = 0.537). The regression results for SVIs from TM vs.
soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model
Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture.