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Displaying 341 - 345 of 662Price determination and efficiency in the market for water rights in New Mexico's Middle Rio Grande Basin
Water rights markets in the western United States have expanded over the last 40 years, as a result of population growth in the West and Southwest, and limited development of new storage. Until 2008, house prices, home construction and population growth appeared to be locked in an ever-increasing upward trend. With little historical experience to the contrary, water right market prices similarly appeared to be driven by real estate development.
high-resolution GIS null model of potential forest expansion following land use changes in Norway
During recent decades, forests have expanded into new areas throughout the whole of Norway. The processes explained as causing the forest expansion have focused mainly on climate or land use changes. To enable a spatially explicit separation of the effects following these two main drivers behind forest expansion, the authors set out to model the potential for natural forest regeneration following land use abandonment, given the present climatic conditions.
Forest Fuel Reduction and Biomass Supply: Perspectives from Southern Private Landowners
Removing excess biomass from fire-hazardous forests can serve dual purposes: enhancing the health and sustainability of forest ecosystems and supplying feedstock for energy production. The physical availability of this biomass is fairly well-known, yet availability does not necessarily translate into actual supply. We assess the perception and behavior of private forestland owners in the southern United States with respect to thinning overstocked forests for bioenergy production.
Mean shift-based clustering of remotely sensed data with agricultural and land-cover applications
The mean shift (MS) algorithm is based on a statistical approach to the clustering problem. Specifically, the method is a variant of density estimation. We revisit in this article the MS paradigm and its use for clustering of remotely sensed images. Specifically, we investigate further the classification accuracy of remotely sensed images as a function of various MS parameters, such as the variant used, kernel type, dimensionality, kernel bandwidth, etc.
Employing lidar data to identify butterfly habitat characteristics of four contrasting butterfly species across a diverse landscape
Lidar and orthophotograph-derived land cover are combined with in situ vegetation measurements to assess habitat characteristics typifying four species of butterflies with differing habitat preferences across a large spatial extent (∼30,000 ha) in northern Idaho, USA. Lidar data are employed to characterize both vegetation structure and topography, whereas digital orthophotographs and in situ vegetation measurements are employed to quantify surrounding land use and larval host plant cover, respectively.