Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

Contém resumo

The late adopter scale: A measure of late adopters of technological innovations

Diffusion of Innovation is a topic of interest for researchers and practitioners. Although substantial research is conducted on user categories, researchers often focus on the first half of the curve, ignoring the late adopters. We conduct two studies to measure the attributes of late adopters. In our first study of mobile phone users, we develop the Late-Adopter Scale. We then test it on a sample of laptop users. This scale is multi-dimensional, presents nomological and discriminant validity and has three dimensions: 1) rate of adoption, 2) resistance to innovation, and 3) skepticism. Findings reveal that all three Late Adopter Scale dimensions are significantly associated with low price preference. Moreover, in both samples skepticism is associated with high preference for simple products, lower leading edge status, and lower product involvement. Discussion focuses on implications of this new scale to theory and practice of new product development and diffusion of innovation.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.