No abstract

The purpose of this study is to examine the use of multi-resolution object-based classification methods for the classification of Unmanned Aircraft Systems (UAS) images of wetland vegetation and to compare its performance with pixel-based classification approaches. Three types of classifiers (Support Vector Machine, Artificial Neural Network and Maximum Likelihood) were utilized to classify the object-based images, the original 8-cm UAS images and the down-sampled (30 cm) version of the image. The results of the object-based and two pixel-based classifications were evaluated and compared. Object-based classification produced higher accuracy than pixel-based classifications if the same type of classifier is used. Our results also showed that under the same classification scheme (i.e. object or pixel), the Support Vector Machine classifier performed slightly better than Artificial Neural Network, which often yielded better results than Maximum Likelihood. With an overall accuracy of 70.78%, object-based classification using Support Vector Machine showed the best performance. This study also concludes that while UAS has the potential to provide flexible and feasible solutions for wetland mapping, some issues related to image quality still need to be addressed in order to improve the classification performance. ARTICLE HISTORY

No abstract

Managing Pinus elliotii forests for timber production and/or carbon sequestration is a common management objective, but can negatively affect water yield due to high losses from evapotranspiration. Thus, understanding the trade-offs and potential synergies among multiple ecosystem goods services, as well as the drivers influencing these interactions, can help identify effective forest management practices. We used available data from 377 permanent plots from the USDA Forest Service Forest Inventory and Analysis Program for 2002-2011, and a forest water yield model to quantify provision levels and spatial distribution and patterns of carbon sequestration, timber volume and water yield for Pinus elliotii ecosystems in North Florida, USA. A ranking-classification framework and statistical analyses were used to better understand the interactions among ecosystem services and the effect of biophysical drivers on ecosystem service bundles. Results indicate that increased biomass reduced water yield but this trade-off varied across space. Specific synergies, or acceptable provision levels, among carbon sequestration, timber volume and water yield were identified and mapped. OPEN ACCESSForests 2014, 5 1410Additionally, stand age, silvicultural treatment, and site quality significantly affected the provision level of, and interactions among, the three ecosystem goods and services. The framework developed in this study can be used to assess, map, and manage subtropical forests for optimal provision of ecosystem services.

2016-11-03T14:11:40