A Neural Network Method for Efficient Vegetation Mapping

This paper describes the application of a neural network method designed to improve the efficiency of map production from remote sensing data. Specifically, the ARTMAP neural network produces vegetation maps of the Sierra National Forest, in Northern California, using Landsat Thematic Mapper (TM) data. In addition to spectral values, the data set includes terrain and location information for each pixel. The maps produced by ARTMAP are of comparable accuracy to maps produced by a currently used method, which requires expert knowledge of the area as well as extensive manual editing. In fact, once field observations of vegetation classes had been collected for selected sites, ARTMAP took only a few hours to accomplish a mapping task that had previously taken many months. The ARTMAP network features fast on-line learning, so the system can be updated incrementally when new field observations arrive, without the need for retraining on the entire data set. In addition to maps that identify lifeform and Calveg species, ARTMAP produces confidence maps, which indicate where errors are most likely to occur and which can, therefore, be used to guide map editing

Reflections of Personal Experiences in Lima, the La Joya of Peru

This article reflects my faculty professional development travel abroad prodigious experiences encountered as a traveler to Lima, Peru. My travel was sponsored in part by the Kennesaw State University (KSU) Year of Peru initiative and the KSU Psychology Department. The Peruvian global engagement experience enriched my future academic pursuits of teaching, research, mentoring undergraduate and graduate students, and professional faculty development. Tangentially, the Peru experience also intensified and heightened my appreciation for Lima’s diversified history, culture, and people

A Neural Network Method for Mixture Estimation for Vegetation Mapping

While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife habitat studies, can benefit from knowledge of mixes. This paper examines various algorithms that use data from the Landsat Thematic Mapper (TM) satellite to estimate mixtures of vegetation types within forest stands. Included in the study are maximum likelihood classification and linear mixture models as well as a new methodology based on the ARTMAP neural network. Two paradigms are considered: classification methods, which describe stand-level vegetation mixtures as mosaics of pixels, each identified with its primary vegetation class; and mixture methods, which treat samples as blends of vegetation, even at the pixel level. Comparative analysis of these mixture estimation methods, tested on data from the Plumas National Forest, yields the following conclusions: (1) accurate estimates of proportions of hardwood and conifer cover within stands can be obtained, particularly when brush is not present in the understory; (2) ARTMAP outperforms statistical methods and linear mixture models in both the classification and the mixture paradigms; (3) topographic correction fails to improve mapping accuracy; and (4) the new ARTMAP mixture system produces the most accurate overall results. The Plumas data set has been made available to other researchers for further development of new mapping methods and comparison with the quantitative studies presented here, which establish initial benchmark standards.National Science Foundation (IRI 94-0165, SBR 95-13889); Office of Naval Research (N00014-95-1-0409, N00014-95-0657); Region 5 Remote Sensing Laboratory of the U.S. Forest Servic

Relocating, Downsizing, and Merging: Inventory Projects to Manage Change in a Digital Environment

With a new library location and newly created librarian position, the Health Sciences Library (HSL) of the University Libraries at the University of Memphis needed a comprehensive inventory. Having previously completed a small-scale inventory, technical services librarians led the project to assess the HSL collection before the newly hired librarian arrived. Beyond ensuring that all materials were in the collection and reflected properly in the integrated library system (ILS), an up-to- date inventory asserts the value of the physical collections to a variety of campus stakeholders. This chapter offers ideas for working collaboratively with personnel across library departments to conduct and complete a major technical services project