AmericaView’s national applied research goals are broad and generally accomplished through the Research Committee. The Research Committee is one of several special interest groups of the AmericaView national consortium. The committee was established to effectively organize and support StateView consortia members who are particularly interested in applied remote sensing research. Because applied research can take many forms, the committee collaborates on various projects driven by state and regional applied research needs as described in our StateView fact sheets.
The Research Committee exists to support research efforts in the areas of objective-based analysis, ecosystem science, classification meta-analysis, and cyber infrastructure. Specifically, the Research Committee provides a forum for:
- Scholarly exchange of insights and experiences;
- Developing and sharing of applied research expertise and resources;
- Fostering and developing collaborative applied research and infrastructure projects;
- Developing cooperative remote sensing research projects for applications by the consortium.
StateViews cooperate in a range of applied research projects depending on common needs and funding from AmericaView and other organizations and agencies. Organized around the need to better understand the structure and function of Earth’s ecosystems and the sustainability of natural resources, StateViews identify, develop, and seek funding for cooperative projects that benefit society. Data used in these projects, such as Landsat and MODIS, is designed to provide wide coverage at moderate resolutions, and is often best suited to landscape-scale and regional applications.
Working through our StateView lead institutions, AmericaView supports a wide range of undergraduate research activities in cooperation with university scientists, government agencies, non-profit organizations, and the private sector. StateViews partners, such as local planning agencies, state departments of natural resources, and federal partners including the National Park Service, U.S. Forest Service, and Bureau of Land Management provide a variety of exciting opportunities for undergraduate students to engage in applied geospatial projects. StateViews also support undergraduate students through scholarships, research assistantships, internships, and travel grants for field work and conference presentations. Examples of student research projects include mapping forest ice storm damage, analyzing changes in floodplain land use, assessing wildfire damage, quantifying agricultural productivity, detecting and mapping invasive species, and monitoring rangeland conditions using satellite imagery and aerial photographs. These educational and professional experiences increase students’ geospatial knowledge and skills and significantly enhance their graduate school and employment potential.
Graduate research occurs in cooperation with StateView academic scientists and their partners who direct remote sensing research laboratories at their respective colleges and universities. Most of our StateView Principal Investigators are tenured academic faculty members who work on a wide variety of theoretical and applied research sponsored by the federal and state government, foundations, and other organizations. Graduate students are critical to the research missions and gain valuable experience as a central part of their academic training.
The AmericaView Classification Methods Accuracy Comparison (ACMAC) project.
ACMAC is an AmericaView project lead by Dr. Rick Lawrence at Montana State University as part of MontanaView. The purpose of ACMAC is to provide the tools and infrastructure to conduct rigorous comparisons of classification algorithms. ACMAC is implemented in the R statistical programming language. Two tools are provided by ACMAC to support classification algorithm comparisons: (1) a collection of 30 datasets, each including separate training and validation data, in a series of comma-delimited (csv) files and (2) sample R code for automatically analyzing the 30 dataset collection (or other user provided datasets) using Random Forest (the Random Forest algorithm can readily be replaced with most other classification algorithms available in R). To download a zip file containing these ACMAC tools, click here.