An Efficient Remote Sensing Solution
What’s glossy, full of colors, says 1000 words without a single letter, and is stacked 20 inches high on my desk? Imagery magazines! I love a good read and when a colleague arrived at my door with an armload of imagery journals looking for a home I thought I’d won the lottery. The publications range in scope from research journals to industry publications for geospatial professionals to remote sensing and optical satellite imagery resource solutions.
This week I want to share an article that caught my eye in the June edition of Photogrammetric Engineering and Remote Sensing: “An Efficient Remote Sensing Solution to Update the NCWI”, B.R. Stein, B. Zheng, I. Kokkinidis, N. Kayastha, T. Seigler, K. Gokkaya, R. Gopalakrishnan, and W. Hwang. This article features the winning project which answered the 2012 GeoLeague Challenge sponsored by the ASPRS Student Advisory Council.
The goal of the 2012 GeoLeague Challenge was to develop a strategy for updating the National Coastal Wetlands Inventory (NCWI) that was not only time and cost-efficient but also addressed shortcomings in current approaches and increase accuracy. Further, the proposed solution should be scalable to the national level and repeatable for efficient inventory update projects every five to 10 years.
Here are a few key points from the article I found exciting:
1) The authors base their approach around the “additive benefits of a data fusion” and propose the utilization of Landsat, LiDAR, and Radar data in an “unprecedented” data fusion combination to utilize key quality components from various data sources.
Why is this exciting? The success of this project can be modeled across multiple disciplines ranging from land use and land planning, to forestry and environmental monitoring, and to any other industry looking at data fusion as a new way to utilize imagery to solve problems.
2) The proposed workflow involves utilizing various data combinations with various analytic methods on a small scale, then applying the best combination(s) to larger scale study areas and eventually applying them to national study areas.
Why is this exciting? Scientific and time-tested algorithms like maximum likelihood classification are coupled with newer approaches to geospatial data analysis such as object-based imagery classification. Accuracy we have not been able to achieve in the past may be reached by combining multiple analytic approaches with new data combinations - thanks to increasingly rich data and new technology.
3) All facets of this proposal are so well constructed it is truly impressive. The scientific information, business case, timeline, implementation plan, and funding considerations are comprehensive and concise.
Why is this exciting? I won’t be a bit surprised when I see a follow-up article outlining this group’s achievements and notification that the NCWI updates are successfully underway – thanks to this “Efficient Remote Sensing Solution…”