Re-sampling remotely sensed data to improve national and regional mapping of forest conditions with confidential field data

Ray L. Czaplewski

Treesearch

Re-sampling remotely sensed data to improve national and regional mapping of forest conditions with confidential field data

Informally Refereed

Authors:

Ray L. Czaplewski

Year:

2005

Type:

Other

Station:

Rocky Mountain Research Station

Source:

In: Marsden, Michael; Downing, Marla; Riffe, Mark, comps. Workshop proceedings: quantitative techniques for deriving national-scale data; Westminster, CO, July 26–28, 2005. FHTET-05-12. Fort Collins, CO: U.S. Dept. of Agriculture, Forest Service, Forest Health Technology Enterprise Team: 263-284

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Abstract

Forest Service Research and Development (R&D) and State and Private Forestry Deputy Areas, in partnership with the National Forest System Remote Sensing Applications Center (RSAC), built a 250-m resolution (6.25-ha pixel) dataset for the entire USA. It assembles multi-seasonal hyperspectral MODIS data and derivatives, Landsat derivatives (i.e., summary statistics for the 70 or so Landsat pixels within each 6.25-ha pixel), and other national geospatial datasets (e.g., climate, soils, ecoregions). All datasets are re-sampled to the same 250-m grid used for standard MODIS derivatives. There are approximately 100 layers in this “stack” of full-coverage geospatial data.

Keywords

remote sensing, hyperspectral data, mapping, MODIS, Landsat

Citation

Czaplewski, Raymond L. 2005. Re-sampling remotely sensed data to improve national and regional mapping of forest conditions with confidential field data. In: Marsden, Michael; Downing, Marla; Riffe, Mark, comps. Workshop proceedings: quantitative techniques for deriving national-scale data; Westminster, CO, July 26–28, 2005. FHTET-05-12. Fort Collins, CO: U.S. Dept. of Agriculture, Forest Service, Forest Health Technology Enterprise Team: 263-284