Skip to Main Content
Moderate-resolution data and gradient nearest neighbor imputation for regional-national risk assessmentAuthor(s): Kenneth B. Jr. Pierce; C. Kenneth Brewer; Janet L. Ohmann
Source: In: Pye, John M.; Rauscher, H. Michael; Sands, Yasmeen; Lee, Danny C.; Beatty, Jerome S., tech. eds. 2010. Advances in threat assessment and their application to forest and rangeland management. Gen. Tech. Rep. PNW-GTR-802. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest and Southern Research Stations: 111-121
Publication Series: General Technical Report (GTR)
Station: Pacific Northwest Research Station
PDF: View PDF (298.68 KB)
DescriptionThis study was designed to test the feasibility of combining a method designed to populate pixels with inventory plot data at the 30-m scale with a new national predictor data set. The new national predictor data set was developed by the USDA Forest Service Remote Sensing Applications Center (hereafter RSAC) at the 250-m scale. Gradient Nearest Neighbor (GNN) imputation was designed by the USDA Forest Service Pacific Northwest Research Station (hereafter PNW) to assign a plot identifier, and, therefore, a link to associated plot data, to each pixel within a target raster. Gradient Nearest Neighbor was implemented at 30-m resolution in three separate multimillion-hectare regions of the Western United States. Concurrently, RSAC developed a set of spatial predictor surfaces at 250-m resolution for use in producing nationally consistent data products. These data have been used for modeling forest types and forest biomass for the conterminous United States and Alaska. These predictor data have also been used for large regional applications. In this study, we substituted the 250-m predictor data for the 30-m predictor data used thus far in GNN. Our objective was to quantify the difference in performance using the lower spatial resolution predictors. We remodeled the same three regions that were mapped at 30 m with the 250-m data set and compared the error structure of the two modeling efforts. For species presence/absence models in the two areas with large environmental gradients, the Sierra Nevada and northeastern Washington, the species models performed substantially the same at the two resolutions. For the region with reduced environmental heterogeneity and moderate environmental gradients, coastal Oregon, species models did not work well with either the 30-m or 250-m studies. Models geared towards mapping forest structure did not perform as well as the 30-m models and may be insufficient for risk-assessment use.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationPierce, Kenneth B. Jr.; Brewer, C. Kenneth; Ohmann, Janet L. 2010. Moderate-resolution data and gradient nearest neighbor imputation for regional-national risk assessment. In: Pye, John M.; Rauscher, H. Michael; Sands, Yasmeen; Lee, Danny C.; Beatty, Jerome S., tech. eds. 2010. Advances in threat assessment and their application to forest and rangeland management. Gen. Tech. Rep. PNW-GTR-802. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest and Southern Research Stations: 111-121.
KeywordsGradient Nearest Neighbor, imputation, regional analysis, species distributions, vegetation mapping.
- Mapping change of older forest with nearest-neighbor imputation and Landsat time-series
- Imputed forest structure uncertainty varies across elevational and longitudinal gradients in the western Cascade mountains, Oregon, USA
- A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data
XML: View XML