Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method

Hector Franco-Lopez, Alan R. Ek, Marvin E. Bauer

Research output: Contribution to journalArticlepeer-review

318 Scopus citations

Abstract

Mapping forest variables and associated characteristics is fundamental for forest planning and management. Considerable effort has been made in Northern Europe to develop techniques for wall-to-wall mapping of forest variables. Following that work, we describe the k-nearest neighbors (kNN) method for improving estimation and to produce wall-to-wall basal area, volume, and cover type maps, in the context of the USDA Forest Service's Forest Inventory and Analysis (FIA) monitoring system. Several variations within the kNN were tested, including: distance metric, weighting function, feature weighting parameters, and number of neighbors. Specific procedures to incorporate ancillary information and image enhancement techniques were also tested. Using the nearest neighbor (k = 1), Euclidean distance, a three date 18-band composite image, and feature weighting parameters, maps were constructed for basal area, volume, and cover type. The empirical, bootstrap based, 95% confidence interval for the basal area root mean square error (MSE) is (8.21, 9.02) m2/ha and for volume (48.68, 54.58) m3/ha. For the 13 FIA forest cover type classes, results indicated useful map accuracy and the choice of k = 1 retained the full range of forest types present in the region. The 95% confidence interval, obtained using the bootstrap 0.632+ technique, for the overall accuracy (OA) in the 13 cover type classification was (0.4952, 0.5459). Recommendations for applying the kNN method for mapping and regional estimation are provided.

Original languageEnglish (US)
Pages (from-to)251-274
Number of pages24
JournalRemote Sensing of Environment
Volume77
Issue number3
DOIs
StatePublished - Oct 11 2001

Keywords

  • Estimation
  • Forest inventory
  • k-nearest neighbors

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