Hooman Latifi

Environmental Engineering · 2010–2016

A researcher in the Science corpus: 5 papers; trusts 39 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

Whom Hooman Latifi trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. N. Crookston
  2. Arne Nothdurft
  3. Matti Maltamo
  4. Erik Næsset
  5. Trevor J. Hastie
  6. Andrew Thomas Hudak
  7. Johannes Breidenbach
  8. A. R. Stage
  9. J. Ohmann
  10. J. Cadima

Who trusts Hooman Latifi overall

  1. S. Howard
  2. Petteri Packalen
  3. D. Bickford
  4. Barbara Koch
  5. Fabian Ewald Fassnacht
  6. Hailemariam Temesgen
  7. Lars T. Waser

Papers from Hooman Latifi's perspective

Ordered by how strongly the researchers Hooman Latifi trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Random Forests
  2. Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A.
  3. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method
  4. Partitioning Error Components for Accuracy-Assessment of Near-Neighbor Methods of Imputation (Forestry sciences)
  5. Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data
  6. Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics
  7. Comparison of Nearest Neighbor Methods for Estimating Basal Area and Stems per Hectare Using Aerial Auxiliary Variables (Forestry sciences)
  8. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data
  9. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data
  10. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data

Their papers