Erkki Tomppo

Nature and Landscape Conservation · 2001–2013

A researcher in the Science corpus: 9 papers; trusts 26 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Nature and Landscape Conservation — 33 citations into it; #922 of 6,941 by trust here, #2,947 by citations
• Ecology — 27 citations into it; #1,619 of 10,717 by trust here, #5,442 by citations
• Environmental Engineering — 21 citations into it; #729 of 5,479 by trust here, #1,593 by citations
• Ecology, Evolution, Behavior and Systematics — 19 citations into it
• Statistics and Probability — 16 citations into it
• Plant Science — 13 citations into it
• Insect Science — 11 citations into it
• Artificial Intelligence — 9 citations into it

Whom Erkki Tomppo trusts overall

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

1. Ronald E. McRoberts
2. J. Ohmann
3. V. Lemay
4. Hector Franco-Lopez
5. Matthew J. Gregory
6. Hailemariam Temesgen
7. Jiahua Chen
8. A. Ek
9. H. Mäkelä
10. Eamonn J. Keogh

Who trusts Erkki Tomppo overall

1. Ronald E. McRoberts
2. Steen Magnussen
3. Piermaria Corona
4. Peter Wolter
5. Dengsheng Lu
6. Hooman Latifi
7. Luis Diaz-Balteiro
8. Philip A. Townsend
9. Arne Nothdurft
10. Johannes Breidenbach

Papers from Erkki Tomppo's perspective

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

1. A Coefficient of Agreement for Nominal Scales
2. Imputing tree-lists from aerial attributes for complex stands of south-eastern British Columbia
3. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method
4. Comparison of Nearest Neighbor Methods for Estimating Basal Area and Stems per Hectare Using Aerial Auxiliary Variables (Forestry sciences)
5. Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems
6. Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories
7. Semiparametric Regression: Preface
8. Resampling and exchangeable arrays
9. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery
10. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence

Their papers

• Specialist species of wood‐inhabiting fungi struggle while generalists thrive in fragmented boreal forests (2013) — 247 citations
• Harmonizing national forest inventories (2012) — 89 citations
• Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data (2011) — 72 citations
• Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories (2009) — 76 citations
• Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery (2009) — 84 citations
• Common tree definitions for National Forest Inventories in Europe. (2009) — 127 citations
• Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica (2008) — 48 citations
• Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery (2007) — 214 citations
• Improving the accuracy of multisource forest inventory estimates to reducing plot location error — a multicriteria approach (2001) — 75 citations