Johannes Breidenbach

Environmental Engineering · 2008–2020

A researcher in the Science corpus: 7 papers; trusts 20 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 45 citations into it; #1,363 of 5,479 by trust here, #1,477 by citations
• Nature and Landscape Conservation — 36 citations into it; #1,668 of 6,941 by trust here, #2,835 by citations
• Insect Science — 23 citations into it; #893 of 2,259 by trust here, #855 by citations
• Ecology — 20 citations into it
• Statistics and Probability — 17 citations into it
• Artificial Intelligence — 6 citations into it
• Economics and Econometrics — 5 citations into it

Whom Johannes Breidenbach trusts overall

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

1. Erik Næsset
2. N. Crookston
3. Torsten Hothorn
4. Chao Chen
5. Marco Heurich
6. Matti Maltamo
7. Leo Breiman
8. Andy Liaw
9. Arne Nothdurft
10. Ronald E. McRoberts

Who trusts Johannes Breidenbach overall

1. Hooman Latifi
2. Petteri Packalen
3. Matti Maltamo
4. Arne Nothdurft
5. Johan Holmgren
6. Jari Vauhkonen
7. Zhen Zhen
8. Barbara Koch
9. Gerald Kändler
10. Erik Næsset

Papers from Johannes Breidenbach's perspective

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

1. Random Forests
2. Modern Applied Statistics With S
3. Combining ALS and NFI training data for forest management planning: a case study in Kuortane, Western Finland (European Journal of Forest Research)
4. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data
5. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data
6. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser
7. Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories
8. DEM Generation from Laser Scanner Data Using Adaptive TIN Models
9. Partitioning Error Components for Accuracy-Assessment of Near-Neighbor Methods of Imputation (Forestry sciences)
10. Using Random Forest to Learn Imbalanced Data

Their papers

• National mapping and estimation of forest area by dominant tree species using Sentinel-2 data (2020) — 70 citations
• Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data (2020) — 49 citations
• Unit-level and area-level small area estimation under heteroscedasticity using digital aerial photogrammetry data (2018) — 48 citations
• Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data (2010) — 71 citations
• Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data (2010) — 271 citations
• Spatial prediction of forest stand variables (2009) — 48 citations
• Estimation of diameter distributions by means of airborne laser scanner data (2008) — 48 citations