Barbara Koch
Environmental Engineering · 2007–2020
A researcher in the Science corpus: 8 papers; trusts 43 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 45 citations into it; #2,566 of 5,479 by trust here, #1,912 by citations
- Nature and Landscape Conservation — 38 citations into it; #3,952 of 6,941 by trust here, #4,376 by citations
- Ecology — 36 citations into it; #8,204 of 10,717 by trust here, #6,853 by citations
- Insect Science — 22 citations into it; #1,159 of 2,259 by trust here, #1,460 by citations
- Global and Planetary Change — 21 citations into it
- Statistics and Probability — 17 citations into it
- Artificial Intelligence — 15 citations into it
- Analytical Chemistry — 10 citations into it
Whom Barbara Koch trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- N. Crookston
- Arne Nothdurft
- Matti Maltamo
- Trevor J. Hastie
- Erik Næsset
- Andrew Thomas Hudak
- A. R. Stage
- J. Ohmann
- Johannes Breidenbach
- J. Cadima
Who trusts Barbara Koch overall
Papers from Barbara Koch's perspective
Ordered by how strongly the researchers Barbara Koch trusts endorse each paper. Same corpus, a different order for every researcher.
- Random Forests
- Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A.
- Partitioning Error Components for Accuracy-Assessment of Near-Neighbor Methods of Imputation (Forestry sciences)
- Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method
- Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data
- Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics
- Comparison of Nearest Neighbor Methods for Estimating Basal Area and Stems per Hectare Using Aerial Auxiliary Variables (Forestry sciences)
- Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data
- Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data
- A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments (IEEE Transactions on Geoscience and Remote Sensing)
Their papers
- Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks (2020) — 334 citations
- A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales (2014) — 348 citations
- Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass (2014) — 413 citations
- Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality (2014) — 150 citations
- Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands (2012) — 50 citations
- Comparative testing of single-tree detection algorithms under different types of forest (2011) — 390 citations
- Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors (2010) — 220 citations
- Agricultural land abandonment and natural forest re-growth in the Swiss mountains: A spatially explicit economic analysis (2007) — 524 citations