Peter Wolter

Environmental Engineering · 2009–2011

A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Environmental Engineering — 19 citations into it; #3,159 of 5,479 by trust here, #4,337 by citations
• Nature and Landscape Conservation — 16 citations into it; #3,801 of 6,941 by trust here, #6,048 by citations
• Ecology — 15 citations into it; #5,790 of 10,717 by trust here, #9,389 by citations
• Global and Planetary Change — 9 citations into it

Whom Peter Wolter trusts overall

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

1. Ronald E. McRoberts
2. Allan Aasbjerg Nielsen
3. Johan Holmgren
4. Hector Franco-Lopez
5. James A. Hanley
6. Erkki Tomppo
7. Andrew Thomas Hudak
8. A. Ek
9. Gordon Bonan
10. Philip A. Townsend

Who trusts Peter Wolter overall

1. Philip A. Townsend
2. Ali Shamsoddini
3. Jochem Verrelst
4. John C. Trinder
5. Aditya Singh
6. Gustau Camps-Valls

Papers from Peter Wolter's perspective

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

1. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method
2. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery
3. Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories
4. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning
5. National-Scale Biomass Estimators for United States Tree Species (Forestry sciences)
6. Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery
7. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data
8. An improved strategy for regression of biophysical variables and Landsat ETM+ data.
9. Large area forest classification and biophysical parameter estimation using the 5-Scale canopy reflectance model in Multiple-Forward-Mode
10. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003) (IEEE Transactions on Geoscience and Remote Sensing)

Their papers

• Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota (2011)
• Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data (2009) — 141 citations