Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data

[1]  P. Mahalanobis On the generalized distance in statistics , 1936 .

[2]  R Daubenmire,et al.  Vegetation: identification of typal communities. , 1966, Science.

[3]  R. J. Pike,et al.  Elevation-Relief Ratio, Hypsometric Integral, and Geomorphic Area-Altitude Analysis , 1971 .

[4]  Albert R. Stage,et al.  An Expression for the Effect of Aspect, Slope, and Habitat Type on Tree Growth , 1976 .

[5]  R. Colwell Remote sensing of the environment , 1980, Nature.

[6]  John M. Chambers,et al.  Graphical Methods for Data Analysis , 1983 .

[7]  Beat Kleiner,et al.  Graphical Methods for Data Analysis , 1983 .

[8]  M. Hill,et al.  Data analysis in community and landscape ecology , 1987 .

[9]  E. Tomppo,et al.  Satellite image-based national forest inventory of finland for publication in the igarss'91 digest , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[10]  S. Running,et al.  Forest ecosystem processes at the watershed scale: Sensitivity to remotely-sensed leaf area index estimates , 1993 .

[11]  A. O. Nicholls,et al.  Determining species response functions to an environmental gradient by means of a β‐function , 1994 .

[12]  Albert R. Stage,et al.  Most Similar Neighbor: An Improved Sampling Inference Procedure for Natural Resource Planning , 1995, Forest Science.

[13]  M. Nilsson Estimation of forest variables using satellite image data and airborne Lidar , 1997 .

[14]  J. Means Use of Large-Footprint Scanning Airborne Lidar To Estimate Forest Stand Characteristics in the Western Cascades of Oregon , 1999 .

[15]  W. Cohen,et al.  Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA , 1999 .

[16]  W. Cohen,et al.  Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .

[17]  J. Means,et al.  Predicting forest stand characteristics with airborne scanning lidar , 2000 .

[18]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[19]  W. Cohen,et al.  An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon , 2001 .

[20]  Matti Maltamo,et al.  The Most Similar Neighbour reference in the yield prediction of Pinus kesiya stands in Zambia , 2001 .

[21]  M. Lefsky,et al.  Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests , 2001 .

[22]  W. Cohen,et al.  Lidar remote sensing of above‐ground biomass in three biomes , 2002 .

[23]  E. Tomppo,et al.  Selecting estimation parameters for the Finnish multisource National Forest Inventory , 2001 .

[24]  M. Bauer,et al.  Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method , 2001 .

[25]  M. Maltamo,et al.  Forest stand characteristics estimation using a most similar neighbor approach and image spatial structure information , 2001 .

[26]  Janet L. Ohmann,et al.  Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A. , 2002 .

[27]  R. Dubayah,et al.  Estimation of tropical forest structural characteristics using large-footprint lidar , 2002 .

[28]  R. Dubayah,et al.  Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest , 2002 .

[29]  Ronald E. McRoberts,et al.  Stratified estimation of forest area using satellite imagery, inventory data, and the k-Nearest Neighbors technique , 2002 .

[30]  Mats Nilsson,et al.  Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass , 2002 .

[31]  Tomas Brandtberg Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America , 2003 .

[32]  Hailemariam Temesgen,et al.  Imputing tree-lists from aerial attributes for complex stands of south-eastern British Columbia , 2003 .

[33]  A. Pekkarinen,et al.  Local radiometric correction of digital aerial photographs for multi source forest inventory , 2004 .

[34]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[35]  Erkki Tomppo,et al.  Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: a genetic algorithm approach , 2004 .

[36]  Andrew P. Robinson,et al.  The relationship between effective plant area index and Landsat spectral response across elevation, solar insolation, and spatial scales in a northern Idaho forest , 2004 .

[37]  A. Pekkarinen,et al.  Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data , 2004 .

[38]  Hailemariam Temesgen,et al.  Comparison of Nearest Neighbor Methods for Estimating Basal Area and Stems per Hectare Using Aerial Auxiliary Variables , 2005, Forest Science.

[39]  G. A. Blackburn,et al.  Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi‐spectral remotely sensed data , 2005 .

[40]  S. Reutebuch,et al.  Light detection and ranging (LIDAR): an emerging tool for multiple resource inventory. , 2005 .

[41]  W. Cohen,et al.  Patterns of covariance between forest stand and canopy structure in the Pacific Northwest , 2005 .

[42]  W. Cohen,et al.  Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest , 2005 .

[43]  D. Roberts,et al.  Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales , 2005 .

[44]  P. Gessler,et al.  Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data , 2006 .

[45]  Erkki Tomppo,et al.  Model-based prediction error uncertainty estimation for k-nn method , 2006 .

[46]  M. Maltamo,et al.  Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data , 2006 .

[47]  Nicholas L. Crookston,et al.  Partitioning error components for accuracy-assessment of near-neighbor methods of imputation , 2007 .

[48]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[49]  Andrew Thomas Hudak,et al.  A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Nicholas L. Crookston,et al.  Aggregating pixel-level basal area predictions derived from LiDAR data to industrial forest stands in North-Central Idaho , 2008 .

[51]  Nicholas L. Crookston,et al.  yaImpute: An R Package for kNN Imputation , 2008 .