Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data
暂无分享,去创建一个
Petteri Packalen | Matti Maltamo | Timo Lähivaara | Aku Seppänen | Petri Varvia | M. Maltamo | P. Packalen | P. Varvia | A. Seppänen | T. Lähivaara
[1] Göran Ståhl,et al. Model-assisted estimation of biomass in a LiDAR sample survey in Hedmark County, NorwayThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time. , 2011 .
[2] Yong Pang,et al. Characterizing forest canopy structure with lidar composite metrics and machine learning , 2011 .
[3] Sébastien Da Veiga,et al. Gaussian process modeling with inequality constraints , 2012 .
[4] E. Næsset. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser , 2004 .
[5] E. Næsset,et al. Forestry Applications of Airborne Laser Scanning , 2014, Managing Forest Ecosystems.
[6] S. Popescu,et al. Lidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers , 2009 .
[7] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[8] Virpi Junttila,et al. Sparse Bayesian Estimation of Forest Stand Characteristics from Airborne Laser Scanning , 2008 .
[9] Andrew Gordon Wilson,et al. Deep Kernel Learning , 2015, AISTATS.
[10] Petteri Packalen,et al. Comparing nearest neighbor configurations in the prediction of species-specific diameter distributions , 2018, Annals of Forest Science.
[11] E. Næsset,et al. Using pre-classification to improve the accuracy of species-specific forest attribute estimates from airborne laser scanner data and aerial images , 2014 .
[12] Rajnish Chauhan,et al. Light Detection and Ranging , 2017, Encyclopedia of GIS.
[13] Petteri Packalen,et al. Uncertainty Quantification in ALS-Based Species-Specific Growing Stock Volume Estimation , 2017, IEEE Transactions on Geoscience and Remote Sensing.
[14] M. Maltamo,et al. A Two Stage Method to Estimate Species-specific Growing Stock , 2009 .
[15] S. Magnussen,et al. Alternative mean-squared error estimators for synthetic estimators of domain means , 2016 .
[16] M. Maltamo,et al. Variable selection strategies for nearest neighbor imputation methods used in remote sensing based forest inventory , 2012 .
[17] M. Maltamo,et al. The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs , 2007 .
[18] S. Reutebuch,et al. Light detection and ranging (LIDAR): an emerging tool for multiple resource inventory. , 2005 .
[19] Mark J. Schervish,et al. Nonstationary Covariance Functions for Gaussian Process Regression , 2003, NIPS.
[20] R. Nelson,et al. Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway , 2011 .
[21] Andrew Gordon Wilson,et al. Gaussian Process Kernels for Pattern Discovery and Extrapolation , 2013, ICML.
[22] M. Maltamo,et al. Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data , 2008 .
[23] E. Næsset. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .
[24] William C. Horrace,et al. Some results on the multivariate truncated normal distribution , 2005 .
[25] José Cristóbal Riquelme Santos,et al. A Preliminary Study of the Suitability of Deep Learning to Improve LiDAR-Derived Biomass Estimation , 2016, HAIS.
[26] Virpi Junttila,et al. Linear Models for Airborne-Laser-Scanning-Based Operational Forest Inventory With Small Field Sample Size and Highly Correlated LiDAR Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[27] Thomas B. Schön,et al. Linearly constrained Gaussian processes , 2017, NIPS.
[28] Daniel Mandallaz,et al. Sampling Techniques for Forest Inventories , 2007 .
[29] E. Næsset,et al. Remote sensing and forest inventories in Nordic countries – roadmap for the future , 2018 .
[30] Geoffrey E. Hinton,et al. Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes , 2007, NIPS.
[31] John B. Bradford,et al. Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[32] Edwin V. Bonilla,et al. Multi-task Gaussian Process Prediction , 2007, NIPS.
[33] Mikko Kolehmainen,et al. Neural Networks for the Prediction of Species-Specific Plot Volumes Using Airborne Laser Scanning and Aerial Photographs , 2010, IEEE Transactions on Geoscience and Remote Sensing.