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@PhdThesis{Monnet11c,
  Title                    = {Using airborne laser scanning for mountain forests mapping: support vector regression for stand parameters estimation and unsupervised training for treetop detection.},
  Author                   = {Monnet, Jean-Matthieu},
  School                   = {Université de Grenoble},
  Year                     = {2011},

  Abstract                 = {Numerous studies have shown the potential of airborne laser scanning for the mapping of forest resources. However, the application of this remote sensing technique to complex forests encountered in mountainous areas requires further investigation. In this thesis, the two main methods used to derive forest information are tested with airborne laser scanning data acquired in the French Alps, and adapted to the constraints of mountainous environments. In particular, a framework for unsupervised training of treetop detection is proposed, and the performance of support vector regression combined with dimension reduction for forest stand parameters estimation is evaluated.},
  Keywords                 = {Remote sensing, airborne laser scanning, LiDAR, forest mapping, support vector regression, unsupervised training, treetop detection},
  Owner                    = {jimbo},
  Timestamp                = {2014.05.15},
  Url                      = {http://tel.archives-ouvertes.fr/tel-00652698/fr/}
}

@Article{Monnet2014,
  Title                    = {Cross-Correlation of Diameter Measures for the Co-Registration of Forest Inventory Plots with Airborne Laser Scanning Data},
  Author                   = {Monnet, Jean-Matthieu and Mermin, Éric},
  Journal                  = {Forests},
  Year                     = {2014},
  Number                   = {9},
  Pages                    = {2307--2326},
  Volume                   = {5},

  Doi                      = {10.3390/f5092307},
  ISSN                     = {1999-4907},
  Owner                    = {jean-matthieu},
  Timestamp                = {2018.05.04},
  Url                      = {http://www.mdpi.com/1999-4907/5/9/2307}
}

@inproceedings{Monnet10,
  TITLE = {{Tree top detection using local maxima filtering: a parameter sensitivity analysis}},
  AUTHOR = {Monnet, Jean-Matthieu and Mermin, Eric and Chanussot, Jocelyn and Berger, Fr{\'e}d{\'e}ric},
  URL = {https://hal.archives-ouvertes.fr/hal-00523245},
  BOOKTITLE = {{10th International Conference on LiDAR Applications for Assessing Forest Ecosystems (Silvilaser 2010)}},
  ADDRESS = {Freiburg, Germany},
  HAL_LOCAL_REFERENCE = {D{\'e}partement Images et Signal},
  PAGES = {9 p.},
  YEAR = {2010},
  MONTH = Sep,
  KEYWORDS = {DETECTION ; ANALYSE DE SENSIBILITE ; LASER ; FORET ; CANOPEE ; LIDAR DETECTION ; ALGORITHME DE DETECTION ; TREE DETECTION},
  PDF = {https://hal.archives-ouvertes.fr/hal-00523245/file/GR2010-PUB00029356.pdf},
  HAL_ID = {hal-00523245},
  HAL_VERSION = {v1},
}

@article{Eysn15, 
  title={A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space}, 
  volume={6}, 
ISSN={1999-4907}, 
  url={http://dx.doi.org/10.3390/f6051721}, 
  DOI={10.3390/f6051721}, 
  number={12}, 
  journal={Forests}, 
  publisher={MDPI AG}, 
  author={Eysn, Lothar and Hollaus, Markus and Lindberg, Eva and Berger, Frédéric and Monnet, Jean-Matthieu and Dalponte, Michele and Kobal, Milan and Pellegrini, Marco and Lingua, Emanuele and Mongus, Domen and et al.}, 
  year={2015}, 
  month={May}, 
  pages={1721–1747}
  }
  
@article{Glad20,
  author = {Glad, Anouk and Reineking, Björn and Montadert, Marc and Depraz, Alexandra and Monnet, Jean-Matthieu},
  title = {Assessing the performance of object-oriented LiDAR predictors for forest bird habitat suitability modeling},
  journal = {Remote Sensing in Ecology and Conservation},
  volume = {6},
  number = {1},
  pages = {5-19},
  keywords = {Habitat suitability models, LiDAR, object-oriented metrics, point-cloud area-based metrics},
  doi = {10.1002/rse2.117},
  url = {https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.117},
  eprint = {https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.117},
  abstract = {Abstract Habitat suitability models (HSMs) are widely used to plan actions for species of conservation interest. Models that will be turned into conservation actions need predictors that are both ecologically pertinent and fit managers’ conceptual view of ecosystems. Remote sensing technologies such as light detection and ranging (LiDAR) can describe landscapes at high resolution over large spatial areas and have already given promising results for modeling forest species distributions. The point-cloud (PC) area-based LiDAR variables are often used as environmental variables in HSMs and have more recently been complemented by object-oriented (OO) metrics. However, the efficiency of each type of variable to capture structural information on forest bird habitat has not yet been compared. We tested two hypotheses: (1) the use of OO variables in HSMs will give similar performance as PC area-based models; and (2) OO variables will improve model robustness to LiDAR datasets acquired at different times for the same area. Using the case of a locally endangered forest bird, the capercaillie (Tetrao urogallus), model performance and predictions were compared between the two variable types. Models using OO variables showed slightly lower discriminatory performance than PC area-based models (average ΔAUC = −0.032 and −0.01 for females and males, respectively). OO-based models were as robust (absolute difference in Spearman rank correlation of predictions ≤ 0.21) or more robust than PC area-based models. In sum, LiDAR-derived PC area-based metrics and OO metrics showed similar performance for modeling the distribution of the capercaillie. We encourage the further exploration of OO metrics for creating reliable HSMs, and in particular testing whether they might help improve the scientist–stakeholder interface through better interpretability.},
  year = {2020}
}

@article{BALTSAVIAS1999199,
title = {Airborne laser scanning: basic relations and formulas},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {54},
number = {2},
pages = {199-214},
year = {1999},
issn = {0924-2716},
doi = {10.1016/S0924-2716(99)00015-5},
url = {https://www.sciencedirect.com/science/article/pii/S0924271699000155},
author = {E.P Baltsavias},
keywords = {Airborne laser scanning, Terminology, Basic relations, Formulas, 3D accuracy analysis},
abstract = {An overview of basic relations and formulas concerning airborne laser scanning is given. They are divided into two main parts, the first treating lasers and laser ranging, and the second one referring to airborne laser scanning. A separate discussion is devoted to the accuracy of 3D positioning and the factors influencing it. Examples are given for most relations, using typical values for ALS and assuming an airplane platform. The relations refer mostly to pulse lasers, but CW lasers are also treated. Different scan patterns, especially parallel lines, are treated. Due to the complexity of the relations, some formulas represent approximations or are based on assumptions like constant flying speed, vertical scan, etc.}
}

@InProceedings{Axelsson00,
  Title                    = {{DEM} generation from laser scanner data using adaptive {TIN} models},
  Author                   = {Axelsson, P.},
  Booktitle                = {XIXth ISPRS Congress, IAPRS},
  Year                     = {2000},
  Pages                    = {110--117},
  Volume                   = {XXXIII},
}