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Comparison of methods for the automatic classification of forest habitat types in the Southern Alps—Application to ecological data from the French national forest inventory

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Abstract

The monitoring of habitats at plant association level, has been developed by the French-National Forest Inventory (NFI) progressively since 2011, whereas ecological and floristic data exist since the mid-1980s. The NFI habitat monitoring is the French tool of surveillance of forest habitats decreed by Natura 2000 Directive (article 11). Determination of plant association in NFI plots concerns all the habitats, whether they are of community interest or not. The objective of this study is to compare different methods of automatic classification of floristic and ecological surveys into forest habitat groups. Indeed, enriching the old surveys, which contain only ecological, floristic and trees data, with information on habitats would increase the accuracy of the calculated statistical results on habitats. The uncertainty of the attribution of a habitat outside the field (ex-situ) by experts was quantified by comparison with the determination in the field (in situ). This result was used as a benchmark to compare to the error rates obtained by two methods of automatic classification: an algorithm inspired by the habitat identification key used in the field and Random forest, a learning classification method. The classification performance was evaluated for three levels of habitat groupings. The results showed that the lower the level of clustering, the higher the error rate. Depending on the classification method used and the level of aggregation, the error rates varied between 5 and 15%. In all cases, the error rates were below the estimated uncertainty of the expert attribution of ex-situ habitat.

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Data availability

The data from the forest inventory are available online on the website (the habitat type data is not already at disposal because of verification necessities): https://inventaire-forestier.ign.fr/spip.php?rubrique159. The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The codes used in this study are available from the corresponding author upon request.

References

  • Archaux F, Gosselin F, Bergès L, Chevalier R (2006) Effects of sampling time, species richness and observer on the exhaustiveness of plant censuses. J Veg Sci 17:299–306

    Article  Google Scholar 

  • Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis-a brief tutorial. Inst Signal Inf Process 18(1998):1–8

    Google Scholar 

  • Bissardon M, Guibal L, GIP Aten (2003) CORINE biotopes – Version originale – Type d’habitats français. École nationale du génie rural, des eaux et des forêts (ENGREF), Nancy

  • Bonhême I (2021) La détermination des habitats naturels par l’inventaire forestier: les objectifs et les concepts utilisés. IGN, Institut national de l’information géographique et forestière, Saint-Mandé

  • Bontemps J-D, Denardou A, Hervé J-C et al (2020) Unprecedented pluri-decennial increase in the growing stock of French forests is persistent and dominated by private broadleaved forests. Ann Sci 77:1–20. https://doi.org/10.1007/s13595-020-01003-6

    Article  Google Scholar 

  • Breiman L (2001) Random Forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Breiman L (1999) Random Forests-Random Features. University of California

  • Černá L, Chytrý M (2005) Supervised classification of plant communities with artificial neural networks. J Veg Sci 16:407–414. https://doi.org/10.1111/j.1654-1103.2005.tb02380.x

    Article  Google Scholar 

  • Chirici G, Mura M, McInerney D et al (2016) A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data. Remote Sens Environ 176:282–294. https://doi.org/10.1016/j.rse.2016.02.001

    Article  Google Scholar 

  • Council EC (2006) Council Directive 92/43/EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Off J Eur Union 206:7–50

    Google Scholar 

  • Couvreur J-M, Smits Q, Dufrene M (2015) Evaluation of the “observer effect” in botanical surveys of grasslands. Biotechnol Agron Soc Environ 19:132–142

    Google Scholar 

  • Cutler DR, Edwards TC, Beard KH et al (2007) Random forests for classification in ecology. Ecology 88:2783–2792. https://doi.org/10.1890/07-0539.1

    Article  PubMed  Google Scholar 

  • De Cáceres M, Font X, Vicente P, Oliva F (2009) Numerical reproduction of traditional classifications and automatic vegetation identification. J Veg Sci 20:620–628. https://doi.org/10.1111/j.1654-1103.2009.01081.x

    Article  Google Scholar 

  • Delhaye S, Gattus J-C, Brusten T, et al (2021) Les habitats forestiers des Alpes du Sud

  • Drake JM, Randin C, Guisan A (2006) Modelling ecological niches with support vector machines. J Appl Ecol 43:424–432. https://doi.org/10.1111/j.1365-2664.2006.01141.x

    Article  Google Scholar 

  • Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35:352–359. https://doi.org/10.1016/S1532-0464(03)00034-0

    Article  PubMed  Google Scholar 

  • European Commission DG Environment (2013) Interpretation manual of European Union habitats. Eur 28:1–44

    Google Scholar 

  • Gégout J-C, Coudun C (2012) The right relevé in the right vegetation unit: a new typicality index to reproduce expert judgement with an automatic classification programme. J Veg Sci 23:24–32. https://doi.org/10.1111/j.1654-1103.2011.01337.x

    Article  Google Scholar 

  • Giannetti F, Barbati A, Mancini LD et al (2018) European forest types: toward an automated classification. Ann for Sci 75:1–14. https://doi.org/10.1007/s13595-017-0674-6

    Article  Google Scholar 

  • Glele Kakaï RL, Salako V, Padonou E, Lykke AM (2016) Méthodes statistiques multivariées utilisées en écologie. Annales Des Sciences Agronomiques 20:139–157

    Google Scholar 

  • Guillaume S, Charnomordic B (2011) Learning interpretable fuzzy inference systems with FisPro. Inf Sci 181:4409–4427. https://doi.org/10.1016/j.ins.2011.03.025

    Article  Google Scholar 

  • Guillaume S, Charnomordic B (2012) Fuzzy inference systems: an integrated modelling environment for collaboration between expert knowledge and data using Fispro. Expert Syst Appl 39:8744–8755. https://doi.org/10.1016/j.eswa.2012.01.206

    Article  Google Scholar 

  • Khaneboubi, M (2016) Introduction à Random Forest avec R. http://mehdikhaneboubi.free.fr/random_forest_r.html

  • Kočí M, Chytrý M, Tichý L (2003) Formalized reproduction of an expert-based phytosociological classification: a case study of subalpine tall-forb vegetation. J Veg Sci 14:601–610. https://doi.org/10.1111/j.1654-1103.2003.tb02187.x

    Article  Google Scholar 

  • Legendre P, Legendre L (1998) Numerical Ecology, 2nd edn. Elsevier, Amsterdam

    Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:5

    Google Scholar 

  • Liu C, Zhang L, D CJ et al (2003) Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data. Forest Sci 49:619–631

    Google Scholar 

  • Machado D, Silva S, Curi N, Duarte de Menezes M (2019) Soil type spatial prediction from Random Forest: different training datasets, transferability, accuracy and uncertainty assessment. Scientia Agricola 76:243. https://doi.org/10.1590/1678-992X-2017-0300

    Article  Google Scholar 

  • Maciejewski L, Pinto PE, Wurpillot S et al (2020) Vegetation unit assignments: phytosociology experts and classification programs show similar performance but low convergence. Appl Veg Sci 23:698–709. https://doi.org/10.1111/avsc.12516

    Article  Google Scholar 

  • Mikolajczak A, Maréchal D, Sanz T et al (2015) Modelling spatial distributions of alpine vegetation: A graph theory approach to delineate ecologically-consistent species assemblages. Eco Inform 30:196–202. https://doi.org/10.1016/j.ecoinf.2015.09.005

    Article  Google Scholar 

  • Milberg P, Bergstedt J, Fridman J et al (2008) Observer bias and random variation in vegetation monitoring data. J Veg Sci 19:633–644. https://doi.org/10.3170/2008-8-18423

    Article  Google Scholar 

  • Morrison LW (2016) Observer error in vegetation surveys: a review. J Plant Ecol 9:367–379. https://doi.org/10.1093/jpe/rtv077

    Article  Google Scholar 

  • Mountassir A, Benbrahim H, Berrada I (2012) An empirical study to address the problem of Unbalanced Data Sets in sentiment classification. pp 3298–3303

  • Oliver I, Broese EA, Dillon ML et al (2013) Semi-automated assignment of vegetation survey plots within an a priori classification of vegetation types. Methods Ecol Evol 4:73–81. https://doi.org/10.1111/j.2041-210x.2012.00258.x

    Article  Google Scholar 

  • Otto H-J (1998) Écologie forestière, Institut pour le développement forestier

  • Peters J, De Baets B, Verhoest N et al (2007) Random forests as a tool for ecohydrological distribution modelling. Ecol Model 207:304–318. https://doi.org/10.1016/j.ecolmodel.2007.05.011

    Article  Google Scholar 

  • Pouteau R, Meyer J-Y, Taputuarai R, Stoll B (2012) Support vector machines to map rare and endangered native plants in Pacific islands forests. Eco Inform 9:37–46. https://doi.org/10.1016/j.ecoinf.2012.03.003

    Article  Google Scholar 

  • R Core Team (2020) R: A Language and Environment for Statistical Computing

  • Rameau J-C, Gauberville C, Drapier N (2000) Gestion forestière et diversité biologique: Identification et gestion intégrée des habitats et espèces d’intérêt communautaire, Volume 2, France, domaine continental. CNPF-IDF, Paris

  • Renaux B, Timbal J, Guberville C, et al (2019a) Contribution au prodrome des végétations de France : Déclinaison des classes forestières françaises issues des Querco roboris-Fagetea sylvaticae Braun-Blanq. & Viegler 1937, concepts, historique et méthode ; Quercetea pubescentis et Quercetea robori-petraeae. Documents Phytosociologiques

  • Renaux B, Timbal J, Guberville C, et al (2019b) Contribution au Prodrome des végétations de France : les Carpino betuli-Fagetea sylvaticae Jakucs 1967. Documents Phytosociologiques

  • Siraj-Ud-Doula M, Alam MA (2018) Ecological Data Analysis Based on Machine Learning Algorithms

  • Stevens JP, Blackstock TH, Howe EA, Stevens DP (2004) Repeatability of phase 1 habitat survey. J Environ Manage 73:53–59. https://doi.org/10.1016/j.jenvman.2004.05.009

    Article  CAS  PubMed  Google Scholar 

  • Thébaud G, Bernard C-E (2017) Contribution au prodrome de végétations de France : les forêts de conifères circumboréales ou montagnardes sur sols acides des classes des Vaccinio-Piceetea Braun-Blanq. in Braun-Blanq. et al. 1939 des Junipero-Pinetea sylvestris Rivas-Mart. 1965 et des Roso pendulinae-Pinetea mugo Theurillat in Theurillat et al. 1995. Documents phytosociologiques pp 284–421

  • Thessen A (2016) Adoption of machine learning techniques in ecology and earth science. One Ecosyst. https://doi.org/10.3897/oneeco.1.e8621

    Article  Google Scholar 

  • Tichý L, Chytrý M, Botta-Dukát Z (2014) Semi-supervised classification of vegetation: preserving the good old units and searching for new ones. J Veg Sci 25:1504–1512. https://doi.org/10.1111/jvs.12193

    Article  Google Scholar 

  • Vittoz P, Guisan A (2007) How reliable is the monitoring of permanent vegetation plots? A test with multiple observers. J Veg Sci 18:413–422. https://doi.org/10.1111/j.1654-1103.2007.tb02553.x

    Article  Google Scholar 

  • Willner W (2011) Unambiguous assignment of relevés to vegetation units: the example of the Festuco-Brometea and Trifolio-Geranietea sanguinei. Tuexenia 31:271–282

    Google Scholar 

  • Ye J, Janardan R, Li Q (2004) Two-Dimensional Linear Discriminant Analysis.

Download references

Acknowledgements

We would like to thank the forest inventory teams who collected the data on which we were able to work, as well as the team leaders and the ecologist auditor (Desiderio C., Delhaye S., Salmon-Legagneur I., Pietri V., Benoit-De-Coignac S.) who participated in the ex-situ plot classification survey. Thanks also to Dalmasso M. for her help and for the forms that allowed the survey to be carried out. We also thank the lecturers from Bordeaux Sciences Agro and Montpellier SupAgro (Bombrun L., Brunel G., Jones H., Fontez B.) for their precious help in the field of classification and statistics. Finally, we thank Gow M.-V., Cuny H. and Dassot M. for their careful English language review.

Funding

The internship was financed by the National Institute of Geographic and Forestry Information (IGN). The collection of habitat data is financed by the Ministry of Ecological Transition. The collection of all other forest inventory data is financed by the National Institute of Geographic and Forestry Information (IGN) supported by the Ministries of Agriculture and Alimentation and Ecological Transition.

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Authors and Affiliations

  1. IGN (Institut National de l’information géographique et forestière), Saint-Médard-en-Jalles, France

    Charlotte Labit & Ingrid Bonhême

  2. Bordeaux Sciences Agro, Gradignan, France

    Charlotte Labit

  3. IGN (Institut National de l’information géographique et forestière), Aix-en-Provence, France

    Sébastien Delhaye

Authors
  1. Charlotte Labit
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  2. Ingrid Bonhême
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Contributions

Three authors participated in the elaboration of this paper: CL, IB and SD. All authors contributed to the conception and design of the study and to the extraction of data from the national forest inventory database. In addition, SD participated in the field data collection and in the elaboration of the identification key for forest habitats in the Alpine region. The data analysis and automatic classification steps were carried out by CL and all authors participated in the interpretation of the results. The first version of the manuscript was written by CL, with corrections by IB and SD. All authors have read and approved the final manuscript.

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Correspondence to Charlotte Labit, Ingrid Bonhême or Sébastien Delhaye.

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The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

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Communicated by Daniel Sanchez Mata.

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Supplementary Information

Below is the link to the electronic supplementary material.

10531_2022_2487_MOESM1_ESM.pptx

Supplementary file1 Continuation of the structure of key inspired algorithm to classify more plots previously unclassified (PPTX 45 kb)

10531_2022_2487_MOESM2_ESM.pptx

Supplementary file2 (PPTX 43 kb) Variables identified as most discriminating in the classification into two habitat groups by Random forest (PPTX 43 kb)

10531_2022_2487_MOESM3_ESM.pptx

Supplementary file3 Correspondence between the different levels of the phytosociological classification and the different grouping levels used in the study (PPTX 55 kb)

10531_2022_2487_MOESM4_ESM.pptx

Supplementary file4 Ecological variables used for classification by field operators (a), key inspired algorithm (b) and Random forest classification (c) (PPTX 63 kb)

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Labit, C., Bonhême, I. & Delhaye, S. Comparison of methods for the automatic classification of forest habitat types in the Southern Alps—Application to ecological data from the French national forest inventory. Biodivers Conserv 31, 3257–3283 (2022). https://doi.org/10.1007/s10531-022-02487-6

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