Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.

  1. Garcia-Hidalgo, Miguel
  2. Blázquez-Casado, Ángela
  3. Águeda, Beatriz
  4. Rodriguez, Francisco
Revista:
Forest systems

ISSN: 2171-5068

Año de publicación: 2018

Volumen: 27

Número: 3

Tipo: Artículo

DOI: 10.5424/FS/2018273-13686 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Forest systems

Resumen

Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde stand types.Area of study: 1500 ha forest in Monteverde, North Tenerife, Canary Islands.Material and methods: RF, SVML, SVMR and ANN algorithms are used to classify the three Monteverde stand types.  Before training the model, feature selection of LiDAR, orthophotography, and Sentinel-2 data through VSURF was carried out.  Comparison of its accuracy was performed.Main results: Five LiDAR variables were found to be the most efficient for classifying each object, while only one Sentinel-2 index and one Sentinel-2 band was valuable.  Additionally, standard deviation and mean of the Red orthophotography colour band, and ratio between Red and Green bands were also found to be suitable.  SVML is confirmed as the most accurate algorithm (0.904, 0.041 SD) while ANN showed the lowest value of 0.891 (0.073 SD).  SVMR and RF obtain 0.902 (0.060 SD) and 0.904 (0.056 SD) respectively.  SVML was found to be the best method given its low standard deviation.Research highlights: The similar high accuracy values among models confirm the importance of taking into account diverse machine-learning methods for stand types classification purposes and different explanatory variables.  Although differences between errors may not seem relevant at a first glance, due to the limited size of the study area with only three plus two categories, such differences could be highly important when working at large scales with more stand types.ADDITIONAL KEY WORDSRF algorithm, SVML algorithm, SVMR algorithm, ANN algorithm, LiDAR, orthophotography, Sentinel-2ABBREVIATIONS USEDANN, artificial neural networks algorithm; Band04, Sentinel-2 band 04 image data; BR, brezal; DTHM, digital tree height model; DTHM-2016, digital tree height model based on 2016 LiDAR data; DTM, digital terrain model; DTM-2016, digital terrain model based on 2016 LiDAR data; FBA, fayal-brezal-acebiñal; FCC, canopy cover; HEIGHT-2009, maximum height based on 2009 LiDAR data; HGR, height growth based on 2009 and 2016 LiDAR data; LA, laurisilva; NDVI705, Sentinel-2 index image data; NMF, non-Monteverde forest; NMG, non-Monteverde ground; P95-2016, height percentile 95 based on 2016 LiDAR data; RATIO R/G, ratio between Red and Green bands orthophotograph data; RED, Red band orthophotograph data; Red-SD, standard deviation of the Red band orthophotograph data; RF, random forest algorithm; SVM, support vector machine algorithm; SVML, linear support vector machine algorithm; SVMR, radial support vector machine algorithm; VSURF, variable selection using random forest. 

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Información de financiación

Research by Ángela Blázquez-Casado was funded by DI-14-06953 fellowship (Ministerio de Economía, Industria y Competitividad, Spanish Government). Research by Beatriz Águeda was funded by PTQ-16-08411 fellowship (Ministerio de Economía, Industria y Competitividad, Spanish Government).The Authors wish to thank the Cabildo de Tenerife staff for their invaluable help on the fieldwork. LiDAR data was provided by CNIG-PNOA (SpanishGovernment), who also provided orthophotographic images. Sentinel images were provided by ESA (European Space Agency). Tecnosylva provided support to the authors in the image segmentationprocess. We also thank Adam Collins for the English language advice.

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Referencias bibliográficas

  • Arozena ME, Panareda, JM, 2013. Forest transition and biogeographic meaning of the current laurel forest landscape in Canary Islands, Spain. Physical Geography 34: 211-235. https://doi.org/10.1080/02723646.2013.817181
  • Genuer R, Poggi JM, Tuleau-Malot C, 2015. VSURF: An R Package for Variable Selection Using Random Forests. https://journal.r-project.org/archive/2015/RJ-2015-018/RJ-2015-018.pdf.
  • Henrich V, Krauss G, Götze C, Sandow C, 2012. IDB - https://www.indexdatabase.de/, Entwicklung einer Datenbank für Fernerkundungsindizes. AK Fernerkundung, Bochum 45: 10.
  • Kuhn M, Jed Wing A, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, et al., 2018. caret: Classification and Regression TrainingR package version 6.0-78. https://cran.r-project.org/web/packages/caret/index.html.
  • McGaughey R, 2007. Fusion/LDV: Software for Lidar Data Analysis and Visualization; USDA Forest Service, Pacific Northwest Research Station: Portland, OR, USA.
  • Nitze I, Schulthess U, Asche H, 2012. Comparison of machine learning algorithms Random Forest, Artificial Neural Network and Support Vector Machine to maximum likelihood for supervised crop type classification. Proc GEOBIA, Rio de Janeiro (Brazil), May 7-9, pp: 35.
  • OTB Development Team, 2017. The ORFEO Tool Box Software Guide. Updated for OTB-5.10.0
  • QGIS Development Team, 2017. QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://www.qgis.org/en/site/
  • Sinergise, 2018. Sentinel 2 EO products. https://www.sentinel-hub.com/develop/documentation/eo_products/Sentinel2EOproducts.
  • Valbuena R, Maltamo M, Packalen P, 2016. Classification of forest development stages from national low-density LiDAR datasets: a comparison of machine learning methods. Revista de Teledetección 45: 15-25. https://doi.org/10.4995/raet.2016.4029
  • Vega Isuhuaylas LA, Hirata Y, Ventura Santos LC, Serrudo Torobeo N, 2018. Natural forest mapping in the Andes (Peru): a comparison of the performance of machine-learning algorithms. Remote Sens 10: 782. https://doi.org/10.3390/rs10050782
  • Xu C, Manley B, Morgenroth J, 2018. Evaluation of modelling approaches in predicting forest volume and stand age for small-scale plantation forests in New Zealand with RapidEye and LiDAR. Int J Appl Earth Obs Geoinformation 73: 386-396. https://doi.org/10.1016/j.jag.2018.06.021
  • Zhu XX, Tuia D, Mou L, Xia GS, Zhang L, Xu F, Fraundorfer F, 2017. Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources. IEEE Geosci Remote Sens Mag 5: 8-36. https://doi.org/10.1109/MGRS.2017.2762307
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