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

Forest Systems

View Publication Info
Field Value
Title Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.
Creator Garcia-Hidalgo, Miguel
Blázquez-Casado, Ángela
Águeda, Beatriz
Rodriguez, Francisco
Subject RF algorithm; SVML algorithm; SVMR algorithm; ANN algorithm; LiDAR; orthophotography; Sentinel-2
Description 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. 
Publisher INIA
Contributor Ángela Blázquez-Casado, Ministerio de Economía, Industria y Competitividad, Spanish Government
Beatriz Águeda, Ministerio de Economía, Industria y Competitividad, Spanish Government.
Date 2018-12-19
Type info:eu-repo/semantics/article
Format application/pdf
Source Forest Systems; Vol 27, No 3 (2018); eSC03
Forest Systems; Vol 27, No 3 (2018); eSC03
Language eng
Rights info:eu-repo/semantics/openAccess
Copyright (c) 2018 Forest Systems

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us


Copyright © 2015-2018 Simon Fraser University Library