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Open AccessArticle

Object-Based Classification Approaches for Multitemporal Identification and Monitoring of Pastures in Agroforestry Regions using Multispectral Unmanned Aerial Vehicle Products

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MARETEC—Marine, Environment and Technology Centre, Instituto Superior Técnico, LARSyS, Universidade de Lisboa, 1049-001 Lisboa, Portugal
2
Terraprima—Serviços Ambientais, Sociedade Unipessoal, Lda, 2135-199 Samora Correia, Portugal
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 814; https://doi.org/10.3390/rs12050814
Received: 2 January 2020 / Revised: 28 February 2020 / Accepted: 29 February 2020 / Published: 3 March 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Sown Biodiverse Pastures (SBP) are the basis of a high-yield grazing system tailored for Mediterranean ecosystems and widely implemented in Southern Portugal. The application of precision farming methods in SBP requires cost-effective monitoring using remote sensing (RS). The main hurdle for the remote monitoring of SBP is the fact that the bulk of the pastures are installed in open Montado agroforestry systems. Sparsely distributed trees cast shadows that hinder the identification of the underlaying pasture using Unmanned Aerial Vehicles (UAV) imagery. Image acquisition in the Spring is made difficult by the presence of flowers that mislead the classification algorithms. Here, we tested multiple procedures for the geographical, object-based image classification (GEOBIA) of SBP, aiming to reduce the effects of tree shadows and flowers in open Montado systems. We used remotely sensed data acquired between November 2017 and May 2018 in three Portuguese farms. We used three machine learning supervised classification algorithms: Random Forests (RF), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). We classified SBP based on: (1) a single-period image for the maximum Normalized Difference Vegetation Index (NDVI) epoch in each of the three farms, and (2) multi-temporal image stacking. RF, SVM and ANN were trained using some visible (red, green and blue bands) and near-infrared (NIR) reflectance bands, plus NDVI and a Digital Surface Model (DSM). We obtained high overall accuracy and kappa index (higher than 79% and 0.60, respectively). The RF algorithm had the highest overall accuracy (more than 92%) for all farms. Multitemporal image classification increased the accuracy of the algorithms. as it helped to correctly identify as SBP the areas covered by tree shadows and flower patches, which would be misclassified using single image classification. This study thus established the first workflow for SBP monitoring based on remotely sensed data, suggesting an operational approach for SBP identification. The workflow can be applied to other types of pastures in agroforestry regions to reduce the effects of shadows and flowering in classification problems. View Full-Text
Keywords: remote sensing; multitemporal classification; machine learning algorithms; land cover classification; tree shadows; flowering remote sensing; multitemporal classification; machine learning algorithms; land cover classification; tree shadows; flowering
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MDPI and ACS Style

Vilar, P.; Morais, T.G.; Rodrigues, N.R.; Gama, I.; Monteiro, M.L.; Domingos, T.; Teixeira, R.F.M. Object-Based Classification Approaches for Multitemporal Identification and Monitoring of Pastures in Agroforestry Regions using Multispectral Unmanned Aerial Vehicle Products. Remote Sens. 2020, 12, 814.

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