Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain

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Title: Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain
Authors: García Ferrández, Miguel Antonio | Moutahir, Hassane | Casady, Grant M. | Bautista, Susana
Research Group/s: Análisis de Datos y Modelización de Procesos en Biología y Geociencias | Gestión de Ecosistemas y de la Biodiversidad (GEB)
Center, Department or Service: Universidad de Alicante. Departamento de Matemática Aplicada | Universidad de Alicante. Departamento de Ecología | Universidad de Alicante. Instituto Multidisciplinar para el Estudio del Medio "Ramón Margalef"
Keywords: MODIS | NDVI | HMM | Greenbrown | TIMESAT
Knowledge Area: Matemática Aplicada | Ecología
Issue Date: 2-Mar-2019
Publisher: MDPI
Citation: García MA, Moutahir H, Casady GM, Bautista S, Rodríguez F. Using Hidden Markov Models for Land Surface Phenology: An Evaluation Across a Range of Land Cover Types in Southeast Spain. Remote Sensing. 2019; 11(5):507. doi:10.3390/rs11050507
Abstract: Land Surface Phenology (LSP) metrics are increasingly being used as indicators of climate change impacts in ecosystems. For this purpose, it is necessary to use methods that can be applied to large areas with different types of vegetation, including vulnerable semiarid ecosystems that exhibit high spatial variability and low signal-to-noise ratio in seasonality. In this work, we evaluated the use of hidden Markov models (HMM) to extract phenological parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI). We analyzed NDVI time-series data for the period 2000–2018 across a range of land cover types in Southeast Spain, including rice croplands, shrublands, mixed pine forests, and semiarid steppes. Start of Season (SOS) and End of Season (EOS) metrics derived from HMM were compared with those obtained using well-established smoothing methods. When a clear and consistent seasonal variation was present, as was the case in the rice croplands, and when adjusting average curves, the smoothing methods performed as well as expected, with HMM providing consistent results. When spatial variability was high and seasonality was less clearly defined, as in the semiarid shrublands and steppe, the performance of the smoothing methods degraded. In these cases, the results from HMM were also less consistent, yet they were able to provide pixel-wise estimations of the metrics even when comparison methods did not.
Sponsor: This research was funded by Ministerio de Economía y Competitividad grant numbers CGL2017-89804-R, CGL2014-59074-R, and CGL2015-69773-C2-1-P.
URI: http://hdl.handle.net/10045/89090
ISSN: 2072-4292
DOI: 10.3390/rs11050507
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/rs11050507
Appears in Collections:INV - ADMPBG - Artículos de Revistas
INV - GEB - Artículos de Revistas

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