Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess <i>Pinus pinaster</i> Aiton. Forest Defoliation in South-Eastern Spain
Climate change is increasing the vulnerability of Mediterranean coniferous plantations. Here, we integrate a Landsat time series with a physically-based distributed hydrological model (Watershed Integrated Management in Mediterranean Environments—WiMMed) to examine spatially-explicit relat...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-09-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/11/19/2291 |
id |
doaj-10e2ff888cb3467d986188520403bc75 |
---|---|
record_format |
Article |
spelling |
doaj-10e2ff888cb3467d986188520403bc752020-11-25T01:50:57ZengMDPI AGRemote Sensing2072-42922019-09-011119229110.3390/rs11192291rs11192291Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess <i>Pinus pinaster</i> Aiton. Forest Defoliation in South-Eastern SpainAntonio Jesús Ariza Salamanca0Rafael María Navarro-Cerrillo1Francisco J. Bonet-García2Ma José Pérez-Palazón3María J. Polo4Department of Forestry Engineering, Laboratory of Silviculture, dendrochronology and climate change. DendrodatLab-ERSAF, Andalusian Institute for Earth System Research, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 Córdoba, SpainDepartment of Forestry Engineering, Laboratory of Silviculture, dendrochronology and climate change. DendrodatLab-ERSAF, Andalusian Institute for Earth System Research, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 Córdoba, SpainDepartment of Ecology, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 Córdoba, SpainDepartment of Hydraulic Engineering, Laboratory of River Dynamics and Hydrology, Andalusian Institute for Earth System Research, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 Córdoba, SpainDepartment of Hydraulic Engineering, Laboratory of River Dynamics and Hydrology, Andalusian Institute for Earth System Research, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, E-14071 Córdoba, SpainClimate change is increasing the vulnerability of Mediterranean coniferous plantations. Here, we integrate a Landsat time series with a physically-based distributed hydrological model (Watershed Integrated Management in Mediterranean Environments—WiMMed) to examine spatially-explicit relationships between the mortality processes of <i>Pinus pinaster</i> plantations and the hydrological regime, using different spectral indices of vegetation and machine learning algorithms. The Normalized Burn Ratio (NBR) and Moisture Stress Index (MSI) show the highest correlations with defoliation rates. Random Forest was the most accurate model (R<sup>2</sup> = 0.79; RMSE = 0.059), showing a high model performance and prediction. Support vector machines and neural networks also demonstrated a high performance (R<sup>2</sup> > 0.7). The main hydrological variables selected by the model to explain defoliation were potential evapotranspiration, winter precipitation and maximum summer temperature (lower Out-of-bag error). These results show the importance of hydrological variables involved in evaporation processes, and on the change in the spatial distribution of seasonal rainfall upon the defoliation processes of <i>P. pinaster</i>. These results underpin the importance of integrating temporal remote sensing data and hydrological models to analyze the drivers of forest defoliation and mortality processes in the Mediterranean climate.https://www.mdpi.com/2072-4292/11/19/2291forest disturbance<i>pinus</i> plantationslandsat time-series datahydrological modelmachine learningdefoliation mapping |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Antonio Jesús Ariza Salamanca Rafael María Navarro-Cerrillo Francisco J. Bonet-García Ma José Pérez-Palazón María J. Polo |
spellingShingle |
Antonio Jesús Ariza Salamanca Rafael María Navarro-Cerrillo Francisco J. Bonet-García Ma José Pérez-Palazón María J. Polo Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess <i>Pinus pinaster</i> Aiton. Forest Defoliation in South-Eastern Spain Remote Sensing forest disturbance <i>pinus</i> plantations landsat time-series data hydrological model machine learning defoliation mapping |
author_facet |
Antonio Jesús Ariza Salamanca Rafael María Navarro-Cerrillo Francisco J. Bonet-García Ma José Pérez-Palazón María J. Polo |
author_sort |
Antonio Jesús Ariza Salamanca |
title |
Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess <i>Pinus pinaster</i> Aiton. Forest Defoliation in South-Eastern Spain |
title_short |
Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess <i>Pinus pinaster</i> Aiton. Forest Defoliation in South-Eastern Spain |
title_full |
Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess <i>Pinus pinaster</i> Aiton. Forest Defoliation in South-Eastern Spain |
title_fullStr |
Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess <i>Pinus pinaster</i> Aiton. Forest Defoliation in South-Eastern Spain |
title_full_unstemmed |
Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess <i>Pinus pinaster</i> Aiton. Forest Defoliation in South-Eastern Spain |
title_sort |
integration of a landsat time-series of nbr and hydrological modeling to assess <i>pinus pinaster</i> aiton. forest defoliation in south-eastern spain |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-09-01 |
description |
Climate change is increasing the vulnerability of Mediterranean coniferous plantations. Here, we integrate a Landsat time series with a physically-based distributed hydrological model (Watershed Integrated Management in Mediterranean Environments—WiMMed) to examine spatially-explicit relationships between the mortality processes of <i>Pinus pinaster</i> plantations and the hydrological regime, using different spectral indices of vegetation and machine learning algorithms. The Normalized Burn Ratio (NBR) and Moisture Stress Index (MSI) show the highest correlations with defoliation rates. Random Forest was the most accurate model (R<sup>2</sup> = 0.79; RMSE = 0.059), showing a high model performance and prediction. Support vector machines and neural networks also demonstrated a high performance (R<sup>2</sup> > 0.7). The main hydrological variables selected by the model to explain defoliation were potential evapotranspiration, winter precipitation and maximum summer temperature (lower Out-of-bag error). These results show the importance of hydrological variables involved in evaporation processes, and on the change in the spatial distribution of seasonal rainfall upon the defoliation processes of <i>P. pinaster</i>. These results underpin the importance of integrating temporal remote sensing data and hydrological models to analyze the drivers of forest defoliation and mortality processes in the Mediterranean climate. |
topic |
forest disturbance <i>pinus</i> plantations landsat time-series data hydrological model machine learning defoliation mapping |
url |
https://www.mdpi.com/2072-4292/11/19/2291 |
work_keys_str_mv |
AT antoniojesusarizasalamanca integrationofalandsattimeseriesofnbrandhydrologicalmodelingtoassessipinuspinasteriaitonforestdefoliationinsoutheasternspain AT rafaelmarianavarrocerrillo integrationofalandsattimeseriesofnbrandhydrologicalmodelingtoassessipinuspinasteriaitonforestdefoliationinsoutheasternspain AT franciscojbonetgarcia integrationofalandsattimeseriesofnbrandhydrologicalmodelingtoassessipinuspinasteriaitonforestdefoliationinsoutheasternspain AT majoseperezpalazon integrationofalandsattimeseriesofnbrandhydrologicalmodelingtoassessipinuspinasteriaitonforestdefoliationinsoutheasternspain AT mariajpolo integrationofalandsattimeseriesofnbrandhydrologicalmodelingtoassessipinuspinasteriaitonforestdefoliationinsoutheasternspain |
_version_ |
1724999216241049600 |