Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control

Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cu...

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Main Authors: Alberto López-Amoedo, Xana Álvarez, Henrique Lorenzo, Juan Luis Rodríguez
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/15/2983
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spelling doaj-591c965267e848ca8ed8393c27cbd7a42021-08-06T15:30:43ZengMDPI AGRemote Sensing2072-42922021-07-01132983298310.3390/rs13152983Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut ControlAlberto López-Amoedo0Xana Álvarez1Henrique Lorenzo2Juan Luis Rodríguez3Asefor Ingeniería Forestal, S.L.E. Centro de Emprendemento Monte Gaiás, Cidade da Cultura, 15707 Santiago de Compostela, SpainSchool of Forestry Engineering, University of Vigo, Campus A Xunqueira s/n, 36005 Pontevedra, SpainCINTECX, GeoTECH Research Group, Universidade de Vigo, 36310 Vigo, SpainCINTECX, GeoTECH Research Group, Universidade de Vigo, 36310 Vigo, SpainLand fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans.https://www.mdpi.com/2072-4292/13/15/2983remote sensingforest cover<i>Eucalyptus globulus</i><i>Pinus pinaster</i>time series
collection DOAJ
language English
format Article
sources DOAJ
author Alberto López-Amoedo
Xana Álvarez
Henrique Lorenzo
Juan Luis Rodríguez
spellingShingle Alberto López-Amoedo
Xana Álvarez
Henrique Lorenzo
Juan Luis Rodríguez
Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control
Remote Sensing
remote sensing
forest cover
<i>Eucalyptus globulus</i>
<i>Pinus pinaster</i>
time series
author_facet Alberto López-Amoedo
Xana Álvarez
Henrique Lorenzo
Juan Luis Rodríguez
author_sort Alberto López-Amoedo
title Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control
title_short Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control
title_full Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control
title_fullStr Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control
title_full_unstemmed Multi-Temporal Sentinel-2 Data Analysis for Smallholding Forest Cut Control
title_sort multi-temporal sentinel-2 data analysis for smallholding forest cut control
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2021-07-01
description Land fragmentation and small plots are the main features of the rural environment of Galicia (NW Spain). Smallholding limits land use management, representing a drawback in local forest planning. This study analyzes the potential use of multitemporal Sentinel-2 images to detect and control forest cuts in very small pine and eucalyptus plots located in southern Galicia. The proposed approach is based on the analysis of Sentinel-2 NDVI time series in 4231 plots smaller than 3 ha (average 0.46 ha). The methodology allowed us to detect cuts, allocate cut dates and quantify plot areas due to different cutting cycles in an uneven-aged stand. An accuracy of approximately 95% was achieved when the whole plot was cut, with an 81% accuracy for partial cuts. The main difficulty in detecting and dating cuts was related to cloud cover, which affected the multitemporal analysis. In conclusion, the proposed methodology provides an accurate estimation of cutting date and area, helping to improve the monitoring system in sustainable forest certifications to ensure compliance with forest management plans.
topic remote sensing
forest cover
<i>Eucalyptus globulus</i>
<i>Pinus pinaster</i>
time series
url https://www.mdpi.com/2072-4292/13/15/2983
work_keys_str_mv AT albertolopezamoedo multitemporalsentinel2dataanalysisforsmallholdingforestcutcontrol
AT xanaalvarez multitemporalsentinel2dataanalysisforsmallholdingforestcutcontrol
AT henriquelorenzo multitemporalsentinel2dataanalysisforsmallholdingforestcutcontrol
AT juanluisrodriguez multitemporalsentinel2dataanalysisforsmallholdingforestcutcontrol
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