Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery

This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kind...

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Autores principales:	Maurizio Sarti, Marco Ciolfi, Marco Lauteri, Pierluigi Paris, Francesca Chiocchini
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	remote sensing forest inventories rural landscapes biodiversity ecological network Oceanography GC1-1581 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/8fd8e9442cb64d9f9b42133ca71008fe
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spelling	oai:doaj.org-article:8fd8e9442cb64d9f9b42133ca71008fe2021-11-04T15:51:55ZTrees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery2279-725410.1080/22797254.2021.1986678https://doaj.org/article/8fd8e9442cb64d9f9b42133ca71008fe2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/22797254.2021.1986678https://doaj.org/toc/2279-7254This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kinds of tree out of forest (TOF) structures: forest patches (FP), isolated trees (IT), tree hedgerows (THR), distinguished from forest (F). Ground truth validation allows estimating the accuracy of classification. Four optical bands and six spectral indices are compared detecting images’ T areas: B2, B3, B4 and B8 bands, Negative Luminance (NL), Normalized Difference Vegetation index (NDVI), Green NDVI (GNDVI), Blue NDVI (BNDVI), Panchromatic NDVI (PNDVI) and Enhanced Vegetation Index (EVI). NL shows a relatively better capability for TOF detection and classification, with overall accuracy (OA) exceeding 92% and p-value = 10−5. Experiments were conducted on optical data acquired by Sentinel-2 in 2016 over the Alfina highland, central Italy. The tree characteristics were extracted exploiting GNU Octave Image Package. Our results show that this new approach could be extended to the detection and mapping of TOF within large areas of agroforestry landscape.Maurizio SartiMarco CiolfiMarco LauteriPierluigi ParisFrancesca ChiocchiniTaylor & Francis Grouparticleremote sensingforest inventoriesrural landscapesbiodiversityecological networkOceanographyGC1-1581GeologyQE1-996.5ENEuropean Journal of Remote Sensing, Vol 54, Iss 1, Pp 609-623 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	remote sensing forest inventories rural landscapes biodiversity ecological network Oceanography GC1-1581 Geology QE1-996.5
spellingShingle	remote sensing forest inventories rural landscapes biodiversity ecological network Oceanography GC1-1581 Geology QE1-996.5 Maurizio Sarti Marco Ciolfi Marco Lauteri Pierluigi Paris Francesca Chiocchini Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery
description	This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kinds of tree out of forest (TOF) structures: forest patches (FP), isolated trees (IT), tree hedgerows (THR), distinguished from forest (F). Ground truth validation allows estimating the accuracy of classification. Four optical bands and six spectral indices are compared detecting images’ T areas: B2, B3, B4 and B8 bands, Negative Luminance (NL), Normalized Difference Vegetation index (NDVI), Green NDVI (GNDVI), Blue NDVI (BNDVI), Panchromatic NDVI (PNDVI) and Enhanced Vegetation Index (EVI). NL shows a relatively better capability for TOF detection and classification, with overall accuracy (OA) exceeding 92% and p-value = 10−5. Experiments were conducted on optical data acquired by Sentinel-2 in 2016 over the Alfina highland, central Italy. The tree characteristics were extracted exploiting GNU Octave Image Package. Our results show that this new approach could be extended to the detection and mapping of TOF within large areas of agroforestry landscape.
format	article
author	Maurizio Sarti Marco Ciolfi Marco Lauteri Pierluigi Paris Francesca Chiocchini
author_facet	Maurizio Sarti Marco Ciolfi Marco Lauteri Pierluigi Paris Francesca Chiocchini
author_sort	Maurizio Sarti
title	Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery
title_short	Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery
title_full	Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery
title_fullStr	Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery
title_full_unstemmed	Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery
title_sort	trees outside forest in italian agroforestry landscapes: detection and mapping using sentinel-2 imagery
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/8fd8e9442cb64d9f9b42133ca71008fe
work_keys_str_mv	AT mauriziosarti treesoutsideforestinitalianagroforestrylandscapesdetectionandmappingusingsentinel2imagery AT marcociolfi treesoutsideforestinitalianagroforestrylandscapesdetectionandmappingusingsentinel2imagery AT marcolauteri treesoutsideforestinitalianagroforestrylandscapesdetectionandmappingusingsentinel2imagery AT pierluigiparis treesoutsideforestinitalianagroforestrylandscapesdetectionandmappingusingsentinel2imagery AT francescachiocchini treesoutsideforestinitalianagroforestrylandscapesdetectionandmappingusingsentinel2imagery
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Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery

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