Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery

Europe’s mountain forests, which are naturally valuable areas due to their high biodiversity and well-preserved natural characteristics, are experiencing major alterations, so an important component of monitoring is obtaining up-to-date information concerning species composition, extent, and locatio...

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Main Authors: Marcin Kluczek, Bogdan Zagajewski, Tomasz Zwijacz-Kozica
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/15/3/844
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author Marcin Kluczek
Bogdan Zagajewski
Tomasz Zwijacz-Kozica
author_facet Marcin Kluczek
Bogdan Zagajewski
Tomasz Zwijacz-Kozica
author_sort Marcin Kluczek
collection DOAJ
description Europe’s mountain forests, which are naturally valuable areas due to their high biodiversity and well-preserved natural characteristics, are experiencing major alterations, so an important component of monitoring is obtaining up-to-date information concerning species composition, extent, and location. An important aspect of mapping tree stands is the selection of remote sensing data that vary in temporal, spectral, and spatial resolution, as well as in open and commercial access. For the Tatra Mountains area, which is a unique alpine ecosystem in central Europe, we classified 13 woody species by iterative machine learning methods using random forest (RF) and support vector machine (SVM) algorithms of more than 1000 polygons collected in the field. For this task, we used free Sentinel-2 multitemporal satellite data (10 m pixel size, 12 spectral bands, and 21 acquisition dates), commercial PlanetScope data (3 m pixel size, 8 spectral bands, and 3 acquisitions dates), and airborne HySpex hyperspectral data (2 m pixel size, 430 spectral bands, and a single acquisition) with fusion of the data of topographic derivatives based on Shuttle Radar Topography Mission (SRTM) and airborne laser scanning (ALS) data. The iterative classification method achieved the highest F1-score with HySpex (0.95 RF; 0.92 SVM) imagery, but the multitemporal Sentinel-2 data cube, which consisted of 21 scenes, offered comparable results (0.93 RF; 0.89 SVM). The three images of the high-resolution PlanetScope produced slightly less accurate results (0.89 RF; 0.87 SVM).
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spelling doaj.art-7f7c50507e79437aa87338e4c82809fb2023-11-16T17:55:03ZengMDPI AGRemote Sensing2072-42922023-02-0115384410.3390/rs15030844Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral ImageryMarcin Kluczek0Bogdan Zagajewski1Tomasz Zwijacz-Kozica2Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, PolandDepartment of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warszawa, PolandTatra National Park, Kuźnice 1, 34-500 Zakopane, PolandEurope’s mountain forests, which are naturally valuable areas due to their high biodiversity and well-preserved natural characteristics, are experiencing major alterations, so an important component of monitoring is obtaining up-to-date information concerning species composition, extent, and location. An important aspect of mapping tree stands is the selection of remote sensing data that vary in temporal, spectral, and spatial resolution, as well as in open and commercial access. For the Tatra Mountains area, which is a unique alpine ecosystem in central Europe, we classified 13 woody species by iterative machine learning methods using random forest (RF) and support vector machine (SVM) algorithms of more than 1000 polygons collected in the field. For this task, we used free Sentinel-2 multitemporal satellite data (10 m pixel size, 12 spectral bands, and 21 acquisition dates), commercial PlanetScope data (3 m pixel size, 8 spectral bands, and 3 acquisitions dates), and airborne HySpex hyperspectral data (2 m pixel size, 430 spectral bands, and a single acquisition) with fusion of the data of topographic derivatives based on Shuttle Radar Topography Mission (SRTM) and airborne laser scanning (ALS) data. The iterative classification method achieved the highest F1-score with HySpex (0.95 RF; 0.92 SVM) imagery, but the multitemporal Sentinel-2 data cube, which consisted of 21 scenes, offered comparable results (0.93 RF; 0.89 SVM). The three images of the high-resolution PlanetScope produced slightly less accurate results (0.89 RF; 0.87 SVM).https://www.mdpi.com/2072-4292/15/3/844vegetation mappingmountain ecosystemwoody plant speciesthe TatrasclassificationSentinel-2
spellingShingle Marcin Kluczek
Bogdan Zagajewski
Tomasz Zwijacz-Kozica
Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery
Remote Sensing
vegetation mapping
mountain ecosystem
woody plant species
the Tatras
classification
Sentinel-2
title Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery
title_full Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery
title_fullStr Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery
title_full_unstemmed Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery
title_short Mountain Tree Species Mapping Using Sentinel-2, PlanetScope, and Airborne HySpex Hyperspectral Imagery
title_sort mountain tree species mapping using sentinel 2 planetscope and airborne hyspex hyperspectral imagery
topic vegetation mapping
mountain ecosystem
woody plant species
the Tatras
classification
Sentinel-2
url https://www.mdpi.com/2072-4292/15/3/844
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AT bogdanzagajewski mountaintreespeciesmappingusingsentinel2planetscopeandairbornehyspexhyperspectralimagery
AT tomaszzwijaczkozica mountaintreespeciesmappingusingsentinel2planetscopeandairbornehyspexhyperspectralimagery