An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery
Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this...
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doaj-f75ff79f9a9f4c909579ef98b4a575812021-05-31T23:40:58ZengMDPI AGRemote Sensing2072-42922021-05-01131868186810.3390/rs13101868An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite ImageryMartina Deur0Mateo Gašparović1Ivan Balenović2Institute for Spatial Planning of Šibenik-Knin County, Vladimira Nazora 1/IV, 22000 Šibenik, CroatiaChair of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, 10000 Zagreb, CroatiaDivision for Forest Management and Forestry Economics, Croatian Forest Research Institute, Trnjanska cesta 35, 10000 Zagreb, CroatiaQuality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (<i>Quercus robur</i> L., <i>Carpinus betulus</i> L., and <i>Alnus glutinosa</i> (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.https://www.mdpi.com/2072-4292/13/10/1868pansharpeningrandom forestobject-based classification (OBIA)pixel-based classificationWorldView-3 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Martina Deur Mateo Gašparović Ivan Balenović |
spellingShingle |
Martina Deur Mateo Gašparović Ivan Balenović An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery Remote Sensing pansharpening random forest object-based classification (OBIA) pixel-based classification WorldView-3 |
author_facet |
Martina Deur Mateo Gašparović Ivan Balenović |
author_sort |
Martina Deur |
title |
An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery |
title_short |
An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery |
title_full |
An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery |
title_fullStr |
An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery |
title_full_unstemmed |
An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery |
title_sort |
evaluation of pixel- and object-based tree species classification in mixed deciduous forests using pansharpened very high spatial resolution satellite imagery |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-05-01 |
description |
Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (<i>Quercus robur</i> L., <i>Carpinus betulus</i> L., and <i>Alnus glutinosa</i> (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas. |
topic |
pansharpening random forest object-based classification (OBIA) pixel-based classification WorldView-3 |
url |
https://www.mdpi.com/2072-4292/13/10/1868 |
work_keys_str_mv |
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