Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images
Historical black-and-white (B&W) aerial images have been recognized as an important source of information for assessing vegetation dynamics. However, the use of these images is limited by the lack of multispectral information, as well as by their varying quality. It is therefore important to stu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/9/2135 |
| _version_ | 1797503026369396736 |
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| author | Zuyuan Wang Christian Ginzler Birgit Eben Nataliia Rehush Lars T. Waser |
| author_facet | Zuyuan Wang Christian Ginzler Birgit Eben Nataliia Rehush Lars T. Waser |
| author_sort | Zuyuan Wang |
| collection | DOAJ |
| description | Historical black-and-white (B&W) aerial images have been recognized as an important source of information for assessing vegetation dynamics. However, the use of these images is limited by the lack of multispectral information, as well as by their varying quality. It is therefore important to study and develop methods that are capable of automatic and accurate classification of these B&W images while reducing the need for tedious manual work. The goal of this study was to assess changes over 30 years in woody vegetation cover along alpine treeline ecotones using B&W aerial images from two time points. A convolutional neural networks model was firstly set up based on three structure classes calculated from Airborne Laser Scanning data using the B&W aerial images from 2010. Then, the model was improved by active addition of training samples of those that were wrongly predicted from historical B&W aerial images from 1980. A comparison with visual image interpretation revealed generally high agreement for the class “dense forest” and lower agreement for the class “group of trees”. The study illustrates that vegetation changes at the treeline ecotone can be detected in order to assess areawide long-term vegetation dynamics at a fine spatial resolution. |
| first_indexed | 2024-03-10T03:44:37Z |
| format | Article |
| id | doaj.art-15b361c2ae2e42f79a593094d8774cea |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:44:37Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-15b361c2ae2e42f79a593094d8774cea2023-11-23T09:11:08ZengMDPI AGRemote Sensing2072-42922022-04-01149213510.3390/rs14092135Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial ImagesZuyuan Wang0Christian Ginzler1Birgit Eben2Nataliia Rehush3Lars T. Waser4Department of Land Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, SwitzerlandDepartment of Land Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, SwitzerlandDepartment of Land Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, SwitzerlandDepartment of Forest Resources and Management, Scientific Service NFI, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, SwitzerlandDepartment of Land Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, SwitzerlandHistorical black-and-white (B&W) aerial images have been recognized as an important source of information for assessing vegetation dynamics. However, the use of these images is limited by the lack of multispectral information, as well as by their varying quality. It is therefore important to study and develop methods that are capable of automatic and accurate classification of these B&W images while reducing the need for tedious manual work. The goal of this study was to assess changes over 30 years in woody vegetation cover along alpine treeline ecotones using B&W aerial images from two time points. A convolutional neural networks model was firstly set up based on three structure classes calculated from Airborne Laser Scanning data using the B&W aerial images from 2010. Then, the model was improved by active addition of training samples of those that were wrongly predicted from historical B&W aerial images from 1980. A comparison with visual image interpretation revealed generally high agreement for the class “dense forest” and lower agreement for the class “group of trees”. The study illustrates that vegetation changes at the treeline ecotone can be detected in order to assess areawide long-term vegetation dynamics at a fine spatial resolution.https://www.mdpi.com/2072-4292/14/9/2135convolutional neural networkhistorical black-and-white imagerytreeline ecotonewoody vegetation change |
| spellingShingle | Zuyuan Wang Christian Ginzler Birgit Eben Nataliia Rehush Lars T. Waser Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images Remote Sensing convolutional neural network historical black-and-white imagery treeline ecotone woody vegetation change |
| title | Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images |
| title_full | Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images |
| title_fullStr | Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images |
| title_full_unstemmed | Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images |
| title_short | Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images |
| title_sort | assessing changes in mountain treeline ecotones over 30 years using cnns and historical aerial images |
| topic | convolutional neural network historical black-and-white imagery treeline ecotone woody vegetation change |
| url | https://www.mdpi.com/2072-4292/14/9/2135 |
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