Mapping Large-Scale Bamboo Forest Based on Phenology and Morphology Features
Bamboo forest is a unique forest landscape that is mainly composed of herbal plants. It has a stronger capability to increase terrestrial carbon sinks than woody forests in the same environment, thus playing a special role in absorbing atmospheric CO<sub>2</sub>. Accurate and timely bamb...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/2/515 |
| _version_ | 1797437393685446656 |
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| author | Xueliang Feng Shen Tan Yun Dong Xin Zhang Jiaming Xu Liheng Zhong Le Yu |
| author_facet | Xueliang Feng Shen Tan Yun Dong Xin Zhang Jiaming Xu Liheng Zhong Le Yu |
| author_sort | Xueliang Feng |
| collection | DOAJ |
| description | Bamboo forest is a unique forest landscape that is mainly composed of herbal plants. It has a stronger capability to increase terrestrial carbon sinks than woody forests in the same environment, thus playing a special role in absorbing atmospheric CO<sub>2</sub>. Accurate and timely bamboo forest maps are necessary to better understand and quantify their contribution to the carbon and hydrological cycles. Previous studies have reported that the unique phenology pattern of bamboo forests, i.e., the on- and off-year cycle, can be detected with time-series high spatial resolution remote sensing (RS) images. Nevertheless, this information has not yet been applied in large-scale bamboo mapping. In this study, we innovatively incorporate newly designed phenology features reflecting the aforementioned on- and off-year cycles into a typical end-to-end classification workflow, including two features describing growing efficiency during the green-up season and two features describing the difference between annual peak greenness. Additionally, two horizonal morphology features and one tree height feature were employed, simultaneously. An experiment in southeast China was carried out to test the method’s performance, in which seven categories were focused. A total of 987 field samples were used for training and validation (70% and 30%, respectively). The results show that combining the time-series features based on spectral bands and vegetation indices and newly designed phenology and morphology patterns can differentiate bamboo forests from other vegetation categories. Based on these features, the classification results exhibit a reasonable spatial distribution and a satisfactory overall accuracy (0.89). The detected bamboo area proportion in 82 counties agrees with the statistics from China’s Third National Land Survey, which was produced based on high resolution images from commercial satellites and human interpretation (correlation coefficient = 0.69, and root mean squared error = 5.1%). This study demonstrates that the new scheme incorporating phenology features helps to map bamboo forests accurately while reducing the sample size requirement. |
| first_indexed | 2024-03-09T11:19:35Z |
| format | Article |
| id | doaj.art-d231c82501fb45f79c0e4c5079cebb30 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:19:35Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d231c82501fb45f79c0e4c5079cebb302023-12-01T00:23:01ZengMDPI AGRemote Sensing2072-42922023-01-0115251510.3390/rs15020515Mapping Large-Scale Bamboo Forest Based on Phenology and Morphology FeaturesXueliang Feng0Shen Tan1Yun Dong2Xin Zhang3Jiaming Xu4Liheng Zhong5Le Yu6Huaiyin Institute of Technology, Huaian 223001, ChinaSino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, ChinaHuaiyin Institute of Technology, Huaian 223001, ChinaDepartment of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UKEast China Survey and Planning Institute, National Forestry and Grassland Administration, Hangzhou 310019, ChinaAnt Group, Beijing 100020, ChinaDepartment of Earth System Science, Tsinghua University, Beijing 100084, ChinaBamboo forest is a unique forest landscape that is mainly composed of herbal plants. It has a stronger capability to increase terrestrial carbon sinks than woody forests in the same environment, thus playing a special role in absorbing atmospheric CO<sub>2</sub>. Accurate and timely bamboo forest maps are necessary to better understand and quantify their contribution to the carbon and hydrological cycles. Previous studies have reported that the unique phenology pattern of bamboo forests, i.e., the on- and off-year cycle, can be detected with time-series high spatial resolution remote sensing (RS) images. Nevertheless, this information has not yet been applied in large-scale bamboo mapping. In this study, we innovatively incorporate newly designed phenology features reflecting the aforementioned on- and off-year cycles into a typical end-to-end classification workflow, including two features describing growing efficiency during the green-up season and two features describing the difference between annual peak greenness. Additionally, two horizonal morphology features and one tree height feature were employed, simultaneously. An experiment in southeast China was carried out to test the method’s performance, in which seven categories were focused. A total of 987 field samples were used for training and validation (70% and 30%, respectively). The results show that combining the time-series features based on spectral bands and vegetation indices and newly designed phenology and morphology patterns can differentiate bamboo forests from other vegetation categories. Based on these features, the classification results exhibit a reasonable spatial distribution and a satisfactory overall accuracy (0.89). The detected bamboo area proportion in 82 counties agrees with the statistics from China’s Third National Land Survey, which was produced based on high resolution images from commercial satellites and human interpretation (correlation coefficient = 0.69, and root mean squared error = 5.1%). This study demonstrates that the new scheme incorporating phenology features helps to map bamboo forests accurately while reducing the sample size requirement.https://www.mdpi.com/2072-4292/15/2/515bamboo forest mappingphenology featuresmorphology featurestree height |
| spellingShingle | Xueliang Feng Shen Tan Yun Dong Xin Zhang Jiaming Xu Liheng Zhong Le Yu Mapping Large-Scale Bamboo Forest Based on Phenology and Morphology Features Remote Sensing bamboo forest mapping phenology features morphology features tree height |
| title | Mapping Large-Scale Bamboo Forest Based on Phenology and Morphology Features |
| title_full | Mapping Large-Scale Bamboo Forest Based on Phenology and Morphology Features |
| title_fullStr | Mapping Large-Scale Bamboo Forest Based on Phenology and Morphology Features |
| title_full_unstemmed | Mapping Large-Scale Bamboo Forest Based on Phenology and Morphology Features |
| title_short | Mapping Large-Scale Bamboo Forest Based on Phenology and Morphology Features |
| title_sort | mapping large scale bamboo forest based on phenology and morphology features |
| topic | bamboo forest mapping phenology features morphology features tree height |
| url | https://www.mdpi.com/2072-4292/15/2/515 |
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